Advances in Psychology Volume 102 Cognitive Issues in Motor Expertise

COGNITIVE ISSUES IN MOTOR EXPERTISE

ADVANCES IN PSYCHOLOGY

102 Editors:

G . E. STELMACH

P. A. VROON

NORTH-HOLLAND AMSTERDAM LONDON NEW YORK TOKYO

COGNITIVE ISSUES IN MOTOR EXPERTISE

Edited by

JANET L. STARKES Department of Kinesiology McMaster University Hamilton, Ontario, Canada

FRAN ALLARD Department of Kinesiology University of Waterloo Waterloo, Ontario, Canada

1993

NORTH-HOLLAND AMSTERDAM LONDON NEW YORK TOKYO

NORTH-HOLLAND ELSEVIER SCIENCE PUBLISHERS B.V. Sara Burgerhartstraat 25 P.O. Box 21 I , 1000 AE Amsterdam, The Netherlands

ISBN: 0 444 89302 4 1993 ELSEVIER SCIENCE PUBLISHERS B.V. All rights reserved

No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical. photocopying, recording or otherwise, without the prior written permission of the publisher, Elsevier Science Publishers B.V., Copyright & Permissions Department, P.O. Box 521, 1000 AM Amsterdam, The Netherlands. Special regulations for readers in the U.S.A. - This publication has been registered with the Copyright Clearance Center Inc. (CCC), Salem, Massachusetts. Information can be obtained from the CCC about conditions under which photocopies of parts of this publication may be made in the U.S.A. All other copyright questions, including photocopying outside of the U.S.A., should be referred to the copyright owner, Elsevier Science Publishers B.V., unless otherwise specified. No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. This book is printed on acid-free paper. Printed in The Netherlands

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Acknowledgement This book took almost two years to complete. The inspiration for the book came as a result of a roundtable on expertise hosted by Anders Ericsson and Jacqui Smith at the Max Planck Institute (Berlin) in 1989. The book was initiated in Japan, while the first editor was a foreign researcher with the Japanese government and Ibaraki University. Colleagues and close friends in Japan are the first people we are indebted to. Crossing hemispheres, preparation of the book continued and we are grateful to the many contributors, without whom the book would not have been possible. Deanna Goral was responsible for converting and formatting each of the chapters for the book and her computer expertise, perseverance, and good humour were much appreciated. Finally, David LeClair has played a special role not just as a co-author but in the preparation of the subject and author indexes. We would like to thank each of these individuals for their invaluable help in preparing the book.

Janet Starkes Hamilton, Ontario

Fran Allad Waterloo, Ontario

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Permissions We thank the following authors/publishers for allowing us to reproduce/redraw previously published figures and tables. Heldref Publications. Figure 15.1 (Keele & Ivry, copyright 1987) Davis & Geck and Ralph K. Davies Medical Center, Figure 12.1 (Alpert, Bucke & Buncke, 1975) University of South Carolina Press. Figure 12.3 (Starkes, copyright 1990) Cambridge University Press. Figure 12.5 (Allard & Starkes, copyright 1991) Psychological Review. Figure 16.1 and 16.2 (Bryan & Haner, copyright 1897) Human Kinetics Publishers. Figure 16.4 (Schmidt, copyright 1988)

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List of Contents V

Acknowledgement

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Permissions

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List of Contents

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List of Conmbutors

Part One

Preliminaries: Approaches to the study of expertise

Chapter 1

Motor experts: Opening thoughts Janet L. Starkes

Chapter 2

Chapter 3

3

Cognition, expertise, and motor performance Fran Allard

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The role of three dimensional analysis in the assessment of motor expertise Heather Camahan

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Part Two

Domains

Chapter 4

Determinants of video game performance Donna M. Baba

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Analyzing diagnostic expertise of competitive swimming coaches Rebecca Rutt Leas and Micheline T.H.Chi

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Declarative knowledge in skilled motor performance: byproduct or constituent? Fran Allard, Janice Deakin, Shane Parker, and Wendy Rodgers

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Chapter 5

Chapter 6

Chapter 7

The relationship between expertise and visual information processing in sport Werner Helsen and J.M. Pauwels

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Chapter 8

Chapter 9

Chapter 10

Chapter 11

Cognitive Issues in Motor Expertise

The perceptual side of action: Decisionmaking in sport Craig J. Chamberlain and Alan J. Coelho

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Knowledge representation and decisionmaking in sport Sue L. McPherson

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Neuropsychological analyses of surgical skill Arthur L. Schueneman and Jack Pickleman

189

The skill of speech production Kevin G. Munhall

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Part Three

Acquisition and Developmental Aspects

Chapter 12

A stitch in time: Cognitive issues in microsurgery Janet L. Starkes, Irene Payk. Peter Jennen, and David LeClair

225

Motor expertise and aging: The relevance of lifestyle to balance Michael J. Stones, Blair Hong, and Albert Kozma

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The development of expertise in youth sport Karen E. French and Michael E. Nevett

255

Chapter 13

Chapter 14

Part Four

Theoretical considerations and evaluations of the approach

Chapter 15

A modular approach to individual differences in skill and coordination Steven K. Jones

213

Three legacies of Bryan and Harter: Automaticity, variability and change in skilled performance Timothy D. Lee and Stephan P. Swinnen

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Chapter 16

List of Contents

Chapter 17

Part Five

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Strategies for improving understanding of motor expertise [or mistakes we have made and things we have learned!!] Bruce Abemethy, Katherine T. Thomas, and Jerry T. Thomas

317

Editors’ Epilogue: Where are we now?

359

Author Index

363

Subject Index

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List of Contributors BRUCE ABERNETHY Department of Human Movement Studies, University of Queensland, St. Lucia, Qld 4072, Australia FRANALLARD Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1 DONNA M. BABA The Usability Group Inc., Willowdale, Ontario, Canada, M2J 4V8 HEATHERCARNAHAN Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada. N2L 3G1 CRAIG J. CHAMBERLAIN University College of the Fraser Valley, Abbotsford, British Columbia, Canada, V2S 4N2 MICHELENE T.H. CHI 821 Learning Research and Development Centre, 3939 O’Hara Street, Pittsburgh, Pennsylvania, 15260 ALAN J. COELHO Department of Physical Education, Eastern Washington University, Cheney, Washington, 99004-2499 JANICE DEAKIN Department of Physical and Health Education, Queen’s University, Kingston, Ontario, Canada, K7L 3N6 KAREN E. FRENCH Department of Physical Education, University of South Carolina, Columbia, South Carolina, 29208 WERNER HELSEN Leuven University, Institute for Physical Education, Motor Learning Lab, Tervuunevest 101, 3030 Leuven, Belgium BLAIR HONG Gerontology Centre and Department of Psychology, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada, AlB 3x9

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Cognitive Issues in Motor Expertise

PETER JENNEN Faculty of Medicine, University of Limburg, Maastricht, The Netherlands STEVEN K. JONES Department of Psychology, University of Oregon, Eugene, Oregon, 97403-1227 ALBERT KOZMA Gerontology Centre and Department of Psychology, Memorial University of Newfoundland, St. John’s, Newfoundland. Canada, A1B 3x9 DAVID LeCLAIR Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada, L8S 4K1 TIMOTHY D. LEE Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada, L8S 4K1 SUE L. McPHERSON Department of Health, Physical Education and Recreation, Western Carolina University, Cullowhee, North Carolina, 28723 KEVIN G. MUNHALL Department of Psychology, Queen’s University, Kingston, Ontario, Canada, KIN 6N5 MICHAEL E. NEVETT Department of Physical Education, University of South Carolina, Columbia, South Carolina, 29208 SHANE PARKER Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1 J.M. PAUWELS Leuven University, Institute for Physical Education, Motor Learning Lab, Tervuursevest 101, 3030 Leuven, Belgium IRENE PAY K Microsurgery Lab, McMaster University Medical Centre, Hamilton, Ontario, Canada, L8S 4K1 JACK PICKLEMAN Loyola University Medical Centre, Loyola University Chicago, Maywood, Illinois, 60153 WENDY RODGERS Department of Kinesiology, University of Windsor, Windsor, Ontario, Canada, N9B 3P4

List of Contributors

REBECCARUTTLEAS 821 Learning Research and Development Centre, 3939 O’Hara Street, Pittsburgh, Pennsylvania, 15260 ARTHUR L. SCHUENEMAN Loyola University Medical Center, Loyola University Chicago, Maywood, Illinois, 60153 JANET L. STARKES Department of Kinesiology, McMaster University, Hamilton, Ontario,Canada, L8S 4K1 MICHAEL J. STONES Gerontology Centre and Department of Psychology, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada, AlB 3 x 9 STEPHAN P. SWINNEN Department of Kinanthropology, Catholic University of Leuven, 3001 Heverlee, Belgium JERRY T. THOMAS Department of Exercise Science and Physical Education, Arizona State University, Tempe, Arizona, 85287 KATHERINE T. THOMAS Department of Exercise Science and Physical Education, Arizona State University, Tempe, Arizona, 85287

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Part 1 Preliminaries: Approaches to the study of motor expertise

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COGNITIVE ISSUES IN MOTOR EXPERTISE J.L.Stakes and F. Allard (Editors) 0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 1 MOTOR EXPERTS: OPENING THOUGHTS JANET L. STARKES Department of Kinesiology, McMaster University Hamilton, Ontario, L8S 4Kl This volume contains a series of papers which focus on investigations of expert motor performance. The situations considered range from swimmers to surgeons, with all authors attempting to discover what differentiates expert from novice individuals. The experimental methods used also cover a wide range, from conventional laboratory studies, to protocol analysis, to a re-analysis of data that are nearly 100 years old.

In these opening remarks, I will point out some issues that should be kept in mind while reading the following papers. Many of these issues arise from the application of an approach developed to study the nature of expertise in cognitive tasks to motor tasks. The first such issue is what constitutes a motor expert? What is a motor expert? Intuitively it seems simple to describe a motor expert - someone who's very good at doing something motoric. But how good does someone have to be? How does one measure "good"? How often do they have to be good and how motoric must the task be? Defining what constitutes motor expertise depends very much on one's world view. Some authors (Sloboda, 1991) believe that each of us exhibits some degree of expertise in certain domains, perhaps music, sports, or one's own language. In this view people can achieve competence in a task simply because of its function in everyday life. An analogy might be that many people are expert drivers because of the number of hours they routinely spend in traffic or commuting. If one adopts this notion of expertise the exclusivity of the skill may be subject to cultural interpretation. For example, today we rarely think of one's driving skill as an unusual or exclusive ability. On the other hand if one is a skilled equestrian, we think of that person as unique, perhaps worthy of study as an expert. At the turn of the century, when the primary mode of transportation was horse, one was hardly viewed as an expert if they could ride well. The real expert was someone who could adeptly manage one of the new fangled horseless carriages. So to some extent who we class as an expert depends on the relative exclusivity of the skill. Once a skill becomes part of the repertoire of normal people most cease to view it as a task worthy of study.

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In most cases "exclusivity" seems to be a hallmark of the expertise approach. Salthouse (1991) feels that only those individuals in the highest percentiles of the normal dismbution of skill should be termed "experts". If we think of a normal distribution of skill, these would be individuals who exhibit performance 2-3 standard deviations above the norm. He also suggests that the reason experts seem so impressive is that they have learned to circumvent the normal limits of human information processing. Thus experts often seem to "have all the time in the world' or are "able to see two plays ahead in tasks that involve complex decision making. What then are the normal processing limitations that are inherent in motor skills, and how are they different from strictly cognitive tasks? Again Salthouse (1991) suggests that different tasks present different kinds of limitations for the performer. In his analysis, for chess the limitations may be not knowing what to do and not knowing what to expect from one's opponent. For a musician the limitations are: not knowing what to expect in musical sequences, insensitivity to sensory/ perceptual discriminations such as pitch or timing, and lack of proficiency in producing pitch, timing, intensity, etc. In sports, the limitations inherent might be: not knowing what to expect from one's own or an opponent's actions, insensitivity to critical sensory/ perceptual information such as trajectory of ball flight, and lack of proficiency in performing appropriate actions. The main difference between motor and strictly cognitive skills lies not in fact that information must be anticipated and discriminated but that the most appropriate action must be selected and performed. In general there are two basic approaches to the study of expertise, one we will call the bottom-up approach and one the top-down approach (Salthouse. 1991). T h e bottom-up approach involves three basic steps. First a domain is studied and detailed analyses are conducted to determine what processes are associated with expertise, and what mechanisms are responsible for the processes. Case studies of experts in the domain are usually performed or canied out and hypotheses formed. Next quantitative methods are. used and data are collected on moderate sized groups of experts and novices. The goals in this stage are usually to assess characteristics of experts and determine whether they are consistent between subjects, along the competence continuum, and hold for different procedures and paradigms. The third stage in the bottom-up approach is to determine whether there are common principles underlying expertise across domains. This is a test of generalizability of the model. Unfortunately, while stage three would be the ultimate in theory building, most would say that expert-novice research has been stuck at stage two for some time. There has been very little cross-domain research or work on the generalizability of models by those using the bottom-up approach. In the top-down approach one begins by speculating about what characteristics might be critical to experts in general. The principles are then systematically and empirically investigated. One general characteristic that has been investigated is the ability of experts to circumvent the normal limits of human information processing (Salthouse, 1991). In transcription typing the work of both Gentner (1988) and Salthouse (1984) suggest that skilled typists circumvent limits by taking advantage of the fact that relevant (to be typed) information is constantly available. Thus they can process subsequent stimuli before all of the performance is complete on earlier stimuli. As a result the better the typist the farther ahead in the text they look while typing. If typing were

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a simple reaction time task, typists would exhibit a maximum typing rate of approximately 22 words per minute. Since skilled typists often exceed 75 words per minute they have developed systems for overlapping or parallel processing of information. The task is no longer a serial, discrete task but a dynamic and continuous one that can circumvent the limits imposed by nonparallel processing. While space precludes a more complete discussion of principles in topdown analysis (see Salthouse, 1991) the notion of circumvention of limits could be potentially very valuable in the study of motor expertise. In most cases where speeded motor responses are required (musical performance, human speech, video game controller manipulation) reaction and movement time do not appear to constrain skilled performance. This is one principle that may prove generalizable to many domains. Another controversy that surrounds expert performance is whether task specific sensory information is handled differently by someone once they become skilled. While a substantial amount of work has examined these issues in traditional motor learning studies using learning paradigms, there are little data on established motor experts using real world skills. To date there are three prominent theories, each of which address this question, each of which make very different predictions, and for each of which there is supportive data (Robertson, Collins, Elliott & Starkes, 1992). Pew (1966) was the first to suggest that as one becomes expert in a motor skill there is a reduced need for the continuous monitoring of sensory infomation. That is subjects switch from a control system using sensory feedback and intermittent corrections (closed-loop system) to one in which movement becomes primarily preprogrammed and does not use feedback (open-loop system). A second theory predicts that as one becomes expen the importance of visual feedback is lessened and one begins to rely more on proprioceptive feedback. Since the original theory was proposed (Fleishman & Rich, 1963) others have modified it to suggest that while kinesthetic information becomes very important, motor programs may also differentiate skill levels (Fischman & Schneider, 1985). Finally the specificity of learning theory suggests that learning is specific to the feedback condition in which the skill was acquired. The more practice one has in a skill using a particular source of feedback, the more important that source of feedback becomes (Proteau, Marteniuk, Girouard, & Dugas, 1987). While each of these theories have empirical support, each also has detractors. The advantage of examining these theories using real world experts and skills is that subjects have undergone far more learning and practice n i a l s than one could ever simulate. in laboratory settings. In spite of this most of the current research continues to examine learning on conmved laboratory tasks where amount of learning and practice are necessarily constrained. This is one area where traditional motor learning and expert - novice research could benefit by amalgamating paradigms. Issues unique to the empirical assessment of movement 1. Skills that are assessed on the basis of motor output run the whole gamut from human speech, to transcription typing, to playing the piano, to ballet, to sports, to surgery. How then does one ever begin to get a handle on types of movement output or relative importance of movement output to overall task performance? The most common distinction in the motor

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literature and one still very much of value is the difference between "open" and "closed skills (Poulton, 1957). Closed skills are distinguished by performance in a consistent, usually stationary environment, and open skills are performed in a moving, dynamic environment. Thus skills such as typing, playing a piano, or performing a figure skating routine would be closed skills, and ones like speech, controlling a joystick in a video game, and sports such as boxing or soccer are open skills. In most open sports part of the dynamic nature of the environment comes from the opponent being present during the competition (judo, volleyball). In closed skills, competitors typically take turns competing or performing (gymnastics, ballet). A third distinction in open and closed skills is the role of motor patterns. For a closed skill the motor pattern & the skill, and it's critical that the performer reliably reproduce the standardized pattern. For open skills it is the outcome of the movement that must be effectively produced and a standardized motor pattern is rarely of help. Motor skills need to be further divided depending on their motor requirements. Some skills may require extreme accuracy but deemphasize speed (surgery, darts), others require speed (racquet sports, swimming), others require strength (shot put, pole vault), still others require endurance (triathalon, microsurgery). A task analysis of motor output may indicate various tradeoffs in the underlying requirements of a particular skill. A skill taxonomy of motor requirements is available elsewhere (Allard & Starkes, 1991). Before embarking on any expennovice analysis however, it is important to understand the underlying nature of the skill at hand. In the typical expert -novice paradigm, one or more experts in a particular domain are compared with subjects of lesser skill. In research to date however, the range of skill assessed has been as diverse as the number of domains examined. In one study "experts" might be movement professionals with many years experience, in another varsity level athletes, and in a third developmental study - 12 year olds ranked at a national level. Across studies one person's "expert" is another's "skilled adolescent". Probably the term "expert" is only appropriate for individuals who have spent a significant part of their life in preparation and training within their domain and who perform consistently at a very high level.

2.

3. Another controversy in the assessment of expertise is how much of expert behavior can be explained by experience and training versus how much by individual abilities the subject brings to the task. As Posner (1988) points out, most of us believe that there are important underlying differences among people in how readily they could become experts. Some people also seem to have more potential to develop abilities in one domain, say music, over another. More recent efforts by cognitive psychologists have been directed at measuring the cognitive processes underlying various domains. Some researchers (Deary & Mitchell, 1989; Adam & Wilberg, 1992) feel that high-speed visual processing abilities play a significant role in "open" skills and suggest that performance is related to information processing capacity and rate. Still others suggest that different combinations of abilities may play a critical role in skill at different stages of acquisition and proficiency (Ackerman, 1988; Fleishman, 1966; 1972).

Elsewhere, I have argued that generations of expert-novice research have gained little

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from the assessment of underlying component abilities (Starkes & Deakin, 1984) yet I do believe that a certain proportion of variance in expertise must be accounted for in some way by ability constraints. If different combinations or constellations of abilities become important at different stages of skill then perhaps this has hindered the search for isolated underlying psychomotor traits that would help explain performance. Or perhaps given the amount of time and training it takes to become expert, the problem of producing one may not be so much in finding someone with the underlying prerequisite abilities as finding someone with sufficient motivation to persevere. Another problem in discussing expertise is that within any one expert, skill and experience are inherently confounded. It is very rare to find individuals who consistently excel but have little experience. It is far more common to find people that have significant amounts of experience in a domain but are not experts. If one were able to separate the contributions of each then the relative weighting of ability vs. training could be assessed. 4.

One experimental approach to this nature - nurture question has to date not been used in studies of motor expertise. The approach involves the study of laboratory engineered expertise. In motor skills it would be very revealing to be able to plot the rise of someone to expertise in a longitudinal study. Ericsson and Hanis (1989) (cited in Ericsson & Smith, 1991) have done this in the domain of chess. They were able after 50 hours of practice to train a subject with no chess-playing experience to recall chess positions at a level of accuracy approaching chess masters. Likewise Chase and Ericsson (1981) trained subjects on digit span tasks to exhibit skilled memory processes. Others have decomposed and trained performance in mental calculation (Chamess & Campbell, 1988).Plotting improvements in motor expertise from novice to expert would help us understand learning plateaus, motivational changes, the role of mentors, competitive performance, etc. better. In the real world of skill the ideal situation would be to follow a group of average novices longitudinally,examine changes in performance and determine who of the group emerges as expert and why. 5.

As research has proliferated several authors have attempted to delineate characteristics of those we term experts. Simon & Chase (1973) were the first to observe that over 10 years of preparation and training was required to compete in chess at the international level. Bloom's (1985) insightful study of experts in music, sports and science confirmed that individuals needed a decade of training to excel in any of these fields. Both Bloom and more recently Ericsson and Crutcher (1990) have shown that people who perform internationally probably became interested in their skill domain before age 6 and spent the majority of their adolescence and young adulthood in training. 6. Consistency of performance is critical in assessing whether someone is an expert. Not everyone who gets a "hole-in-one" is an expert golfer. Likewise those who win at games of chance, complete a single major art work or piece of literature don't exhibit the stable performance characteristics of an expert. Expertise is characterized by stable, measurable performance over long periods of time (Ericsson & Smith, 1991). Interestingly many of the

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cognitive tasks assessed in the literature: chess, bridge, Go, etc. have scoring systems where one can assess both performance level relative to others and changes over time. This may or may not be the case in motor skills. For example gymnastics has a well delineated point system for each skill and an international ranking system. Assessing whether an individual surgeon or Super Mario player is an "expert" poses somewhat more of a problem. 7. In many skills like surgery or microsurgery whether one is considered expert is often influenced by one's perceived abilities. This makes the judgement somewhat more social in nature. For research purposes however, subjects should not be designated as experts on the basis of professional credentials, reputation, peer evaluation, or some correlate of competence, but on the basis of actual performance in the task. Patel and G m n (1991) suggest that standardized categories along the competence continuum (layperson, beginner, novice, intermediate, subexpert and expert) would be useful. An alternative category system is provided by Dreyfus and Dreyfus (1986) who see experts proceeding on the continuum in the following sequence: novice, advanced beginner, competence, proficiency, and finally expertise. Nevertheless as Salthouse (1991) notes, establishing boundaries between each subgroup is just as difficult and artificial a distinction as dichotomizing experts and novices.

8. As Ericsson and Smith (1991) note, two features distinguish the expertise approach within what has previously been called a bottom-up analysis. First, it is necessary to design a series of representative tasks that capture the superior performance seen in the domain and elicit it under laboratory conditions. Second it is necessary to discover the mediating mechanisms of superior performance and analyze the types of learning and adaptation of the mechanisms that occur both in real world performance and the laboratory tasks. Within the expertise approach and particularly with motor output the issue of measurement becomes critical. In motor behaviour research there has been an explosion of measurement technology in the last ten years. We now have infrared, auditory, and optical systems for plotting and analyzing movement in three dimensions. From eye movement systems to motion analyzers the complexity of measurement has mushroomed. This has complicated the issue of what are the best measures of expert performance in the lab and in the real world skill. An interesting and innovative study by Ripoll (1991) illustrates the value of this technology in analyzing expert performance. Using a corneal reflection eye movement analysis system he was able to demonstrate that in expert table tennis players the kind of visual information processing engaged in while performing hitting drills is very different from what occurs during actual competition. So we are beginning to understand that the underlying nature of performance even within expert subjects may vary depending on task uncertainty or demands of the specific situation (competition, importance of performance, etc.) Camahan (this volume) considers when three dimensional systems are of benefit to movement analyses and inherent drawbacks in the various systems. A more basic question that must be the focus of every expert-novice comparison is what propomon of the expert's skill is tapped by any one lab task and what proportion of the expert-novice difference can be reliably

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explained by the task. For many reasons the study of movement & different from the study of more traditional cognitive skills. Let's consider for a moment the difference between the recall of digits or letters and the recall of a series of ballet steps. In a digit recall task the subject is shown a list of digits for a limited period and immediately, or after a short time either with or without interference, is asked to recall the letters in sequence. Recall requires very little time, no special planning on the part of the articulators to say each digit, and one digit out of sequence may or may not influence subsequent recall. Serial position effects are common, and delay interval effects performance very little, unless interference has occurred. 9.

Now let's consider a dancer recalling a series of ballet steps. In a lab we might show the subject a series of structured or random ballet steps performed by another dancer on video, or simply list the steps on a stimulus card. Like the digit task the steps could require immediate recall, a short delay or follow interference. Recall consists of performing the steps in sequence and could be scored by a professional or videotaped for later analysis. But this is where the similarity ends. As the dancer sees the stimuli there is a translation process that must occur from labelling and understanding the steps to deciding how one's own body is going to reproduce the steps for recall. In the real world situation dancers use a system called "marking" for doing this. As they watch the steps they use their hands to "mark" where their feet will position for recall. Marking is usually done simultaneously with visual presentation of the stimuli and often in the delay interval before recall - a kind of motor equivalent of verbal rehearsal. Recall differs from digit recall in several ways. First it simply takes longer. Taking longer means that for each sequential step the delay interval has inherently also been manipulated. Because each step follows on another the previous step may serve as interference, and in fact if it was incorrect, may leave the body in a position totally incompatible with performing the next step correctly (on the wrong foot, or with incompatible arm position). As such, an error in recall may have far more serious effects on subsequent items, than would be the case in digit recall. While the serial nature of motor performance may affect recall differently, Salthouse's (1991) observation that expertise may circumvent the limits of seriality may be important. To date we have very little information on how serial production systems of motor behaviour change with expertise.

Another aspect that is difficult to assess in motor recall is the relative size of elements in a sequence. While it is true that digits may be made up of lines, angles, etc. it is fairly well accepted that a string of 8 digits has eight elements. But what constitutes a movement element? A dance step may involve foot, arm,hand and head placements, body elevation, and attitude. When a dancer recalls one step is it necessarily one element or an "inherent chunk" already comprised of several inseparable elements. All of these issues make the assessment of motor recall difficult but far from uninteresting. 10.

From a neuropsychological perspective we also know that motor information is indeed

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handled differently. The classic example of this is the case of Henry M. (Milner, Corkin, & Teuber, 1968), who as a young man underwent bilateral mesial temporal lobe ablations to treat epilepsy. Upon recovery from surgery H.M. suffered permanent memory deficit. He continued to show severe anterograde amnesia and was unable to learn new names or his address. While he could readily remember events from childhood, events prior to surgery were also lost. Further assessment of cognitive and learning skills revealed that H.M. was able to learn a variety of motor tasks. He could retain skill in star-mirror drawing and pursuit rotor, even though he could not remember ever attempting the tasks. In one study of the visual maze he was tested 2 years after training and showed 75% retention in spite of the fact he had no knowledge of ever being tested previously (Milner et al., 1968) In real life H.M. was able to work in a rehabilitation centre, mounting cigarette lighters on cardboard frames, a task he learned to do quite skilfully (Blakemore, 1977). Gardner (1975) has reported similar evidence from a patient with severe amnesic symptoms following a closed head injury. Although unable to recall the teaching sessions, the patient was able to learn and recall piano melodies. The study of amnesic disorders clearly shows that the acquisition and retention of procedural skill is very different from that of verbal on perceptual information. 11. In studies that involve children who are "motor experts" it is necessary to consider several inherent issues. In tasks that are primarily cognitive, examining children's acquisition and representation of knowledge has contributed greatly to our understanding of the development of expertise. An example of this was the pioneering work of Chi, who was able to develop a network representation of a four year old "dinosaur expert" (Chi & Koeske, 1983). Translating these questions to children who become very good at computer games, or tennis for example becomes more difficult.

First. regardless of how good the child becomes at skills that involve gross movements, it is likely that they could still be surpassed by an adult of even moderate ability, simply because of size and power limitations. With child prodigies in games such as chess this is not the case. Second, problems exist in assessing motor skill at various stages of development. A tennis example illustrates this. Take the case of a twelve year old nationally ranked player who is very skilled, consistent in performance, and uses a two handed backhand. The backhand is effective, strong and most importantly shows very low variability. A year later as the player develops she switches to a one hand backhand because with muscular and further neural maturation she now has the strength and control to master it. What was once a high skill, low variability stroke now becomes more efficient in the long run, but temporarily far more variable. Developmentally, children are likely to reach several performance plateaus, characterized by effective performance with low variability. In moving to the next skill level there may be. disruptions in performance and high variability. As long as variability is induced by constant physical changes it is difficult to assess "expertise" levels in motor skills. 12.

A final issue in the study of motor expertise is whether protocol analysis is an appropriate

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methodology for the elaboration of procedural skills. Protocol analysis uses think - aloud reports of an individual's introspections in a systematic way to catalogue a subject's current state of knowledge about a given problem and how the individual progresses to new knowledge by retrieving new inferences or using formal operators specific to the domain of knowledge (Reitman Olson & Biolsi, 1991). Protocol analysis and its methodological considerations and limitations are discussed elsewhere in great detail (Ericsson & Simon, 1984). While protocol analysis has been very useful in eliciting information from experts in such areas as physics problem solving, chess and bridge, it may not be entirely appropriate for use in motor skills. As Ericsson and Simon (1984) indicate, protocol analyses may be performed both concurrent with the task or retrospectively. Concurrent analysis while it is more powerful may be very difficult. and in some cases not be appropriate in skills where the subject is attempting to do the motor skill at the same time. As well, in concurrent analysis the assumption is essentially that the verbal output reflects what is operating in short term memory. In skilled motor performance however, automatic processes may bypass STM and not be accessible for report. A second issue with protocol analysis has to do with the attentional controls necessary to show that their elicitation had no influence on performance. A standard control procedure when using protocol analysis has been to have the subject perform both with and without giving

think - aloud protocols. Obviously having to think aloud should have no detrimental influence on performance. It is difficult to conceive of a motor skill where having to think aloud would not affect performance, and indeed few motor skill researchers have implemented this control procedure. A third criticism of the approach comes from researchers who produce expert systems. Ever since the advent of artificial intelligence, researchers have tried to produce "artificial experts" by having the computer follow rules used by masters in a piuticular domain. While computers have become far faster and more accurate than people in applying rules, master level performance has remained out of reach (Dreyfus & Dreyfus. 1986). Two problems arise in the production of expert systems from protocols.

[An expert's] knowledge is currently acquired in a very painstaking way; individual computer scientists work with individual experts to explicate the expert's heuristics - to mine those jewels of knowledge out of their heads one by one ... the problem of knowledge acquisition is the critical bottleneck in artificial intelligence. (Feigenbaum & McCorduck, 1983, pp. 7980.)

So while the first problem may be accessing the vast amounts of knowledge held by the expert, the second means that protocol analysis may have serious shortcomings. [A]n expert's knowledge is often ill-specified or incomplete because the expert himself doesn't always know exactly what it is

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he knows about his domain. (Feigenbaum & McCorduck, 1983, p.85.)

New insights into the modelling of expert performance Symbolic Connectionism Expertise research has evolved through two generations of theory building. The first view of the expert was as one who was particularly skilled at heuristic search. The early insightful works of Newell and Simon (1972) conceived of problem solving as search, and suggested that a relatively small number of heuristic methods for serial search could be applied across a number of domains. Very quickly this hypothesis was refuted by work in chess which showed the importance of large amounts of domain specific knowledge (Chase & Simon, 1973; de Groot. 1965). And hot on the heels of the chess work came studies in domains as varied as physics, Go, diagnosis of X-rays, and basketball. Complex problem solving in such real world tasks brought a certain ecological validity to research, and an excitement about the prospects of engineering real world expertise. This second generation of work on expertise, characterized by domain specific research, seemed to show that domain specific declarative knowledge was the way to go - and the more the better. But declarative memory wasn’t the only answer. Expertise depended on learning how to do something well and so procedural learning became of interest. Previous notions of heuristic search seemed to best describe how novices not experts functioned. As Holyoak (1991) suggests since most of the second generation theories were based on serial production systems (Newell, 1973) there was the first real opportunity for interdisciplinary studies of expertise both in cognitive science and artificial intelligence. In artificial intelligence production systems became the basis of the first expert systems. In cognitive science production systems became the core of Anderson’s ACT theory (Anderson, 1976, 1983, 1987), essentially a theory of knowledge compilation. From knowledge compilation the picture emerged of novices who first solve problems by weak methods (usually working backwards from the goal) and who gradually with more successful solutions develop automatic generation of specialized productions (allowing forward solutions from problem state to the goal). The eventual result is that the expert is able to reach solutions both more quickly and more efficiently. The move toward a third generation of theory building has been fuelled by both the popularity of connectionism or neural networks that has infused most other areas of cognitive science, and the problems that current theories have in accounting for many findings with regard to expert performance. Holyoak (1991) lists all of the major consistencies in expert performance demonstrated by second generation studies as follows: (1) experts perform complex tasks in their domains much more accurately than do novices; (2) experts solve problems in their domains with greater ease than do novices; (3) expertise develops from knowledge initially acquired by weak methods, such as means-end analysis; (4) expertise is based on the automatic evocation of actions by

Motor Expens

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conditions; (5) experts have superior memory for information related to their domains; (6) experts are better at perceiving patterns among task-related cues; (7) expert problem-solvers search forward from given information rather than backward from goals; (8) one's degree of expertise increases steadily with practice; (9) learning requires specific goals and clear feedback; (10) expertise is highly domain-specific; (1 1) teaching expert rules results in expertise; (12) performances of experts can be predicted accurately from knowledge of the rules they claim to use. However, he is then able to point to empirical inconsistencies with regard to each of these findings, and is disappointed with the lack of universality in any of these correlates of expertise. A third generation expertise approach may begin to answer some of the inconsistencies faced by the second generation of domain specific theories. Holyoak (1991) has termed this paradigm shift "symbolic connectionism". Though these terms may at first appear contradictory, he is of the opinion that while there is strength in the connectionist or neural network modelling of knowledge, the demise of the importance of symbols (inherent in second generation theories) is premature at this point in time. The connectionist viewpoint is described well by Tienson (1990) and generally involves a neural network of processing units or nodes that are connected by links. Each node is connected to many others, to and from which signals may be sent. A given node may receive or send signals to just a few other nodes or up to several dozen other nodes. The input to a node is similar to a simple electric message, or synaptic transmission in that it is essentially "on" or "off'. The signal may vary in strength however, and all of the input directed at a particular node determines its state of activation. Finally, sometimes a network allows for signals that may either excite or inhibit activation. Between nodes the links are similar to synaptic connections and thus have some degree of resistance. Consequently, the strength of a signal to node b may be both a function of the strength of the signal from a, but also the strength of the connection between the two nodes. The strength of connections between nodes is termed "weight". A higher weight means that a stronger signal was received along a connection with less resistance. While the properties of nodes are considered fixed, the weights may be determined by experience. Thus connectionist systems are capable of learning and becoming expert is essentially "getting your weights changed" Tienson, 1990, p.387). A primary difference between information processing architecture and connectionist architecture is that with connectionism there is no central processing or executive unit. All connections are local, so that each node knows only what it knows in relation to all of the units to which it is connected. As such no one node in the system knows what the system as a whole knows. Unlike the notion of parallel processing there are not parallel independent processes that are programmed or hardwired, there is simply simultaneous local processing throughout the whole system. While space prevents discussion of characteristics of such a system that allow such phenomena as feed forward processing, dismbuted representations and back propagation, learning occurs within the system. In conventional architecture, information is stored within

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memory systems. In a connectionist system information is actively represented by a pattern of activation. When that pattern is not in use it is nowhere in the system, yet information remains "in" the system'' in the sense that the weights between connections have been changed. Memories are not stored, they are recreated as symbols in active representation again and again. Connectionist theories will continue to have a major impact on how cognitive scientists view processing and learning, and although currently neural networks cannot answer all of the inconsistencies in expert-novice findings they may offer new ways of looking at some of the problems. Just how our view may change is illustrated in the following example. In a later chapter of this volume I discuss the associative learning problems encountered when macrosurgeons begin to learn microsurgical techniques. Essentially the visual cues that used to elicit a series of macromovements (i.e., rotating of wrists to perform sutures), must under the microscope, elicit a whole new set of micromovements. Nevertheless, the old condition-action links must be preserved when the surgeon returns to conditions and performs surgery at the macro level. Holyoak (1991, p.325-326) points out that a connectionist-style solution to this problem might preserve the existing excitatory connections from the visual cues to the required macromovements and add new inhibitory links between the two different sets of visual-to-motor connections. The context then would allow the surgeon to "flip-a-switch'' to choose which set of connections was appropriate for that task. This would allow the new skill (micromovements) to build on the old (by using preexisting connections among condition cues) while minimizing the amount of interference between the two. While symbolic connectionism is only one possible direction for the development of expert-novice paradigms, it certainly merits consideration in future. Many of these issues are addressed in the following chapters, in which the basic method of comparing expert to novice performers is applied across a wide range of skills and situations. We do not promise solutions, but we certainly hope to hold your interest.

References Ackerman, P.L. (1988). Determinants of individual differences during skill acquisition: Cognitive abilities and information processing. Journal of Experimental Psychology: General, 11 7 , 288-318. Adam J.J., & Wilberg, R.B. (1992). Individual differences in visual information processing rate and the prediction of performances in team sports: a preliminary investigation. Journal of Sports Sciences, 10, 261-213. Allard. F., & Starkes, J.L. (1991). Motor skill experts in sports, dance, and other domains. In K.A. Ericsson & J. Smith (Eds.). Toward a general rheory of expertise (pp.123-152). Cambridge: Cambridge University Press. Anderson, J.R. (1976). Language, memory, and rhoughr. Hillsdale, N.J.: Erlbaum. Anderson, J.R. (1983). The archirecrure of cognition. Cambridge, MA: Harvard University Press.

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Anderson, J.R. (1987). Skill acquisition: compilation of weak-method problem solutions. Psychological Review, 94, 192-210. Blakernore, C. (1977). Mechanics of the mind (pp.93-98). Cambridge: Cambridge University Press. Bloom, B.S. (1985). Developing talent in young people. New York Ballantine Books. Chamess, N., & Campbell, J.I.D. (1988). Acquiring skill at mental calculation in adulthood: A Experimental Psychology: General, 117, 115-129. task decomposition. Journal of Chase, W.G., & Ericsson. K.A. (1981). Skilled memory. In J.R. Anderson (Ed.),Cognitive skills (pp.141-189). Hillsdale, N.J.: Erlbaurn. and their acquisition Chase, W.G., & Simon, H.A. (1973). The mind's eye in chess. In W.G. Chase (Ed.) Visual information processing (pp.215-281). New York: Academic Press. Chi, M.T.H., & Koeske, R.D. (1983). Network representation of a child's dinosaur knowledge. Developmental Psychology, 19, 29-39. de Groot, A. (1978). Thought and choice in chess. The Hague: Mouton (Original work published in 1946) D e w , I.J., & Mitchell, H. (1989). Inspection time and high-speed ball games. Perception, 18, 789-792. Dreyfus, H., & Dreyfus, S. (1986). Why skills cannot be represented by rules. In N.E. Sharkey (Ed.), Advances in Cognitive Science 1 (pp.315-335). Chichester: Ellis Honvood Ltd. Ericsson, K.A., & Crutcher, R.J. (1990). The nature of exceptional performance, In P.B. Baltes, D.L. Featheman, & R.M. Lerner (Eds.), Life-span development and behavior Vol.10 (pp.187-217), Hillsdale, N.J.: Erlbaum. Ericsson, K.A., & Simon, H. A. (1984). Protocol analysis: verbal reports as data. Cambridge, MA: Bradford Boods / MIT Press. Ericsson, K.A., & Smith, J. (1991). Prospects and limits of the empirical study of expertise: and introduction. In K.A. Ericsson & J. Smith (Eds.) Toward a general theory of eqmiw (pp. 1-38). Cambridge: Cambridge University Press. Feigenbaum, E., & McCorduck, P. (1983). The fifih generation: artificial intelligence and Japan's computer challenge to the world. Reading, MA: Addison-Wesley. Fischman, M.G., & Schneider T. (1985). Skill level, vision and proprioception in simple onehand catching. Journal of Motor Behaviour, 17, 219-229. Fleishman, E.A. (1966). Human abilities and the acquisition of skill. In B.A. Bilodeau (Ed.), Acquisition of skill.(pp.147-167). New York Academic Press. Fleishman, E.A. (1972). Structure and measurement of psychomotor abilities. In F. Urbach (Ed.). The contribution of behavioral science to instructional technology. V01.3. The psychomotor domain of learning. Washington, D.C.: Gryphan House. Fleishman, E.A., & Rich, S. (1963). Role of kinesthetic and spatial-visual abilities in perceptual motor leaming. Journal of Experimental Psychology, 66(5), 301-3 12. Gardner, H. (1975). The shattered mind. New York: Alfred Knopf. Gentner, D.R. (1988). Expertise in typewriting. In M.T.H. Chi, R. Glaser & M.J. Farr (Eds.). The nature of expertise (pp. 1-21). Hillsdale, N.J.: Erlbaum. Holyoak, K. (1991). Symbolic connectionism: toward third-generation theories of expertise. In K.A.Ericsson & J. Smith (Eds.) Toward a general theory of expertise (pp.301-336).

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Cambridge: Cambridge University Press. Milner, B. Corkin, S., & Teuber, H.L. (1968). Funher analysis of the hippocampal amnesic syndrome: 14 year follow-up study of H. M. Neuropsychologia, 6: 215-234. Newell, A. (1973). Production systems: Models of control smctures. In W.G. Chase (Ed.) Visual informarion processing (pp. 463-526). New York: Academic Press. Newell, A., & Simon, H.A. (1972). Human problem solving. Englewood Cliffs, N.J.: PrenticeHall. Patel, V.L.,& Groen, G.J. (1991). The general and specific nature of medical expertise: a critical look. In K.A. Ericsson & J. Smith (Eds.). Toward a general theory of expertise (pp.93125). Cambridge: Cambridge University Press. Pew, R.W. (1966). Acquisition of hierarchical control over the temporal organization of skill. Journal of Experimental Psychology, 71, 746-77 I . Posner, M.I. (1988). Introduction: What it is to be an expert? In M.T.H. Chi, R. Glaser & M.J.Farr (Eds.) The nature of experrise (pp.xxix-xxxvi). Hillsdale, N.J.: Erlbaum. Proteau, L., Marteniuk, R.G., Girouard, Y., & Dugas, C. (1987). On the type of information used to control and leam an aiming movement after moderate and extensive training. Human Movement Science, 6 , 18I - 199. Poulton, E.C. (1957). On prediction is skilled movements. Psychological Bulletin, 54,467-478. Reitman Olson, J., & Biolsi, K.J. (1991). Techniques for representing expert knowledge. In K.A. Ericsson & J. Smith (Eds.) Toward a general theory of expertise (pp.240-285). Cambridge: Cambridge University Press. Ripoll, H. (1991). The understanding-acting process in sport: The relationship between the semantic and the sensorimotor visual function. International Journal of Sport Psychology, 22, 221-243. Robertson S.. Collins, J., Elliott, D., & Starkes, J. (1993). The influence of skill and intermittent vision on dynamic balance. Manuscript submitted for publication. Salthouse, T.A. (1984) Effects of age and skill in typing. Journal ofExperimenra1 Psychology: General, 113,345-371. Salthouse, T.A. (1991). Expertise as the circumvention of human processing limitations. In K.A. Ericsson & J. Smith (Eds.) Toward a generul theory of expertise (pp.286-300). Cambridge: Cambridge University Press. Simon, H.A., & Chase, W.G. (1973). Skill in chess. American Scientist, 61, 394-403. Sloboda, J. (1991). Musical expertise. In K.A. Ericsson & J. Smith (Eds.) Toward a general theory of expertise, (pp. 153-17I). Cambridge: Cambridge University Press. Starkes, J.L., & Deakin, J. (1984). Perception in sport: a cognitive approach to skilled performance. In W.F. Straub & J.M. Williams (Eds.) Cognitive sport psychology, (pp.115-128). Ithaca, New York: Sport Science Associates. Tienson, J.L. (1990). An i n d u c t i o n to connectionism. In J.L. Garfield (Ed.) Foundations of cognitive science. (pp.381-397). New York: Paragon House.

COGNITIVE ISSUES IN MOTOR EXPERTISE J.L. Stakes and F. Allard (Editors)

0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 2 COGNITION, EXPERTISE, AND MOTOR PERFORMANCE FRAN ALLARD Department of Kinesiology, University of Waterloo Waterloo, Ontario, N2L 3Gl

The purpose of this book is to collect and evaluate recent work on the study of experts in motor performance. Some of the following chapters deal with describing differences in motor performance that discriminate skilled from less skilled individuals, and some of the chapters deal with cognitive differences. It is the latter proposition-that cognitive skill is an important component of skilled motor performance-that is contentious. Understanding how the motor system is controlled to produce even simple movements has proven to be very difficult (see, for example, Jordan & Rosenbaum, 1989). For sure, it is not feasible that a "central executive" specifies the contractions of each and every muscle involved in performing a movement; the control of movement must be distributed throughout the nervous system. How then could cognition be relevant to such a system in any role other than determining the overall goal of the movement? Or as baseballs' Yogi Berra said about batting: "How can you hit and think at the same time?" (Dickson,l992). This chapter will make the case that the study of expert sport performers shows the importance of cognition in the production of movement, however messy this proves to be for theories of motor control. The approach used by many investigators in this book is known as the "expertise" approach (Ericsson & Smith, 1991). In this approach, recognized experts in a particular skill domain are compared to non-experts in tests thought to reflect components required to perform well in the domain. If experts are better than non-experts at the experimental tasks, the tasks must reflect some knowledge or ability required for expert performance. The archetypal use of the expertise approach is Chase and Simon's (1973) investigations of the nature of expertise in chess. One of Chase and Simon's tasks required players of different skill levels to study a chess board for 5 seconds, then to recall the positions of as many pieces as possible on a second board. Half of the boards presented were actual game positions, while the remaining boards contained randomly placed pieces. Better players correctly recalled the position of more pieces than lesser players only for the actual game boards, leading Chase and Simon to conclude that chess skill was a function of understanding and encoding the relationships between pieces, rather than superior memory skill. Therefore, in the case of chess, expertise is something acquired through practice at the game rather than an inherent difference between players. The notion of

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expertise as something acquired by the performer, rather than an innate talent possessed by the individual, be this talent thought to be general (IQ) or specific abilities (spatial ability, movement speed), is the defining characteristic of the expertise approach. Expertise is the interaction of the individual with a particular environment, not a main effect of individual or of task (environmental) demands. From this brief description of the expertise approach, it is apparent that any experimenter wishing to use this approach is faced with two problems: determining a criterion for what constitutes an expert, and selecting tasks that evaluate the components of the skill. Ericsson and Smith (1991) have recently proposed solutions for both of these problems. They define expertise as "cases in which the outstanding behaviour can be attributed to relatively stable characteristics of the relevant individuals" @.. 2), thus confining the use of the term expert to individuals with a consistent record of excellence in a particular domain. Ericsson and Smith's advice on task selection in a particular skill domain is to "capture the essence of superior performance under standardized laboratory conditions by identifying representative tasks" (p. 12). "Representative tasks" are tasks that allow evaluation of knowledge needed to perform the full-blown skill, for example, making an opening bid for bridge players (Charness, 1979). These tasks, because they are parts of the whole skill, permit an analysis of the cognitive processes important for their Performance, processes such as search, recognition, computation. The final step in understanding expertise is describing how it is acquired what kinds of knowledge are critical for the particular domain? What type of learning best produces the knowledge? (See Ericsson and Smith for a much better statement of what is summarized here). The expertise approach has been utilized in many cognitive domains such as the study of the nature of skill in the games of chess (Chase & Simon, 1973) and bridge (Chamess, 1979). in medical diagnosis (Patel & Groen, 1991). in computer programming (Adelson & Soloway, 1988), in music (Sloboda, 1991), and in physics problem solving (Anzai, 1991). The work on expertise in cognitive skills is the focus of two recent books: Chi, Glaser, and Farr (1988) and Ericsson and Smith (1991). But what is the application of this approach to the study of expertise in motor performance? Before looking at the role of knowledge in motor skill, we will look at other explanations that have been proposed to explain experthovice differences in the context of sport and motor performance. Approaches to the Study of Expertise in Skilled Motor Performance The study of what makes one athlete better than another has been of interest to sport scientists for many years; this work has resulted in a rich and multifaceted literature in the traditional fields of exercise physiology, biomechanics, sport sociology, social psychology, and motor leaming/control. Many investigators in motor conuolflearning have been concerned with issues of defining and describing expertise, and have adopted a number of different approaches to the problem. Investigators have conceptualized expert motor skill as being a feature of the individual, as being dependent on the pickup of information readily available in the environment,

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or as being the product of a particular individual in a particular environment. In order to contrast the different ideas on the nature of skill offered up by investigators in motor learning and control, let us focus on one particular skill: the skill involved in hitting a rapidly moving ball, as must be done in baseball or cricket batting, and in racquet sports such as tennis, table tennis, badminton, and squash.

Skill is in the person: The Abilities Approach According to this approach, each individual is comprised of bundles of fundamental motor abilities, which are combined in the performance of more complex skills. If an individual is assessed as having lots of a particular ability (i.e., speed of response), this individual should perform better than lesser scoring individuals in any task requiring this ability (i.e.. returning a hard serve in tennis). As well, this speedy individual should be good at all tasks requiring speeded responses: batting a baseball, flying a fighter jet, playing video games. (a)

Edwin Fleishman is the individual who has been most influential in presenting the case for the importance of abilities in human performance. According to Fleishman, "An ability refers to a more general capacity of the individual related to performance in a variety of human tasks ... Both learning and genetic components underlie ability development" (Fleishman & Quaintance, 1984, p. 162-163). Fleishman distinguishes between abilities and skills, with skills being defined as "... the level of proficiency on a specific task or group of tasks. The development of a given skill or proficiency on a given task is predicated in part on the possession of relevant basic abilities" (Fleishman & Quaintance, 1984, p. 163). Fleishman's factor analytic studies have shown there to be eleven psychomotor abilities: control precision, multilimb coordination, response orientation, reaction time, speed of arm movement, rate control, manual dexterity, finger dexterity, arm-hand steadiness, wrist-finger speed, and aiming. (Fleishman & Quaintance, 1984). As well, there are nine physical proficiency factors that have been described by Fleishman and his colleagues: extent flexibility, dynamic flexibility, explosive strength, static strength, dynamic strength, trunk smngth, gross body coordination, gross body equilibrium, and stamina (cardiovascular endurance). (Fleishman & Quaintance, 1984). By determining which abilities are important for particular tasks, Fleishman's measures should be useful for job selection. However, the relationship between abilities and actual performance is complicated by the finding that the pattern of abilities required in a particular task changes with practice (Fleishman & Hempel, 1954). In terms of predicting which individuals will do best on a complex motor task, the abilities approach has not proven to be particularly powerful (see Baba, chapter 4, this volume). As well, Starkes and Deakin (1985) have shown that field hockey players of differing levels of expertise (national team, university team, a class of beginners) do not differ on tests of abilities that would appear to be components of the game (dynamic visual acuity necessary to follow a rapidly moving ball, simple reaction time necessary for reaction to opponents moves, coincident timing necessary for predicting when a ball or an opponent will be at a particular point). Thus,

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athletes of different performance levels cannot be discriminated by measuring their abilities.

Were all motor skills to be constructed from the building blocks of abilities, individuals who are good at a particular motor skill such as tennis should also be good at other skills that require the same kind of abilities, for example squash. As Jones (this volume) points out, the specificity of learning that is so characteristic of motor skills (see Schmidt, 1988) is conuary to this idea. There is something so compelling about the notion that abilities determine motor skill that a second generation of abilities research has evolved (the idea that would not die). Jones (this volume) describes a "modular approach" to motor skill performance, with separate modules replacing abilities in being responsible for the timing, force, and sequencing of skilled motor acts.

In yet another variation on the abilities theme, Ackerman (1987) has united abilities with the idea that a motor skill learner passes from a cognitive stage to an automatic stage (Fitts, 1964) as a skill is acquired. Since skill learning begins with the cognitive stage, it is general cognitive ability which should predict early performance. Only following much practice, when the learner is in the automatic stage, will psychomotor abilities such as perceptual speed be related to performance. Thus psychomotor abilities constrain the final level of skill acquisition, rather than predict initial acquisition. Others have proposed that the ability to perform basic information processing operations is related to sport skill. For example, Deary and Mitchell (1989) have reported a substantial correlation for inspection time and cricket batting average. Inspection time is the exposure duration needed by a subject to determine with 85% accuracy which of two parallel vertical lines is longest when the lines are followed immediately by a mask. The ability to make rapid discriminations about visual stimuli would seem to be important in fast ball games such as cricket. The ability to allocate attention has been advanced as an important aspect of sport skill

(see Nougier, Stein, & Bonnel, 1991, for a recent review), although it is difficult to distinguish between attentional differences in individuals caused by participation in a sport, and trait-like individual differences in attention. Finally, Whiting (1991) proposes that the relationship between abilities and skill may be non-linear. This conclusion is based on a study of table tennis players of varying abilities who were tested for choice reaction time and for time to decide direction (right, left, straight) of a projected table tennis ball (Whiting & Hutt, 1972). CRT was highly correlated with decision time (r;.83) for non-players, moderately correlated for average players (r=.67), but not significantly correlated for advanced players (p.41).Thus abilities may be important early in learning, while other factors dominate after exposure to the game.

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Inspite of the popularity of the idea, there is little experimental support for the notion that the level of accomplishment of an individual performer can be predicted from measured abilities. The role of such higher level abilities as timing or inspection time await further studies to confirm their importance in skilled motor performance.

All the information is in the environment: the ecological perspective Ecological psychology has made a major impact in the area of motor learninglcontrol. According to this perspective, perception and action are woven together such that perception guides action and action provides richer perceptual data. The perception-action link is so direct that no cognitive activity intervenes between them.

(b)

Investigators working from this perspective often use highly skilled performers, for example Lee, Lishman, and Thomson’s (1982) study of the use of vision to regulate the approach to the take-off board in long jumpers, and Bootsma and van Wieringen’s (1990) study of table tennis players hitting an attacking forehand drive. Both of these studies illustrate the exquisite motor control of the expert performers observed, and show how variability across trials in the performance of experts is often functionally linked to accomplishing the goal of the action. Thus the long jumper varies stride length over trials as she approaches the take-off board in order to decrease footfall position and hit the board with greater precision. Table tennis players show a coupling between the initiation time of the drive and the acceleration of the swing, such that drives initiated early are performed more slowly (with less acceleration). Neither Lee et al. (1982) nor Bootsma and van Wieringen (1990) have compared experts to novices on long jumping or on hitting table tennis drives. Because novices have the same perceptual-motor systems as do skilled performers, the same coupling of perception with action should be a feature of the performances of both types of subjects. Presumably, much of the variability in the performance of novices is due to noise in the system which occurs in addition to functional variability. On the other hand, variability in experts’ performance is functionally related to goal acquisition, making the nature of the relationship between the action and the environment easier to see in experts. Thus, experts are simply more experienced with exwcting the perceptual invariants or in producing adjustments to performance as a consequence of perceptual information than are less skilled performers. Thus ecological investigators would expect no influence of cognition on motor performance, and little difference in the perceptual information that controls the performance of skilled and less skilled performers. However, there are many studies that have shown differences in the nature of the information used by skilled and less skilled athletes in the performance of sport related experimental tasks, studies done from an information processing perspective. Skill is the product of both the individual and the environment: information processing differences in skill According to the information processing approach to skilled motor performance, any skilled action is the product of a chain of events, beginning with the analysis of sensory data,

(c)

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followed by a decision about what movement to perform, and ending with the execution of the selected motor pattern (e.g., Marteniuk, 1976). This approach attempts to determine what happens at each step in the journey taken by information as it passes from one stage to the next by careful experimentation which isolates each relevant stage of processing. Of major concern in many information processing studies is the time it takes to perform the component task being investigated. In a classic example of this approach, Keele and Posner (1968) determined the time it takes to utilize visual information to correct an ongoing movement by holding the required movement constant and varying the presence of vision. Subjects learned to perform a six inch movement from a home position to one of two targets in a set of specified movement times. While subjects performed the timed target acquisition task, the room lights were extinguished on half of the trials as soon as the subject lifted off the home position, and did not go on again until the subject touched the target. Subjects moving at the shortest movement time (target time 150 msec., actual time 192 msec) showed no decrement in performance for trials in which the lights went out. For the longer movement times, lights on trials were significantly more accurate than lights off trials. This study shows that it takes a subject somewhere around 200 milliseconds to implement visual information about the accuracy with which a movement is being executed. A critical problem for the information processing approach comes in generalizing from laboratory findings to the performance of real world motor skills . That the results of the Keele and Posner study are generalizable has been established in a very clever study by Peter McLecd (1987, Experiment 1). He tested skilled cricket batsmen hitting balls bowled by machine. Pieces of dowel parallel to the flight of the ball were placed under a carpet at the point where the ball bounced. On one third of the mals, the ball did not hit the doweling, and the ball came straight through. On the other trials, the ball hit a piece of dowel and kicked left or right. A marker was placed on the end of the bat, allowing McLeod to analyze movements of the bat from a film record. The critical point was when in time the movement of the bat was different for balls moving left or balls moving right, a measure of how fast the batsman could react to the pitch. McLeod found this time to be 192 msec, virtually identical to Keele and Posner’s results with a pointing task. By its very nature, the information processing approach produces many small bits and pieces of knowledge about human performance, such as the estimate of the time it takes to utilize visual information that has just been reviewed. The problem comes in putting all the pieces together in order to understand the performance of a skilled motor act. The information processing approach is like taking apart an alarm clock; it is always easier to take the clock apart than it is to put it back together again. To provide an example of the alarm clock problem in skilled motor perforniance, consider the information processing steps involved in batting a baseball. According to an information processing analysis, batting a pitched ball involves first perceiving the speed and location of the incoming pitch, deciding on whether or not to swing at the pitch, and, should the decision to swing be positive, executing the swing. In the best tradition of information processing, Slater-Hammel and Stumpner (1950) set out to assess the

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time taken by these operations, studies done in order to determine how long a batter could afford to watch a pitch and still have enough time left to execute a swing. Slater-Hammel and Stumpner measured two types of "batting reaction time"; the time taken for the subject to initiate a swing of the bat in response to a visual signal (starting RT) and the time to adjust the trajectory of the moving bat by lifting it off a rail along which it was being moved (movement RT). Starting RT was measured to be .206 sec and movement RT was .269 sec. Translating these time into distances from the plate for a 95 mph and a 70 mph pitch, led Slater-Hammel and Stumpner to conclude that "To have sufficient time for a starting reaction time, the ball would have to be from 22 to 30 feet from home base. A movement reaction time would require that the ball be from 28 to 38 feet from home base." (p. 355). Hubbard (1955) pointed out several problems that arise when Slater-Hammel and Stumpner's data are applied to the real world task of hitting a baseball. Slater-Hammel and Stumpner's calculation of ball location at swing initiation considered only the time to initiate the swing, or to initiate a correction to the ongoing swing. The time it takes to actually swing the bat over the plate, the movement time of the bat, was not included in the calculation, and when it is included, there are great problems for an information processing approach. Slater-Hammel and Stumpner assumed batting to be a serial information processing task, involving a decision followed by a response. Thus the information processing time for batting can be determined by adding together batting reaction time (starting or movement RT) and batting movement time. Hubbard used Slater-Hammel and Stumpner's values of .21 sec for starting RT and .27 for movement RT, and the values of .16 seconds or .12 sec for movement time in his calculations. The time the batter actually has to decide and swing is a function of the time it takes for the pitched ball to reach the plate, a time which ranges from .83 seconds for a 50 mph pitch to .43 seconds for a 100 mph pitch. As Slater-Hammel and Stumpner had done, Hubbard translated times into distances, and calculated the distance of the ball from the plate when the batter had to have completed all information processing required to swing at the pitch. Unfortunately, for most pitches, the alarm clock problem emerges; the batter does not have enough time to do the required information processing. In Hubbard's words: To find the point at which the batter would have to get his signal to start to react, we multiply ball flight (60 ft. roughly) by the fraction, interval to "react and move" divided by interval of ball flight at the speeds of pitched balls. A ball travels 60 ft. at 50 mph in.83 sec., at 70 mph in .58 sec., at 95 mph in .43 sec., and at 100 mph, in .41 sec. Using mean figures for "starting reaction time" plus "movement time", we get for 50 mph. 32 ft., for 70 mph. 45 ft., for 95 mph, 52 ft., and for 100 mph. 54 ft. Using mean figures for "movement reaction time" plus "movement time", we get corresponding estimates of 32, 45, 60, and 63 ft. Using .20 sec. "movement time" the corresponding distances for "starting reaction time'' would be 30,42,57, and 60 ft., and for "movement reaction time" 34,49,65, and 69 ft. Note that for fast balls or for something like "movement reaction time" the batter may have to get his signal us or before rhe pitcher releases the bull. (Hubbard, 1955, p. 368).

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Hubbard (1955) also makes the point that it is incorrect to think of batting as a reaction time task. Unlike the standard RT task where the subject has little idea as to when the signal to respond will be given, the batter can see the pitcher wind up in preparation for the pitch, and has a very good idea of when the ball will become visible. In fact, Hubbard and Seng (1954) filmed major league batters during batting practice and showed that the step with the front foot made in anticipation of the actual swing was stvted at the release of the pitch, the start of the swing occurred very shortly (.04 sec) after the front foot was planted, and the temporal duration of the step was related to pitch speed. They also observed highly consistent swing times for their batters, a finding that has also been observed for cricket batsmen (McLeod & Jenkins, 1991). and for table tennis players (Bootsma & van Wieringen, 1990). Hubbard (1955) argues that batters gather visual information continuously and use the accruing visual information to decide whether to continue or to check the swing. A main difference between the information processing approach and direct perception is the emphasis of information processing on laboratory investigations of performance elements suspected as being important for skill. As has been shown, this approach has identified temporal constraints on motor behaviour, such as the time taken to make corrections to an ongoing movement on the basis of visual information. It has not done a good job of integrating the findings of the many lab studies that have been done into a coherent model of skilled motor performance. Modern Studies in Information Processing in Skilled Motor Performance Despite serious problems in explaining performance in fast ball games and heavy criticism from experimenters in direct perception, sport information processing research is flourishing (e.g.. the special issue on Information Processing and Decision Making in Suort of the International Journal of Suon Psvchology (Ripoll, 1991). Modem information processing research deals with the temporal constraints posed by fast ball games by proposing that motor skill experts use predictive information, information that is available before the pitch is released or the ball is served, to guide their response. Abernethy (1991) has summarized a series of studies performed in his lab at Queensland showing skilled badminton players are better than novice players at predicting the landing position of a shuttle when viewing film of an opponent’s stroke that is cut off at various times before, at, or immediately following racquet contact. Further investigations have been done in which films of players performing different strokes were edited to eliminate critical aspects of visual information (player’s arm,racquet, arm and racquet, head and face, unessential background information). Both expert and novice players showed poorer prediction of shuttle landing position when arm and racquet cues were not present. When arm cues were available, only the experts showed a significant improvement in accuracy, showing experts were able to anticipate the landing position of the shuttle from arm or racquet cues, while novice players relied on racquet cues. Eye movement recordings for both expert and novice players confirmed that increased attention was being paid to arm and racquet cues. Ripoll and his colleagues (summarized in Ripoll, 1991) have also used recordings of eye

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movements to determine the information pick up of skilled motor performers. Ripoll distinguishes between two different functions of visual information available to an athlete. "Semantic" visual information is used mainly in open sport situations, and acts to "identify and interpret the situation" (p. 222). The experiments of Abernethy (1991) just summarized show the use made by semantic visual information by badminton players. The second function of visual information -"sensorimotor" visual functioning- is vision used in the service of actually performing an action, "for example, calculating the time to contact needed to release the strike and coordinate the visual and motor systems involved in the stroke" (p. 222). Ripoll experimentally discriminated sensorimotor from semantic functiorhg by measuring the eye movements of expert table tennis players in two situations. The first situation had the players return balls in drill situations where there was no uncertainty about the stroke to be used in the return. This situation assessed the sensorimotor function of vision: the only information required to make the stroke was the flight path of the incoming ball. The second situation involved playing a match, with lots of uncertainty about what the opponent would do next. Eye movements observed in the drill condition were anticipations, looking ahead of the current position of the ball and in the direction of the shot just played. Rarely was the opponent fixated in this condition. In the match condition, the opponent and the early flight of the ball became the focus of attention as the player determined the type and location of the incoming stroke. Ripoll's data are important because they show that ecological perception is only a part of the story. In the drill situation, the players well might be using time to contact to hit the return stroke. However, when the situation becomes more complex, the expert player seeks predictive information, as would be expected from Abemethy's work, in addition to the strictly sensory information utilized in the drill condition. As mentioned earlier, Nougier, Stein, and Bonnel (1991) have proposed that experts in motor performance differ from novice performers in how efficiently attentional resources are deployed. Thus, another major difference between experts and novices is that experts reduce the amount of information that needs to be processed in the very short periods of time available to them. Nougier et al. provide examples of how conceptual models of attention used in cognitive psychology (i.e., controlled vs. automatic processing, cost-benefit analysis, signal detection theory) may be used to assess attentional differences in expert and novices. As well as differing in cognitive information processing, expert athletes well might differ from novices in the variability with which they perform skill elements. "Variability" here means that what is requested of the motor system is executed with greater consistency for experts. Such consistency of performance is critical for the closed skills of figure skating, gymnastics, and diving. As well, studies of timing variability for skilled open skill performers in table tennis ( Bootsma & van Wieringen, 1990) and cricket (McLeod & Jenkins, 1991) show little movement time variability. Indeed, McLecd, McLaughlin, and Nimmo-Smith (1985) have shown that unpractised subjects are capable of a high degree of consistency and accuracy in timing the initiation of a striking movement. Clearly, more work needs to be done to evaluate actual

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performance differences in expert and novice athletes. These studies require experimenters who have enough experience with sophisticated biomechanical techniques to measure and analyze three dimensional records of complex movement patterns (see Carnahan, this volume). Thus, there am many possible locations to search for information processing differences between expert and novice motor performers. One problem is that no matter where you look for differences. you most always find them. Rarely is any effort ma& to determine how much each new expert-novice difference in information processing contributes to overall differences in performance. McLeod and Jenkins (1991), commenting on the many studies of information processing differences for expert and novice athletes, point out that the magnitude of the cognitive differences observed is often much smaller than differences in sport performance for the two groups. The interesting fact about many of these studies is not that there is an expertnovice difference (it would be remarkable if there were not one), but how small the effects are in many of the studies. Take Abernethy’s (1989) study of experthovice differences in the ability to predict where a badminton shot would send the shuttle, for example. His expert group included players up to national level; his novices had never played the game competitively. It would be difficult to imagine a wider span of skill differences than this, and yet he found a superiority of about 10% for his experts. Similarly small differences appear in many other studies of experthovice differences. The task for sports science is not to go on showing experthovice differences in yet more sports. Their mere existence is neither surprising nor interesting. What is required is to show whether the differences that exist are sufficient to explain the dramatic differences in performance between experts and novices or whether we should be looking elsewhere. (McLeod & Jenkins, 1991, p. 291). I n Summary To summarize, the existing literature in motor controVleaming shows little support for expednovice differences being primarily due to differences in simple abilities such as speed of response or visual acuity. The importance of more complex ideas about the nature of abilities, for example, the idea that abilities are modular, remains to be determined, as does the contention that simple abilities consaain the final level of performance rather than predict the initial level of performance.

Ecological psychologists who study perception-action links have almost always studied skilled performers; for skilled performers, variability in performance is typically functional, while for less skilled individuals, variability is also produced by noise somewhere in the system. A major plank in the platform of ecological psychology is to eliminate explanations of motor performance that rely on cognition. It is difficult to know to what non-cognitive explanation

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is able to account for the reduction in the noise in the system with practice. Finally, information processing studies have shown many differences between expert and novice performers, especially for skills that must be performed very rapidly such as hitting a ball or a badminton shuttle. Most notably, experthovice differences have been shown for picking up information predictive of what is to come and for attentional focus as evaluated through eye movement recording. Whether the magnitude of experthovice differences observed in these tasks is sufficient to account for experthovice differences in performance has not been established.

The Expertise Approach and Motor Skill: Can Knowledge Influence Performance? This chapter began with a brief outline of the study of expertise as investigated by cognitive psychologists, then described what has been discovered about the nature of motor skill expertise by investigators in motor learning and sport science. Before making a case for the application of the expertise approach used in cognitive psychology to motor skill, it is important to consider the relationship between cognitive and motor skills: are there any similarities between cognitive and motor skills? One major difference between the two types of skill comes from the declarative nature of much of cognitive skill and the procedural nature of much of motor skill. Because of their declarative nature, cognitive skills are amenable to investigation using such experimental tools as protocol analysis. Motor skills are performed rapidly and, in comparison to cognitive skills, unconsciously. Given such differences, is there any evidence that a factor shown to be critical for skill in cognitive tasks, knowledge, is important in skilled motor performance? In other words, can declarative knowledge impact motor performance? There are two different types of knowledge that are of importance to expert motor performers. First, as in all human activities, motor experts perform in context, in a particular environment. Basketball players, for example, need to know the rules of the game, tactics and strategies, and terminology, in order to play the game effectively. This type of knowledgeknowledge about the game-is strictly verbal or declarative, and may be related to performance skill. However, sports commentators and sports fans, individuals who often are unable to perfom the skills required of the game, share this declarative knowledge with the coaches and players who are able to perform the skills. Thus knowledge about the game may be more a product of familiarity with the particular sporting environment than an essential component to skilled performance. This issue is the focus of the chapter by Allard, Deakin, Parker, and Rodgers in this book. The second category of knowledge is knowledge that can be used in the effective performance of motor skills in a particular domain-knowledge required for performance. Thus the baseball batter must know the speed and location of the incoming pitch in order to know when and where to swing the bat. The second category of knowledge sounds identical to Ripoll’s (1991) sensorimotor function of vision. A difference is that Ripoll’s concern is with describing the different uses to which visual information can be put in performing motor skills. The concern

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here is with knowledge-the contents of the mind-and not with information from the senses. Is there any evidence that skilled motor performers are able to use such “inside the head” knowledge to improve ongoing performance? Indeed, what form would such evidence take?

A first criterion for the impact of knowledge is that it should improve performance. Therefore, a particular player should perform better at those times when he or she has knowledge than those times when knowledge is not available. Knowledge about what is likely to happen next is used all the time in sports. Knowing that a hockey player almost always shoots high to the left lets a goaltender adjust his or her position before the shot is taken to be optimally placed to make the save. Such anticipatory preparation is performed before an actual movement takes place, and does not impact the actual performance of the skill. Thus, a second criterion for a skill able to show the impact of knowledge is that knowledge can only operate during the actual execution of the skill. Finally, ecological psychology emphasizes the close relationship between perception and action, such that incoming perceptual information can be used to drive action in a virtually continuous fashion. A third criterion for showing evidence for the role of knowledge in motor control is that the skill is performed rapidly, with little opportunity for the intervention of incoming sensory information. Thus, data supportive of the importance of knowledge for skill should have the following characteristics: 1. Adding knowledge while all other sources of information remain constant should result in improved performance. The knowledge should not produce a physical adjustment performed in advance 2. of the movement that improves performance. There should be minimal time during the execution of the skill for guidance or 3. correction based on incoming sensory information. A sport skill that fulfils all the above requirements is batting a baseball. According to the rules, the batter must remain in the batters’ box for the duration of a pitch, and can consequently adjust only position (i.e., take a position in the front or back of the batters’ box ) and posture (is., take an open or closed stance) before each pitch. The time taken by a major league pitch to travel to the batter is very short, ranging from 580 msec for a 70 mph pitch to 410 msec for a 100 mph pitch. It takes the batter from 280 msec to 190 msec to swing the bat to make contact with the pitch (Breen, 1967). a time that remains constant for a particular batter (Hubbard & Seng, 1954) regardless of the speed of the pitch. According to Kirkpatrick (1963), the ball crosses the plate in about 10 msec., making the timing of the swing crucial. As well as timing the swing, the batter must also control the location of the bat. Kirkpatrick (1963) describes the physical factors that come into play when striking a ball with a bat as follows.

The state of the bat at the moment of contact with the ball is defined by 13 independent variables, all of which are subject to the batter’s control. These qualities are the 3 positional coordinates of the mass centre (or other reference point) of the bat, 3 coordinates of angular orientation, 3 of linear momentum, 3

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of angular momentum, and 1 coordinate of time. In his control of any one of these variables, the batter may err in either the positive or the negative sense, so it appears that he is faced at the outset with 26 roads to failure. (p. 606) It is important to point out that the task for a batter in a real game is not simply to make contact with the ball: the ball must be hit so that it lands within the field of play, and so that it eludes the best efforts of the defensive team to make the batter out. Most often, for reasons of strategy, the demands are even more specific; for example, the batter may be called upon to hit a long fly ball to the outfield (a "sacrifice fly") in order to score a runner from third base, or to hit to the right side of the field to advance a runner who is on first base. Given the demands on the batter, is there any evidence that knowledge aids the performance of the batter? Each confrontation between a pitcher and a batter during a baseball game is known as

an "at bat". The goal of the pitcher is to get the player out. This can be done in a variety of ways, the main ways being by throwing three strikes (a smke being any pitch the batter swings at or any pitch passing across the plate in an area roughly between the batter's knees and chest) past the batter, by the batter hitting a ball in the air that is caught by a defender, or by the batter making contact with the ball but not making it to a particular base before the ball does. The batter's goal is to get at least to first base which can be accomplished by hitting the ball out of the range of the defenders, or by having the pitcher throw four balls. Each pitch thrown during an at bat occurs in the context of a particular ball-strike count. There are twelve possible counts, and according to baseball lore, some counts work in favour of the pitcher, while others favour the batter. In particular, those counts in which the batter has two strikes are thought to favour the pitcher, if the batter does not swing at the next pitch, he/she faces the possibility of striking out. Such an anxious batter is likely to swing at a bad pitch. Other counts work in favour of the batter, in particular, those counts in which the batter has two or three balls and no strikes. If the pitcher does not throw a smke on the next pitch, he/she faces the possibility of walking the batter. Knowing that the pitcher cannot afford to miss the smke zone means the batter can watch for a pitch that is easy to hit. As sports writer Leonard Koppett describes the confrontation between pitcher and batter: The first rule of effective pitching is to stay ahead of the hitter most of the time. If the first pitch is a smke, the arithmetic shifts way over in favor of the pitcher. Now he can miss the strike zone three times without issuing a base on balls, and has to hit it only once to give the hitter the problems that come with a two-strike count. In other words, if the first pitch is a strike, the pitcher has plenty of margin for error in wying to get the batter to swing at borderline strikes. Even if only one of the next four pitches hits the mark, he still has an even chance with the count 3-2. On the other hand, if the first pitch is a ball, things are not so good. If the second pitch is also a ball, the pitcher is in a real hole with a 2-0 count; he musr come in with three of the next four pitches, and the chances of throwing

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F.AIlard something the hitter wants are much greater. (Koppett, 1991, p. 56-7.)

Any data that show hitting performance changes with the count would qualify batting a baseball as evidence for knowledge impacting motor control according to the three criteria described earlier. Baseball is a sport justifiably famous for the size of the statistical data base that exists for almost every component of the game. In fact, each pitch thrown in a major league game is recorded for posterity. Using the pitch by pitch data, Dewan and Zminda and Stats, Inc.(1990, p. 257) have calculated the probability of a hit occurring as a function of the count on which the at bat ended. The averages include the at bats of all major league players excluding pitchers in both American and National Leagues for the 1989 season, a total of 576,150 at bats. These data shown that batters are more successful for favourable counts (3-0,3-1,2-0), with the mean of the averages for these counts being ,343, than for unfavourable counts (0-2, 1-2, 2-2), the mean of these averages being .175. The data show even more dramatic differences for slugging percentage (.244 and 3 8 ) and on base percentage (.179 and .638). Thus major league hitters do show improved hitting as a function of the ball-strike count.

It might be argued that batters, realizing that the ball will most likely be a strike for favourable counts, decide to swing and hope for the best. Were this the case, there should be a higher percentage of swings taken in favourable counts. In fact, the number of swings taken (the sum of swinging strikes, foul balls, and balls in play) as a percentage of pitches seen is lower for favourable counts (36% of pitches) than for unfavourable counts (58%). Whatever the players are doing to improve hitting, it is not a simple "response bias"; players do not swing more often in favourable counts (data from Dewan et al., 1990). The batting data are important because the information available from the environment does not change as a function of the count. This means the same information about time to contact is available in all counts. Pitchers work very hard to not "telegraph" the type of pitch by throwing different pitches with the same arm motion, thus reducing predictive cues as much as possible. This is not to say that time to contact and advance cues are not important for batting. It is to say that professional batters use all the information available in the situation, including declarative information such as the ball-smke count and knowledge of the pitcher's repertoire. The improved performance shown by batters is a warning that skill must be studied in context: it would be impossible to observe effects of knowledge were batters to be evaluated while hitting balls delivered by a pitching machine or while making decisions based on controlled views of single pitches. The use of knowledge to aid motor performance is not unique to batting a baseball. The game of cricket, so incomprehensible to the North American mind (this one, anyway) is even more strategic because of fewer constraints on the positioning of the fielders, the incredible variety of pitcher a batsman must face, and the length of time of a match. A cricket batsman must certainly be able to make contact with the ball, but equally important in scoring runs are such factors as where the shot is placed, and how many runs are taken.

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The batting data show that knowledge is an important aspect to motor control. How professional batters use knowledge to improve performance remains to be determined; possible candidate explanations include pre-programming the swing, becoming more selective when in a favourable count, or having less uncertainty about the location of the up coming pitch. It is interesting that baseball players and managers, as well as close observers of the game such as sports writers, have often spoken of the importance of knowledge for successful hitting. Ty Cobb, a prodigious hitter with a life time batting average of .366, was not known for his intellectual approach to the game. Even Cobb realized the importance of knowledge to his skill:

The longer I live, the longer I realize that batting is more a mental matter than it is physical. The ability to grasp the bat, swing at the proper time, take a proper stance, all these are elemental. Batting rather is a study in psychology, a sizing up of pitcher and catcher, and observing little details that are of immense importance. It's like the study of crime, the work of a detective as he picks up clues. (cited in Dickson, 1992, p. 86.) According to Thomas Boswell: Inside the mind, that's where the secrets are in this game of timing and deceit, anticipation and disinfoxmation. (Boswell, 1990, p. 242) To recapitulate, it is the contention of this chapter that knowledge-cognition is vital in real world skilled motor performance. Knowledge is important for formulating the intended goals of actions; as well, knowledge facilitates actual performance. Salthouse (1991) conceptualizes the study of expertise as the study of how people have learned to overcome normal constraints on information processing; in his words, skilled performance boils down to a "circumvention-oflimitations" (Salthouse, 1991, p. 299). As an example of this perspective, Salthouse cites his work on the acquisition of skill in transcription typing (Salthouse, 1984). Transcription typing requires reacting to visually presented material by making successive keysmkes. Thus, typing speed should be constrained by the time it takes for a visual reaction time to be made. As Salthouse describes below, the constraints should be serious indeed.

...reaction times to successively presented stimuli in a recent study (Salthouse, 1984) averaged more than 550 msec per response, which, assuming 5 characters per word, would correspond to a maximum typing rate of less than 22 words per minute. Even the rate of repetitive finger tapping, without any requirement to choose among alternative stimuli or to select among distinct responses, seems to place severe constraints on maximum speeds of typing. As an illustration, in the same study in which reaction times were measured, I found that the interval between successive finger taps averaged 163 msec, which would yield a typing rate of less than 74 words per minute. (Salthouse, 1991, p. 296)

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In actual fact, Gentner (1988) estimates a professional typist averages 50 words per minute, with some individuals capable of typing at much faster rates. Salthouse's (1984) work shows that typists circumvent reaction time limits by parallel processing. Highly skilled typists look further ahead in the material to be typed than do slower typists, which allows them to overlap successively performed keystroke movements. The consequence of this parallel processing is, in Salthouse's words, to convert "a serial and discrete task, subject to severe processing limitations, into a dynamic and continuous task in which those constraints are relatively unimportant". (Salthouse, 1991, p. 299). The challenge remains to determine how other types of motor skill experts are able to overcome constraints. Perception and action well may be linked in a seamless web, but the study of sport experts shows that knowledge and action are also tightly coupled, such that "The intent of a manoeuvre is always an intrinsic part of its execution" (Koppett, 1991, p. 11).

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theory of expertise (pp. 153-171). Cambridge: Cambridge University Press. Starkes, J.L., & Deakin, J.M. (1985). Perception in sport: A cognitive approach to skilled performance. In W.F. Straub & J.M. Williams (Eds.), Cognitive sport psychology (pp.115-128). Lansing, NY: Sport Science Associates. Whiting, H.T.A., (1991). Action is not reaction! A reply to McLeod and Jenkins. Infernutiom1 J O W M ~of Sport Psychology, 22, 296-303. Whiting, H.T.A., & Hutt, J.W.R. (1972). The effects of personality and ability on speed of decision regarding the directional aspects of ball flight. Journal of Motor Behavior, 4 , 89-97.

COGNITIVE ISSUES IN MOTOR EXPERTISE J.L.Starkes and F. Allard (Editors) 0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 3 THE ROLE OF THREE DIMENSIONAL ANALYSIS IN THE ASSESSMENT OF MOTOR EXPERTISE HEATHER CARNAHAN Department of Kinesiology University of Waterloo, Waterloo, Ontario, N2L 3GI Many of us are interested in what makes one individual more skilled than another, and the factors that influence the development of motor expertise (see Allard & Starkes, 1989; Schmidt, 1988). There are many perspectives that can be taken to address the topic of skill ranging from examining the manner in which skills are learned, to comparing the motor performance of expert and novice individuals. Those who study motor skill learning have investigated the variables that influence the acquisition and retention of skill, for example, practice scheduling and the presentation of feedback (for reviews see Chamberlin & Lee, 1992; Salmoni, Schmidt & Walter, 1984). While this approach has examined variables that affect the end result or final product of a movement, little attention has been paid to how the form of a movement changes with learning. Gentile (1972) however, states that effective motor skill teaching requires an analysis and understanding of the nature or characteristics of the to be learned skill. Thus, before we can teach skills, we have to understand what is it about a movement that makes it "skilled". An alternative approach to looking at skilled performance is to look at expert and novice differences, where the perceptual abilities or cognitive strategies associated with skilled performance are examined and compared (Allard & Bumett, 1985; Allard, Graham & Paarsalu, 1980; Allard & Starkes, 1980; Charness, 1979; Starkes & Deakin, 1985). In this situation, expert and novice performers are often categorized based on years of experience or national and international competitive ranking; not on some objective measure of quality or form of movement. On what basis are we defining motor skill? Welford (1976) describes skill as a quality of performance which is developed through training, practice and experience. While this definition is acceptable, to actually apply this definition to the categorization of real movements is very difficult. Rarely do we objectively quantify the form of an expert performer when we investigate skill. The goal of this chapter is to review how our definitions of skill may be altered depending on how we define what constitutes a skilled movement. As well, I hope to touch on how information about the form of a movement may influence our thinking about what really is expert performance. When the acquisition of skilled performance is evaluated, or we want to investigate the

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role of perception or cognition in skilled performance we are left with one common problem; that is, how do we quantify skill? In real life, we may use summary statistics like batting average or runs batted in to describe the skill level of a baseball player (see James, 1991; Siwoff, Hirdt, Hirdt & Hirdt, 1992) . Entire volumes are published each year full of statistics which attempt to capture the essence of baseball skill. We want to be able to say that if one player has better stats than another player, then he/she is the more skilled player. This rationale even has legal precedent, and is used in arbitration settlements when professional athletes negotiate their salaries which are directly related to their playing skills. If we as researchers could also adequately define skill based on one summary performance number or statistic then our job would be much easier. When we assess skill acquisition in a laboratory setting, performance is often described in terms of the final result of a movement, by using measures such as reaction time, movement time, terminal accuracy, force output, etc. (see Schmidt, 1988). These measures tell us nothing about how the movement was performed, only about the end result of the action. However, it is quite possible to have very different movement patterns produce identical movement outputs. If the final output is the same, would both movement patterns represent equally skilled movement? Before we can look at the sorts of variables that influence skill, we need to define precisely what constitutes skilled movements. However, this is not a very straightforward task. Is skill based on outcome only, or does the form of the movement matter? Can an individual be considered skilled if their outcome is poor but their form is perfect? Most likely these two aspects of skill (outcome and form) are very highly related, that is, superior movement form will result in superior outcome. For example, the shot putter that can adequately coordinate the generation of forces in all their joints will most likely put the shot the farthest, or the skater with the best technique will jump the highest and spin the fastest. In these examples, superior form will generally result in superior outcome. However, this is not always m e . We can all think of examples where a performer is extremely successful in terms of outcome, with a particularly unconventional style or motor pattern. The converse is also possible, in which a performer has a consistent and acceptable motor pattern, but is unable to produce a highly successful movement outcome (Gentile 1972). Form also plays a critical role in activities like diving, dance, gymnastics, or skating where the actual form of the movement is the main objective of the skill. These types of skills can be categorized as "closed" skills, which are performed in a static, unchanging environment (Poulton, 1957). The evaluation of success in these types of skills is based on how the skills are performed. As Allard and Starkes (1992) point out, "for closed skills, motor patterns ARE the skill; it is critical that the performer be able to consistently and reliably reproduce a defined, standard pattern" (pp.127). In these types of closed skills, it is difficult to quantify and thus understand the movement attributes that characterize expert performance. For years, judges have attempted to quantify whether one movement form is superior to another. But, anyone who has watched diving, figure skating or gymnastics competitions knows that the classifications made by judges are not without question. It is difficult to not let prior experience, political views and opinions interfere with judgements of movement form (Ste-Marie & Lee, 1991). A

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more objective approach would be to quantify or measure movement form with some sort of tool. However, individuals studying motor expertise have rarely done this. This is partly because until recently, the technology necessary to accurately quantify natural human movement has not been available, either because the technology has simply not existed, or it has been too expensive. But, over recent years, two and three dimensional video and optoelectric systems have been developed that are now accessible to many motor behavior researchers. While these systems allow an objective and systematic analysis of movement patterns, they art not the only types of systems that can be used for quantifying movements. There are direct measurement techniques which rely on devices such as goniometers, accelerometers, graphics tablets or manipulanda hooked up to potentiometers. These systems generally constrain movements to one plane and will not be discussed in this chapter in any detail. For a discussion of these types of systems see Winter (1990). Instead I would like to focus on the quantification of natural unconstrained movements using imaging measurement, techniques which are most often used in the quantification of manual reaching and grasping, gait, and complex skills like diving, running and throwing.

How Are the Data Collected? Two and three dimensional imagery systems can be divided into three main types: cinematography, optoelecmc and video, with the latter two being the most frequently used. With video systems, video television cameras are used to record movement on a video tape, after which the video image of the movement is digitized and the position of the image recorded by a computer. With optoelecmc systems, small markers which emit infrared light are attached to the subject, and specialized infrared cameras record the position of the markers; no image of the actual subject is recorded. Each system has advantages and disadvantages which depend on the environmental conditions and type of movements to be measured. There are many parameters that must be considered in deciding what type of measurement system is preferred. Below are listed some of the most important ones: Sampling Rate The sampling rate of most video systems is limited to 60 Hz. (High speed video cameras can sample at higher frequencies but their cost is often prohibitive). This is generally acceptable for most human movement, which is usually low frequency (3 to 10 Hz). However, if higher sampling rates are needed optoelecmc systems can sample up to several thousand Hz. Higher sampling rates are preferable for monitoring a skill in which a high-impact is being monitored, such as hammering. Impact results in a high frequency component to the movement, and this is more effectively measured with higher sampling rates. Higher sampling rates are also useful if acceleration is going to be examined, essentially because the process of differentiation involves calculating differences between sample points, so with more samples the derivative is more representative. Sampling theorem states that the minimal sampling rate for a signal should be 2N+1, where N refers to the frequency component of the signal being measured. Thus, if human movement is 10 Hz, then a minimum of 21 Hz sampling rate will be adequate (Winter, 1990). However, a more generous estimate of 10 times the frequency component of the signal is probably more acceptable.

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Range and Accuracy Video systems generally have a larger range. Optoelectric systems are limited in both potential distance away from the camera and working space volume, based on the power or range of the light emitted from the diodes. Although it depends on calibration of the system and the size of the volume being used, optoelecmc systems are currently more accurate than video based systems. Some optoelecmc systems (e.g. Optotrak) are accurate to the fraction of a millimeter at close volumes. However, for most types of motor skills that may be of interest, this degree of accuracy is probably not necessary. A carefully calibrated video system should provide adequate accuracy (e.g. 2 to 5 mm error). Rotation Video systems are a little more forgiving of markers rotating out of view of the cameras than are optoelecmc systems. With an automatic digitizing video system, if a marker goes out of view, the position of the marker can be hand digitized to replace the missing portions of data. This is because a video image of the subject exists and the human operator can "guesstimate" the position of the missing marker even if it is not in view. The disadvantage of this of course is that a human operator is necessary during the digitization process and this can be extremely time consuming. As well, error is introduced into the system. With an optoelecmc system, data is automatically digitized. However, if a marker if obscured or rotates out of view, the data can only be replaced with interpolation techniques where missing portions of curves are reconstructed using various splines. However, if the appropriate order of spline is not used, the reconstructed data could misrepresent the original missing part of the curve. Regardless of how the data are gathered (and even when using 3 dimensional systems), movements generally have to be planar for all the markers to be seen by all the cameras. Once a subject rotates away from the camera, the markers on their body become obscured from camera view. However, with improved software and multiple camera systems, this is less of a problem. With enough cameras, a marker can be tracked from one set of cameras to an adjacent set. However, this is an expensive solution since additional cameras are needed, as well as complicated software to integrate the information from the various camera pairs. This approach has been successfully used by biomechanicians, however, because of the complexity of the procedure, has not yet been adopted by those looking at skilled performance from a cognitive perspective

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Physical Constraints With video systems, small adhesive markers are placed on the subject demarcating points of interest such as the wrist, fingers, elbow, knee etc. The subject is free to move naturally after the markers have been positioned. With optoelecuic systems, small light emitting diodes are taped to the body. Subjects are constrained to some extent by the wires leading to the light emitting diodes, yet the impact of this restraint will depend on the type of activity subjects perform. Another constraint to consider is that of the actual physical or geographical location of the data collection session. Video systems are generally more flexible in where they can be used. For example, they can be set up out of doors or in actual competitive settings. Actual

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diving competitions have been filmed and later digitized for biomechanical analysis of the dives (Miller. Hennig, Pizzimenti, Jones & Nelson.1989; Miller, Jones, Pizzimenti, Hennig & Nelson, 1990). Optoelecmc systems are generally constrained to the indoor laboratory setting. Since they depend on detecting the location of small infrared lights, any additional infrared light in the testing environment (like that produced by the sun) will interfere with the accuracy of the system. As well, an optoelectric system could not be used for quantifying aquatic activities such as swimming or diving because of the electric feed required to power the light emitting diodes which are placed on the subject. Volume of Data An important factor to consider when assessing the merits of motion analysis is the volume of data generated with video and optcelectric systems (Winter, 1990; 1991). If movement time or accuracy is used to describe a movement, then one or two numbers can be used to represent the performance. However when kinematic or kinetic information are used to describe movement many hundreds or even thousands of data points are collected. For example, if only one marker was placed on the arm to represent the three dimensional translational motion of a reaching movement, which took one second, and was sampled at 100 Hz,there will be 300 data points to represent that skill. You can imagine how many data points are involved in representing the movement of an entire arm and hand where markers are placed on the fingers, wrist, elbow and shoulder. These large volumes of data increase the cost of research because the volumes of numbers are time consuming to deal with, require powerful computers, and take up large amounts of disk space etc. There is a need to evaluate whether the added expense in dollars and time is providing sufficient unique information to warrant the investment. Will this abundance of information be used to develop new theories regarding motor skills, or will we get caught in the fashionable urge to collect volumes of data, for its own sake? While I may have painted a somewhat discouraging scenario, it is my opinion that this approach is warranted, and that as additional data are collected, patterns will emerge which will direct theoretical development.

How Are the Data Analyzed? Once the data are collected there are many ways they can be represented (see Enoka, 1988; Winter, 1990; 1991). At the first level of analysis are the temporal measures; these measures deal with the timing aspects (e.g., movement time) of the whole movement. AS previously mentioned, these are the types of measures that have typically been used to quantify skilled movement. At the next level of analysis are the kinematic measures, which describe linear or angular motion, but do not consider the forces involved in the movement. Imaging measurement systems will provide an output of displacement as a function of time, and other kinematic variables such as velocity, acceleration or jerk can be derived from displacement. More detail regarding how kinematic information can be used to describe skill will be outlined later. The next level of analysis involves kinetic variables, which describe movement in terms of the forces required to generate the motion. Kinetic variables can be calculated from the kinematic information. Of primary concern in this type of analysis are the individual muscle forces or moments of force generated by the muscles across a joint. A related level of analysis

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is termed energetics. One energetic variable, power, is the rate of doing work or the rate of energy change, and is the product of both a kinetic (force) and kinematic (velocity) variable. Power patterns reveal the rate of generation or absorption of force by the muscles (see McFadyen, 1990; 1991; Winter, 1990; 1991). Kinematics There are many stages to data analysis and of course the nature of the analysis chosen depends on how the data were collected, and the questions being asked. In his recent book Winter (1991) does an excellent job of describing the most common kinematic parameters measured for gait. The approach described by Winter could be applied in the analysis of most action patterns. Below, is a description of how the most common data analysis stages are applied to the analysis of reaching and grasping movements, since currently most movement analysis research involves the upper limbs or fine manual skills. Data Smoothing The first stage of data analysis involves removing the noise or unwanted portion of the signals. Polynomial fitting, harmonic analyses, spline curve fitting and filtering will all remove noise, with filtering probably being the most satisfactory (Winter, 1991). Smoothing the data is usually necessary if the curves are going to be differentiated to examine velocity (Winter, Quanbury, Hobson,Sidwall, Reimer, Trenholm, Steinke & Shlosser, 1974) . However, with any type of signal processing, because small distortions may be introduced into the signal, the nature of the distortion is dependent on the characteristics of the raw signal and the specific technique used to process the signal. Caution must be used when interpreting processed data, because a deviation in a curve may not be due to a particular physiological process (e.g. visually based correction in a movement) but instead could be a signal processing artifact caused by something like an underdamped filter. A recent trend in the evaluation of the role of visual feedback in the control of manual aiming has been to evaluate the number and nature of oscillations in acceleration profiles of arm movements. These deviations have then been interpreted as indications of visual feedback processing (van Donkelaar & Franks,l991; Young, Allard & Marteniuk,l988). However, it has not yet been clearly established that the deviations in acceleration profiles are actually corrections. A post-hoc approach has been used to define corrections; that is, if there is a deviation in a profile then feedback must have been used to modify the trajectory. However, an alternative explanation is that a motor program is used to generate an aiming movement with little use of feedback. However, as the movement evolves, noise is introduced into the motor system, resulting in trajectory deviations. Recent evidence has shown however, that in situations where visual feedback is available, there are more trajectory deviations, when compared to no visual feedback situations (Chua, 1992). Thus, although there is mounting evidence that oscillations in acceleration curves are associated with feedback corrections, it has still not been clearly established what causes a deviation in a movement trajectory. The speed at which expert and novice volleyball players can use visual information has

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been examined in a study in which players were required to detect the presence of a ball in a slide of a game situation (Allard & Starkes, 1980). These researchers found that skilled volleyball players were faster than non players at visually detecting balls in slides of volleyball settings. Subjects indicated their perception of the ball with a verbal response. While a verbal indicator of perception was used in this study, in a real game situation, a physical response would be required (most likely a movement directed toward the ball). It is possible that similar, and potentially more sensitive measures of perception could be determined by monitoring how the kinematics of part of the skill (for example the volley action, or the leg action) is affected by perceptual skill level. How the use of visual information changes with training and improved skill could perhaps be investigated by examining the trajectory deviations in skilled and unskilled movement. This approach was used in a recent laboratory study where subjects were required to reach towards and grasp small illuminated dowels in a semi-darkened mom ( Paulignan, MacKenzie, Marteniuk & Jeannerod, 1990). Unexpectedly, on some mals as the subjects initiated their reach towards the dowel, the small light beneath the dowel was extinguished and a light under another dowel, which was located either to side of the original dowel was illuminated. This gave the illusion to the subject that the dowel position has actually jumped to a new position. Paulignan et al. (1991) found that modifications made to the reaching movement in response to this visual perturbation occurred very early in the movement trajectory, even though subjects reported that the dowel seemed to move just before the hand actually reached the target. Applying these findings to the volleyball situation, it is possible that kinematic modifications to reaching or spiking movements in response to visual stimuli (the ball) could be occurring much sooner than would be recorded if subjects only produced a verbal report. This is another example, where information about how a movement is performed may provide unique insights into cognitive abilities or strategies.

Angles One way to describe the motion of the limb is to define joint angles and to measure how each angle changes and varies with the other. An angle can be defined by any three markers, with the middle marker being placed on the axis of joint rotation. One problem with this, however, is that external markers never truly represent the joint center, thus adding error to the calculation of the true angle. A complicated but more accurate alternative is to place several markers on the two limb segments involved in the angle, and use this information to mathematically define them as rigid tubes. Then, an instantaneous joint center can be calculated to provide a more accurate measure of the angle. This approach however, is computationally much more difficult, so a researcher may choose to accept the error associated with the easier method. The amount of error acceptable in calculating a joint angle is of course dependent on the reason it is being measured and the goal of the study . Independent of how the markers are placed on a limb to calculate the angle, the angle can be defined several ways: First, the angle can be described relative to itself, which means that regardless of how the joint angle is oriented in space, the angle described by two joined

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segments remains the same. Terms like flexion or extension describe angles in these terms. Alternatively, joint angles can be described in terms of a world based reference or planes, that is sagittal, frontal or horizontal planes. In this case, the orientation of the joint angle relative to external spatial coordinates is important. Once again, the way one chooses to describe a joint angle depends on the type of inferences one is attempting to make from the data. Figure 3.1 illustrates wrist and elbow angle changes when the subject throws a small ball.

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Figure 3.1. This Figure shows the wrist joint angle (created by markers placed on the knuckles, wrist and elbow) and elbow joint angle (created by markers place on the wrist, the elbow, and the shoulder) of a subject throwing a small ball. The angles are measured relative to themselves. You can see that as the wrist is drawn into flexion, the elbow angle remains unchanged, then both the elbow and wrist joints extend.

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Cross Correlation Once the joint angles of a limb arc defined, they can be compared to each other by a technique called cross correlation. Two angle curves (the curve is the plot of joint angle as a function of time) are correlated to each other. One curve can then be shifted in the time domain, and the remaining overlapping points are then correlated. Using this procedure, the phase shift at which the curves are the most highly related can be determined. This type of analysis can quantify similarity in the shape of two curves, and will reveal at what time lag the similarity in shape is maximal. It is a useful technique to use when you suspect that the shapes of two curves are similar, but they are phase shifted in the time domain. Figure 3.2 illustrates cross correlation.

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Figure 3.2. This Figure shows how two angles (in this case the wrist and elbow angles plotted in Figure 3.1) can be crosscorrelated to examine similarity in trendr. The overlapping points along the two curves are correlated, then one curve is shifted, and the remaining overlapping points are again correlated.

The results of a cross correlational analysis can be interpreted in two different ways. With the first perspective, the assumption is made that if a movement is highly skilled, the movements of the joint angles are highly related. This approach has been used in the comparison of skilled and unskilled dart throwers (Leavitt, Marteniuk & Camahan, 1988). In

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this study expert and novice dart throwers were required to throw darts at a regulation dart board. Movements of the throwing arm were recorded with an optoelecmc system, and angles of the wrist, elbow and shoulder (in the sagittal plane) were crosscorrelated. Higher correlation values between the joints were found for the expert when compared to the novice dart throwers. Thus, the assumption was made that the more skilled an individual is, the more highly correlated the joint angles will be. However, an alternative perspective has also been used to describe skill; expert performers possess the ability to uncouple movements of the joints. In a recent study, Swinnen, Walter, Beirihckx, and Meugens (1991) had subjects perform skills in which the two hands were required to move together, at different tempos and in opposite directions. In this situation, expert performance was manifest by an uncoupling of the two limbs, or in other words a controlled discoordination. Others (Kelso, Putnam & Goodman, 1983; Kelso, Southard & Goodman, 1979; Marteniuk, MacKenzie & Baba, 1984) have shown that when making bimanual movements, the trajectories of the two hands are coupled. However, Swinnen et al. (1991) showed that with practice, movements of the limbs can be uncoupled. Subjects in the Swinnen et al. study were required to generate unsynchronous flexion and extension movements about the left and right elbows. Although this was difficult early in practice and tended to be very "unnatural", with practice subjects were able to achieve the skill. In the Swinnen et al. (1991) study , skill was defined by a dyscoordination or uncoupling of the motion in the left and right elbow joint angles. That is, the elbow joints were eventually able to move at differing tempos. Thus, it appears that depending on the nature of the skill, skilled performance can be defined by either the coupling or uncoupling of the limbs or joint angles. This apparent contradiction can be found in real life examples as well. For example, in a situation where maximum force is required, such as throwing an implement, it makes biomechanical sense to have the angles of the elbow, wrist and shoulder extend in unison to generate maximal force. However, there are different kinds of skills where it is important to have independence of the effectors, such as playing a piano, where the fingers of the hands must play different tempos, and flex and extend independently.

The Grasp (Aperture) Within the past ten years, many researchers have become involved in investigating how reaching and grasping movements are controlled (see Jeannerod. 1988 for a review). The initial step in these investigations was to determine how typical prehension movements unfold. It was proposed that prehension movements are comprised of two phases, the transport and the grasp (Jeannerod, 1984). The grasp phase is usually quantified by measuring the hand aperture, or the distance between the thumb and the forefinger. This measure is used to represent how the hand is opening up and closing around an object. Jeannerod (1984) has shown that the size of the peak aperture is highly correlated with the size of the object subjects are reaching towards to grasp. Thus, it seems to be a relatively effective measure. However, it has been suggested that it would be more fruitful to examine the grasping characteristics of the entire hand. The preshaping and grasping posture of all the fingers should be quantified in order to really understand grasp fomiation (Proteau, 1992). Normal healthy adults appear to be very expert at grasp formation. However, one might not think of simple reaching as a skilled activity since we all seem to be very good at it. But, when an individual has some type of brain injury or

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nervous system disorder, the ability to generate the appropriate aperture size when grasping can be lost. After stroke it has been shown that subtle disruptions in reaching performance can be quantified by monitoring the kinematics of reaching, grasping and pointing movements (Charlton, Roy, Marteniuk & MacKenzie, 1988; Fisk and Goodale,1988; Goodale, Milner, Jakobson & Carey, 1990). Goodale et al. (1990) have argued that kinematic analyses of reaching movements reveal differences between patients and normals that may not be clinically observable, and that this approach could be used to evaluate recovery of function. Thus, a measure as simple as hand aperture can be used to quantiiy the "skill " inherent in reaching and grasping. To illustrate this Figure 3.3 is data on hand aperture for a normal subject reaching to grasp an object.

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Figure 3.3. This shows an aperture profile of a subject reaching to grasp a stationary object located on a table in front of him. These data were collected by an optoelectric system at 200 H z , and were filtered at 7 H z with a dual pass buttenvorth filter.

Transport The transport phase of a reaching movement describes how the limb moves through space to reach a target location. When the kinematics of the transport phase of a reaching movement are evaluated, distinct differences can again be found between skilled and abnormal movement (caused by brain injury). A dependent measure that is often used to describe the

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transport phase of a reaching movement is the velocity of the limb. The velocity profile is usually characterized by a bell shaped curve (Hollerbach & Atkeson, 1987 ) with the skewness of the curve being affected by such factors as the accuracy of the task (MacKenzie, Marteniuk. Dugas, Liske & Eickmeier,l987; Marteniuk, MacKenzie, Jeannerod, Athenes & Dugas, 1987). In individuals with brain damage, the smoothness of the curve can be used to quantify the magnitude of the motor control deficit Deviations in a kinematic profile can be interpreted to suggest patients are relying on visual feedback as opposed to preprogrammed control, or alternatively, deviations can suggest patients have more "noise" in their neural system. Measures like the velocity of the limb or closure of the hand around an object are very sensitive measures, and may be used as tools for assessing cognitive processes in performing skilled movement. Marteniuk et al. (1987) have shown that the symmetry of the velocity profile during reaching is influenced by the context of an object or task. For example, when individuals generate reaching movements towards objects of similar visual impact, a tennis ball and a light bulb, very different reaches are generated. Subjects use the information they already have acquired through experience regarding the fragility of objects when picking up the light bulb and spend a larger proportion of their movement trajectory slowing down so that they can make a very controlled grasp of the bulb. Conversely, when individuals pick up the tennis ball, they spend a smaller proportion of the trajectory slowing down before contact with the object. All of these adjustments are made prior to contact with the object, and are based on subject's prior experience and expectations in dealing with these types of objects. This is just one example of how a kinematic approach can be used to gain insight into cognitive processes, and the types of information individuals use in planning and controlling a reaching movement. Figure 3.4 shows wrist velocity changes in a reaching and grasping task, while Figures 3.5 and 3.6 arc the same task completed by a patient with a neural disorder.

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Figure 3.4. This shows a wrist resultant velocity profile for the same trial shown in Figure 3.3. The Formulae for calculating resultanr is 2 = 2 + )? + 2.

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Statistical Approaches As shown in Figures 3.1 to 3.5, kinematic variables are often represented as curves.

While there is a lot of information inherent in a curve, there are a limited number of statistical approaches that can be used. One approach is to use variables such as coefficient of variation to describe the variability associated with a group of curves, however inferential statistics such as analysis of variance (ANOVA) are not used to make comparisons between groups of curves. An alternative approach is to summarize a curve by picking off landmark points (e.g., peak velocity, peak aperture, time to peak velocity etc.) and entering these data into an ANOVA. MANOVA or similar analysis. This procedure provides a way to statistically deal with the variability between mals or subjects, but by doing so, information about the rest of the curve is lost (Winter, 1987). Peaks should not be picked in isolation; instead they should be analyzed in conjunction with qualitative analysis of the curve shapes. ._1

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Figure 35. The Figure shows several velociry and aperture profiles for a patient wirh an undiagnosed neural disorder, as he reached and grasped a small object on a table top. These curves are for the parienr's leji hand which was severely affected.

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Figure 3.6. These curves show the right hand (which was normal) of the same patient in Figure 3.5. Kinetics A moment of force is the sum of muscular, ligament and friction forces which act on the angular rotation of a joint (Winter, 1991). However, friction and ligament forces are assumed to be negligible, so the net moment is generally considered to reflect the forces due to muscular activity. Thus, moments of force reflect the muscular activity that causes the kinematic patterns we observe. Put another way, they are one step closer to the neural signal. Several variables go into the calculation of moment of force; ground reaction forces (which for gait are derived from a force plate imbedded in the ground and for free upper limb reaching moments are considered to be zero), kinematic information for the linked segments involved in the analysis, and tabled information from an anthropometric model which includes lengths and masses of the

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segments. The most extensive kinetic analyses have been conducted on walking, which at first glance might not be thought of as "skilled' activity. Often when we consider expert performance we tend to imagine sporting or work activities. But, we should not let this preclude the examination of more everyday activities such as grasping and walking, where the most thorough kinetic analyses have been conducted. However, sports biomechanicianshave compiled kinetic descriptions of athletes performing many skills, and perhaps this information should be examined by those individuals interested in the development of expertise (e.g.. de Koning, de Groot & van Ingen Schenau, 1991; Miller et al., 1989; 1990; Schot & Knutzen, 1992). At each progressive level of analysis (temporal, kinematic, or kinetic) the complexity of the analysis and interpretation increases. Although we may see one particular pattern of kinematics, there are a multitude of patterns of muscle force that could create that pattern (Winter, 1984). Winter has shown that kinematic gait patterns can be produced by very different muscle patterns and forces about the leg. It is apparent that in trying to define or describe skill, there are many levels of analysis that can be used. When assessing sports like gymnastics or diving we tend to see a particular predefined kinematic pattern and label that as a skilled performance. However, one must be aware that more than one pattern of muscle activation can be adopted to produce a single kinematic pattern. There has not been enough research examining kinetic patterns during skilled performance or learning to satisfactorily address this issue. However, in a recent study, the kinematic patterns of the leg were evaluated during the learning of a kicking task (Young, 1990). Subjects in this experiment were required to generate time constrained (400 ms) kicking movements, with a 1.67 kg weight strapped to their foot. An optoelecmc imaging system was used to record movement kinematics for the leg. As you would expect, the temporal accuracy of the movements increased over trial blocks. More interesting however, was the finding that kinematic variability did not decrease as a function of practice. Variability We tend to associate skill with consistency of performance, especially if we are thinking in terms of kinematic patterns (see Roy, Brown & Hardie, in press). However, the opposite might be m e . Perhaps motor skill involves the ability to utilize various differing muscle patterns to generate similar kinematic outputs. Variability. or the ability to respond to it in the environment, might be an important atmbute of skill. If this is m e , then the way we think of the cognitive processing associated with skill would need to change. Perhaps a skilled performer does not plan to be consistent. Instead, a skilled performer could ,with experience, develop a repertoire of movement strategies, or the ability to deal quickly with various sources of feedback to amend movements in response to both environmental and internal perturbation. This suggestion is not really very different from Abbs, Gracco and Cole's (1984) updated description of a motor program, "a program is more likely the representation of the dynamic processes whereby the appropriate sensorimotor contingencies are set up to ensure cooperative complementary contribution of the multiple actions to a common, predetermined goal" (pp.214215).

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The notion of motor equivalence addresses the observation that there are many kinematic solutions to a particular movement goal. That is, a single movement goal can be reached in a multitude of ways and from various different starting points. If the movement goal is to reach a particular position in space with the hand (as in picking up a ball), there are many different ways the arm can do this, because of its multiple degrees of freedom (Bernstein, 1967). This is especially helpful if there is an obstacle between an individual's starting position and their reaching target (Cruse, 1986). A temporal or spatial (accuracy) analysis would not be sensitive to this flexibility. For example, movement time or accuracy may not reflect the type of path chosen to reach a goal, or the muscles selected to move the arm. However, if the kinematics or kinetics describing the movement are examined, then the functional variability in achieving a seemingly consistent movement goal becomes apparent. Perhaps skill is the ability to successfully vary kinematic and kinetic patterns in response to physical and cognitive influences. It has been demonstrated that kinematic trajectories can change as a function of the intent of a movement. Maneniuk et al. (1987) showed that subjects spent a larger proportion of their reaching trajectories slowing down before picking up an object when their intent was to place it carefully after picking it up, as opposed to throwing it into a large box. Even though the object and the environment remained the same, the objective of the two tasks differed, and this resulted in altered kinematic patterns. Which Comes First, Kinematics o r Skill? When we describe a motor act as being skilled, generally this judgement is based on the outcome of the movement. That is, the athlete that jumped the furthest, hit the most home runs, or shot the arrow the most accurately is the most skilled performer. Our strategy has then been to use kinematic or kinetic measures to more fully describe the movement characteristics of the skilled performance. However, when enough normative data have been collected, and "kinematic norms" have been established, it may be possible to then predict whether or not a movement will be successful based on a particular kinematic pattern. It may even become possible to successfully alter existing kinematic patterns to resemble ideal movement patterns in order to facilitate the development of skill. This approach is being used in gait research, where movement parameters of pathological gait can be compared to a pool of nonnative data (Winter, 1991). While using kinematics in this manner may be a long way off, the flexibility exhibited by kinematic and kinetic patterns may reflect control strategies utilized by skilled performers and should be considered in theories of cognition and motor skill.

References Gracco, V.L.,& Cole, K.J.(1984). Control of multi-joint movement coordination: Abbs, J.H., Sensorimotor mechanisms in speech motor programming. Journal of Moror Behavior, 16, 195-231. Allard, F.,& Burnett, N. (1985). Skill in sport. Canadian Journal of Psychology, 39, 294-312. Allard, F., Graham, S., & Paarsalu, M.E. (1980). Perception in sport: Basketball. Journal of Sport Psychology, 2, 14-21. Allard. F., & Starkes, J.L. (1989). Motor skill experts. Paper presented at "The Study of

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Expertise: Prospects and Limits". Berlin, June. Allard, F., & Starkes, J.L. (1980). Perception in sport: Volleyball. Journal of Sport Psychology, 2 , 22-23, Bernstein, N. (1967). The co-ordinarion and regulation ofmovemenrs. Oxford: Pergamon Press. Chamberlin, C.J. & Lee, T.D. (in press). Arranging practice conditions and designing instruction. In R.N. Singer, M. Murphey & L.K. Tennant (Eds.), Handbook on research in sport psychology. New York Macmillan. Chamess, N. (1979). Components of skill in bridge. Canadian Journal of Psychology, 33, 116. Charlton, J.L., Roy, E.A., Marteniuk, R.G. & MacKenzie, C.L. (1988). A kinematic analysis of prehension in apraxia. Society for Neuroscience Abstract, 14, 1234. de Koning, J.J., de Groot. G. & van Ingen Schenau, G.J. (1991). Speed skating the curves: A study of muscle coordination and power production. International Journal of Sport Biomechanics, 7, 344-358. Enoka, R.M. (1988). Neuromechanical basis of kinesiology. Champaign, IL: Human Kinetics. Chua, R. (1992). Visual regulation of manual aiming. Unpublished master's thesis, McMaster University, Hamilton, Ontario. Cruse, H. (1986). Constraints for joint angle control of the human arm. Biological Cybernetics, 54, 125-132. Fisk, J.D. & Goodale, M.A. (1988). The effects of unilateral brain damage on visually guided reaching: Hemisphere differences in the nature of the deficit. Experimenral Brain Research, 72, 425-435. Gentile, A.M. (1972). A working model of skill acquisition with application to teaching. Quesr, 17, 3-23. Hollerbach, J.M. & Atkeson, C.G. (1987). Deducing planning variables from experimental arm trajectories: Pitfalls and possibilities. Biological Cybernetics, 56, 279-292. James, B. (1991). Stars 1992 major league handbook. Lincolnwood, IL: Sports TeamAnalysis & Tracking Systems, Inc. Jeannerod, M. (1984). The timing of natural prehension movements. Journal of Motor W , 16,235-254, Goodale, M.A., Milner, A.D., Jakobson, L.S. & Carey, D.P. (1990). Kinematic analysis of limb movements in neuropsychological research: subtle deficits and recovery of function. Canadian Journal of Psychology, 44(2), 180-195. Kelso, J.A.S., Putnam, C.A. & Goodman,D. (1983). On the space-time structure of human interlimb coordination. Quarterly Journal of Experimental Psychology, 35A, 347-375. Kelso, J.A.S., Southard, D.L. & Goodman, D. (1979). On the nature of human interlimb coordination. Science, 203, 1029-1031. Leavitt, J.L., Marteniuk, R.G. & Camahan, H. (1987). Arm movement trajectories and movement control strategies of expert and non-expert dart throwers. Neuroscience Abracts. MacKenzie, C.L., Marteniuk, R.G., Dugas, C., Liske, D & Eickmeier, B. (1987). Three dimensional movement trajectories in Fitts' task: Implications for control. Quurterly Journal of Experimental Psychology, 39A, 629-647.

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Marteniuk, R.G., MacKenzie, C.L. & Baba, D.M. (1984). Bimanual movement control: Information processing and interaction effects. Quarterly Journal of Experimental Psychology, 36A, 335-365. Marteniuk, R.G.,MacKenzie, C.L. Jeannerod, M., Athenes, S. & Dugas, C. (1987). Constraints on human arm movement trajectories. Canadian Journal of Psychology, 41, 365-378. McFadyen, B.J. (1990). A "power plane" technique for analysis of goal-directed mechanical strategies. Proceedings of the Sixth Biennial Conference of the Canadian Society for Biomechanics Quebec. (pp. 141-142) Quebec, Canada. McFadyen, B.J. (1991). A power portrait and its application to the study of human movement. Proceedings of the Annual International Conference of the IEEE-EMBS, 13, (pp. 2210221 I). Miller, D.I., Hennig, E., Pizzimenti, M.A., Jones, I.C., & Nelson, R.C (1989). Kinetic and kinematic characteristics of 10-M platform performances of elite divers: 1. Back takeoffs. International Journal of Biomechanics, 6 , 60-88. Miller, D.I., Jones, 1.C.. Pizzimenti, M.A., Hennig, e., Nelson, R.C. (1990). Kinetic and kinematic characteristics of 10-M platform performances of elite divers: 11. Reverse takeoffs. International Journal of Sport Biomechanics, 6 , 283-308. Paulignan, Y.,MacKenzie, C., Marteniuk, R.G.,& Jeannercd, M. (1990). The coupling of arm and finger movements during prehension. Experimental Brian Research, 79, 431 -435. Poulton, E.C. (1957). On prediction in skilled movements. Psychological Bulletin, 54,467-478. Proteau, L. (1992). Personal Communications. Roy, E.A., Brown, L., & Hardie, M. (in press). Movement variability in limb gesturing: Implications for understanding apraxia. In K. Newell & D. Corcos (Us.), Variability in Motor Control. Champaign, Illinois: Human Kinetics. Salmoni, A.W., Schmidt, R.A., & Walter, C.B. (1984). Knowledge of results and motor learning: A review and critical reappraisal. Psychological Bulletin, 95, 355-386. Schmidt, R.A. (1988). Motor control and learning: A behavioral emphasis (2nd ed.). Champaign, 1L Human Kinetics. Schot, P.K., & Knutzen, K.M. (1992). A biomechanical analysis of four sprint start positions. Research Quarterly for Exercise and Sport, 63, 137-147. Siwoff, S., Hirdt, S., Hirdt, T., Hirdt, P. (1992). The 1992 Elius baseball analyst. New York: Simon & Schuster. Ste-Marie, D.M., & Lee, T.D. (1991). Prior processing effects on gymnastic judging. Journal of Experimental Psychology: Learning Memory and Cognition, 17, 126-136. Starkes, J.L., & Deakin, J.M. (1985). Perception in sport: A cognitive approach to skilled performance. In Straub, W.F., & Williams, J.M. (Eds.), Cognitive sport psychology. Lansing, NY: Sport Associates. Swinnen, S.P.. Beirinckx, M.B., Meugens, P.F., Walter, C.B. (1991). Dissociating the structure and metrical specifications of bimanual movement. Journal of Motor Behavior, 23,263279. van Donkelaar, P., & Franks, I.M. (1991). The effects of changing movement velocity and

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complexity on response preparation: Evidence from latency, kinematic, and EMG measures. Experimental Brain Research, 83, 6 18-632. Welford, T. (1976). Skilled performance: Perceptual and motor skills. Glenview, Illinois: Scott, Foreman & Company. Winter, D.A. (1990). Biomechanics and motor control of human movement. New York: John Wiley & Sons. Winter, D.A. (1987). Are hypotheses really necessary in motor control research? Journal of Motor Behavior, 19, 216-279. Winter, D.A. (1984). Kinematic kinetic patterns in human gait: Variability and compensating effects. Hwnan Movement Science. 3, 51-76. Winter, D.A. (1991). The biomechanics and motor control of human gait: Normal, elderly and pathological. Waterloo, Ontario: University of Waterloo Press. Winter, D.A., Quanbury, A.Q., Hobson, D.A., Sidwall, H.G., Reimer, G.D., Trenholm, B.G., Steinke, T., & Shlosser, H. (1974). Kinematics of normal location: A statistical study based on T.V. data. Journal of Biomechanics, 7, 419-486. Young, R.P. (1990). The nature of motor-control strategies underlying the learning of a kicking task. Unpublished doctoral dissertation, University of Waterloo, Waterloo, Ontario. Young, R.P., Allard, F., & Marteniuk, R.G. (1988). The kinematics of visually-feedback based error corrections. SCAPPS Abstracts, 19, 26.

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Part 2

Domains

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COGNITIVE ISSUES IN MOTOR EXPERTISE J.L. Stakes and F.Allard (Editors) 0 1993 Elsevier Science Publishers B.V.All rights reserved.

CHAPTER 4 DETERMINANTS OF VIDEO GAME PERFORMANCE DONNA M. BABA The Usability Group Inc. Willowdab, Ontario, M2J 4V8 The purpose of the studies conducted was to obtain an initial understanding of what underlies or determines performance in video games. Although the popularity of playing video games has grown rapidly through the 1980's and 1990's. the study of video game skill is largely uncharted territory. What do people learn when playing video games? What comprises skill in this domain? Why are some people better at video games than others? Seeking answers to these seemingly simple and basic questions became a labyrinthine search employing several different approaches to the study of perceptual motor skills, including an individual differences in psychomotor abilities approach, an expert-novice approach to skilled performance, and various learning paradigms before some preliminary answers became apparent. While the majority of this paper focuses on sharing the preliminary answers eventually found, a brief summary of the earlier, less fruitful searches also is provided. In some respects, the null results are equally informative in that they run counter to "common sense" explanations and indicate what is unlikely to underlie video game performance. A complete description of all of these studies can be found in Baba (1986). Psychomotor Abilities and Video Game Performance An appealing, common sense explanation of what determines video game performance

and skill lies in the domain of psychomotor abilities. It is presumed that individuals differentially possess some general trait or set of abilities that are particularly supportive of video game performance. This explanation suggests, for example, that some players are better than others because they have faster reaction speed, better manual dexterity, and better hand-eye coordination. This type of explanation is assumed to underlie some real world applications of video games. For example, video games are appearing in classrooms for learning-disabled children and neuropsychology clinics, where therapists prescribe various games to exercise different cognitive and perceptual motor components such as: memory, reaction time, hand-eye coordination, reasoning, and sequencing ability (Stewart, 1983). And, the United States military

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has been examining whether video games could be used for personnel training, and as reliable and valid aptitude tests for various military jobs like, pilots, radar operators, and artillery operations (Jones, Kennedy & Bittner, 1981; Kennedy, Bittner, Harbeson & Jones, 1982). All of these uses and intended uses of video games are based on the assumption that video games have something in common with real world skills. The assumption is that the same set of basic abilities underlie or determine performance in both domains. Therefore, training and improving the basic abilities in one task domain (i.e., video games) should benefit performance in another task domain (i.e., real world skill). While this assumption is implicit in the use of video games, therapists, educators and military personnel are careful to point out that they have little beyond anecdotal evidence to support this assumption.

To substantiate this view empirically, measures of psychomotor abilities must be found to correlate strongly with video game performance, and/or strong correlations must be found between individuals’ performances on various video games. That is, if psychomotor abilities do, in fact, determine video game performance then, the ordering of individuals based on their levels of psychomotor abilities ought to strongly predict the ordering of these same individuals on their video game performance, and/or the ordering of individuals should be essentially the same across different video games. Several video game experiments were conducted to test this hypothesis, and neither of these lines of substantiating evidence was found for video games. In one study employing 175 subjects, individuals’ performances on 2 different video games had low correlations with their measures on 10 psychomotor abilities (e.g., Fleishman, 1972) thought to underlie performance in these games (correlations corrected for attenuation ranged from -.26 to .25). In another study of 17 different video games, it was found that test-retest reliability of video game scores was low to high (reliability .09 to .92). however, the majority of intercorrelations among the different video games were very low to moderate in strength. Furthermore, in a study of group differences, when the same 10 psychomotor abilities of highly skilled and novice groups of video game players were compared, there was little difference between these groups in their psychomotor abilities, even though there was a substantial difference in their video game performance. These results found with video game performance are in keeping with past research findings in perceptual motor skills in general. Past research does not support the assumption that individuals’ performance on one perceptual motor task can be used to predict their performance on another task even though both tasks appear to require the same basic abilities (see Henry, 1958; Marteniuk, 1974, 1976; Schmidt, 1982 for review). The correlation between performance on different tasks is typically low (range = -.40 to +.40). Also, the research on mining of motor skills shows that the amount of transfer - the gain (or loss) in proficiency in one skill as a result of practice on some other - is positive but quite small unless the two tasks are so similar as to be practically identical (Henry, 1958; Schmidt, 1982).

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The apparent lack of relationship found in the video game experiments makes the psychomotor abilities explanation for video game performance difficult to defend. It should be noted that these results do not negate the fact that improvements in real world skills could occur with coincident video game playing. Rather, these results seriously challenge the common sense reason (i.e., improved psychomotor abilities) for any observed improvements. Expert-Novice Difference and Video Game Performance In a further attempt to identify determinants of video game performance, several experiments were conducted to examine expert-novice differences in video game performance. The basic strategy of this skilled performance approach is to compare the performance of highly skilled performers and less skilled performers on tests of components that appear important for determining performance. If a component is in fact important for determining performance, then highly skilled performers should perform better on the component test than less skilled performers. The video game performance components examined were movement control, and game-specific knowledge. Nine subjects served in these studies, who were expert performers on a video game called "Lady Bug" by Coleco (1982). Their experience with the Lady Bug game ranged from approximately 200 to 350 hours of play, and the weakest player could reliably achieve a minimum score of 80,000 points while the strongest player's minimum reliable score was 500,000 points. In contrast, the 9 novice players who served as test subjects achieved an average score of 8,673 points over 18 games (2 games each). The Lady Bug game is a maze-running game where the player moves the Lady Bug with a joystick control through the maze (similar in concept to the popular "Pac Man" video game). The object of the game is to a attain a high score by having Lady Bug eat the dots, letters, hearts and vegetables along the maze pathways, while avoiding the skulls or being eaten herself by one of four predatory insects. When the player clears the screen, a new screen is presented and each successive screen consists of faster and "smarter" predatory insects. There are turnstiles within the maze that can be moved only by the Lady Bug, and these can be used to dodge and escape the insects. Thus, much of the maze configuration can be changed at will by the player. In observing game play, one obvious difference between expert and novice performance is the skill with which Lady bug movements are controlled and executed. In one study, this apparent movement control difference was examined directly and quantified by having the subjects follow predefined paths in the Lady Bug maze. The pathways differed in their complexity in terms of the number of turns required, and ease/difficulty of turn execution. Over all pathway conditions, the novices attained only 73% of the speed of experts, and made twice the number of errors as experts on easy turns and 24 times the number of errors on difficult turns. These large and robust differences indicate that movement control and execution skill is an important determinant of video game performance. In contrast to the experts, the Lady Bug movements under the control of novices are slow, gross and jerky. Novices have difficulty

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making direction changes in one smooth movement, and they cannot gently nudge turnstiles into closed position. This lack of reliable movement control is often directly responsible for Lady Bug’s demise. The fact that expert video game players display superior movement skills in comparison to novices is not surprising. Superior movement control on the part of highly skilled performers is readily apparent in most perceptual motor skills. The more interesting questions pertain to the nature of skilled movement performance and its contribution to the overall skill demonstrated by the experts. For example, is skilled Lady Bug performance mostly determined by superior movement control, and does it matter what control device an expert uses to play the game? This same question pertains to many sport skills. Most elite athletes have superior movement control, and specific preferences when it comes to equipment characteristics. The question is, to what extent is their elite performance dependent on the equipment characteristics? Answers to these types of questions can provide insight into the nature of movement skill, and the relationship of movement skill to skilled performance. To obtain answers to these questions, the expert and novice Lady Bug players played two different yet, in many respects, similar video games (Lady Bug and Ms. Pacman) with two different joystick controls (Coleco joystick and Atari joystick). In total there were 4 experimental conditions: Lady Bug with Coleco joystick, Lady Bug with Atari joystick, Ms. Pacman with Coleco joystick, and Ms. Pacman with Atari joystick, and all subjects participated in all conditions. In both games the object is to clear the maze of game elements while avoiding being eaten by 4 pursuing creatures. As a player advances in either game, the creatures become quicker and “smarter” to thwart the player’s advancement. The subjects played each game-joystick combination for 20 minutes, and the results for total points and mean game score are shown in Figure 4.1. Analysis of variance for total points and mean game score revealed main effects of skill F(1.14) = 18.77, p<.05, F(1,14) = 17.07, p<.05, and condition F(3,42) = 23.56, p<.05, F(3,42) = 12.92, p<.05, and a skill by condition interaction F(3,42) = 10.94, p<.05. F(3.42) = 7.32, p<.05. Analysis of the interaction revealed that expert Lady Bug performance was significantly worse with an Atari joystick than with a Coleco joystick, but expert perfomiance on the Ms. Pacman game was the same with either joystick. The novice’s performance did not differ across all 4 conditions. Also, expert Lady Bug scores were higher than expert Ms. Pacman scores, and novices were significantly worse than experts in both Lady Bug conditions. However, novices scored the same as experts in both Ms. Pacman conditions. These results indicate that the experts’ movement skill is specific to playing Lady Bug with a Coleco joystick, and since they showed no Coleco joystick advantage in the Ms. Pacman game, it would seem that their movement skills are specific to the particular combination of Lady Bug game with Coleco joystick. Also, the results indicate that the experts’ skill is game specific since the experts were no better than the novices in the novel Ms. Pacman game. The experts’ skill in Lady Bug did not transfer to a conceptually similar video game.

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Video game performance

I

Total Game Score Video Game Joystick

Ms. Pacrnan Atari

Lady Bug Coleco

Lady Bug Atari

ME. Pacrnan Coleco

173,290

137,853

79,501

78,174

Experts

Mean

(n=9)

S.D.

47,558

28.234

16,817

18,597

Novices

Mean

80,273

73,208

67,021

67,014

(n=9)

S.D.

36,223

29,644

8,877

8,727

Mean Game Scorn Video Game Joys! ick

Lady Bug Coleco

Lady Bug Atari

Ms. Pacrnan Coleco

Ms. Pacrnan Atari

Experts

Mean

88,860

34,956

14,877

13,143

(n=9)

S.D.

60,925

9,269

7,161

8,615

Novices

Mean

14,646

12,464

6,940

6,803

(n=9)

S.D.

9.555

7,381

3,117

3,752

Figure 4.1. Total and Mean Game Scores for Game by Joystick Conditions.

Although expert Lady Bug performance was worse with an Atari joystick, their overall game performance did not deteriorate to the level of the novices even in this condition. Even with perturbed motor control, experts’ Lady Bug game performance was still superior to that of novices. This suggests that there is more than superior movement control accounting for expert game performance. In fact, in playing the Ms. Pacman game, a few of the experts commented that a pattern probably exists in Ms. Pacman like in Lady Bug, and if they could discover the pattern they could beat the game. This reference to patterns may be important for skilled performance. All of the Lady Bug experts have learned the same patterns of maze configurations which are intimately tied to their strategies for clearing the maze. The expens in comparison to the novices, not only display superior movement control, but their movements appear to be more purposeful. The experts’ comments on knowing and using defined maze patterns is reminiscent of

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what Chase and Simon (1973 a,b) found to be the distinguishing difference between highly skilled and less skilled chess players. That is, presumably there are arrangements or patterns of maze walls that are meaningful to expert Lady Bug players, as there are arrangements or patterns of chess pieces that are meaningful to chess Masters. Chase and Simon (1973a.b) had chess players of different skill levels perform two parallel tasks involving 5-sec presentation and recall. In both tasks the players saw pieces arranged on a chess board for 5 seconds, and they were required to recall what had been presented by reconstructing the situation on another chess board. In one task the presented chess pieces had been arranged in a structured game situation. In the other task the chess pieces had been randomly arranged on the board. Chase and Simon found an increase in the number of pieces accurately recalled with increased chess skill, but only for the structured chess situations. When the pieces had been randomly arranged there were no skill differences in the number of pieces recalled. This latter result showed that chess skill is not due to general memory ability - experts were reduced to memorizing individual pieces like novices, and were no better than novices. Rather, chess skill is related to the amount of specific chess knowledge stored in memory. This superior recall effect also has been shown to exist in bridge (Charness, 1979). Go (Reitman, 1976), Gomoku (Eisenstadt & Kareev, 1975) and basketball (Allard, Graham, & Paarsalu, 1980). What this research has shown is that a large organized knowledge base underlies skilled performance in each of the domains, and the domain specific knowledge drives or enables rapid perceptual encoding or recognition processes to greatly reduce the processing load on the performer (Chase & Chi, 1981). In playing a game, the skilled player immediately recognizes familiar game situations (i.e., patterns or structure), which are associated with particular strategies and plausible moves. Recognition of the patterns results in rapid retrieval of highly plausible strategies or courses of action that the player should consider. These processes explain why chess Masters are able to think of very good moves quickly, and how a chess Master is able to defeat many weaker players in simultaneous play (Chase & Chi, 1981). These processes also explain why bridge experts are able to generate bids and plan the play of a hand faster and more accurately than weaker players (Chamess, 1979). While speed is not necessarily important in games like chess and bridge, speed is of the essence in most action-oriented video games. Encoding processes that enable rapid contact with patterns stored in long term memory could be an important determinant of video game performance. The video game player has very little time to think about what to do. Rapid encoding of the game situation, and rapid retrieval of good action plans may spell the difference between game termination or getting to play the next screen. Therefore, two experiments were conducted to determine if the superior recall effect as shown in other skill domains, could be found with highly skilled Lady Bug players. The 5-second recall task was adapted for use on the Lady Bug game. In one experiment the Lady

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63

Bug players were given 5 second views of static Lady Bug displays which varied in the amount of structure present (with respect to turnstile patterns and position of game elements), and were required to reconstruct the displays on a model of the Lady Bug maze. In another experiment the players were shown videotaped, dynamic displays of actual expert or novice game play, and were required to reconstruct the maze as it was last seen when the screen went blank.

In both studies, a skill by game structure interaction was not found. Rather, both the experts and novices performed better with structured than unstructured displays. This would suggest that skilled Lady Bug players do not have game specific knowledge that enables them to rapidly encode the game situation, unlike chess, bridge, gomoku, go and basketball players. However, in examining what the experts and novices actually did in performing the experimental task, the more likely explanation for the null finding is that the recall tasks were inappropriate for tapping the video game knowledge of the experts. Both the experts and novices focused on sections of the maze and adopted an area-by-area recall strategy. They also attempted to use anchor points within these sections to remember where a group of elements started or stopped to fill in large areas of the maze with dots. This was an advantageous strategy because one characteristic of structured displays is that the maze elements are clustered together. That is, experts clear out discrete sections of the maze one at a time. Even though with the dynamic displays, experts would comment on the skill of the player and the use or non-use of turnstile patterns, the experts still would use the area-by-area recall strategy as opposed to their game knowledge to reconstruct the maze. Thus, it was premature to conclude only on the basis of this type of task that game knowledge does not conmbute to skilled video game performance. Rather, the possibility of game knowledge conmbutions was examined in a more direct fashion, and is described in the next section. Game Knowledge and Movement Control in Video Game Performance In this study, the contribution of both game knowledge and movement control to the acquisition of Lady Bug performance was compared. This experiment examined learning of the Lady Bug video game by novices with game knowledge training versus no training, and movement control training versus no training. Method Four groups of novice Lady Bug players (4 subjects per group) were employed in this experiment. One group received movement training only (Movement group). This training consisted of practicing movements outside of the game context much like drills used in sport domains. A second group received game knowledge training only (Strategy group), which consisted of instruction in game strategy and tactics much like "chalktalks" delivered by coaches in sport domains. A third group received both the movement training and the game knowledge training (hereafter referred to as the "Both" group), and a fourth group received no training at all (Control group). Then all four groups of novices were monitored over 50 games of Lady Bug play to observe training effects on performance and rate of performance improvement. The specific procedure that was used is described below.

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The sequence of events for all 4 groups of novices was as follows. At the start of the experiment, baseline performance measures were obtained for each subject. Each subject played 5 games of Lady Bug and performed 5 mals on each of the four movement pathways used in the movement control experiment described earlier. These game scores and movement speed measures are hereafter referred to as pre-training game scores, and pre-training movement speed. After the baseline measures were obtained, the novices received training (i.e., movement training for the Movement group, strategy training for the Strategy group, movement and strategy training for the Both group, and a 2 week no training interval for the Control group). After the training administration, each subject played 5 games of Lady Bug and performed 5 trials on each of the four movement pathways again. These game scores and movement speed measures were collected to establish the training effects, and are hereafter referred to as post-training game scores and post-training movement speed. Then the subjects took the Colecovision and Lady Bug game home to play 50 games. The subjects were given data collection sheets and instructions to record their game scores and the date and time of game play. After this 50-game learning phase, the subjects played 3 more games which were videotaped, and the subjects were interviewed about their Lady Bug learning experience. The movement training provided to the Movement group and the Both group consisted

of having the subjects move the Lady Bug through the 4 pathways used in the movement control experiment. The subjects received part and whole task training on the pathways in that they practiced the more difficult segments (e.g.. difficult turn sequences) separately, as well as running the complete pathways. The subjects were informed of movement performance weaknesses, and were coached on how to improve their movement performance. In total, the nature of the movement training (exercises and instruction methods) was like that of basketball and hockey drills, which are directed at practice of specific movement skills outside of the game situation. Movement training continued until the subjects could perform the 4 different pathway conditions within time criteria that were outside the lower limit of the 95% confidence interval about the novice mean performances obtained in the movement control experiment. Therefore, after movement training, these novices could be considered significantly better in their movement skill than untrained novices. In knowledge training, which was provided to the Strategy group and the Both group, the subjects were given a half-hour to read and study a Lady Bug strategy booklet. The game strategy booklet outlined the experts’ strategy for clearing the maze. The experts provided step-by-step instructions on how to clear the maze, and these instructions along with maze diagrams that clarified the instructions formed the content of the booklet. The instructions described: what to look for when the maze first appears, when to get the hearts and letters. what maze areas should be cleared and the sequence for clearing the areas, and how turnstiles should be positioned when clearing areas and how they should be left after the areas are cleared.

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After studying the booklet, the strategy elements were shown and explained to the subjects on the model of the Lady Bug maze. During this demonstration emphasis was placed on the rationale behind each step of the strategy. Following this, each subject demonstrated their knowledge and understanding of the strategy on the Lady Bug maze by showing how the maze should be cleared, and explaining why certain parts should be cleared first, and turnstiles should be left in certain positions. This was followed by viewing taped excerpts of expert Lady Bug play so the novices could see how the strategy was implemented. The novices were considered knowledgeable when they could demonstrate the strategy and rationale, and answer questions on specific maze configurations without errors. The Both group received movement training first, and when movement criteria were achieved, they received strategy training. The Both group and the Strategy group were given the strategy booklet, along with the instructions and data sheets for the 50 game learning phase. This was done to ensure that their Lady Bug performance would not be affected by temporary lapses, or errors in recall of the Lady Bug game strategy over time.

Results All dependent variable means and standard deviations are presented in Figure 4.2. The analysis of variance for pre-training and post-training game scores revealed a main effect of condition, F(1.12) = 14.32, p<.05, and a group by condition interaction, F(3,12) = 3.55, p<.05. Analysis of the interaction indicated a condition effect only in that each group’s post-training game scores were significantly greater than their pre-training game scores. The analysis of variance for the movement speed scores revealed a group main effect, F(3.12) = 5.02, p<.05, a condition main effect, F(1,12) = 31.77, p<.05, and a group by condition interaction, F(3,12) = 15.64, p<.05. Analysis of the interaction showed that at pre-training, the Both group’s movement speed was significantly less than that of the Movement group. All other pairwise comparisons were not significant. However, at post-training, the groups who had received movement training, were significantly faster than the groups who had not received this training, indicating that the movement training was effective. The analysis of variance for the time taken to play 50 games revealed a main effect of group, F(3,12) = 6.72, pc.05. Post hoc analysis showed that the Strategy group took more time than all the other groups, and the Both group took more time than the Movement and Control groups. The time taken by the Movement and Control groups were not significantly different. The learning curves for the 4 groups are presented in Figure 4.3. The 50 learning games were divided into 10 blocks of 5 consecutive games, and the mean of the 5 games was used as the game score for each block. As can be seen, the Strategy and Both groups’ learning curves rise rapidly, while those of the Control and Movement groups rise very slowly. The analysis of variance for the game scores over blocks revealed a main effect of block F(9,108)= 10.05, p<.05, and a group by block interaction, F(27,108) = 2.79, p<.05. Analysis of the

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interaction showed that all 4 groups were the same at blocks 1 and 2. At block 3, the Strategy group was significantly better than the other 3 groups, and the latter 3 groups did not differ from each other. At block 4, the Strategy and Both groups were no different from each other, but were significantly better than the Control group. At block 5, the Strategy group was significantly better than the other 3 p u p s , and the latter 3 groups did not differ from each other.

I

1

I

GameScorcw Pre-Training Mean S.D.

n (games) ~

I

Post-Training Mean S.D.

~

~

~

ControlGroup

20

6,038

1,691

7,396

2,696

Movement Group

20

8,779

3,245

12,701

7,356

Strategy Group

20

9,065

7,413

9,579

11,721

Both Group

20

5,184

3,271

13,530

6,959

Movement Speed (cmlsec) Pre-Training S.D. Mean

n (trials)

Post-Training Mean S.D.

Control Group

80

6.16

1.93

6.07

1.86

Movement Group

80

7.34

1.19

8.66

0.45

Strategy Group

80

6.53

1.49

6.41

1.53

Both Group

80

5.08

1.97

7.78

1.17

Learn Rate Mean S.D.

Tlme (Hn) Mean S.D.

n

Y-Intercept Mean S.D.

Control Group

4

7,756

2,805

965

531

3.2

0.3

Movement Group

4

14,595

2,321

585

213

4.4

1.2

Strategy Group

4

19,476

10,586

4,543

2,978

18.0

10.3

Both Group

4

8,991

7,894

4.835

2.478

12.7

3.1

~~

Figure 4.2. Novice Lady Bug Learning.

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The Control and Movement groups did not differ from each other over all 10 learning blocks. The Strategy and Both groups were significantly different than the Control and Movement groups from block 6 to block 10. The Strategy and Both groups differed from each other on blocks 6 and 7, but they did not differ on blocks 8, 9, and 10. In summary, the performance of the 4 groups was the same in the early blocks, but by the sixth block the Strategy and Both groups performed significantly better than the Movement and Control groups. Game Scorn x1ooo

75

C

0-

-

Control Gmup I .Movement Only Group -0 Strategy Only Group

70

65 60

55 50 45

40

35

30 25

20 15 10

5

1

2

3

4

5 6 Blocks

7

(5 Consecutive Games Per Block)

Figure 4.3. h d y Bug Learning Curves Over 50 Games.

8

9

10

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D.M. Baba

The analysis of variance for the y-intercepts showed no significant p u p differences, F(3.12) = 1.87, p>.05, while the analysis of variance for the learning rate variable revealed a group main effect, F(3.12) = 5.37, p<.05. Post hoc analysis showed that the Strategy and Both groups' learning rates were the same, and both were significantly greater than that of the Control group and Movement group. The Control and Movement group learning rates were not significantly different from each other.

Discussion The post-training results show some benefit of movement skills training very early in Lady Bug performance. It appears that the two groups who received movement training (Movement and Both groups) showed more of an improvement relative to their pre-training game scores, than the two groups who did not receive movement training (Strategy and Control groups). However, the benefits of movement training to game performance were short lived (as shown over the 50 game learning curves) suggesting that these early improvements may reflect attainment of a basic, prerequisite level of movement skill needed for any reasonable play of the game. The 50 game learning results, in contrast to the memory recall experiments, demonstrate a large conmbution of game knowledge to Lady Bug performance. The two groups that received strategy training (Strategy and Both groups) clearly improved their performance at a faster rate, and attained significantly higher performance levels after 50 games than the Movement and Control groups. Furthermore, it appears that game knowledge or strategy plays the dominant role over movement skill in determining Lady Bug performance. The movement skills of the Both group did not give them an advantage over the Strategy alone group. The Both group never achieved significantly better game scores than the Strategy group in any of the 10 blocks of the learning phase. This is not to say that the Movement and Control group subjects had no strategy; rather, the strategies they developed were far less effective. None of these subjects discovered the hide pattern and optimum strategy within their 50 games of learning experience. The "hide pattern" is a configuration of turnstiles within which the Lady Bug is relatively safe from the predatory insects. This pattern is included as part of the experts' strategy for clearing the maze. That novice performance and rate of performance improvement were greatly enhanced when the novices were provided with the experts' game knowledge indicates that game knowledge is an important determinant of performance. Also, this result indicates that the use of the memory recall tasks in the earlier studies may have been inappropriate for showing expert-novice game knowledge differences. That is, it is reasonable to assume that game knowledge differences which contribute to perfomlance actually exist between experts and novices, when the results of this study show that novices who share a proportion of knowledge known by experts, perform (after 50 games practice) significantly better than novices without such knowledge.

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The movement skill results however, appear inconsistent with previous findings. That movement skill was shown to have no significant effect on Lady Bug performance over 50 games of learning in this experiment is inconsistent with the results of the movement control studies which showed that experts have superior movement skills. This inconsistency may be explained by the difference between novices who are just starting to learn the game (early in learning) versus experts who have mastered the game (late in learning). What the results show is that superior movement skill is important for game performance later in learning, but not necessarily so early in learning. The importance of movement skill to performance in different phases of learning can be seen by contrasting the game performance of the Strategy and Both groups after 50 games of practice with that of Lady Bug experts. While the Strategy and Both groups’ performance is significantly better than the novices who had no game knowledge training, the Strategy and Both groups’ performance does not approach the skill level of the Lady Bug experts. The former groups’ game score mean was 70,000-75,000 points, while that of the experts was 225,000 points. Also, the speed and efficiency of expert game play attests to the importance of movement skills. For example, in the first 5 minutes of game play the experts average 36,961 points and they are usually on the fourth screen of the game. In contrast, players in the Strategy and Both groups (after 50 games experience) average 13,025 points and they are usually on the second screen of the game. Thus, it can be seen that even though the Strategy and Both groups’ game scores are relatively high, their performance lacks the quality of expert play in term of speed and efficiency.

The superior movement skill of the Lady Bug experts also enables them to engage in a different and more aggressive style of Lady Bug play than that shown by the Strategy and Both groups. The Strategy and Both groups were seen to adhere to the learned snategy regardless of the screen level they were playing. They would employ the hide position with the fast and intelligent insects, as well as the slow and duller insects. In contrast, experts set up the hide pattern but they do not use the hide position for the first 3 to 5 screens. The experts know that they can outrun and outmaneuver these slower and duller insects. On the screens with faster and more intelligent insects, the experts do employ the hide position. However, they often venture out from the hide position to clear sections of the maze or to lure the insects into a trap. The subjects in the Strategy and Both groups reported trying to do the same thing (which also was observed in their videotaped games after the learning phase). However, unlike experts who have superior movement skills, these novices would get their Lady Bug killed more often in these attempts than experts. The contribution of movement skill and style of play is not reflected in Lady Bug game scores because the game score is not influenced by the amount of time taken to clear the screens. There are no time limits in the Lady Bug game, nor are there bonus points for clearing the

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screens faster. If there were time limits or bonuses, the expert Lady Bug players would outperform players who have knowledge of the basic strategy but have only moderate movement skills. That is, once players can use the basic strategy, game score alone is an insufficient measure or discriminator of performance. Efficiency must be taken into consideration to further distinguish between skill levels. In conclusion, these data suggest that game knowledge of effective strategy is the main determinant of Lady Bug performance early in learning (i.e., over the first 50 games). This element is necessary for achieving high game scores, and for achieving rapid performance improvements early in learning. Movement skill, on the other hand, appears to be a secondary determinant of performance. Early in learning, players need only have adequate movement skill to execute the strategy effectively. However, later in learning superior movement skill contributes to speed and efficiency in game performance and it modulates the strategies and tactics that players employ thus, determining their style of game play. General Discussion Why are some people more skilled or proficient than others at perceptual motor tasks like playing video games? What determines performance? One view, the abilities view, suggests that performance differences are determined largely by differences in people's inherent abilities. People who are better performers have more and/or better "hardware" components than those who are less skilled. The differences in hardware may refer to things such as: better visual acuity, better manual dexterity, more processing capacity, faster processors or more memory. This view presumes that performance is determined largely by endowment of abilities or traits that are generic or general in the sense that they may be seen to underlie or support the performance of many different tasks. The data, however, show that video game performance is not determined by such task-independent psychomotor abilities, although it is conceivable that psychomotor abilities may determine the ultimate level of proficiency that a player can attain (Schmidt, 1982). An alternative view, the skilled performance view, suggests that performance differences are determined largely by components that change and develop with practice or experience with the particular task, and not so much by any task-independent "hardware" abilities (Allard & Burnett, 1985; Allport, 1980; Anderson, 1982; Kolers, 1985). This view posits that people who are better at the task possess more task-specific knowledge and task-specific skills which have been acquired through practice, than lesser performers. These studies have shown that skilled performers have stored a large repertoire of domain-specific patterns which enable rapid encoding and recognition of game situations, and the rapid generation of effective courses of action. The video game data support this view and suggest that skill specificity is found in both the cognitive aspects (game knowledge) and motor aspects (movement skill) that contribute to game performance. Why the importance of game knowledge to video game performance was shown in the learning task and not the memory recall tasks is open to many alternative explanations. The

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explanation proposed here centers on the type of knowledge that appears important for video game performance. In the recall tasks it was presumed that the nature of game knowledge is that of static turnstile patterns, much like patterns of chess pieces on a chess board. In contrast, in the learning task game knowledge consisted of a basic strategy which was a plan of sequential actions or a recipe, much like a recipe for a cake or assembly instructions for a model airplane. It is possession of this latter type of game knowledge (i.e., recipe) that is important for video game performance. It should be noted that the static nunstile patterns are embedded and form an integral part of the recipe thus, the experts' claim that it is important to know and use turnstile patterns is valid. However, as the memory recall data suggests, knowledge of turnstile patterns alone is not of primary importance in determining performance. It is not enough to know what the turnstile patterns are (i.e., the ingredients), the player needs to know when and how to form and use them; it is this necessary knowledge that is provided by a recipe (i.e., combining ingredients together). That it is recipe type knowledge that is important for skilled performance is supported by the content found in the popular literature on video gaming. Video game experts share their recipes or how-to knowledge in magazines like, "Joystik", "Video Games Player" and "Electronic Games". These sources usually provide the player with step-by-step instructions, the various contingencies at each step and diagrams illustrating the steps to take to achieve particular game goals. This information is very much like the information that was given in the learning study to the groups of Lady Bug players who received game knowledge training. Although there is a large "cerebral" component to video game performance similar to that shown in the cognitive skills domain, unlike cognitive skills, video game skill also involves a large movement control component. Knowing what, when, why and how to maneuver is not enough; the player must be able to perform the motor acts or movements. What the studies conducted have shown is that superior movement skill contributes to skilled video game performance, however, movement skill is highly specific to the particular task - particular joystick with particular game.

In examining what might underlie the experts' superior movement skills, an analysis of the Coleco and Atari joysticks revealed that the Atari joystick requires slightly less force and less movement displacement to achieve closed switch positions. While it is possible that the experts' motor control system is controlling or programming force and/or movement displacement, this is unlikely to be true. Players do not modulate the force and/or displacement of their movements, rather players of all ages and skill levels operate in an all-or-none fashion. They apply far more force than is necessary and displace the stick as far as it will go (i.e., well beyond closed switch positions). Rather, the variable most likely contributing to skilled movement control is timing. The players must make their movements at precisely the right time in order to be successful at

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executing a turn or stopping the Lady Bug in an exact spot In order to time the movements precisely, the following variables at least must be integrated for skilled movement performance: Lady Bug’s time-to-contact the pivot point where a turn will be executed, the player’s reaction time and the player’s movement time to attain closed switch position. The seemingly minor decrease in the deadspace of the Atari joystick relative to the Coleco joystick could have caused the significant decrement in expert Lady Bug performance. The shorter movement time to closed switch position of the Atari stick may have been enough to disrupt experts’ skilled timing control in the Lady Bug game. Timing specificity also may account for why the experts showed no Coleco joystick advantage when they played Ms. Pacman. Regardless of which joystick is used, the experts would be unfamiliar with gauging Ms. Pacman’s movement in terms of time-to-contact pivot points for turn execution. Aside from affecting their game scores, the experts’ relative loss of movement control affected their style of game play. The experts started out playing Lady Bug like they normally do and were shocked by their relative lack of movement control. How the experts attempted to cope with their loss of control was to modify their normal style of play. The experts modified their actions in the game to ny to fit the level of control they had by playing more cautiously and adhering more closely to the basic strategy. While both game knowledge and movement skill appear important for video game performance, the data suggest that the components are differentially important in different stages of learning. Early in learning, it appears that game knowledge plays the more dominant role in determining performance. However, later in learning, it appears that movement skill is more important in determining performance. This finding is in line with the factor analytic studies of perceptual-motor skills conducted by Fleishman (Fleishman & Hempel, 1954b, 1955; Fleishman & Rich, 1963). Fleishman’s studies showed that the relative contribution of components to performance may change with practice. The factor that was specific to the criterion task increased in the amount of variance it accounted for indicating that skill becomes more specific with increased practice. The importance of game knowledge early in learning makes sense in the domain of perceptual motor skills. While it is necessary in playing a game or sport to be able to execute the prerequisite movements, being able to execute the movements very well is of little use when the player has little knowledge of what to do in the game, when to do it and how to do it (Keele, 1982; Welford, 1976). For example, many athletes can display the best of technique or form, and yet not be outstanding in the game. What is missing is how effectively their knowledge about the game can be brought to bear at critical times (Keele, 1982). The findings of these video game studies also lend support to Anderson’s (1982) view with respect to acquisition of cognitive skills. Anderson (1982) suggests that skill development involves a transition from declarative to procedural ksuch that the first or declarative stage is dominated by declarative/verbally mediated knowledge. The second or compilation stage is

Video game performance

13

marked by a translation of declarative knowledge into procedural knowledge, and in the final or procedural stage, procedural knowledge becomes more highly tuned to the specific task demands.

The pattern of results obtained in the video game studies may be suggestive of Anderson’s (1982) proposal of a translation from declarative to procedural knowledge. For instance, the novices who possessed declarative knowledge of the experts’ recipe, or a set of verbal instructions performed better and improved their performance significantly faster suggesting that declarative knowledge may be important early in learning. Meanwhile. expert performance appears to be dominated by superior movement skills, and the experts performed the same as novices on the declarative knowledge based tasks of memory recall. This may suggest that procedural knowledge (often associated with perceptual motor tasks) may be important for performance late in leaming. In conclusion, the results indicate that psychomotor abilities do not underlie or determine performance to any significant degree, however, it is suggested that these abilities may represent limitations on performance. Instead, the determinants of performance are game specific knowledge and game specific movement skill. It appears that game specific knowledge is the primary determinant in that it underlies both novice and expert performance (i.e., early and late in learning), while game specific movement skill is a secondary determinant in that it was found to be dominant in expert performance only (i.e., late in learning influencing efficiency and style of game play). References Allard, F., & Bumett, N. (1985). Skill in sport. Canadian Journal of Psychology, 39(2), 294-312. Allard, F., Graham, S.,& Paarsalu, M.E. (1980). Perception in sport: Basketball. Journal of Sport Psychology, 2 , 14-21. Allport, D.A. (1980). Patterns and actions: cognitive mechanisms are content specific. In Guy Claxton (Ed.), Cognitive Psychology: New Directions. London: Routledge 8t Kegan Paul. Anderson, J.R. (1982). Acquisition of cognitive skill. Psychological Review, 89(4), 369-406. Baba, D.M. (1986). Determinants of Video Game Performance. Unpublished doctoral dissertation, University of Waterloo, Waterloo, Ontario. Chamess, Neil (1979). Components of skill in bridge. Canadian Journal ofPsychology, 33(1), 1-16. Chase, W.G., & Chi, M.T.H. (1981). Cognitive skill: Implications for spatial skill in large scale environments. In J.H. Harvey (Ed.), Cognition, Social Behuviour and the Environment. Hillsdale, New Jersey: Erlbaum. Chase, W.G., & Simon, H.A. (1973a). The mind’s eye in chess. In. W.G. Chase (Ed.), Visual Information Processing. New York: Academic Press. Chase, W.G., & Simon,H.A. (1973b). Perception in chess. Cognitive Psychology, 4 , 55-81. Eisenstadt, M., & Kareev, Y. (1975). Aspects of human problem solving: The use of internal

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representations. In D.A. Norman & D.E. Rumelhart (Eds.). Exploration in Cognition. San Francisco: Freeman. Fleishman, E.A., & Hempel, W.E. (1954b). Changes in factor structure of a complex psychomotor test as a function of practice. Psychometrika, 19(3), 239-252. Fleishman, E.A., & Hempel, W.E. (1955). The relation between abilities and improvement with practice in a visual discrimination reaction task. Journal of Experimental Psychology, 66(5), 301-312. Fleishman, E.A., & Rich, S. (1963). Role of kinesthetic and spatial-visual abilities in perceptual motor learning. Journal of Experimental Psychology, 66(1), 6-1 1. Henry, F.M. (1958). Specificity vs. generality in learning motor skill. Proceedings, College Physical Education Association (pp. 126-128). Jones, M.B., Kennedy, R.S., & Bittner, A.C. (1981). A video game for performance testing. American Journal of Psychology, 94( I), 143-152. Keele, S.W. (1982). Component analysis and conceptions of skill. In J.A.S. Kelso (Ed.) Human Moror Behaviour, An Introduction. New Jersey: Lawrence Erlbaum Associates. Kennedy, R.S., Bittner, A.C., Harbeson, M., & Jones, M.B. (1982). Television computer games: a "new look in performance testing. Aviation, Space and Environmental Medicine, J ~ ~ u w49-53. Y, Kolers, P.A. (1985). Skill in reading and memory. Canadian Journal of Psychology, 39(2), 232-239. Marteniuk, R.G. (1974). Individual differences in motor performance and learning. In J.H. Wilmore (Ed.), Exercise and Sport Sciences Reviews, 2, 103-130. Marteniuk, R.G. (1976). Information Processing in Motor Skills. New York: Holt, Rinehart & Winston. Reitman, J.S. (1976). Skilled perception in Go: Deducing memory structures from inter-response times. Cognitive Psychology, 8, 336-356. Schmidt, R.A. (1982). Motor Control and Learning: A Behavioral Emphasis. Champaign, Illinois: Human Kinetics Publishers. Stewart, D.M. (1983). Video therapeutics. American Way, April, 43-45. Welford, A.T. (1976). Skilled Pevormance: Perceptual and Motor Skills. Glenview, Illinois: Scott, Foreman and Company.

Acknowledgement This research was based on a doctoral dissertation submitted to the University of Waterloo, and was supported in part by Natural Sciences and Engineering Research Council of Canada graduate scholarships. I wish to thank Fran Allard whose enthusiasm and intellectual support have been invaluable throughout by Ph.D. program and career. Also, special thanks are extended to IBM Canada Limited, and in particular to Coleco of Canada Limited for providing their video game equipment.

COGNITIVE ISSUES IN MOTOR EXPERTISE 1.L. Stakes and F. AUard (Editors) 0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 5 ANALYZING DIAGNOSTIC EXPERTISE OF COMPETITIVE SWIMMING COACHES REBECCA RU?T LEAS AND MICHELENE T.H. CHI 821 Learning Research and Development Centre 3939 O’Hara Street. Pittsburgh, PA 15260 Introduction: Expertise and Diagnosis in Competitive Swimming This study is an attempt to understand the diagnostic knowledge and skills of expert competitive swimming coaches. The ability of a competitive swimming coach to effectively diagnose the weaknesses and strengths of a swimmer’s stroke and to prescribe a remedy is recognized as one of the very most important skills in developing high levels of coaching expertise. This diagnostic task, particularly from an underwater view, entails the consideration of three dimensions of movement analysis, in a medium which is loo0 times more dense than air. Whether instructing young athletes on basic fundamentals or fine tuning the elite athlete’s skills, knowing and understanding how to effectively analyze the competitive strokes, how to articulate the desired movements, and to increase the swimmer’s efficiency, is the hallmark of a successful and effective swimming coacWdiagnostician. Sport studies on experthovice differences in coaches have primarily focused on quantitative and descriptive characteristics of coaches and their skills. To clarify this point, most studies have focused on the outcome of the results of coaching actions and have not investigated the knowledge base and structures which enabled the coach to obtain those results. An example would be the study of the number, types, and intervals of feedback which a coach gives an athlete or team. The sport literature abounds with these sorts of analyses (Barrette, Feingold, Rees, & Pieron, 1987; Darst, Zakrajsek, & Mancini, 1989) which may be of great utility for pedagogical purposes but which do not investigate the state of knowledge that created the outcomes. Currently, little is known about the coach’s knowledge base, its organization and how that interfaces with diagnosing, even though several experthovices studies have been carried out on coaches’ diagnostic abilities in a variety of sport domains such as tennis (Armstrong & Hoffman, 1979; DiCicco, 1990). golf (Skrinar & Hoffman, 1979), gymnastics (Imwold, 1980, Imwold & Hoffman), and track and field (Pinheiro, 1989). The present study used verbal reports obtained through interviews and clinical diagnosis of the competitive freestyle swimming stroke to elicit and identify the coach’s diagnostic

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knowledge. Such a methodology has been pioneered by scientists in cognitive psychology, artificial intelligence (Al), decision science, medicine, and sport. Research on the diagnostic knowledge base of expert coaches in the sport domain, however, is rather sparse and in the area of competitive swimming it is virtually nonexistent. The pragmatic need to understand competent coaching arises from two sources: First the efficiency for swimming is lower than any other sport (Costill, Maglischo, & Richardson, 1992) and second, relatively few coaches attain expert diagnostic skills while the majority remain rather average, even after 10 or more years of coaching. What are the underlying mechanisms which really distinguish the expert swimming coach from less expert peers? Interest in the determination of expertise in sport coaches and in the general teaching profession has increased greatly in the last 10 years (Berliner, 1986; Siedentop & Eldar, 1989). This desire to objectivize and quantify expertise is not a trivial task, particularly in classrooms, on sport fields, and in pools which are notorious for providing many challenging variables that need to be accounted for or controlled in order for scientific study to proceed. Expertise in Sport: Competitive Swimming How do we define and operationalize expertise in a practical domain such as a sport setting? Unequivocally, the answer is, "it depends". The upshot of most references and definitions of experts and expertise in sports settings is that they are most often vague and tenuous at best. The common folk view of expertise (Lesgold, 1984). which assigns the status of expert as strongly related to experience, seems to prevail. Many experts who have invested ten- to twenty thousand hours of training in a specific skill to develop such expertise are simply written off as being intuitive, gifted, and imaginative owners of special knowledge or innate abilities. Some suffer a less complimentary fate and are thought to excel because of mere hours of practice and persistence. Relatively little attention has been directed toward understanding the expert coach's complex development of specially honed cognitive skills in a specialized domain and requiring the investment of a considerable amount of time. That some individuals apparently never learn much from their years of experience (Berliner, 1987) is a commonly observed phenomenon whose explanation continues to elude researchers. The sport of competitive swimming, like most other sports, has been hard pressed to clearly define expertise. Typically, expertise is attributed to a coach according to a number of result oriented criteria. Years of experience and accomplishment (including the number of championship teams, number of national championship qualifiers, national champions, AllAmericans, Olympic Trials qualifiers, and number of Olympians) are typical descriptors. Researchers in many fields have concurred that the development of expertise is a process that occurs over a fairly lengthy period of time. Hayes (1981) argued that at least 20,000 hours of experience are needed to achieve expert status. That is quite revealing when one realizes there are only about 2,000 working hours per year. A recent study by Could, Giannini, Krane, and Hodge (1990) surveyed backgrounds and histories of 130 elite national level coaches in 30 different Olympic sports in the United States. The average number of years of coaching for this elite group was 15 and the majority of coaches had competed in the sport they coached. Even

Diagnostic expertise

I1

more striking is that 48% of the coaches had competed on an international team and 21% had competed on an Olympic team. In the 1991 NCAA women's national championship basketball tournament, both the first and second place team head coaches had played elite basketball at Division I institutions. Personal experience in a sport seems to be in some way associated with achieving coaching expertise and with providing the coaching with additional insight into technical elements of the performance. Perhaps the link is the "perspective" (Thorndyke, 1984) one brings to the arena as a result of experience. The previous list of experiences is qualified according to the level of competition in which the swimming coach participates (e.g., age g o u p versus senior level swimming; high school or summer teams versus year around club swimming). Regardless of level, some form of the aforementioned criteria are the closest definition of expertise that one can generally find in the sport of competitive swimming. These criteria, with the exception of experience, only reflect the of expertise in action and do not reveal the special skills or mental processes and knowledge structures that the expert possesses and utilizes to obtain the results. Additionally, the exact contribution of experience to the development of expertise has continued to elude researchers. What has been established is that long periods of study and practice are a necessary but not a sufficient condition for becoming an expert (Chi, Bassok, Lewis, Reiman, & Glaser, 1989; L. S. Shulrnan, [personal communication, April 25, 19911).

a

Rather than focusing on experience, Klein, Caldenvood, and MacGregor (1989) examined the role of knowledge in expert performance. They concluded that explicit knowledge, sometimes referred to as declarative knowledge, is not sufficient for proficient performance. What remains to be determined is exactly what role experience and various types of knowledge do play in the development of expert human performance. Clinical Diagnosis in Sport Clinical diagnosis in sport is defined as the act of directly observing an athlete's performance for the purpose of analyzing the technique to identify the possible errors or weaknesses and to recommend remedial action. In swimming, and in sport in general, clinical diagnosis is the first step in a chain of action through which performance skills are improved. For the swimmer, developing efficient swimming technique is one of the most important factors in achieving fast swimming times. Thus, understanding what the expert smoke diagnostician knows and can do should be very helpful in providing insight and guidance for the development of novice swimming coaches. Gaining entrance to the intricacies and internal saucture of expert swimming diagnosticians' skills should paint a clearer picture of what it really is that expert coaches know and do and should provide an additional dimension to what is currently known about coaching expertise in competitive swimming in particular, and sport in general. Stroke Prescription

On the heels of diagnosis follows prescription, the next most important sequential step in the coach's effort to improve a swimmer's skill. Determining that a stroke is incorrect is not enough. Simply knowing it is wrong will not abet performance. Athletes often complain to

78

RR. Leas and M.T.H. Chi

coaches that their technique feels off or wrong but are completely bewildered as to how to correct it. Novice observers can often tell that the stroke does not look quite right but cannot carry the analysis to its next extension. Knowing what it is that is wrong is one thing, knowing how to fix it is quite another. It takes an experienced and knowledgeable coach to concretely and correctly interpret what is wrong. Further, the coach must be able to figure out how it can be fixed and to successfully communicate this knowledge to the athlete. The skills of interpreting and recommending are the essence of diagnosis/prescription.

-

The Knowledge Base The knowledge base responsible for the psychological processes must be explored to account for the underlying diagnostic and prescriptive skills of stroke analysis by the coach. Although relatively little is known about the nature of coaches' thought processes and their knowledge of the subject matter in adjusting instruction to the level of the athlete (Putnam, 1987). a model of sport skill diagnosis and prescription was developed by Hoffman (1983) to clarify the sequential nature of the diagnostic/prescriptive process. This model defines the difference between the athlete's response and the desired response as a discrepancy. The recognition of the nature and extent of the discrepancy as well as the identification of the cause of the discrepancy are the basic skills of diagnosis. The recommendation and application of a remedy comprise the prescriptive skill. Upon completion of this process, the initial discrepancy should be reduced or eliminated.

This skill of recognizing and understanding the cause of these discrepancies, which in swimming revolve around stroke cues, is analogous to what Chase and Simon (1973b) referred to as having a "critical eye". Experts are able to "see" (Chase & Simon, 1973b) more than less expert individuals. By "see", it is not meant that the novice cannot see the explicit cues, but that only the experts realize the importance or meaningfulness of certain patterns and configurations (Chi & Bjork, 1991). Thus the experts are able to "see" beyond the explicit cues and take the analysis to a deeper level and determine precisely what the cause of the error is and how it can be fixed. Because recognition and identification of causes of stroke cues in a dynamic sports skill is a complex, knowledge-rich task, it demands that the problem solver has command of a large body of domain-specific knowledge in order for the problem solver to find a solution to the problem. (Knowledge-lean tasks, on the other hand, are problems which can be solved without domain specific knowledge.) Studies of knowledge-rich tasks have occurred primarily in non-sport domains such as algebra, physics, thermodynamics, chess, bridge, geometry, medical diagnosis, public-policy formation, and computer programming (VanLehn, 1989).

Methods Description of Study This study was designed to investigate the differences in the diagnostic knowledge base of expert and novice competitive swimming coaches. Think aloud protocols were elicited for two tasks. In the interview task, the coaches reported their vision of the ideal freestyle stroke. A coach's conception of a good competitive swimming stroke may be reflected in the elaboration

Diagnostic expertise

19

of a stroke prototype and the particular qualities of the stroke that may be more specifically referred to as a reference of correctness. A sample from one of the expert protocols reflects these kind of parameters.

...And to counter torque, what you have to do is, you have two choices, you can counter torque with a physical exertion from the legs, that is you can kick yourself to a horizontal position, or you can counter torque by constructing the strokes such that there is more weight, connected weight, in front of your floatation point, that is the center of air, generally around the lungs... It is these images of the prototype that provide the coach with a basis of comparison by which to judge the observed performance and to offer prescriptive recommendations. The importance of the coach having a mental prototype corresponding to a schema against which to compare and contrast the observed performance has been established by numerous researchers in the field of sport skill analysis (Armstrong, 1986; Mosston, 1986; Christina & Corcos, 1988; Kreighbaum & Barthels, 1990; Schmidt, 1991). Each coach was permitted to describe the stroke for as long as they needed and the responses were audiotaped.

In the diagnosing task, the coaches analyzed the competitive freestyle stroke. Diagnosing stroke technique is a skill and so the development of expertise in that skill would seem to be dependent on the evolution of a coach's procedural knowledge. An example is "the feet and hips are swinging laterally causing excessive form drag but it is all caused by the overreaching of the hands across the body's center line on the entry". The coach saw the &of the problematic movement surface in the hip and leg motion and then correctly nasoned that the cause was in the hand entry, quite a distance away from the hips and legs. A coach's ability to elaborate on dynamic features requires adequate knowledge about the principles which guide movement in a water medium (hydrodynamics) as well as an understanding of stroke mechanics (biomechanics). This latter method seems ecologically valid inasmuch as the clinical diagnosis of strokes is normally based on a verbal interchange, either on tape or directly between coach and swimmer. Thus one would not expect verbalization to alter the problem solving process and its contents. The think aloud technique has also been successfully used by knowledge engineers (Fischoff, 1989; Klein et al., 1989; Klein, 1990) and many other researchers in tasks such as examining chess players (Chase & Simon, 1973a; de Groot, 1966). physicians (Elstein, Shulman, & Sprafka, 1978; Feltovich, Johnson, Moller, & Swanson, 1984; Gale & Marsden, 1983; Kuipers & Kassirer, 1984; Pate1 & Groen, 1986; Wortman, 1972), psychologists (Ericsson, Chase, & Faloon. 1980; Ericsson & Simon, 1980; 1984; Flanagan, 1954; Johnson, 1988; Lawrence, 1988; Leinhardt, 1983; 1986). and teachers (Cummings,Murray, & Martin, 1989; Jones, 1989; Taheri, 1982; DiCicco, 1990). See Chi (in press) for a practical guide on this type of data analysis. Subjects The nature of this research was not to estimate some population value but rather to select subjects with whom an in depth case study approach could be used and from whom the most

80

RR.Leas and M.T.H. Chi

could be learned. Criterion based sampling was utilized to obtain six subjects for an expert group and six subjects for a novice group of competitive swimming coaches. Subjects were chosen from a national population of swimming coaches. To be eligible for selection, novice coaches had a maximum of two years full time head coaching experience or three years part time or assistant coaching experience. Multiple criteria were utilized to select the expert group. First, a minimum of twelve years as full time head coach was required. Second, each of the experts was recognized by their colleagues in United States Swimming ( U S S ) and American Swim Coaches Association (ASCA) as an outstanding coacWdiagnostician. Third, each of the experts had produced anywhere from 20 to 100 of top national caliber swimmers. For the purpose of this chapter, data from the verbal protocols of two expert and two novice subjects will be reported and discussed. One exception is the "measures of coherence" data in table 3 where the complete compliment of subjects was used (six experts and six novices). It should be noted that in the diagnostic task section of this study Expert #2 had very little experience in diagnosing underwater strokes from videotape while Expert #1 had extensive experience. Expert #2 does not have access to an underwater window and thus is restricted to "above water" analysis in the daily coaching job. Both novices had some, but very limited, underwater analysis experience. Materials A videotape was developed as the stimulus condition for the diagnostic task. The tape

was filmed from an underwater window and featured four women collegiate swimmers performing the competitive freestyle stroke. The tape featured swimmers swimming toward and away from the camera for four widths of the pool lasting about 35-45 seconds or nine to eleven seconds per width. This time frame was chosen to replicate what is typically used by coaches in underwater video analysis. The four swimmers differed in their swimming skill levels. Two were national caliber (NCAA Division II) with best times of 51.7 and 53.1 for the 100 yard freestyle. The other two swimmers were much slower, 60.2 and 61.0 and were not national caliber. The 10 second difference in speed for the two groups of swimmers is a significant amount of time in the sport of swimming. Although this difference in speed cannot be detected in the film, the stroke technique which enabled the faster swimmers to excel and which resmcted the slower swimmers, was readily observable. Procedures This study specifically investigated two types of knowledge; conceptual and procedural. The interview task was designed to capture the coach's conceptual knowledge by asking for a description of hisher ideal vision of the competitive freestyle stroke. It was hypothesized that the coach's accessing of specific smke knowledge via hisher schematic representations of a prototypical freestyle stroke and the accompanying parameters for the execution of the stroke should reveal the amount, depth, and nature of the knowledge base. Each coach was permitted to describe the prototype for as long as they needed and the responses were audiotaped.

Diagnostic expertise

81

The diagnostic task was designed to capture the coaches' procedural knowledge and required each coach to watch and diagnose the underwater videotape of four swimmers swimming the freestyle stroke. After each swimmer, the coaches were asked three questions. First, they were asked to rate the swimmer's stroke technique on a scale of 1-10. Second, they were asked to provide a general assessment of the swimmer's stroke. T h i i they were asked to give a qualitative holistic diagnosis of the stroke. The same questions were asked in the same order for each coach and responses were audiotaped.

Results The Interview Task The interview task was designed to probe the coach's knowledge of the ideal freestyle stroke. Four stroke components are recognized as the major categories of movement in the freestyle stroke (Colwin, 1992; Costill et al., 1992; Maglischo, 1982). The first analysis therefore counted the number of protocol citations that fell into each of the four major freestyle stroke components of body position, armstroke, kick, and breathing (see Figure 5.1). What is interesting is that both experts cited each of the four major stroke components while both novices only used two components,both of whom failed to utilitc the "body position" component. Most of the novices' stroke features (mean of 78% for the two novices, versus 55% for the experts) centered on the armstroke component group, suggesting that the novices had limited knowledge of the other components of body position, kick, and breathing. Although it is not surprising that the experts had a greater total number of citations (58) than the novices (22), this would seem to point out the fragmented schematic representation that the novices had as compared to the more complete mental picture which the experts gave.

Novice.

Export. ~

Componontm

Body P o a i t i o n

NO.

t

~

No.

t

NO.

_

_

t

_

~

~

NO.

t

5

13

5

25

0

0

0

0

Annmtroko

20

54

11

55

7

78

10

77

Kick

12

31

1

5

0

0

3

23

Bro~thing

1

3

3

15

2

22

0

0

20

100

9

100

13

100

TOtAl.

38 100

Figure 5.1. Frequency of Stroke Components Cited for Freestyle Stroke Prototype. While Figure 5.1 clearly shows that the experts attend to a more complete view of the

82

RR.Leas and M.TH. Chi

stroke, it does not provide us with an insight into the experts’ knowledge and use of specific stroke features within each of the major stroke components. By utilizing the 15 stroke features most commonly associated with each component of the freestyle (Colwin, 1992; Costill et al., 1992; Maglischo, 1982), a second template was developed from which to compare coaches’ responses. Figure 5.2 decomposes the statements about each “broad” stroke component into one vague and 15 specific stroke feature categories. A sample of vague statements from one of the novice’s prototypes discussing armstroke and kick follows: The hand should be under the body and not outside the hips, the swimmer’s should be moving at the same time but in opposite directions, the swimmer’s arms should have high elbows and the & will be in flutter kick motion.

-

From this sample, it is clear which components are being addressed. The first three comments describe the armstroke component and the last phrase describes the kick component. There are, however, a number of reasons why this excerpt reflects a vague novice description. First, this explanation lacks the kind of sequential and qualitative descriptors common to the elaboration of stroke features by the experts. In other words, one phrase does not biomechanically relate to the next. Second, the above statements focus on specific body parts (see underlined words) whereas the experts focused on process-oriented descriptors (see below underlined words). Third, the descriptions rendered are so general and fragmented that they could apply to other competitive strokes. They do not make it clear which phase of the stroke is being described within each stroke cycle. Lastly, this novice expressed a commonly held misconception which would be considered to be a serious error in the prototype (the swimmer’s arms should be moving at the same time but in different directions) and which would prevent proper execution of the stroke. As compared to the novices, the experts reported the stroke in a much more processoriented manner. An analogous section from one of the experts’ prototypes discussing the armstroke and the kick follows: In the freestyle I look for a nice outsweep, fairly wide at the top and then a downsweeD and an insweee, and then a finish with a full extension of the (elbow) .... the best freestylers utilize a 6-beat kick and that is usually a fairly nmow kick, not very wide, within a radius of about 8 or 9 inches, 10-10 and 1/2 inches at the most .... In this excerpt, the expert cited specific biomechanical movements in the armstroke in a manner which indicates a knowledge of their sequential relationship. Additionally, the description of the kick includes specific parameters of acceptance. This expert also cited a commonly held misconception (full extension of the elbow), however, this error is of much lesser magnitude than the previously cited novice’s error. Across all four broad stroke categories (body position, armstroke, kick, breathing)

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Diagnostic expertise

experts used 10 (El) and 11 (E2) of the 15 available feature categories (a mean of 70%) whereas novices used only four (Nl) and six (N2) of the available feature categories (a mean of 34%). This is another indication that novices’ knowledge about smke features was more deficient.

Expert.

Novice.

2

1 NO.

S

No.

2

1 S

No.

%

No.

S

C 1 Body P o a i t i o n

ceneral

0

0

2

10

0

0

0

0

Soecific F1

Lateral

5

13

1

5

0

0

0

0

F2

Vertical

0

0

2

10

0

0

0

0

5

13

5

25

0

0

0

0

2

22

4

31

Subtotal

C2 Armatroke

General

1

3

4

20

swcific F3

Entry

8

21

1

5

1

11

2

15

F4

Catch

3

8

1

5

1

11

0

0

P5

Downawoop

1

3

1

5

0

0

0

0

P6

Inaweep

1

3

1

5

1

11

0

0

Fl

UpSWOOQ

3

8

1

5

1

11

0

0

P8

Exit

0

0

1

5

0

0

1

8

P9

Recovery

3

8

1

5

0

0

1

8

P10

Timing

0

0

0

0

1

11

2

15

Subtotal

20

54

11

55

7

78

10

77

(table continue.)

RR. Leas and M.T.H. Chi

a4

Expert8

NOViC.8

1 NO.

2 b

NO.

1 b

2

NO.

b

NO.

b

C3 l i c k

CenerlL

2

5

0

0

0

0

3

23

azadLL€ F11

Depth

0

0

0

0

0

0

0

0

I12

Width

5

13

0

0

0

0

0

0

P13

Timing

5

13

1

5

0

0

0

0

Subtotal

12

31

1

s

0

0

3

23

C4 Breathing

G!umzA

0

0

2

10

0

0

0

0

swcitic F14

Po8ition

0

0

0

0

0

0

0

0

F15

Timinq

1

3

1

5

2

22

0

0

Subtotal

1

3

3

15

2

22

0

0

38

loo

20

loo

9

100

13

100

10

67

11

73

6

40

4

27

Total8 Poaturem C i t e d

Figure 5.2. Features Cited in Major Stroke Components for Protorype.

Not only was the novices’ knowledge more deficient in terms of amount, but what they It is commonly accepted that the foundation for speed in a water medium revolves around the swimmer’s body position in the water. Both experts specifically addressed the importance of lateral rotation as it relates to body position. Neither of the novices even mentioned body position anywhere in their prototype. Instead, most of the novices’ comments centered on the amstroke category, again demonstrating that the novices had a limited knowledge of the other stroke components of body position, kick, and breathing.

did know was of lesser importance.

Diagnostic expertise

85

Besides looking at the amount and salience of knowledge experts and novices possess, it was important to assess the extent to which coaches' knowledge is coherently organized. One method to assess coherence is to measure "connectedness" of the coaches' elaborations of the stroke prototype. This method was developed (Lesgold et al., 1988) to analyze the diagnostic X-Ray expertise of physicians, a somewhat analogous task to analyzing videotaped underwater strokes of swimmers. First, "stroke findings" were located in the protocols. Stroke fmdings were defined as the attribution of special stroke properties or characteristics to the stroke or swimmer. Unlike the previous analysis which placed responses either into the vague or specific feature categories, a stroke finding was identified whenever positional or movement attributes in the stroke were indicated. An example of a stroke finding would be "keeping elbows high", "streamlined body", or "full extension of elbow". Second, a "reasoning chain" was then defined by a relationship connecting one or more stroke findings. For example, a coach might first notice excessive eddies and turbulence around the swimmer's feet and then reason that these were created by excessive lateral hip and leg swing. The coach may then take the analysis a step further and point out that lateral hip and leg swing are results caused by overreaching on the entry part of the stroke. Each of these statements would be considered a relationship and would be scored as a reasoning chain of length two: Turbulence (is caused by) -> hip & leg swing (is caused by) -> overreach on entry = reasoning chain of 2. A chain was terminated one of two ways; when the coach ended the sentence or when they began discussing another component. The mean length of chains for experts was 2.6 as compared to 1.6 for novices. Third, a set of stroke findings or reasoning chains that shared a common stroke component, regardless of sequencing, were scored as a "cluster". Consequently, in this analysis, clusters were comprised of and delimited by the four broad stroke categories of body position, armstroke, kick, and breathing. A finding which did not fit into the four aforementioned categories was scored as an independent cluster. Thus, the method of first identifying stroke findings, then reasoning chains, and finally clusters, helps to illuminate the coherence of the coach's ideal prototype of the freestyle stroke and, most imponantly, quantifies the interconnectedness of the protocols. Figure 5.3 presents the mean frequencies for six experts and six novices for the analysis just described. Figure 5.3 presents the means for experts as significantly different from novices (approximately .05)in the categories of number of stroke findings, number of chains, longest chain, mean length of chains, number of clusters, biggest cluster, and mean cluster size. Although experts clearly generated a significantly greater number of stroke findings @=.013), this is not surprising given that they have a more elaborate knowledge base. However, the five subsequent measures of number of chains @=.026), longest chain @=.026), mean length of chains @=.039), number of clusters @=.006), and mean cluster size @=.013) all reflect the coherence and connectedness of the knowledge base. In particular, they measure the coherence

RR. Leas and M.T.H. Chi

86

independent of the amount of protocols (reflecting amount of knowledge) that the coaches articulated.

ExDerts Mean Med.

Novices Mean Med.

Total number of stroke findings

100* 42

17.6

16

Number of chains

28*

14

6.33

6

Longest chain

5.3*

6

2.33

2

Number of different clusters Biggest cluster size

6

4

39.66 22

2.5

2

13

12

Note: The means with an asterisk (*) were significantly different (Pc.05). Figure 5.3. Quantitative Protocol Measures for Freestyle Prototype.

The least significant difference between the two groups was in the biggest cluster category @=.056). This suggests that it is not the case that novices are unable to produce large clusters. Novices’ large clusters came from the component that they were most knowledgeable about, the armstroke. However, the combined results suggest there is a relationship between the amount of knowledge and the connectedness of that knowledge. It should be noted, however, that the correctness of the coaches’ responses is not treated in Figures 5.1, 2, and 3, so that what the subjects cited about the stroke may have, in fact, been false or erroneous. For example, the novices expressed some features about the freestyle stroke which reflected incorrect biomechanics and faulty reasoning. Both novices expressed the idea that the arms should always be moving in opposite directions from each other which is a very naive and common error in diagnosing the freestyle stroke and which would lead to serious stroke difficulties. One of the experts also made an error in recommending the elbow be fully extended at the finish of the stroke, another commonly held misconception about the freestyle stroke. As compared to the novices, however, experts made fewer and less serious errors. In summary of the interview task for the freestyle stroke, the experts explicitly elaborated each of the major stroke components in a manner which flowed and was connected while the novices gave a very narrowly focused, fragmented prototype which dealt almost exclusively with only one component, the armstroke. The coherence of the experts’ knowledge base was further

Diagnostic expertise

87

evidenced by their greater number and size of chains and clusters. These findings are completely consistent with those reported in Chi, Hutchinson and Robin (1989) in which 5- to 7-year old children who were more or less knowledgeable about dinosaurs were asked to describe what they might know about some novel dinosaurs when pictures of them were shown. The novice children typically recited a list of visibly depicted features such as "He has sharp teeth, he has three fingers; he has sharp fingers, sharp toes, a big tail"; whereas an expert child recited a sequence of causally-connected features such as "And so he had webbed feet, so that he could swim, and his nose was shaped like a duck's bill, which gave him his name." The use of connecting words such as "so that" and "which" suggest that the expert child's knowledge of dinosaur features were interconnected so that citing one explicit feature led to the activation of another implicit feature. The Diagnostic Task The diagnostic task consisted of three subtasks. First, both the expert and novice coaches were asked to rate the technique of each of the four videotaped swimmers performing the. freestyle stroke. They rated each swimmer on a scale of 1-10 similar to that used in diving and gymnastics. To facilitate the rating comparison, the four swimmers were ranked according to their best time for the 100 yard freestyle, along with the coaches' ratings assigned to each swimmer (see Figure 5.4). The coaches were not informed of this comparison. It is evident that the experts' rankings correlated perfectly with the swimmers' actual times, whereas the novices were not able to detect the difference between the very good freestyle strokes of swimmer 1 (51 seconds) and 2 (53 seconds) and the very poor strokes of swimmers 3 (60seconds) and 4 (61 seconds). Again, it should be noted that the coaches' ratings were based on technique and not observed speed.

Experts 2 Mean

Time'

1

Swimmer 1

51.7

8

Swimmer 2

53.1

7

Swimmer 3

60.2

5

Swimmer 4

61.0

5.5

4

1

Novices 2 Mean

8.00

9

8

8.50

6

6.5

9.5

6

7.5

4.5

4.75

7

6

6.50

4.75

8

7

7.5

8

'Time for 100 yards freestyle.

Figure 5.4. Ratings of Four Swimmers.

In the second task, the coaches were asked to give an assessment of each swimmer's

RR. Leas and M.T.H.Chi

88

stroke. This was asked to see exactly what it was about the stroke that each of the coaches noticed and attended to first. After examining the protocols, one had to wonder whether these coaches watched the same swimmers! Figure 5.5 presents a collective representation of the general diagnosis rendered by the coaches for swimmer 1.

Itomm Identified

Idontitior Novicom

N L c o body roll

N1 C N 2

Elbow bont on oxtonmion

N1

Should lock olbow out front

N1

Right arm not undornoath body

N2

Loft arm not extending fully

N1 C NZ

CaUS@ And offoct otatmontm

Nl(0) C

Prorcription atatomonta

N l ( 0 ) L N2(0)

NZ(0)

Expert. Hamitation in loft foot of k i c k Littlo

C

unoqual body roll

Rotatom only to tho right Wid.

pull

Broathom to on.

El El C E2

El

C

E2

E2 mido

Stroko unbalancd Caumo and offoct mtatomontm

Proocription mtatamntm

I2 I2

El(1) C Z ( 0 ) Cl(2) C C l ( 2 )

Figure 5 5 . General Diagnoses for Swimmer No. 1

A number of items are immediately evident from this table. First, there is little overlap between the experts and the novices in the features identified either as problematic or nonproblematic for the same swimmer. Second, novices' analyses focused on the flaws of specific body parts (underlined) in a static context (for instance, "elbow bent extension"), whereas the experts focused on process aspects of the stroke such as "wide pull" or "stroke unbalanced". Thus the stroke features identified by the experts and used for diagnosis are more

Diagnostic expertise

89

"second-ordered" and "dynamic", in that they combine and relate several components often maintaining the dynamic nature of the movement. This is completely analogous to the "secondorder" features identified by Chi, Feltovich, and Glaser (1981) in their study of physics expertise and evaluating what makes a problem difficult to solve. In Figure 5.5. all but one of the novices' comments dealt with the specific features of the arms. In contrast, all but one of the experts' comments focused on a dynamic, more holistic assessment including all four components of the stroke; the kick, breathing, pull, and body position. It is of course not surprising that the novices' diagnoses tend to be less accurate. Interestingly, in the only stroke function that both the experts and novices focused upon, they had exactly the opposite diagnosis; the novices thought the swimmer had a nice body roll while both experts noted she had virtually none! Neither novice detected the lack of roll to the swimmer's left side, a serious problem resulting in stroke imbalance which often leads to shoulder injury. Lastly, the locked elbow on the entry and extension recommended by novice #1 is biomechanically incorrect and would likely lead to a shoulder injury. Additionally, one expert provided two cause and effect statements and both experts provided two prescriptive remedies in their general diagnosis of swimmer 1. The novices, in contrast, provided neither cause and effect nor prescriptive statements in their diagnosis. After the coaches completed their general diagnosis of the stroke, the third diagnostic task required them to give a holistic impression of the stroke. Expert 1 made a very astute observation in recognizing that swimmer 1 is actually a breaststroker. Expert 1 further explained why this swimmer attends to only the front pomon of the stroke, why she seemingly ignores the back portion of the stroke, and how her breaststroke movements relate to her deficiencies in freestyle. Expert 2 jumped right to the task of trying to solve the problem by recommending remedial sculling work. Both experts responded with a diagnosis within three to five seconds. A sample of the expert protocols follows: First expert: She is a breaststroker trying to swim freestyle. But I would say she's not feeling the stroke from the midway point of the stroke through the back. She is only feeling the front half of the stroke which is a real natural thing for a breaststroker to do because they don't really feel what it is like at the back of a stroke, only up front as in breaststroke; so she just throws away about half of her stroke, which in freestyle is the most powerful portion of the smke, is the last half and not the first half. .. Second expert: I would ask her to just work on the sculling motion of her underwater pull. Compared to the experts' quick and easy continued diagnosis, the novices struggled. Novice 1 was startled and flustered by having to provide a more holistic diagnosis. He asked if the tape recorder could be turned off to have additional time to think. He ended up merely

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reiterating his comments from the general (first) diagnosis as well as incomctly describing the movement of the hand through the armstroke pull. He also incorrectly attributed an increase in power to a phase of the stroke that is not even a power phase. Novice 2 was also stumped as to what further could be added to the general diagnosis. He required three minutes before reporting he had nothing more to add to the diagnosis. A sample of the novice protocols follows: First novice: I think it was a nice stroke. Really, probably, what we are looking for is that full extension out front--that elbow and it looked like she was bringing it up right, you know,to her throat, her thumb was coming up on her up and insweep to her throat and probably could get a lot more power if she reached out front and was able to bring it in right underneath the rib cage. Second novice: I don't have anything else to say. One interpretation of these responses is that experts have a coherent mental model of the swimmer whereas novices can only focus on the different body parts and analyze the efficiency of each part's movement. Thus, experts are able to give a qualitative holistic analysis of swimming much like the "basic approach" analysis expert physicists can give (Chi et al., 1981). Discussion The experts clearly demonstrated a superior knowledge base as reflected by three main indices: 1) amount of knowledge 2) connectedness of knowledge 3) the depth of representation of knowledge. The results in Figures 5.1 and 2 illustrate the experts' larger knowledge base as manifested by their use of all four of the major freestyle stroke components in their prototype, as compared with the novices. Additionally, within each major freestyle stroke component, the experts utilized more of the specific stroke features which incorporate hydrodynamic and biomechanic parameters. In contrast, the novices' prototype highlighted their use of mostly vague descriptions. The coherence of the coaches' knowledge base was examined via the analyses of the prototypical freestyle as reflected by the results in Figure 5.3. Experts not only had more stroke findings, but these findings were embedded in more reasoning chains, longer reasoning chains, more clusters, and larger clusters thus supporting the experts' ownership of a more coherent knowledge base. That experts represent their knowledge at a deeper level was substantiated by the results of the diagnostic task as shown in Figure 5.5. Their general diagnosis of the swimmer clearly showed they use a dynamic process analysis which reflects a holistic and unified understanding of the stroke and movement. Comparatively, novices' diagnoses were static-based, focused on specific body parts, and dwelled mainly on the pull phase of the armstroke. Additionally, they were not able to give a more in-depth holistic impression of the swimmer, nor did they cite causes, effects, or prescriptions. Further, by comparing the coaches' verbal responses from the

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interview task (Figures 5.1 and 2) to the diagnoses rendered in the diagnostic task (Figure 5.5). it is clear that the experts consistently approached the description of freestyle in a processoriented manner. Their analysis was based on analyzing the swimmer's movements as they related to movement in water both for the prototype and the real-world diagnostic task. This is decidedly different from the novices' approach which was to consistently describe limb movements as they occurred relative to the swimmer's body. The experts' qualitative, holistic, and process-based diagnosis is clearly more correct since they accurately rate the stroke skill of the swimmers while the novices seemed to lack accurate discrimination abilities. Figure 5.4 showed that what the experts were saying was a good stroke was, in fact, a good stroke. The holistic process diagnoses rendered by the expert coaches are analogous to those given by expert physicists as compared to novices when they were asked to state the features of a physics problem that led to their assessment of the general "basic approach" to solving the problem (Chi, Feltovich & Glaser, 1981). As in the case of the coaches, the novices cited explicit objects in the problem statements (such as "inclined plane") as the features responsible for eliciting their basic approach, whereas experts mentioned process-like "second-order'' features, meaning that features that are not explicitly mentioned in the problem statements. These abstracted features consisted of comments such as "no initial or final conditions." or "interaction objects." The transition of explicit objected-oriented features to process-based features corresponds to a kind of "ontological"conceptual shift that Chi (1992; Chi, Slotta & de Leeuw. in press) has proposed for radical conceptual change that comes with deep understanding and the acquisition of expertise.

References Armstrong, C.W. (1986). Research on movement analysis: Implications for the development of pedagogical Competence. In M. Pieron & G.Graham (Eds.) Sport pedagogy (pp. 2739). Champaign, IL: Human Kinetics. Armstrong, C.W., & Hoffman, S.J. (1979). Effects of teaching experience, knowledge of performer competence, and knowledge of performance outcome on performance error identification. Research Quarterly, 50(3), 3 18-327. Barrette, G.T., Feingold, R.S., Rees, C.R.. & Pieron, M. (1987). Myths, models, andmethods in sporr pedagogy. Champaign, IL: Human Kinetics. Berliner, D.C. (1986). In pursuit of the expert pedagogue. Educational Researcher, AugusVSeptember, 5-13. Berliner, D.C. (1987). Ways of thinking about students and classrooms by more and less experienced teachers. In J. Calderhead (Ed.), Exploring teachers' thinking (pp.60-82). Philadelphia, PA: Cassell. Chase, W.G. & Simon, H.A. (1973a). Perception in chess. Cognitive Psychology, 5, 55-81. Chase, W. G.& Simon, H. A. (1973b). The mind's eye in chess. In W. G. Chase (Ed.), Visual information processing (pp. 215-227). New York: Academic Press. Chi, M.T.H. (In press). A practical guide to content analysis of verbal data: A reflection of

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knowledge representation. Journal of the Learning Sciences, [special issue] on "Methodological Issues in the Study of Learning". Chi, M.T.H. (1992). Conceptual chmge within and across ontological categories: Examples from learning and discovery in science. In R. Giere (Ed.), Cognitive models of science: Minnesota srudies in the philosophy of science. (pp. 129-186) University of Minnesota Press. Chi, M.T.H., Bassok, M., Lewis, M.W., Reiman, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145-182. Chi, M.T.H. & Bjork, R. (1991). Modeling expertise. In D.R. Druckman & R. Bjork (Eds.),In rhe mind's eye: Undersranding human performance @p. 57-79). Washington, DC: Academy Press. Chi, M.T.H., Feltovich, P.J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognirive Science, 5, 121-152. Chi, M.T.H., Hutchinson, J.E., & Robin, A.F. (1989). How inferences about novel domainrelated concepts can be constrained by suuctured knowledge. Merrill-Palmer Quarrerly, 35(1), 27-62. Chi, M.T.H., Slotta. J.D. & de Leeuw, N. (in press). From things to processes: A theory of conceptual change for learning science concepts. Learning and Instruction: The Journal of the European Association for Research on Learning and Instrucrion, Special issue on 'Conceptual Change." Colwin, C.M. (1992). Swimming inro the 21sr century. Champaign, IL: Leisure Press. Costill, D.L., Maglischo, E.W., & Richardson, A.B. (1992). Swimming. Boston, MA: Blackwell Scientific Publications. Cummings, A.L., Murray, H.G., & Martin, J. (1989). Protocol analysis of the social problem solving of teachers. American Educational Research Journal, 26( 1). 25-43. Darst, P.W., Zakrajsek, D.B., & Mancini, V.H. (1989). Analyzing physical education and sporr insnuction. Champaign, k Human Kinetics. de Groot, A. (1966). Perception and memory versus thought: Some old ideas and recent findings. In B. Kleinmuntz (Ed.), Problem solving. New York Wiley. DiCicco, G.L.(1990). The effects of playing and reaching experience on abiliry ro perform a diagnostic rusk. Unpublished doctoral dissertation, University of Pittsburgh, Pittsburgh, PA. Elstein, AS., Shulman, L.S., & Sprafka, S.A. (1978). Medical problem solving: An analysis of clinical reasoning. Cambridge, MA: Harvard University Press. Ericsson, K.A., Chase, W.G., & Faloon, S. (1980). Acquisition of a memory skill. Science, 208, 1 181-1182. Ericsson, K.A., & Simon, H.A. (1980). Verbal reports as data. Psychological Review, 87(3), 2 15-251. Ericsson, K.A., & Simon, H.A. (1984). Prorocol analysis: Verbal reports as dara. Cambridge, MA: MIT Press. Feltovich, P.J., Johnson, P.E., Moller, J.H., & Swanson, D.B. (1984). LCS: The role and development of medical knowledge in diagnostic expertise. In W. J. Clancey & E. H.

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Shortliffe (Eds.), Readings in medical artificial intelligence (pp. 275-319). Reading, MA: Addison-Wesley. Fischoff, B. (1989). Eliciting knowledge for analytical representation. IEEE Transactions on Systems, Man. and Cybernetics, 19(3), 448-461. Flanagan, J.C. (1954). The critical incident technique. Psychological Bulletin, 51(4), 327-358. Gale, J., & Marsden, P. (1983). Medical dingnosisfrom student to clinician. New Yo* Oxford University Press. Gould, D., Giannini, J., Krane. V.. & Hodge, K. (1990). Educational needs of elite U.S. National Team, Pan American, and Olympic coaches. Journal ofTeaching in Physical Education, 9, 332-344. Hayes, J.R. (1981). The complete problem solver. Philadelphia, PA: Franklin Institute Press. Hoffman, S. J. (1983). Clinical diagnosis as a pedagogical skill. In T. J. Templin & J. K. Olson (Eds.), Teaching in Physical Education @p. 35-45), Big Ten Body of Knowledge Symposium. Champaign, 11: Human Kinetics. Imwold, C.H. (1980). The relationship between teaching experience and performance diagnosis of a gymnastics skill. Unpublished doctoral dissertation, University of Pittsburgh, Pittsburgh, PA. Imwold, C.H., & Hoffman, S.J. (1983). Visual recognition of a gymnastics skill by experienced and inexperienced insmctors. Research Quarterly, 54(2), 149-155. Johnson, E.J. (1988). Expertise and decision under uncertainty: Performance and Process. In M.T.H. Chi, R. Glaser, & M.J. Farr (Eds.), The name of expem'se (pp. 209-228. Hillsdale, NJ: Lawrence Erlbaum. Jones, D.F. (1989). A comparative anaIysis of expert and novice basketball coaches during planning and practice sessions. Unpublished doctoral dissertation, University of Pittsburgh. Klein, G. (1990). Knowledge engineering: Beyond expert systems. Informution and Decision Technologies, 16(1), 27-41. Klein, G.A., Calderwood, R. & Macgregor, D. (1989). Critical decision method for eliciting knowledge. IEEE Tramactions on System, Man, and Cybernetics, 19(3), 462-472. Kuipers, B., & Kassirer, J.P. (1984). Causal reasoning in medicine: Analysis of a protocol. Cognitive Science, 8, 363-385. Lawrence, J.A. (1988). Expertise on the bench: Modeling magistrates' judicial decisionmaking. In M. T. H. Chi, R. Glaser, & M. J. Farr (Eds.), The name of expertise (pp.229-259). Hillsdale, NJ: Lawrence Erlbaum. Leinhardt, G. (1983). Novice and expert knowledge of individual student's achievement. Educational Psychologist, 18(3), 165-179. Leinhardt, G. (1983). Expertise in mathematc is teaching. Educational Leadership, April, 28-33. Lesgold, A.M. (1984). Acquiring expertise. In J. R. Anderson & S. M. Kosslyn (Eds.), Tutorials in learning and memory @p. 31-60). San Francisco, CA: W. H. Freeman. Lesgold, A.M., Glaser, R., Rubinson, H., Klopfer, D., Feltovich, P., & Wang, Y. (1988). Expertise in a complex skil1:Diagnosing X-ray pictures. In M. T. H. Chi, R. Glaser, &

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M.J. Farr (Eds.), The nature of expertise (pp.311-342). Hillsdale, NJ: Lawrence Erlbaum. Maglischo, E.W. (1982). Swimming faster: A comprehensive guide to the science of swimming. Chico. CA: Mayfield Publishing. Patel, V.L., & Groen, G.J. (1986). Knowledge based solution strategies in medical reasoning. Cognitive Science, 10, 91-116. Pinheiro, V.E.D. (1989). Motor skill analysis: Diagnostic processes of expet? and novice coaches. Unpublished doctoral dissertation, University of Pittsburgh, Pittsburgh, PA. Putnam, R.T. (1987). Structuring and adjusting content for students: A study of live and simulated tutoring addition. American Educational Research Journal, 24( l), 13-48. Schmidt, R.A. (1991). Motor learning & performance: From principles to practice. Champaign, IL: Human Kinetics. Siedentop, D., & Eldar, E. (1989). Expertise, experience, and effectiveness. Journal of Teaching in Physical Education, 8(3), 254-260. Skrinar, G.S., & Hoffman, S.J. (1979). Effect of outcome information on analytic ability of golf teachers. Perceptual and Motor Skills, 48, 703-708. Taheri, M.A. (1982). Analysis of expertise in planning and interactive decision-making among heolth-relatedphysicalfitness teachers. Unpublished doctoral dissertation, University of Pittsburgh, Pittsburgh, PA. Thomdyke, P.W.(1984). Applications of schema theory in cognitive research. In J. R. Anderson & S , M.Kosslyn (Eds.). Tutorials in learning andmemory (pp.167-191). San Francisco, CA: W.H. Freeman. VanLehn, K. (1989). Problem solving and cognitive skill acquisition. In M. Posner (Ed.), Foundations of cognitive science (pp. 527-579). Cambridge, MA: MIT Press. Womnan, P.M. (1972). Medical diagnosis: An information processing approach. Computers and Biomedical Research, 5, 315-328.

Aknowledgement Preparation of this chapter was supported in part by the Office of Naval Research, Contract #N00014-91-J-1532 to the second author.

COGNITIVE ISSUES IN MOTOR EXPERTISE J.L. Starkes and F. Allard (Editors) 0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 6 DECLARATIVE KNOWLEDGE IN SKILLED MOTOR PERFORMANCE: BYPRODUCT OR CONSTITUENT? FRAN ALLARD*, JANICE DEAKIN**, SHANE PARKER*, WENDY RODGERS*** *Department of Kinesiology, University of Waterloo Waterloo, Ontario, N2L 3GI **Department of Physical and Health Education Queen’s University, Kingston, Ontario, K7L 3N6 ***Department of Kinesiology, Universiry of Windsor Windsor, Ontario N9B 3P4 A recurring theme of the papers in this book is that different forms of knowledge ~ I E important for expert motor performance. In many of the studies described, experimenters have selected subjects on the basis of their skill in performing a particular motor activity - with skilled performance assumed to be a product of procedural memory - then tested subjects on cognitive tasks, tasks which require declarative memory. As shown in many of the chapters, expert-novice differences in declarative tasks have often been shown, leading to the conclusion that declarative memory is an important element of skilled motor performance. However, this conclusion might be a leap of faith; it is entirely possible that declarative knowledge is the consequence of the number of hours spent by expen motor performers in their particular environment, and is not essential for the expression of the actual skill. In a review of approaches to motor skill learning, Salmoni (1989) makes this point (and several others) very effectively:

There are problems with this new approach, however. The first problem with the knowledge-based skill analysis technique is that it assumes that the knowledge is a determinant of game skill, when it could be just as logically the case (since these are non-causal studies) that this game-specific knowledge is a byproduct of experience in the sport. ... Perhaps a more serious problem is that the model assumes that skilled performers are those who have proceduralized the most declarative knowledge and that this procedural knowledge is non-verbal in nature. Yet, the tests being used to distinguish skilled from unskilled players are tapping declarative knowledge. Another issue is whether skill proceeds from declarative to procedural knowledge or vice versa. (p. 225)

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One way to sort out whether declarative knowledge is a correlate or a component of skill is to evaluate the domain specific knowledge possessed by individuals who spend much of their time in a particular athletic domain, but do not actually perform or play, for example, coaches, officials or judges, and fans. Should declarative knowledge be a correlate of the time spent in and around a particular environment, coaches, officials, and fans should have the same kind and amount of declarative knowledge as do performers. In this chapter, we report three studies in the domains of hockey, figure skating and diving, and basketball, in which we have attempted to determine the relationship between what a person does and what a person knows.

You Can't See What You Don't Know: Studies in Picture Sorting Among a battery of tasks used to investigate expert-novice differences in physics problem solving, Chi, Feltovich, and Glaser (1981) employed a problem categorization task, with subjects being required to sort physics problems into categories according to how the problem would be solved. More experienced subjects sorted the problems according to the law or physical principle that would be used to solve the problem. Less experienced subjects sorted according to the Situation described in the problem; for example, placing all inclined plane problems into the same category, regardless of the principle or law required to solve the problem. Chi et al. concluded that experienced subjects used the deep structure,while less experienced subjects used the surface structure of the problems, as the basis for their sorting. Allard and Burnett (1985) reported a similar study in which basketball experts (players on the Canadian National Womens' Team) and novices sorted pictures selected from a textbook on women's basketball (Barnes. 1972). Sorting data were analyzed using hierarchical cluster analysis (Johnson, 1967). The first step in this analysis was to calculate the proportion of time each picture was placed in the same group as every other picture in the set. This results in a proximity matrix, which shows the similarity of each set of pairs of pictures. The items with the highest degree of similarity were then considered to be a single item, and the proximity matrix recalculated for the new item and the remaining items. The process of grouping the most similar items and recalculating the proximity matrix continues until all judgments of similarity have been taken into account (see Reitman Olson & Biolsi (1991) for an excellent account of methods of mapping mental representations). The graphical result of this procedure is a "nested rooted tree" (Reiunan Olson & Biolsi, 1991), with similar items joining to form bigger but fewer roots, and the distance between the roots representing how similar each cluster is to every other cluster. Cluster analysis showed the sorts of the players to be a recapitulation of the chapters in the textbook, with distinct and independent clusters for shots, half-court offenses, high post offenses, screens, rebounds, and one on one defense. Cluster analysis of the sorts of the nonplayers showed little order. and appeared to be one category containing all pictures showing one on one situations, and a second category containing pictures with more than two players. The one on one category for the novices contained pictures showing rebounding, shooting, and one on one defense; pictures that had formed three distinct categories for the experts. As found for physics problems, expert players sorted according to the principle being illustrated in the picture, while novices sorted on the basis of physical characteristics-pictures containing two players vs.

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all other pictures. Parker (1989, reported in Allard & Starkes, 1991) extended the basketball study in several ways. He used three groups of subjects: expert players, coaches, and fans. Expert players (n=14) had an average of 18 years playing experience, and were playing for professional teams, for Major Junior teams, or for university teams when tested. The coaches (n=8) were professional coaches in the NHL, Major Junior, or university ranks, with an average of 10 years coaching. The fans (n=15) were selected from male subjects who had played hockey for a minimum of two years, but had no more than five years playing experience. The fans reported that they currently watched an average of 1.4 games per week. The stimuli used in the sorting task were pictures of professional hockey games in progress, rather than the textbook pictures used in the basketball study. Parker's photos contained varying numbers of players, and were taken from different perspectives. Thus Parker's subjects had a much tougher time deciding what was happening in each picture than did subjects in the basketball study. Subjects were instructed to sort the set of pictures "into categories using whatever rules or principles that seem sensible to you". Cluster analysis for the picture sorts of the three groups showed all three groups classified pictures into five main clusters: face-offs, breakouts, power plays, fore-checking, and good scoring chances. But how similar are the smctures of each of the groups' clusters? Fowlkes and Mallows (1983) have suggested a method for determiningthe similarity between two hierarchical clusters. This method involves comparing the items that comprise the clusters when the number of clusters is the same for two groups. In this method, the number of clusters is termed k, and the similarity metric, which can vary between 0 and 1, is termed B,. Mean values of B, show the greatest similarity for sorts of coaches and players (B,=.454), then players and fans (B,=.4496), followed by coaches and fans (B,=.4369). Although the main categories selected and named for each group of subjects were similar, differences existed in terms of which pictures were assigned to each category. Fans seemed less able to identify what was being shown on each picture than were coaches or players, resulting in differences for the groups for assigning pictures to categories. In other words, group differences were not found at the category level, but in the accuracy with which fans were able to identify instances of their own categories (as though fans had difficulty in assigning tokens to types). In order to check this possibility, we asked subjects to sort a second set of pictures. This set included pictures that were difficult to decipher in that it was difficult to determine which player had the puck, much less what he was frying to do with it. Fourteen pictures of hockey games in progress were selected from the set used by Parker (1989). Rather than randomly selecting pictures as had been done by Parker, we selected pictures so that they fitted into one of four categories. Three of the pictures showed a defenseman bringing the puck out of his own end; pictures that were called "breakouts" by Parker's subjects. Four of the pictures showed forwards attacking the puck carrier, pictures called "forechecking" in Parker's study. Four of the pictures showed the goalkeeper passing the

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puck to another player to begin an attack. The final three pictures used were selected because it was difficult to tell exactly what was happening; it was difficult to tell which player had the puck, or what he was trying to do with it. Subjects (9 non-players and 18 players and coaches) were asked to sort the pictures into categories that ma& sense to them. As well, we added a surprise recognition memory task. Following their sorting, subjects were shown 10 pictures one at a time. and asked whether each picture was one of the set that had just been sorted or a new picture. Cluster analysis of the sorts of the two groups of subjects showed that both groups categorized pictures identically at the level of four categories. As we had hoped, the four categories corresponded to how the pictures had been selected: breakouts, forechecking, goalie handles puck, and ambiguous.Thus with easily identified pictures, experts and novices saw things the same way. The interesting aspect of the sorting data was what happened when the four categories were merged into three; that is, how the four clusters were seen as related to each other. For the novices, the ambiguous pictures were seen as similar to breakouts, while the experts saw the ambiguous pictures as similar to forechecking. One of our expert subjects (McGuire, personal communication) proposed that novices classified the ambiguous pictures according to what was happening at the moment (a player was attempting to advance the puck, a breakout), while the experts classified the same pictures in terms of what was about to happen (the player was about to lose possession of the puck, a forecheck). Another difference in the overall structure of the hierarchical clusters also emerged. The cluster analysis for experts produced three distinct “roots” to the tree, corresponding to the clusters for breakouts, goalie with puck, and the forechecking-ambiguous. In other words. no expert subject judged the pictures across the three root clusters to be in the same category. Novices’ cluster analysis produced two roots to the tree: ambiguous plus breakouts and forecheck plus goalie with puck. Thus, expert subjects had more distinct categories than the novices. The performance of novice subjects in this study was both similar to and different from experts’ performance. If the information in the picture was clear and unambiguous, such as the goalie handling the puck, novices and experts saw the same thing. When knowledge was needed to categorize what was being shown, the two groups differed. The results of the surprise recall test support this contention. The experts correctly identified a mean of 9.5/10 pictures, while the novices identified 8.1, a significant difference (t(25)=2.943, p=.006). Categorization on the basis of existing knowledge structures should result in better memory for the stimuli just sorted, as was shown for the expert subjects. As well, the errors for the experts were evenly split between misses (5/9 or 56% of total errors) and false positives (4/9 or 44%), while the errors of the novices were virtually all misses (14/17 or 82% of total errors), again evidence that the pictures they had just sorted were not processed to the same extent by the novice subjects. The message in the sorting data is that experts use existing knowledge - knowledge developed through playing the game - to interpret what is shown in the pictures, while novice

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subjects-even novice subjects who watch lots of hockey-sort only on the basis of the information present in the picture. In other words, you can't classify what you don't know.

Knowing and Doing: Expertise in Judging The picture sorting studies just described show that experience as a player or a coach changes how pictures of the sport are classified in comparison to how "hockey watchers" classify the same pictures. The main difference between subjects in these studies is that players and coaches perform, while fans simply watch the action. It could be argued that the declarative knowledge that influences experts' sorting behaviour is a factor in the actual performance of the skill; knowing is important for doing. However, as Salmoni (1989) has pointed out, the knowingdoing relationship might not be causal; knowing and doing may not be linked, but rather may be independent manifestations of experience in a particular domain. In order to discriminate between these two positions, it is important to determine whether there is, in fact, a link between knowing and doing. Allard and Starkes (1991) contend that expert motor performance depends on the linking of the knowledge of what must be done at a particular point in time with an appropriate motor program. Were knowing and doing linked, the link should work both ways: if knowing facilitates doing, doing should facilitate knowing. Is there any evidence that the ability to perform a skill influences the ability to perform a cognitive task related to the skill? Rodgers and Allard (1990) have argued that a consideration of the characteristics of expert judges in figure skating and diving provides support for a link between doing and knowing. The task of a judge in a sport such as figure skating is cognitive: it requires what amounts to a psychophysical scaling of the performances of large numbers of athletes. In order to perform this task effectively, the judge must have declarative knowledge of the rules of the sport and the particular competition. As well, the judge must know the repertoire of individual elements that comprise the sport. In an actual competition, the judge must compare the performance of each of the competitors and, as well, determine how well each performance compares to a standard for the particular skill element. Thus, the judge in a figure skating competition must be able to recognize each skill element as it is performed, compare the actual performance to the standard, register each element in memory, then reiterate this process for each element in the skater's routine, and for each competitor. The figure skating judge is also required to rank order the performance of each skater in the competition, a task which requires establishing a representation of what each skater has done in long term memory. Judges work under time pressure; their judgments must be rendered immediately following the performance, and under pressure to be accurate and consistent (each judge is compared to the other judges in the competition). Several steps are required in order to become a judge in figure skating or diving, the sports we will be considering here. Judges are required to attend courses or seminars for each level of certification, and successfully pass written tests at each level. They must spend time "mal" judging at standard tests of skaters and at actual competitions, where their performance

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is assessed by more experienced judges. They then are required to spend a specified amount of time, or judge a specified number of events, before being allowed to progress to the next level. There are nine different levels for figure skating judges, and at least four ranks for diving judges. Should doing aid knowing, it would be expected that judges who have had experience performing the skills would have an advantage judging. We collected questionnaire data from 24 skating judges and 39 diving judges, all of whom were qualified to judge at international competitions. In order to determine their experience in performing the sport, subjects were asked to indicate their highest level achieved in the particular sports’ test program (figure skaters can progress through bronze, silver, and gold test levels), and the highest level at which they had competed. As well, the judges were asked to indicate whether they had ever been selected to judge at World or Olympic Championships. We looked f i s t at the relationship between skating test level and level of judging qualification for the sample of 24 expert judges. This was done by assigning a score for the level of test (gold test was 1, silver was 2, etc.) and the level of judging qualification for each of the judges. Spearman’s rho showed the correlation to be significant, though not particularly high, for skaters; rho=.453, p=.02, df=22. For diving judges, the correlation between competitive level attained diving (international, national, college) and level of judging qualification was also significant, rho=.778, p<.OOl, df=37. The correlations above point to a relationship between performance level and judging level. In the world of judging, being qualified to judge internationally does not necessarily mean automatic assignment to the highest levels of competition. Qualified judges must be selected for these competitions. It seems reasonable to assume that it is the best judges from the qualified group who are selected for these tasks. If high level performance skill is related to level of judging skill, we might expect to see a majority of individuals who have competed at the international level being selected to judge at the international level. We selected from our sample of skating and diving judges those who had competed at World or Olympic championships. There were 5 skating judges who had competed at the highest level, and all 5 had also been selected to judge at the highest level. For the diving judges, 16 had competed at World or Olympic Championships, and 14 of the 16 had judged at the same level. Of the 2 diving judges who had not been selected for World or Olympic judging, 1 had just qualified as an international judge and the other had coached at the world level. To look for statistical support for a relationship between performing at an international level and being selected to judge at an international level, we classified our judges in a two by two table according to whether they had judged at Worlds, and whether they had competed at Worlds (Figure 6.1) then calculated a coefficient of contingency (phi coefficient) on the data. For skaters, phi was equal to .513 (chi square=6.32, df=l, p =.02), and for divers the phi coefficient was .602 (chi square=16.67, p<.Ol). The significant relationship between

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performing and judging at an international level is thus confirmed by the coefficients of contingency: if you are qualified to judge at the most prestigious competitions, it helps to have competed at the same level. There are a variety of reasons why such a relationship might exist. Former athletes might be better acquainted with the politics of the sport, and be in a better position to advance their own judging career. They might be better connected, or be better known to sport officials. They might be highly motivated to succeed in all walks of life. Finally, the ability to perform the skills of the domain might aid the acuity of judgment. In order to see what the judges thought about the relationship between judging and performing, we asked 21 judges who were attending a judging clinic held by the Canadian Figure Skating Association whether they thought skating experience was useful to judges, and why (or why not) this was so. The judges attending the clinic reported unanimously that skating experience was not only useful, but essential, for effective judging. The advantages they felt skaters had as judges were: knowledge of what to expect from skater 0 recognition of elements 0 0 recognition and appreciation of difficulty recognition and appreciation of quality 0 greater understanding of technical aspects 0 easier to learn and recognize new technical aspects 0

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Figure 6.1. Sporting experience of judges selected for world level competitions.

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A total of 65% of the respondents mentioned the technical advantage, and 25% mentioned the quality/difficulty aspect. Further, the judges used phrases such as "better concept", "more in tune with mechanics and difficulty", "better knowledge of what to expect", "easier identification"."enhanced recognition", "understanding more readily", and "better base to build knowledge" to describe the advantage they felt came from skating experience. These descriptions suggest that the skaters have something more than simply more declatative knowledge. The words indicate that there is a greater richness of understanding of the performance being judged, reminiscent of the deep/surface structure difference shown in physics problem solving (Chi et al., 1981). The judges clearly felt that having been a performer afforded advantages that could not be overcome by non-performers via some other means. The judges at the clinic who had not skated were very articulate in describing the difficulty they had experienced in learning the technical skills required to judge the performance of skaters. One judge reported that it is "Very time consuming as a non-skater to recognize each and every step, figure, dance, jump, spin, and you must commit this time to be fair to the skater. You would surely know these basics if you figure. skated. ... It would be for me to feel competent at higher dance levels". Another judge commented "...if you have not skated you are not truly able to fathom the mechanics involved". Another suggested that it would be "extremely difficult for you to judge an element that you have never done yourself. For example, a rocker. How would you know the correct body movement, or sound of a clean turn if you have not experienced it?". And finally, another said "It is next to impossible to comprehend difficulties and relative difficulties if you have not executed the difficulty yourself'. Both the data on the characteristics of judges selected for international competitions and the intuitions of judges about the value of knowing how to perform support the contention that the ability to perform component skills aids the cognitive task of judging. In other words, knowing and doing are linked, and doing can facilitate knowing. A similar suggestion, this time for the importance of doing for teaching, comes from the work of Bloom and his colleagues (1985) who have studied the development of expertise in a variety of domains. These studies have revealed that the characteristics of a teacher or coach change with the level of accomplishment of the performer. For performers at the highest levels, the student seeks a "master teacher". Sosniak (1985) describes how expert concert pianists develop their skills to their fullest as follows: The pianists learned to take charge of their music making while working with master teachers - teachers who were among the most respected faculty members at professional schools of music and who were, or had been, renowned concert pianists themselves. They were able to study with these teachers because of arrangements made by previous teachers or someone they met at a competition or summer camp. The pianists, their parents, and their teachers share the conviction that "you can't teach someone an experience that you haven't gone through yourself'. (p. 421)

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Cognitive Differences in Players, Coaches, and Referees: What you do influences what you know The judging data just presented show that the ability to do a skilled activity gives the performer a different kind of knowledge than a judge who has not learned to skate or to dive. Since all the judges we surveyed were qualified to judge at an international level, they all had equal formal training in the cognitive task of judging. The observation that qualified judges who had also been world class competitors were most often the judges selected for international competitions suggests that knowing how to perform the skills that comprise a particular domain makes the judge better able to evaluate the performances of others. This suggests that knowing and doing are, in fact, linked in such a manner so as to allow one to influence the other, which further suggests that what you do should influence what you know. We next present evidence from basketball players, coaches, and referees that shows this to be the case. The data presented here are part of a project investigating skill in basketball officials (Deakin & Allard, 1992). A sport such as basketball is interesting because there are different forms of expertise within one domain: there are expert players, coaches, and officials. As a first step in evaluating officials, it was important to determine whether declarative knowledge of officials differs from the declarative knowledge of players or coaches, or whether there is "generic declarative knowledge" possessed by all experts within the domain. Subjects were 11 national and international level officials, 11 Canadian Intercollegiate Athletic Union coaches, and 36 players from different calibre womens' teams (12 National Team, 15 Junior National Team, 9 Queen's University players). The officials and coaches tested were among the best in the country, with the officials having an average of 17 years experience, and the coaches having an average 11.5 years coaching experience. Players of different skill levels were tested as a safety factor. If there were no differences observed in declarative knowledge for expert coaches, officials, and players, it would be important to know whether subjects differing in skill level would show declarative memory differences. Subjects were tested on 5 tasks. 1.

Basketball General Knowledge The "Trivia Test" was comprised of questions about the history of basketball taken from The Modern EncvcloDedia of Basketball (Hollander, 1969), as well as questions about the identity of the 1991 mens' and womens' CIAU, NCAA, World champions, and the 1991 NBA champions. This test was designed to include information that would be known by individuals who follow basketball, and was intended to assess the general knowledge or interest in the game of each of the three groups. This test was scored out of 16 points. 2.

The Rules Test This test consisted of 41 questions taken from the 1990 FIBA Official Basketball Rules. There were true-false questions, short answer questions, and multiple choice questions, Officials are typically tested using true-false questions. By varying the format of the questions, we hoped

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to give all groups an equal chance on the test. It is important to point out that all the rules questions were questions of fact (i.e., What are the numbers that may be legally used on players’ uniforms? What is the distance from the 3 point line to the basket?), not questions involving interpreting or applying the rules. Following each question, subjects were required to rate their confidence in their answer on a five point scale. with one meaning little confidence and five meaning high confidence. There were 41 questions in this test, with the test being scored out of 60 points. 3.

The Signals Test This test consisted of pictures of 10 of the 37 hand signals specified in FIBA rules (p.8386). The 10 signals used were drawn randomly from an envelope containing copies of all 37 signals. The caption describing the purpose of each of the signals was then removed, and the signals were arranged on one page in random order. Subjects were instructed to identify each of the signals on the page. This test was scored out of 10 points. 4.

Play Recall Play recall entailed subjects viewing schematic diagrams of five actual basketball plays and five random plays . The real plays were simple offensive plays taken from basketball texts by Barnes (1972). Cousy and Power (1983), Healy and Hartley (1982), and Smith and Spear (1982). Each play showed initial position of the five offensive players, with each player being identified by the numbers one (point guard) to five (post). As is standard in such schematic diagrams, player movements were shown by a solid line, a pass was shown by a broken line, a dribble by a wavy line, and a screen or pick by a square bracket. The starting position of the ball was indicated by a small circle. Random plays were created by dividing the half court into nine sectors, numbering each sector, and randomly drawing out five of the nine sectors. Player numbers from one to five were then drawn and each player placed in each selected sector. Next, the actions (pass, dribble, cut to basket, move to high post, screen, stand still) performed in the real plays were randomly drawn and assigned to players. The total number of actions shown in the schematic plays was equal to the total number of actions in the real plays. Thus, the random plays were random for position, player identity at each position, and player action at each position. This process produced quite bizarre plays, with screens being set for no one in particular, passes being directed to no other player, and post players making cuts into centre court. Real and random plays were equated for the total number of elements in each type of play (62 total elements, mean of 12.4 elements per play) with an element being defined as player starting positions, player actions, and ball position. The plays were drawn on a half court marked and scaled to FIBA regulations. They were presented to subjects in booklets containing each play followed by a blank diagram of a half court to be used for recall. The real and random plays were shuffled and presented in a different random order for each subject.

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5.

FouWiolation Detection Task In this task, subjects viewed 50 short segments of basketball games taken from a training video for NCAA officials. After each segment, a subject was required to determine whether a foul or a violation had occurred in the video, or if no call should be made. If the subject responded with foul or violation, he/she was required to identify (name) the foul or violation. Fifteen of the 50 segments were legal play, 5 were violations, and 30 were fouls. Subjects were asked to record their confidence in each response on a 5 point scale. The results showed that coaches, players, and officials performed differently on each of the tests. For basketball general knowledge, officials (70% comct) and coaches (73% correct) were significantly more accurate than players (55% correct). For the rules test, officials (77% correct) were significantly more accurate than coaches (67% correct), who were significantly more accurate than players (52% correct). For the signals test, officials (97% correct) were much superior to coaches (50% correct), and coaches were significantly more accurate than players (38% correct).

For the recall of schematic plays (collapsing over both real and random plays), players (63% correct) were significantly more accurate than coaches (51% correct), who were more accurate than officials (41% correct). An important aspect to the play recall task is the difference in accuracy of recall for real and random plays, which reflects how much the meaning in the real play is helping the subject in recall. Thus a large difference in recall accuracy for real and random plays shows a subject is sensitive to the structure in the real plays. The real-random recall difference is largest for coaches (15% difference), followed by players (9% difference), and officials (4.5% difference). In fact, a t test on real-random recall for the officials shows no significant difference (t=1.4, df=12, p=.169) for the two types of play. For the fouVviolation task, all groups proved to be equal in accuracy at detecting that a foul or violation had taken place (officials 69% correct, coaches 61% correct, players 63% correct). When subjects were required to identify what foul or violation had taken place, officials (50% correct) were significantly more accurate than coaches (31% comct) or players (30% correct). As may be seen by the low over all accuracy, this task proved to be particularly difficult for all subjects. The officials tested commented that the segments of play went by very quickly, which, combined with no lead up to the particular segment, made the task nearly impossible for them to perfom with confidence. The results of this study show that declarative knowledge is a function of the role of the individual in the spon. In keeping with their on the court responsibilities, officials were best at rules, signals, and naming fouls and violations, Coaches showed the biggest gain in recall accuracy for real vs. random plays, a reflection of their role in developing offensive sets and

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diagnosing the offense of the opponent. Players were the most accurate in the recall of plays following a very brief, five second exposure. It is interesting to note that players were most accurate for recall of both real and random plays, showing how players have learned to perceive and encode what is happening on the court as quickly as possible. Finally, the basketball general knowledge test showed subjects with the longest exposure to the game - the coaches and the officials - had the best store of basketball trivia. Thus, declarative knowledge is not "a byproduct of experience in the sport". as suggested by Salmoni (1989): it differs with the role of the expert within the domain. In Summary The three lines of evidence presented in this chapter, evidence from studies of the classification of pictures, of the advantage in judging a sport enjoyed by those who can also perform the skills required of the activity, and of the different types of declarative knowledge required by experts having different roles within a particular skill domain, all speak to the importance of declarative knowledge in expert motor performance. Declarative knowledge is a constituent of skill, rather than a byproduct of time spent in a particular domain. It now becomes important to determine the nature of the procedural-declarative relationship; how is it possible for knowing and doing to influence each other? How do the two forms of knowledge impact learning? References Allard, F., & Burnett. N. (1985). Skill in sport. Canadian Journal of Psychology, 39.294-312. Allard, F., & Starkes, J.L. (1991). Motor-skill experts in sports, dance, and other domains. In K. A. Ericsson & J. Smith ( a s . ) , Toward a General Theory of Expertise (pp.126-152). Cambridge: Cambridge University Press. Barnes, M. J. (1972). Women's Basketball. Boston: Allyn & Bacon. Bloom, B.S. (1985). Developing talent in young people. New York: Ballantine Books. Chi, M.T.H., Feltovich, P.J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5 , 121-152. Cousy, B., & Power, F. G. Jr. (1983). Basketball: Concepts and techniques (2nd ed.). Boston: Allyn & Bacon. Deakin, J.M., & Allard, F. (1992). An evaluation of skill and judgement in basketball oficiating. Paper presented at the meeting of the North American Society for the Psychology of Sport and Physical Activity, Pittsburgh, PA. FIBA oficial basketball rules. (1990) Fowlkes, E.B., & Mallows, C.L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78, 553-569. Hollander, Z. (1969). The modern encyclopedia of basketball. New York: Four Winds Press. Healy, W. A., & Hartley, J. W. (1982). Twelve great basketball offenses. Reston, Va.: The American Alliance for Health, Physical Education, Recreation and Dance. Johnson, S.C. (1967). Hierarchical clustering schemes. Psychornetrika, 32, 241-254. Parker, S.G. (1989). Organization of knowledge in ice hockey experts. Unpublished master's

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thesis, University of New Brunswick, Fredericton. Reitman Olson, J., & Biolsi, K.J. (1991). Techniques for representing expert knowledge. In K. A. Ericsson & J. Smith (Eds.), Toward a General Theory of Expertise bp.240-285). Cambridge: Cambridge University Press. Rodgers. W. M., & Allard. F. (1990). Expertise and judging. Paper presented at the meeting of the North American Society for the Psychology of Sport and Physical Activity, Houston, Texas. Salmoni, A. W. (1989). Motor skill learning. In D. Holding (Ed.), Human Skills (2nd. ed.) (pp.197-227). Chichester: John Wiley & Sons. Smith, D., & Spear, B. (1982). Basketball-multiple offense and defense. Englewood, NJ.: Prentice Hall.

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CHAPTER 7 THE RELATIONSHIP BETWEEN EXPERTISE AND VISUAL INFORMATION PROCESSING IN SPORT WERNER HELSEN AND J.M. PAUWELS Leuven University, Institute for Physical Education Motor Learning Lab Tervuursevest 101, 3030 Leuven, Belgium Introduction Research comparing the visual-perceptualcharacteristics of expert and novice ball-sport players can be situated chronologically in one of t h e periods. From the 1950s to the 1970s. the relationship was studied between non-task-specificparameters (such as static acuity, dynamic acuity, depth perception, and peripheral visual range) and processing skills (such as visual reaction time, nerve conduction time), on the one hand, and the resultant sport skill, on the other. From the divergent results of these studies, it was argued that these "hardware" components, which reflect the central nervous system's efficiency of operation (Starkes & Deakin, 1984), fall short of explaining the level of expertise often observed in athletes.

Over the last 15 years, researchers have med to explain the differences between experts and non-experts in processing the acquired visual information by means of more cognitive, "software" dimensions. In general, it has been shown that expertise in more cognitive domains, such as chess or the solving of mathematical problems, is based on the acquisition of, the rapid access to, and the efficient use of semantically rich and, therefore, complicated networks of domain-specific declarative and procedural knowledge (Anderson, 1982; Chi et al., 1982; Chi & Glaser, 1980). From this theoretical background, the question is posed whether experts in a particular sport discipline differ from non-experts in the amount and the type of knowledge they possess and in the way the information is processed. These studies, which have attempted to compare the knowledge base of expens and non-experts within particular skill domains have fared far better than the "hardware" based studies. Though the shift in sport literature has been from the assumption of superior "hardware" benefits to the assumption of superior "software" benefits, only a few studies have assessed elements of both within subjects to determine the relative contribution of each (Starkes, 1987, 1990; Starkes & Deakin, 1984).

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Our conmbution is situated in this research context, that is, it is "software" directed. Firstly, we set out to acquire more insight into visual information processing during tactical decision making. In addition to speed and decision adequacy, we were concerned mainly with the visual information that is used by subjects with different levels of competence, as well as the way in which this information is processed with increasing competence.

Perception in sport: a cognitive approach to skilled performance To obtain better insight into problem-solving skills and processes in the acquisition of expertise in a sport discipline, a conceptual framework is sketched based on research on the acquisition of expertise in explicitly cognitive problem-solving tasks, such as chess or the solving of mathematical problems. In general, it has been shown that expertise in a particular discipline is based on the acquisition of, the rapid access to, and the efficient use of domain-specific declarative and procedural knowledge (Anderson, 1982; Chi et al., 1982; Chi & Glaser, 1980). Indeed, it has been demonstrated that experts, because of their years of experience, possess a larger database or, in other words, a greater amount of declarative knowledge. This knowledge is represented in the literature as "schemata", "frames", "scripts", or "knowledge structures". However, of itself, it does not guarantee better performance. As long as one cannot find one's way efficiently, a larger amount of data only compounds the problem. Hence, in the second instance, experts also differ in the efficiency with which they can link environmental information to declarative knowledge. The key to this lies in the experts' ability to compile in meaningful units a larger amount of structured information from long-term memory. These meaningful units can be stored in working memory for further processing, which significantly reduces the processing time. With uaining, knowledge is finally translated into a procedural form that concerns 'knowing how' to perform something. This is represented by means of a "production system" (Anderson, 1982; Chi et al., 1982). Each production is characterized by a condition and an action that is executed if the condition is satisfied. Both of these can consist of actions. The construction of a declarative and procedural knowledge "system" as well as the ability to srnicture environmental information are critical skills that have to be developed if one is to become an expert athlete. From this theoretical background, several authors (Allard & Burnett, 1985; French & Thomas, 1987; Starkes, 1987, 1990; Starkes & Deakin. 1984; Starkes et al., 1987; Thomas et al., 1986; Vickers, 1983, 1986, 1988) have introduced this cognitive approach to the domain of sport.' The question has been addressed whether experts in a particular sport discipline differ from non-experts in the amount and type of knowledge they possess and in the way in which information is processed. It must be noted that this problem is 'From a developmental perspective, the contributions of Wall (1986) and Wall et al. (1985) have to be cited.

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posed from an information processing standpoint. Knowledge-based interaction with the visual environment is an original application of this field of research, contrasting, for example, with the study of knowledge-directed processes on the operation of memory. Indeed, it is surprising that the influence of such knowledge on memory has been broadly investigated yet its influence on perception hardly at all. Thus, this research focusses on applying knowledge issues to perceptual processing and to the interaction between perception and memory. Therefore, we set out to formulate a synopsis of the differences in knowledge structure between experts and non-experts discovered through both indirect and direct research technique? by various authors (Figure 7.1). This review is limited in that only laboratory studies are taken into account. In the studies reported, attempts were made to simulate, within the bounds of acceptable experimental control and replicability: the perceptual demands of real-world activities in a variety of disciplines. The methodology commonly used is characterized by a small-scale but exhaustive approach. Generally a thorough contrastive analysis is conducted in which a small number of experts in a particular area of activity are compared with nonexpens in visual information processing and problem-solving in situations with a quantity-limited information supply. For this reason those studies dealing with the problem of the stabilization of head and eyes on target in succesful basketball- (Ripoll et al., 1981, 1982, 1986; Simonet et al., 1983) and rapid-fire pistol shooting (Ripoll et al., 1985a, 1985b; Ripoll, 1986) are not included in Figure 7.1.

Indirect Research Techniques In the typical "recall" paradigm, subjects are generally shown a "frozen", static scene from a particular discipline for a very short period of time. Immediately thereafter, they must reproduce the presented information as accurately as possible. The score is generally determined by the degree of correspondence between the presented and the reconstructed information. In this regard, the original research of Chase and Simon (1973a, 1973b) in chess is noteworthy.

In the "recognition" paradigm, too, sport-specific information is displayed statically (slides) and/or dynamically (film, video). The subjects are again confronted with an analogous form of information, half of which consists of already presented information and half new

*Fora more complete review of the assumptions and limitations of the signal detection approach, the film-occlusion approach, and the eye-movement registration technique, see Abernelhy (1985). 'Applied research in sport offers two alternative research approaches, the "field and "laboratory" approaches. The field approach, based on observation of the performer in his or her actual performance setting, provides the highest degree of ecological validity. The limitations of this approach in achieving satisfactory experimental control and replicability account in part for the predominance of laboratorybased motor skills research. Nevertheless, it should be stressed that the field approach is still useful for determining the time constraints imposed on decision-making and as evidence for the importance of anticipation in a wide range of fast ball sports (for a review see Abernethy. 1984,1985,1987; Howarth et al.. 1984).

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t 3

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Figure 7.1. List of studies using the different paradigms processing in individual sports and team sport g m s .

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information. A performance score is determined by the accuracy with which the infomation presented earlier is recognized correctly. In the "signal-detection'' approach, subjects must detect as rapidly as possible the presence or absence of a particular element within certain environmental information. Detection speed and accuracy are related to the nature of the environmental information (structured versus unstructured game information). In the "film-occlusion" approach, the actual environmental situation of a sport is filmed from the point of view of a particular player. This film is then shown to subjects, generally within an experimental design with repeated measurements. A perceptual judgement is required of the subjects (Where will the ball land?) or a decision with regard to movement selection (What tennis stroke should be used?). At certain time intervals, information is occluded to determine the importance of specific information zones for the quality of the decisions. A distinction is ma& between temporal occlusion, spatial occlusion, and combinations of the two. In this context, the work of Abernethy and Russell has to be cited, with applications in baseball (Abemethy & Russell, 1984). cricket (Abernethy, 1984; Abemethy & Russell, 1984), and badminton (Abemethy, 1986a. 1988, 1989; Abemethy & Russell, 1987a). Another indirect technique is the "chronometric" approach, in which subjects are shown tactical game situations from a particular sport using slides or film. After presentation of a game scene, they must report the correct tactical decision into a microphone from the standpoint of the ball handler. Generally, the choice of response possibilities is to shoot, pass, or dribble. A score is determined for the number of correct decisions and the response time between the slide presentation and the voiced decision. These techniques are somewhat limited because of their indirect measurement of visual information processing. Nevertheless, they can yield useful information, when they supplement each other and are combined with the data obtained by eye-movement registration techniques.

Eye-movement Registration The direct measure of eye-movement registration has been used extensively in very different applications over the last fifteen years. It is effective because the sensitivity of the human retina for visual stimuli is not the same everywhere: 100% acuity is limited to an area of only 1 .This has concrete implications for eye-movement research because the most sensitive, central part of the retina has to be oriented to the information in the visual field that must be analyzed (Houtmans & Sanders, 1984). This is done by means of ocular saccades. Saccades are rapid, intermittent jumps of eye position used to fixate an object within foveal vision. Research has indicated that afferent messages are inhibited during ocular saccades and that information is recorded during the fixations or pauses (Houtmans & Sanders, 1983). Saccades, therefore, are noninformational, and consequently, most research has focused on information gathemd during ocular fixations. The concept of an "eye fixation" is defined as a period that lasts at least 140 msec. in which eye movements are limited to an area that corresponds in angular size with the fovea (Abernethy & Russell, 1987b; Vickers, 1988). To

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evaluate visual exploration in a dynamic context, it is important to study not only saccades and fixations, but also slow-tracking eye movements. Once an object is fixated, these movements keep it in foveal vision as it moves or as the observer moves. In the same way, the concept of "foveation" can be defied. The basic idea behind the registration of eye movements is the following. It is assumed that the number of fixations, their duration, location. and succession ("scan path") reflect cognitive processing while relevant information from the visual field is selected. First, it may be stated, as does Shepherd (1986). that if the visual information supply is static and relatively complex, the correspondence between the fixation center and the center of visual attention is sufficiently high to be able to consider the fixation point as a valid index of the point of selective information registration. If the visual information supply is dynamic, however, there is the possibility that a certain amount of information is processed peripherally. Second, it seems that fixation duration can be considered a valuable parameter for describing visual exploration in the sense that fixation duration corresponds to the time needed to analyze the information. Finally, it is generally accepted that there is a direct link between the sequence in which certain items are fixated and the priority given by a subject to these items as pemnent information sources. Research in this area has shown that scan paths are influenced primarily by three factors. First, they are dependent on conditions of the external environment; second, they are influenced by subject characteristics; and, third, they depend on the nature of the task and conditions in which the task is performed. For the last fifteen years, Bard and Fleury have done pioneering work in the domain of sport with eye-movement registration. Indeed they have been a source of inspiration for a great number of researchers. Bard and Fleury studied visual exploration in the solving of tactical game problems in basketball (Bard, 1982; Bard & Cani&re, 1975; Bard et al., 1975,1987; Bard & Fleury, 1976a, 1976b, 1 9 7 6 ~1978,1981; Carribre, 1978) as well as in ice hockey (Bard, 1982; Bart et al., 1987; Bard & Fleury, 1980, 1981). More recently, their research focus has been on individual sports: gymnastics (Bard, 1982; Bard et al., 1980). fencing (Bard, 1982; Bard et al., 1981, 1987). and especially tennis (Bard et al., 1987; Goulet et al., 1988).

In experiments examining game tactics, the task of the player is described as a problem-solvingtask. This implies that decisions must be made and that there are certain rules that control in advance the selection of pertinent information. Moreover, most decisions must be made under time pressure, so selection has to be made very rapidly and efficiently. Results of the Indirect and Direct Research Techniques The first two indirect techniques have shown that expert players in very different disciplines can retain, recall, and recognize significantlymore information about structured game situations than can less experienced players when information is presented in brief periods. Therefore, expertise in sport reflects not only procedural knowledge but also a greater level of declarative knowledge. This phenomena is also demonstratedby the results of the "resequencing" task used by Vickers (1986, 1988). It has also been shown that experienced coaches can detect

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errors more easily in sport-specific movement patterns (Armstrong & Hoffman, 1979; Biscan & Hoffman, 1976; Gordon & Osborne, 1972; Hoffman & Armstrong, 1979). Using film-occlusion techniques, it has been demonstrated that the effective use of information prior to and during the ball flight to anticipate, is an important discriminating characteristic of top players. Abernethy (1985, 1987) uses the term "advance cue usage" to describe the perceptual strategy adapted by the performer in selecting the most pertinent information from the mass of potential information confronting him or her. The differentiation between differing skill groups is all the more apparent as information is removed earlier in time. What declarative knowledge, in terms of informative zones, is the most useful obviously depends on the specific sport.

On the basis of the results obtained with indirect techniques, the theoretical assumption is confmed that procedural knowledge, obtained by fraining, and the playing of games (the "action" component of the production system) promotes the acquisition and retention of specific declarative knowledge. Clearly experts differ from non-experts not only in amount and accessibilityof declarative knowledge, but also the efficiency with which they can link environmental information to declarative knowledge. From eye movement registration data, a number of general conclusions are formulated: experts answer faster and more correctly, and with increasing expertise a certain perceptual automation occurs in visual exploration. This is characterized by greater efficiency, by an increase in selectivity and processing speed, and by an enlargement of the useful visual field. Experts answer more c o m t l y with fewer fixations and in a shorter time period than do non-experts. Analysis of the number of fixations further indicates that experts scan the information supply in a more direct, more selective way. The available information is processed faster, so experts can make adequate decisions more rapidly. With regard to the number of fixations and their duration, the results found in team-sport games (Bard & Carrikre, 1975; Bard et al., 1975; Bard & Fleury, 1976a, 1976b. 1976c; Helsen et al., 1986b) differ from those obtained in individual sports (Abernethy, 1986b; Abernethy & Russell, 1987b). However, as Bard et al. (1987) and Goulet et al. (1988) have noted, the task requirements in each are obviously different. In team-sport studies conducted thus far, subjects were allowed to control the time of exposure, and the situation as a whole was presented at the beginning of the slide presentation. In individual-sport studies to date, the exposure time is similar for all subjects and information presentation is not completed until the end of the mal. According to Steiner's typology (1966). in the former case we are dealing with a maximizing task, in which time requirements are emphasized; the shorter the better. In the latter case, an optimizing task, based on the quality of the response, is presented. Since experts process information more rapidly, they analyze a greater amount of information during the allocated time in an optimizing task and consequently have increased performance. The finding that experts have significantly fewer fixations than non-experts in a

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maximizing task in our opinion is linked very closely to the interpretation of the results of recall experiments: experts, by chunking and structuring meaningful information components, can obtain a greater amount of relevant infomation from the context within one single fixation. That the processing time is reduced by the storage of this information in working memory can, in turn, be deduced from experts’ shorter response times and shorter fixation duration. Finally, in relation to enlargement of the useful visual field, we note the possible role of the retinal periphery. It is suggested (Abernethy, 1986b; Abemethy & Russell, 1987b; Bard et al., 1987; Bard & Fleury, 1976a. 1976b, 1976c) that a pre-attentive process done on the basis of what is detected peripherally, allows the subject to bring into foveal vision only those elements pertinent to the solution of the problem.

Purpose In contrast with many experiments reported in the literature and our own previous experiments, in this study we used more dynamic stimuli on 16-mm film. In this experiment, players were presented life-size and moving instead of on static and dimensionally limited slides. Ecological validity was further preserved not only for perceptual aspects but also for motoric components since a sport-specific response was required instead of a verbal response. This is important since in real world activities output processes are integrated with perception and decision processes. Attempts to isolate them experimentally fail to provide meaningful conclusions unless they have been assembled in conditions reflecting real game simulations and reconstructions. These changes make the study unique in the literature.

In addition to speed and decision adequacy of tactical decision making in soccer, attention was focused on the identification and usage of relevant information by subjects with different levels of expertise. Method

Subiects Thirty male subjects participated in the experiment. The expert group’s mean age was 25.7 years and consisted of 15 semi-professional players who averaged some ten years of active competition. The novice group’s mean age was 21.3 years and consisted of 15 undergraduate students in physical education at Leuven University who had limited experience in soccer competition (as verified in a preliminary interview and pre-experimental session). All the subjects were volunteers. Film task desian Using video films, different tactical situations were selected from real soccer matches in European Cup and World Cup games. The nature and number of each of the tactical actions studied on video are discussed in more detail elsewhere (Helsen & Pauwels, 1988). For each situation, the actions of all the players (attackers, defenders, referees) were studied and carefully schematized. Thirty different situations were taken into account (free kicks, penalty kicks, off-side problems, and dribbling, shooting, and passing situations).

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These game situations were then replayed "live" on a real soccer field with all the relevant elements, including advertising panels. The camera (16-mm film) replaced one of the players who was confronted at a specific moment with an important tactical decision. The actors (referee and players) were given clear instructions and then played the situation as recorded on video. Like the player from the actual match, the camera travelled on a track in the game situation and recorded what the player was confronted with during the real game situation. This might be called "acted soccer'' realistically played by experienced soccer players with a camera taking the place of one of the athletes. Apparatus Eye movements were recorded by a NAC-V-Eye-Movement Recorder. With this sytem, a light ray is reflected from the anterior surface of the cornea. The scene-camera records the field of view of the subject. while the eye-cameras record the movements of the eye. Both are recorded on videotape. The concept of an "eye fixation" is defined as a period that lasts at least 140 msec in which the eye movements are limited to an area that corresponds in angular size with the fovea (Abernethy & Russell, 1987; Vickers, 1988). For the evaluation of visual exploration in a dynamic context, it is important to study not only saccades and fixations, but also slow-tracking eye movements. Once an object is fixated, these movements keep it in foveal vision as it moves or as the observer moves. Procedure The eye-movement recording apparatus was fitted onto the subjects' head and stabilized. It was then calibrated for both position and linearity to ensure that the fixation mark (a light spot reflected from the subject's dominant eye) corresponded precisely to the subject's visual orientation to different sectors of the projection screen. The film task was then presented to the subject by projecting the constructed film onto a white screen set 9 m in front of the subjects (10 m x 4 m, 90 of visual angle). The subject was further instructed to return his visual focus to the screen centre immediately after completing his response. Whenever this visual orientation was not apparent from the monitored eye movements, the film was stopped, and the eye-mark was recalibrated. The experimental set-up was explained individually to each subject by means of a standardized introduction program on the film, and five situations were used for familiarization. When each situation started, the ball moved on the screen (2) from one player to another. At a specific moment, the ball was played by one of the attackers in the direction of the subject (1) who became part of the action on the screen. In response to an auditory signal on the film he was required to perform, as quickly and precisely as possible, a tactical decision with the ball just like in a real game situation. The possible answers were; shooting at the goal, dribbling around the goalkeeper or an opponent, and passing to a specific and free teammate. Timing was recorded automatically when the subject started his movement (initiation time), when he touched the ball (balVfoot contact time), and when the ball reached the screen (total response time). The times were transmitted to a microcomputer (4), stored on disk ( 5 ) , and printed out (6). The

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Figure 7.2. The test set-up.

correctness of the decisions was also evaluated for each situation (a number of soccer experts had previously agreed on the right solution). Exuerimental design Five dependent variables were taken into account. The response times (initiation time; balVfoot contact time; total response time) and responsed accuracy provide a product evaluation. To evaluate the ongoing cognitive processes, the fixation duration, number of fixations and fixation location were studied. The effect of expertise is stressed here a s the most important independent variable. A one way ANOVA was performed. Hvuotheses The general purpose of this study was to determine 1) if experts are faster and more adequate in finding tactical solutions; 2) if experts differ from less experienced players in their

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visual search patterns; 3) if, according to the type of solution, the subjects use different visual cues or if their search is exhaustive most of the time.

Resulb Reliability The reliability of the test set-up was determined by the split-half method (Spearman-Brown formula) and resulted in a coefficient of 0.89 for the different dependent variables. The semi-automatic video analysis revealed an acceptable intra-observer reliability of .99 and an inter-observer reliability of .97. The accuracy of the eye-movement-recorder was c 2 of visual angle, which supports the findings of Schwerdt and Lesbats (1986). Reswnse times Figure 7.3 gives the response times for the expert and novice groups.

EXPERTS X

SD SE

V

NONEXPERTS X

SD SE V

rota1 response time in msec.

Responseadequacy in %

1690.92 102.73 27.46 6.07

2364.67 143.37 38.32 6.06

91.76 1.02 0.27 3.72

1877.20 106.36 28.42 5.67

2624.33 161.14 43.10 6.14

82.23 1.92 0.5 1 7.79

time in msec.

in msec.

157.79 135.91 36.32 17.93

968.49 128.19 34.26 13.24

I

Figure 7.3. The response times for the expert and novice group.

The analysis of the data on the response times showed that there were statistically significant differences between both groups for the initiation time (F (1,28) = 17.81, MSe = 18697.87, p c .0002), the balYfoot contact time (F (1.28) = 22.21, MSe = 1171 1.69, p c .OOOI), and the total response time (F (1.28) = 20.29. MSe = 24922.54, p c .OO01). These findings show that the experts had shorter mean response times than the non-experts, which supports the hypothesis that the duration of information processing decreases as a function of the subject’s experience. An analysis of covariance revealed that differences in balVfoot contact time (F (1.25) = 3.81, p > .05) and total response time (F (1,25) = 6.78, p > .01) were no longer significant when the differences in initiation time were taken into account. According to

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the results of the film-occlusion paradigm, it seems that making effective use of information prior to the response signal in order to anticipate (and reduce the initiation times) is an important discriminating characteristic of top players. This is also shown by the strong relationship (r = 0.75) between initiation time and total response time. Resoonse adeauacv (8of correct answers] Statistical analyses showed that the main effect "level of expertise" was significant (F (1,28) = 24.28, MSe = 2.54, p < .OOOI). The experts we= faster in finding the solution and were also better able to find the right solution. Fixation duration Figure 7.4 gives the fixation duration and the number of fixations for the expert and novice groups.

EXPERTS X

SD SE V

NUMBER OF FIXATIONS

FIXATION DURATION (in msec.)

1.71 0.28 0.07 16.20

47 1.07 49.33 13.19 10.47

2.24 0.34 0.09 15.09

444.64 65.69 17.56 14.77

"EXPERTS X SD SE V

Differencesin fixation duration between groups were not significant.These findings agree with the results found in other "optimizing" tasks (Abernethy, 1987b; Abernethy & Russell, 1987b). The available data tentatively suggest that fixation duration for this task is within the range reported for tasks using dynamic stimuli but is substantially longer than that generally seen in tasks where the display is static (Abemethy & Russell, 1987b). Number of fixations The analysis of the data on the number of fixations showed that the differences between the two groups were significant (F (1.28) = 20.32, MSe = 0.10, p < .OOOl). These findings indicate that experts scan the information with fewer fixations.

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Fixation location In Figure 7.5, the fixation location is given for the passing situations.

Figure 7.5. Fixation locarions for passing situations (N=12).

The first finding here is that visual search is never exhaustive. The subjects, whether experts or beginners, tended to select specific cues and to respond as soon as they thought they had sufficient information. Prior to the transition from a tactical decision to a motoric response, the experts fixated primarily the pass receiver (Me = 36.2 % < > Mne = 28.8 %), the free back (Me = 16.95 % < > Mne = 5.15 %; t=2.54, p < 0.05) and the free space (Me = 5.91 % < > Mne = 1.14 %; t=2.47, p c 0.05). The non-experts, however, looked more to the attackers, the goal, and the ball. In this context, it has to be emphasized that the location of fixations had a great impact on the response adequacy, since non-experts often passed the ball to the wrong teammate. As discussed elsewhere for off-side positions (Helsen & Pauwels 1990, 1991). they also repeatedly passed the ball to the teammate in off-side position, instead of making a more appropriate dribbling movement, as did the experts.

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Discussion In general, the hypotheses were confirmed by the results of the research. In terms of a product evaluation, the experts did, indeed, respond faster and more correctly than did non-experts. In terms of process evaluation, a certain perceptual automation in the visual exploration seems to occur as a function of increasing expertise. This automation is characterized, as confmed in the literature, by greater efficiency and an increase in selectivity and processing speed. These factors give rise to shorter information processing times and, hence, shorter response times, and better performance. Clearly this shows the importance of the way in which the available information is analyzed and applied (process) in terms of the speed and correctness with which the ultimate decision is made (product).

In the first instance, the conswction of a task-specific declarative knowledge base is apparent in the visual exploration of the two skill groups. %or to the transition from a tactical decision into a motor response, soccer experts make better functional use of the defenders in general and the free back and the free space, in particular. while non-experts look primarily at the ball, the attackers, and the goal. That experts make better use of the position game of the free back can be explained by the enhanced task of this player in recent years. He must not only act as the organizer of the defense but also provide the attack impulse as the fust and undefended attacker from the second line. Non-experts,however, cannot assess the proper value of the free back position. In the same way, non-experts make much less use of the errors that are made in the defense and of the information available in the free space, as noted by other researchers (Bard & Carrikre, 1975; Bard et al., 1975; Bard & Fleury, 1976a, 1976b, 1976c. 1980, 1981). The same reasoning can be applied in relation to the position of the goalkeeper in a penalty-kick situation and the formation of the wall in a free-kick situation. A greater amount of specific declarative knowledge, however, does not always guarantee better performance. That experts also differ in the efficiency with which they can link environmental information to declarative knowledge is demonstrated by their shorter response times and the greater degree of response correctness. The key to expert ability is that they extract a greater amount of relevant information from the context in one single fixation, probably by the chunking and structuring of meaningful information components. That processing time is reduced by the processing of this information in working-memory can be deduced from the shorter response times. For the novice, the interpretation of information from declarative knowledge has heavy costs both in time and the amount of working-memory required. Interpretation requires retrieval of declarative information from long-term memory, and the individual production steps be small in order to achieve flexibility and generality of the system. Proceduralizationreduces the load on working memory, since long-term information no longer needs to be held there. The concept of proceduralization as "tuning" is a useful one because, even after a skill is compiled into a task-specific procedure, learning continues and performance improves (Anderson, 1982). One learns, for example, very soon in soccer how to shoot a ball or what common specific procedure to use in front of the goal. With further proceduralization, one becomes more judicious about when to shoot the ball and which cues in the environment indicate that a pass or dribble would be preferable. This tuning of search has been characterized

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by three processes: "generalization", the process whereby production rules become broader in their applicability, "discrimination", the process by which rules are narrowed, and "strengthening", the process that allows better rules to be reinforced and poorer rules to be weakened. Clearly these tuning processes help determine the difference in performance between novice and expert players (Anderson, 1982).

This can also explain the efficient scan paths of experts in the verbal evaluation of tactical game situations from soccer presented on slides (Helsen et al., 1986b). Non-experts must often bring the same information back to working memory by repeated fixations. This may be analygous to the verbal repetition of material when one is in an early cognitive phase. Finally, this emerges more clearly as the decisions become more complex and more structured. Perhaps the visual exploration and evaluation is more directional among experts, more "schema driven" instead of "search driven" (Gilhooly & Green, 1988). Tasks in which a dynamic display and time-constraint decision making is used, such as in this experiment, encourage subjects to use more predictable orders of search. Thus they rely more on the interpretative function of the visual system, than when either the display is static or the search task is not time-constrained. However, that priorities are given to a rather limited number of cues does not fully support the original hypothesis that experts "see" totally different informational elements. This interpretation also emerges in the publications of others (Abernethy & Russell, 3987b; Bard et al.. submitted for publication). On the basis of our results, it can be stated that an expert sees what he knows, and the process is more efficient, more selective, and more rapid the more expert he is. From the cortical standpoint, highly skilled players can thus, in a limited sense, be considered skilled scanners. In this regard, we support the statement of Fodor and Pylyshyn (198 1, pp. 189) "What you see when you see a thing depends upon what the thing you see is. But what you see the thing as depends upon what you know about what you are seeing." Conclusion In summary, the theoretical assumption can be accepted that experts in a particular discipline differ from non-experts in the amount and type of knowledge they possess and in the way in which they process available information. Furthermore, the results of these sport-specific tasks provide insight into the knowledge structure of players with differing experiential background and how changes occur as a result of increasing expertise. Both the construction of declarative knowledge and the ability to "compile" and "tuning" can be considered as "software" attributes. They can be studied by means of the indirect and direct paradigms and research techniques described. Sports. in general, and team sports, like hockey and soccer, in particular, are, therefore, very appropriate for study. Each team has eleven players on a large field of play, and each has very specific defensive and offensive duties. In each, players also need to structure relevant game information. The consensus among researchers who apply these techniques is more evident than among those who investigated "hardware" components. In other words, as suggested by Starkes and Deakin (1984). the interaction found between skill level and the processing of structured match information seems to form a stable basis for further research.

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It would be valuable, as has been suggested by Bard et al. (1987). to derive a comprehensive index of perceptual efficiency that would completely chart the various "software" aspects of visual-information processing. According to the various research techniques and results (see Figure 7.1). the following factors need to be taken into account: retrieval capacity, rapidity of detection, quality of advance cue usage, simple and complex decision speed, accuracy and adequacy of response, and the organization of visual search patterns. Bard et al. (submitted for publication) recently suggested also taking into consideration the ability to use information acquirrd through the peripheral visual system and the economy of operations, (i.e. the attentional cost of the operations leading to a decision). Such a multitask approach provides a more realistic picture of domain-specific skill because of the inter- and inm-individual performance variability in individual tasks. The value of this approach has been c o n f i i e d in the stepwise multiple regression analyses of hardware and software variables (Starkes & Deakin, 1984; Starkes, 1987. 1990). For expert field hockey players, the determining factors appear to be how well they are able to encode and use information about the game structure and how well they can predict placement of a shot following a view of ball impact. According to Starkes (1987), these findings are encouraging for two reasons. First, these tasks are closer to actual game performance requirements than many of the other tasks employed, so their importance is intuitively appealing. Second, none of the "hardware" factors figured significantly in the prediction of skilled performers. Such a multitask approach, however, has only been used for fieldhockey and volleyball (see Figure 7.1). Therefore, in a further extension of our study, we investigated the relative importance of both "hardware" and "software" aspects of the visual system in the determination of expertise. The "hardware"-attributes assessed were simple reaction time, peripheral reaction time, static visual acuity, dynamic visual acuity, depth perception, periheral visual range, and visual correction time. The "software"-attributes were complex decision speed and adequacy, number of fixations and fixation duration in solving tactical game problems, presented statically by means of slides and dynamically by means of 16-mm film. A stepwise discriminant analysis was used to determine the most discriminating variables. The results indicated an average squared canonical correlation of .87 with the significant step variables all being "software"-variables. The primary variable was the response adequacy (F=20.91, ~ 0 . 4 5p<.OOOl). , The second and third variables were the number of fixations in solving tactical game problems, presented dynamically by means of 16-mm film (response phase; F=18.12, r 4 . 4 2 , p<.OOO3) and statically by means of slides (F=7.61, r 4 . 2 4 , pc.01). The fourth variable was the number of fixations in solving tactical game problems, presented dynamically by means of 16-mm film (preparation phase; , The fifth variable was the ballcontact time (F=5.19, 14.19, pc.03). F=5.12. ~ 0 . 1 8pc.03). These variables can be divided into decision, perceptual and motor components. The last and only "hardware" variable was peripheral visual range in the vertical dimension (F=5.56, ~ 0 . 2 0 , p<.03). All together these variables explained 87 96 of the variance in performance of both skill groups. Only one of the "hardware" factors figured significantly in the discrimination of subjects of differing skill level. These findings reveal the importance of the "software" dimension of visual search in the determination of skill in soccer. Presumably this is due an overlap of knowledge bases required to perform the test and actual skills. In t h i s way, these

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conclusions of visual search in solving tactical game problems support the findings of Starkes and Deakin (1984), and Starkes (1987, 1990) in fieldhockey. Just like other sportgames, soccer does have very definite structure and cognitive demands, and these do account in part for what makes an expert.

Further Applications A number of issues should be addressed with a view to future research. First, we will note some advantages and disadvantages of the use of eye-movement registration for the study of visual information processing. A number of more theoretical observations will be formulated, and, finally, practical suggestions will be made. Important perspectives are opened by the use of eye-movement registration since visual information processing can be expressed qualitatively and quantitatively with an acceptable degree of accuracy under ecologically valid, and even real conditions. Both the number of fixations and the fixated information units themselves give a quantitative idea of the information units that are considered important. The duration of fixation and the sequence of exploration give a qualitative idea of the relationship between the various information components, and of the priority the subject assigns to those items as information sources. However, the biggest drawback to this technique is that only what is looked at foveally is analyzed. In the literature, it is suggested that the enlargement of the useful visual field in relation to increasing expertise can be explained by the role of the retinal periphery. Indeed, it could be that inexperienced subjects fixate primarily foveally while more experienced players see both foveally and peripherally. So far, only Bard et al. (submitted for publication), and Goulet et al. (submitted for publication) have studied the contribution of the peripheral visual system to the decision-making process in sport situations, using the eye-movement camera. Further research is needed using similar approaches to provide an understanding of the complementary functions of both the peripheral and central visual systems in sport. Even though the theoretical conceptual framework constitutes a valuable point of departure for the study of changes that occur in problem-solving skills and processes with the acquisition of expertise in a specific sport discipline, we must also note its incompleteness. While the various operations essentially proceed serially and chronologically, it has repeatedly been suggested that more parallel processing takes place as a function of increasing expertise. As far as practical applications are concerned, one may ask about the contribution of the results of these studies to the learning of procedural skills. As Anderson (1982) has noted, the human system learns by doing. In motor skills the question remains, how much of skill is attributable to larger bases of declarative knowledge (from watching, problem solving) versus efficient proceduralization of this information within the movement context (physically performing in the context). As already noted, "getting the idea of an action" is the first important step in the acquisition of a skill. Since it appears that the procedural and declarative knowledge that experts have acquired in their sport facilitates the acquisition and retention of

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specific infomation, it is reasonable to suppose that the first steps am facilitated when the task is learned in a meaningful context. A substantial declarative basis in a spon would facilitate the learning of specific skills because such knowledge would provide, as it were, a more fertile soil. On the other hand, one can hardly assess the knowledge smcture of an expert player without noting that much of the declarative base was acquired and proceduralized within the course of movement.

More training-oriented suggestions are concerned primarily with the way in which the visual information registration and processing, in general, and perceptual performance, in particular, can be improved. It must be s a s s e d here that it is not possible to bring a beginning player to the level of an expert simply by perceptual imitation. In the real world, it has been repeatedly demonstrated that, even though the specific information supply is equal for different skill groups, only experts can make the important leap from informative items to their concrete implications. Further research must, therefore, be oriented not only to the disclosure of the information or knowledge that is used by experts in a specific discipline but also to the way in which this information can best be transmitted. In summary, we can conclude that training the visual system in order to obtain better visual information processing must be done in a dynamic, sport-specific context. We note the limited effectiveness of genexal visual training programs, which are being promoted by sports optometrists in the USA and Canada under the motto "Eyerobics". They stimulate only "hardware" aspects of the visual information system in non-specific and also often static environmental situations. In our opinion, this does not lead to more successful sports participation and performance. In this respect, training programs that include anticipatory tasks (like the film-ccclusion tasks described by Haskins (1965) or Burroughs (1984), in addition to the use of search patterns of experts as a prototype, appear to offer the most fruitful alternative.

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Thomas, J.R., French, K.E.. & Humphries, C.A. (1986). Knowledge development and sport skill performance: directions for motor behavior research. Journal of Sport Psychology, 8, 259-212. Tyldesley, D.A., Bootsma R.J., & Bomhoff, G.T. (1982). Skill level and eye-movement patterns in a sport oriented reaction time task. In H. Rider, K. BBs, H. Mechling, and K. Reischle (Eds.), Motorik und Bewegungsforschung: ein Beitrag z w n Lernen im Sport. (pp. 290-296). Schomdorft Verlag Karl Hofmann. Vickers, J.N. (1983). The role of expert knowledge structures in an instructional design model for physical education. Journal of Teaching in Physical Education. 2 , 17-32. Vickers, J.N. (1986). The resequencing task: determining expert-novice differences in the organization of a movement sequence. Research Quarterly for Exercise and Sport, 57. 260-264. Vickers, J.N. (1988). Knowledge structures of expert-novice gymnasts. Human Movement Science, 7, 41-72. Wall, A.E. (1986). A knowledge-based approach to motor skill acquisition. In M.G.Wade, & H.T.A. Whiting (Eds.), Motor development in children: aspects of coordination and control (pp. 33-49). Dordrecht: Martinus Nijhoff. Wall, A.E., Mc Clements, J., Bouffard, M., Findlay, H., & Taylor, M.J. (1985). A knowledge-based approach to motor development: implications for the physically awkward. Adapted Physical Activity Quarterly. 2 , 21-42.

Acknowledgements The authors wish to thank P. Meugens, M. Beirinckx, and M. Vanbuel who provided invaluable guidance in designing the research equipment and the electronics.

COGNITIVE ISSUES IN MOTOR EXPERTISE J.L.Stakes and F. Allard (Editors) Q 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 8 THE PERCEPTUAL SIDE OF ACTION: DECISION-MAKING IN SPORT CRAIG J. CHAMBERLAIN* and ALAN J. COELHO** *Universiry College of the Fraser Valley, Abbotsford, British Columbia, V 2 S 4N2 **Department of Physical Education, Eastern Washington University Cheney, Washington, 99004-2499 High level performance in sport settings is characterized not only by efficient execution of the motor task, but by superior levels of decision-making. In particular. the successful performance of open skills at elite levels can often be defined by the quality of the decisionmaking process. There is a certain obviousness to the statement that an athlete will not experience a successful outcome for a motor action if the skill selected was inappropriate for the demands of the environment. In this context, the perceptual side of action becomes equal to, if not greater than, the execution side for truly expert performance. A study by French and Thomas (1987) demonstrated that the maximum discriminator of expert and novice children on basketball performance was the quality of decisions made rather than the quality of skill execution. Within the conceptual framework adopted for this chapter, decision-making in sports is viewed from a cognitive perspective and is couched within an information processing model, not unlike the model described by Tenenbaum and Bar-Eli (1993). Unlike Tenenbaum and Bar-Eli, however, decision-making is viewed as being synonymous with problem solving, with the connection being particularly apparent at lower levels of expertise. In fact, the establishment of superior decision-making capabilities is viewed as being the result of an athlete assuming a problem-solving approach to knowledge acquisition, encompassing an hypothesis generation-testrevision cycle. As an athlete increases in decision-making expertise, more of the attentional processes required by the problem-solving approach to decision-making are relegated to subconscious levels, providing for an appearance of "automaticity" in performance (see Logan, 1983). When reviewing the extant literature, it can be noted that a great deal of empirical evidence has accumulated with respect to enhancing motor skill execution, with a relatively

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modest amount being directed towards the decision-making process. This imbalance in the literature reflects what occurs in the real world. Coaches and athletes spend an inordinate amount of time developing capabilities for motor skill execution, but relatively little time in developing a capacity for decision-making. As Jones and Miles (1978) noted, "sports coaches spend too much time and energy in promoting good stroke production and not enough in teaching people to 'read' their opponents' movemen ts..." (p. 235). Along a similar line, Bard and Fleury (1981) stated that: The coach usually pays attention to the learning of a motor response, based on the player's experience. It might be as important for him to consider the visual dimension as a major learning and performance factor, grounded this time on the perceptual experience of the subject. (p. 28) Given the underlying importance of decision-making processes to the overall achievement of sport expertise, this imbalance in focus seems inappropriate. The purpose of this chapter, then, is to investigate the research literature that has been

directed towards gaining an understanding of the perceptualkognitive component of producing motor actions in sport settings. The chapter is organized around four main sections. The first section will provide evidence from the empirical data base which has identified experts as having a higher level of decision-making when compared to novices in a specific sport setting. We will attempt to provide some insight into the general characteristics of decision-making that distinguish an expert from a novice, and highlight under what conditions the expenlnovice separation in decision-making capabilities breaks down. The second section will be directed at understanding why an expert has superior decision-making processes when compared to a novice. In the third section, we will provide a critique of the research performed to date. In particular, we will focus on the research methodologies that have been employed and argue that differences in research methodology have at times limited our understanding of the perceptual process. Finally, in section four, we will consider the issue of perceptual training in athletes and will discuss the potential value of video technology for improving decision-making capabilities. Expert/Novice Differences in Decision-Making The robust finding from the literature has been that when placed in a sport-specific context, experts tend to perform more capably than do novices in various elements of the decision-making process. A variety of approaches has been adopted in the research effort, each directed at investigating a particular aspect of the decision-making process. These approaches can be grouped into 4 categories: recall of briefly presented information, signal detection, visual search patterns, and advanced cue utilization. This section will briefly consider the findings from each of these research paradigms. (For a more complete review, see Starkes & Deakin, 1984.) Recall of Briefly Presented Information In what might be considered the classical paradigm for this area of research, subjects are

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presented slides depicting structured or unstructured game situations. Structured game situations would depict orderly events in the game action, such as a particular offensive or defensive set being adopted. Unstructured game situations would depict less orderly events, such as time-outs or following a breakdown in the play being run (i.e., after a turn-over in basketball). After a brief presentation interval, subjects are then asked to recall as accurately as possible the positions of each player viewed on the slide. The main intent of this paradigm is to investigate the encoding and retrieval differences between experts and novices from the particular sport being utilized in the research. This type of approach follows from the studies performed using more highly cognitive tasks, such as the recall of positions in chess (Chase & Simon, 1973), Go (Reitman, 1976), and bridge (Chamess, 1979). In general, the findings have been that a structure by level of expertise interaction effect occurs. That is, experts demonstrate greater recall when compared to novices for structured game information only, and this level of recall was superior to recall of unstructured game information. No differences exist between levels of expertise for recall of the unstructured game information. These results have been obtained using football (Garland & Barry, 1991), basketball (Allard & Burnett, 1985; Allard, Graham, & Paarsalu, 1980; Millslagle, 1988),field hockey (Starkes, 1987), and volleyball (Borgeaud & Abemethy, 1987). The Borgeaud and Abemethy (1987) study represents an interesting variation on this paradigm. The sport of volleyball is quite different from the other sport contexts used. Football, basketball, and field hockey are invasion games, where the object is to perform periodic incursions into the opponent’s temtory. In volleyball, the players are restricted from entering their opponent’s temtory by the net. In a study reported by A l l d and Burnett (1985), expcrt volleyball players did not demonstrate any difference in recall accuracy for structured or unstructured situations. Further, expert volleyball players were superior on recall of schematic diagrams of volleyball situations when compared to novices for both structured and unstructured displays. Borgeaud and Abemethy presented the game information dynamically, having their subjects view a filmed clip of an offensive attack sequence during game action (slructured display) or a film clip of warm-up activity (unstructured display). The task was to recall the position of the players on the court at the end of the filmed sequence. Unlike the data reported by Allard and Burnett (1985), their results did indicate the expected structure by expertise interaction effect; however, the interaction was a result of the novice ncall performance being worst for the structured sequence, rather than the expert recall performance improving with structure. Similar to the Allard and Burnett (1985) data, experts performed equally well on recall of structured and unstructured displays. Two comments can be made regarding this study. First, the presentation of information dynamically, as opposed to statically, would seem to be more ecologically valid. Structure in many sport situations is the result of an emerging pattern of action that often cannot be captured in a single static image. This fact would seem to distinguish research in sport settings to the studies performed using games (chess, go, and bridge). Second, the divergent results of this. and of Allard and Bumett’s (1985), study would seem to indicate differing perceptual qualities

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inherent in the sport of volleyball as compared to basketball or football. Allard and Bumett contend that the difference may be that the pattern of action in volleyball is often constructed to deceive the viewer, so that ignoring the pattern of action may be helpful. Thus signal detection (where is the ball?) becomes more useful as a perceptual tool. However, the patterns of action constructed in basketball and football often have the same goal, i.e., to deceive the defender. In fact, a case could be made that ignoring the pattern of action in volleyball and focussing on the ball would decrease a team’s capability to defend, as they would not be aware of the various locations of attacking players, thus denying the defenders information regarding potential points of attack. A second explanation for the discrepancy in results is offered by Borgeaud and Abernethy (1987). It may be that the unstructured situations for their study, which depict non-game situations (e.g., warm-ups), are less complex and thus induce a floor effect with regard to the error measures employed. Therefore, equal levels of performance by the experts on both structured and unshuctured displays may not be due to the inability of the structured display to result in an increase in performance, but the unstructured situation not causing a decrease in performance. This explanation appears to be quite plausible as, for example, in the basketball studies (Allard. Graham, & Paarsalu, 1980; Millslagle, 1988) the unstructured information still depicted game situations (e.g., action after a turn-over or rebound).

A third explanation for these results is also possible. By nature, volleyball is a game that retains a high degree of structure throughout the course of a competition. Player responsibilities are clearly defined in terms of role (e.g., setter, power hitter, middle hitter, serve receiver) and court position (e.g., center back, right front). In game situations, particularly when advanced level performers are involved, rarely will truly unstructured situations occur. Even non-game events, such as warm-ups and time-outs, tend to have a degree of structure to them. Therefore, the unstructured stimulus displays presented to the subjects may actually contain a great deal of stmcture that would be readily apparent to the more advanced performer, thus resulting in levels of performance on recall for the expert similar to recall levels for the structured displays. If either of these latter two explanations for the divergent results from the volleyball studies is veridical, then the apparent different perceptual qualities of volleyball could be considered an experimental artifact. More research is required before a definitive conclusion can be reached. Signal Detection A second approach to studying the perceptual nature of sport performance has been to investigate the speed and accuracy with which expert athletes, as compared to novices, are able to detect objects within a visual field. In general, subjects are presented brief exposures to sport specific information that is either structured or unstructured in nature. The subjects’ task is to search the visual field for the presence of a ball and respond as rapidly and accurately as possible. The usual finding has been that experts completed the task more rapidly, but with no greater accuracy, than novices (Allard & Starkes, 1980; Millslagle, 1988; Starkes, 1987). This would seem to suggest that experienced athletes emphasize speed of detection over accuracy. However, it may be the nature of the sports investigated (volleyball, basketball, and field

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hockey) that speed of detection is a more critical factor than accuracy. Structure does not seem to be a factor as speed and accuracy of ball detection is equivalent for both s t r u c t d and unstructured presentations. The only exception to this latter conclusion was in the Allard and Starkes (1980) study when non-game slides were presented with an inverted orientation. Both expert and novice subjects had more difficulty in ball detection for inverted non-game slides as compared to correctly aligned game slides. In addition, results have been context-dependent in that the greater speed of detection has been noted within the sport specific context under investigation. Volleyball players did not demonstrate superior signal detection capabilities in non-volleyball situations or for a non-ball target in a volleyball situation(Al1ard & Starkes, 1980). The results to date have not been as congruent as may be indicated in the preceding paragraph. Millslagle (1988) adopted Prinz’s (1977) hurdle technique to investigate whether signal detection in spon settings is under context or target control. In the hurdle technique, a stimulus that is distinct from the target and incongruent with the context of presentation is included in the stimulus display. Detection of the hurdle would assume a context-controlled search. If, as is evident in Allard and Starkes’ (1980) data, the speed of detection is context specific, then it would seem to follow that experienced athletes are operating under context control. However, Millslagle’s data have indicated that both expert and novice athletes operate under target control. This apparent contradiction cannot be readily explained. It may be, as Millslagle (1988) suggests, that the lack of sport research utilizing the hurdle technique does not allow us to make any conclusion regarding the effectiveness of the technique in this type of research. The relative importance of the context for signal detection in sport remains at issue.

Visual Search Patterns A number of studies have compared expert to novice subjects on the actual pattern of visual search utilized when viewing a stimulus display. The technique employed in these studies is to present a stimulus display to the subjects and, with the aid of an eye movement monitoring device, record the points of visual fixation and the serial pattern of search undertaken. As summarized in Goulet, Bard, and Fleury (1989), eye-movement recording allows for the acquisition of several pieces of valuable information, such as the selection and identification of the most informative cues, the quantification of the amount of information selected, and identification of the visual search strategy which reveals the dynamic priority of the subject @. 383). This approach has been used in a variety of sport settings, including ice hockey (Bard & Reury, 1981), tennis (Goulet et al., 1989), basketball (Bard & Fleury, 1976), table tennis (Rip011 & Fleurance, 1988),gymnastics (Bard, Fleury, & Caniere, 1975), badminton (Abernethy & Russell, 1987b). and squash (Abernethy, 1990). In general, the findings have revealed more similarities between experts and novices on the pattern of visual search than differences. The differences found have been that experts tend to have fewer fixations than novices (Bard & Fleury, 1976; Bard et al., 1975; Goulet et al., 1989) and that novices often fixate on the

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object being manipulated (i.e., the ball or puck) more than do experts (Bad & Fleury, 1981; Goulet et al., 1989). Several explanations can, and have, been offered for these results. As will be noted in the next section, experts are able to more accurately predict event occurrence from the use of earlier appearing cues in the stimulus display. Thus display redundancy would result much earlier for the expert as compared to the novice. This would result in a reduction in the number of fixations required, which would lessen the cognitive load placed on the performer. A second, intuitively appealing explanation is that experts simply know where to look. As much of the stimulus display contains irrelevant information, the expert has learned to focus on the fewer relevant locations and ignore the irrelevant, However, the fact that several studies have revealed only minor differences in location of fixations would seem to argue against this hypothesis (e.g., Abemethy, 1990). A third explanation may be that experts are simply more confident in their ability to make an accurate decision. Thus, they make decisions following fewer fixations, using less information in the process. Novices, because they lack the confidence of an expert, are more careful in making a decision and prefer to acquire more information from more sources prior to responding. A study by Franks, Elliott, and Johnson (1985) would seem to support this idea. In this study, expert and novice gymnastic observers viewed paired performances of a front handspring on videotape. The task was to detect whether the performances were identical or different, and if there were differences, where did these differences lie. The results indicated that experts were no more accurate in providing a perceptual judgement regarding similarity of performance, but were more confident in their decisions.

It is most likely that the differences noted in experts’ visual search patterns are a combination of the hypotheses suggested above. The more confident and knowledgeable expert would not only require fewer fixations, but would also need to view the stimulus display for a shorter period of time (Hubbad & Seng, 1954; Whiting, Gill, & Stephenson, 1970). This would explain why inexperienced subjects fixate on the object more often. The one sure source of information for the novice would be the object being manipulated. Being unsure of the connection between an opponent’s action and the resultant object projection, the novice opts for fixating on the source of information which would seem to provide reliable cues for the decisionmaking process. It is interesting to note that some studies have indicated this strategy might be detrimental to performance. Ripoll and Fleurance (1988) described the visual search patterns of expert table tennis players and compared this data to that collected in a pilot study using novices. They found that the expert performers did not track the ball during the flight path. These subjects fixated on the ball during their opponent’s ball strike and the initial segment of the flight and then shifted their point of fixation to the anticipated contact point for the execution of their stroke. Novices, on the other hand, attempted to fixate on the ball for longer periods of time and to observe the entire flight path of the ball. The result was that novices often attempted to contact the ball while

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still looking in a forward direction, thus developing a biomechanically incorrect action. The reason that this occurs is due to the nature of the visual system. In sports where the object travels quite fast, the successive visual saccades required to observe the path of the object are unable to keep pace, thus developing a progressive discrepancy between perceived and actual location of the object @ahill& LaRitz, 1984). The result is that, for high level performance, the need is to make a prediction regarding flight path characteristics earlier and, in effect, to ignort the object and shift focus to the anticipated contact point. Advanced Cue Utilization More recently, the most commonly used approach to the study of decision-making in sport has been to investigate the connection between advanced cue utilization and the ability to make accurate predictions. To accomplish this task, the technique of spatial or temporal occlusion of a visual stimulus display has been employed. Spatial (or event) occlusion results when particular elements of the visual display are masked from the subject’s view during the entire performance. Temporal occlusion results from stopping the sequence of action at various points during the performance (Abemethy, 1989). In either case, subjects are normally asked to make a prediction regarding an event occurrence (e.g., the arrival point of a projected object following performance of the observed task) or to provide a prediction regarding the necessary performance that must be generated in response to the observed task. Although it can be considered an artificial separation, each of these approaches will be considered in isolation here. Event Prediction The normal paradigm adopted in these studies is to have subjects observe a sequence of events from the perspective of a competitor in a sport setting. The sequence is stopped at varying points in time. Following the temporal occlusion, which may or may not be combined with spatial occlusion, the subject must provide a prediction regarding the event occurrence that will result from the sequence observed. The prediction is evaluated for both speed and accuracy of the decision made. This approach has been used in a variety of sport settings, including badminton (Abemethy, 1990; Abernethy & Russell, 1987a). field hockey (Starkes, 1987), squash (Abemethy, 1990), ice hockey (Salniela & Fiorito, 1979), and tennis (Goulet et al., 1989; Isaac & Finch, 1983; Jones & Miles, 1978). The results from these studies have been quite consistent. That is, experts tend to make more rapid and accurate decisions than novices, although the accuracy of the decision appears to be specific to the nature of the performance being utilized. As the type of occlusion (temporal or spatial) provides somewhat different information, each will be considered separately. The Effect of Temuoral Occlusion Although there are a number of subtle differences in the results of the various studies, in general the data have been quite congruent. Decreasing the amount of time available to view a performance results in a decease in the ability of both experts and novices to provide predictions regarding the performance outcome. Experts tend to provide more accurate predictionsregarding event occurrence overall; however, the difference in predictive performance

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between experts and novices decreases as viewing time increases. It would appear that experts gain more information from an opponent’s pre-contact performance than do novices. In most cases, improvement in the novice subjects’ predictive performance occurs with increases in postcontact viewing, with predictions not exceeding chance levels of performance when occlusion occurs pre-contact (e.g., Starkes, 1987). Logically, the period of time just prior to contact should be rich in informative cues to allow an individual to make anticipatory predictions regarding event outcome. Although this generally holds true, it appears that the most beneficial pre-contact time interval is task specific. For example, Abemethy (Abemethy, 1989; Abemethy & Russell, 1987a) demonstrated that for badminton, the critical time period for gaining information appeared to be 83 ms prior to racquet-shuttle contact. This time interval was even useful to novices in improving their predictive performance. However, in a similar study using squash performance (Abemethy, 1990), novices were unable to make use of pre-contact information. In a tennis study (Isaacs & Finch, 1983), neither experts nor novices seem to gain a great deal from pre-contract information. This can be contrasted with a study of expert ice hockey goalies in which occlusion 333 ms prior to stick-puck contact produced greater than chance level predictions, although the level of prediction was superior when a wrist shot was being used as opposed to a slap shot (Salmela & Fiorito, 1979). It would appear that the nature of the sport skill being performed, as well as the skill level of the performer, would significantly impact the ability of a subject to use pre-contact information. From the studies cited to this point, it seems that pre-contact information can generally be useful in providing cues that will drive the anticipatory decision-making process. although this cannot be accepted as being universally true. It is also apparent that the time interval immediately prior to contact is the most important for this to occur. Also, it can be concluded that experts gain more from pre-contact information than do novices. These conclusions could lead one to deduce that it might be best to instruct novices to focus on an opponent’s behaviour beginning at a point immediately prior to object contact. It could be argued that observation of an opponent’s behaviour prior to this point in time may provide too much information or, since preliminary actions are often meant to deceive an opponent, the information may be of a contradictory nature. However, a study by Goulet et al. (1989) produced data which would refute this logic. In their study, subjects were required to predict the landing point for a variety of tennis serves. The tennis serve was divided into three phases. The ritual phase consisted of the initial foot positioning and ball bouncing. The preparatory phase began with the elevation of the arm holding the ball and ended with the ball reaching the apex of its flight path. The execution phase began with the server’s knee flexion and ended at ball-racquet contact (p. 385). Their data indicated that observation of the ritual phase was important to the novice subjects as prediction of ball landing location was best when the ritual phase was observed. However, the expert subjects did not receive any additional advantage from observing this phase. It would appear that the ritual phase, which ends 1209 ms prior to racquet-ball contact, serves as an orienting stimulus to the novice, allowing them to focus on the relevant cues that would be available during the preparatory and execution phases. The experts d o not require this orienting

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stimulus as they are able to rapidly focus their attention on the relevant skill-related information. The Effect of Spatial Occlusion Spatial occlusion of the visual stimulus field has not been used with the same frequency in the research literature as temporal occlusion. This is most likely due to the fact that the question the spatial occlusion technique is being employed to answer (i.e., what elements of the stimulus display offer the more relevant anticipatory cues?) is more effectively approached with a visual tracking technique. However, some interesting data have arisen from spatial occlusion studies. Abemethy and Russell (1987a) occluded the racquet, the racquet and arm, the face and head, or the lower body of a badminton player performing an overhead stroke. Their results indicated that the most useful anticipatory cues were available from the racquet and arm. Novices, however, were unable to make use of arm information. Neither experts nor novices were able to obtain useful information from the face and head, or lower body rcgions. These results are congruent with data from studies on field hockey and volleyball (Lyle & Cook, 1984, Neumaier, 1983, as cited in Abemethy & Russell, 1987a), and with the results of the visual tracking studies cited previously. That is, "...the striking implement...and the most proximal limb...p rovide the most significant sources of information to aid in the anticipation of the forthcoming stroke." (Abemethy & Russell, 1987a. p. 337). In a subsequent study, however, Abemethy (1989) was unable to replicate the expemse by occlusion condition interaction. placing some doubt on the contribution of arm information to overall stroke prediction performance. ResDonse Prediction Several studies have been performed which, rather than having subjects predict characteristics of the stimulus display, required their subjects to predict a response that must be generated (Abemethy & Russell, 1984),or to actually generate a response (Chamberlin. Coelho. & Reuter, 1992; Coelho & Chamberlin, 1991). Each of these studies made use of temporal occlusion of the visual stimulus display. Abemethy and Russell (1984) compared novice and expert batsmen in cricket on their abilities to predict the c o m t batting response that should be generated for a particular bowling sequence. They found evidence to indicate that expert batsmen made more accurate response predictions in less viewing time than did novice batsmen. In addition, the expert batsmen wherc able to make better use of pre-flight information, although observation of ball flight is essential for improvement in response prediction. In two studies performed in our laboratory, we had subjects produce graded,single limb responses that corresponded to the direction of the whole body movements that would be required by a defending player in volleyball (Chamberlin, Coelho, & Reuter, 1992; Coelho & Chamberlin, 1991). Subjects, from the perspective of a center back defender, observed an offensive player execute an attack up to the point of hand-ball contact. Two types of hits (spike

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or tip) were combined with two directions of hits (dawn-the-lineor cross-court), resulting in four possible attacks. The subject’s hand rested on a micro-switch that served as the starting position. The task was for the subject to respond as accurately and quickly as possible in moving to one of four micro-switches that corresponded to the interception location for the defending player and the attack. That is, if the attack was a spike down the line, the correct response would be to move to the micro-switch located laterally and to the right of the subject. A spike cross-court should be responded to with a move laterally and to the left. The correct response for a center tip would be forward and for a tip down the line would be forward and to the right. In the two experiments conducted, subjects observed the attacking player with or without blockers and could respond either at any point in the sequence or were required to wait until the point of hand-ball contact. The results replicated previous findings. That is. expert subjects responded much more quickly and accurately than did the novice subjects, although the greater accuracy in responding was the result of the experts predicting type of attack with more efficiency than direction of attack. This finding was not significantly affected by the presence of the blockers. Although these studies represent a very preliminary investigation of this nature, they would seem to be important since they demonstrate that the requirement of a perception-action link does not alter the findings that have been generated by studies which did not require this linkage. Prediction Errors As most of the sports which have been investigated for decision-making have involved object interception tasks, an interesting pattern of results emerge when the type of prediction error that occurs is taken into consideration. In racquet sports and volleyball, it is necessary for the defender to predict both direction and force to anive at an accurate estimation of object trajectory. Several studies have provided data relating to each of these potential sources of error as well as utilizing a combined error measure. However, interpretation of these results are difficult, as a number of the studies have produced data which, when analyzed, have resulted in unwieldy &-way interactions with type of e m r as one of the affected factors. The pattern for type of prediction error seems to be sport specific. For the racquet sports of badminton and tennis, subjects appear to be more efficient at predicting direction of the stroke than force of the stroke. This conclusion can be drawn from a variety of sources. First, in most of these studies, overall results have indicated lower levels of direction (or lateral) error when compared to depth (or force) errors (Abernethy & Russell, 1987a; Isaacs & Finch, 1983). Second, precontact visual cues are more helpful in resolving direction uncertainty than depth (Abernethy & Russell, 1987a). Finally, spatial occlusion of the arm and racquet causes a significant decline in the prediction of direction (Abernethy & Russell, 1987a). Taken together, these data can be interpreted as indicating that cues necessary to resolve direction uncertainty are more easily discernable than an cues needed to resolve force uncertainty, although methodological concerns with the use of 2-dimensional displays need to be resolved before a definitive conclusion can be reached.

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A study in which goaltenders in ice hockey were asked to predict shot location provides a similar finding to that cited above. Data from Salmela and Fiorito’s (1979) research indicated that goaltenders were better at predicting side (direction) of shot as compared to height. Force was not a variable in this experiment. Also, Abernethy’s (1990) study of shot prediction in squash can be interpreted as being in support of this conclusion. Although overall, subjects were superior at predicting depth of shot and both depth and direction e m s were reduced with increased viewing time of the stroke, direction errors appeared to be reduced to a greater extent.

Level of expertise is also a factor in mediating the pattern of prediction error previously identified. As would follow from the interpretation that cues for force information are more difficult to acquire than direction, novices seem unable to utilize the information provided in these studies to resolve force uncertainty. For example, the data from Isaacs and Finch (1983) indicated that experts improved the percent of correct placements for tennis serves across increased viewing time conditions for both direction and depth errors. However, novices showed an improvement in this measure for direction errors only and a general decrement in this score for depth errors. In Abemethy and Russell’s (1987a) study, a similar e n d can be noted. Experts exhibited a general decrease in prediction errors for both direction and depth across increased viewing conditions, although a greater resolution of direction errors did occur. Novices, on the other hand, demonstrated little capability to resolve depth errors, even with a full viewing condition. The amount of error in predicting direction did decrease significantly, however, with the performance of the novices approaching that of the experts under a full viewing condition. When viewing is permitted post-contact the majority of predictive error difference between the experts and novices results from providing depth information. It would be tempting to hypothesize from these studies that the nature of racquet sports, in general, lead to force resolution being the more difficult perceptual task. However, in Abernethy’s (1990) study on squash, the absolute value for prediction error was less for depth than direction across all temporally occluded viewing conditions except when no occlusion occurred. When occlusion occurred 160 ms prior to contact, the percent errors for depth of the expert performers was approximately 18% whereas the percent errors for direction was approximately 32%. It may be that depth resolution is more critical in squash than tennis or badminton so that the expert performers have developed this perceptual capability, or it may be that depth is more easily resolved in squash than in the other two racquet sports. Despite this contradiction, it can be concluded that, for tennis and badminton, predicting direction would appear to be the more easily achieved perceptual task. This pattern for predictive error is not generic across all sports, however. The finding can be contrasted with the error pattern noted in a study done in our lab (Coelho & Chamberlin, 1991). Recall that in this study, subjects were required to predict the type (tip or spike) and direction (down-the-line or crosscourt) of an attack in volleyball. The two types of hits are differentiated by the force being applied to the ball with a tip being a less forceful hit than a spike. Predictive errors were classified as type (force), direction, or both wrong for each trial. Expert subjects never made a ”both”error. When an error was recorded for the expert subjects it was inevitably a direction

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error, not one of force. For novice subjects, direction and type e m r s occurred with the same frequency. The error rate on direction errors for experts and novices was similar. In this case, then, experts were distinguished from novices by their ability to detect the type of hit, rather than the direction of the hit. It can be concluded from these data that predicting direction, as opposed to force, of the hit offers the more difficult perceptual task for the volleyball player. For the badminton, tennis, and perhaps, the ice hockey performer, the opposite appears to be true. Although predicting force and direction of a projected object has been the most common perceptual task utilized in the literature, other types of predictive task have also been used. For example, Goulet et al. (1989) had subjects predict the type of tennis serve (flat, topspin, or slice) performed by either a right or left handed server. Overall, they found that it was easier to predict the serve of a right handed player and that the flat and topspin serves were easier to predict than the slice serve. There was, however, an interaction with expertise. Novice subjects had difficulty predicting the type of serve for a left handed server, regardless of type of serve. Their best predictions were for a right handed server hitting a flat serve. Expert subjects demonstrated capable prediction levels for the flat and topspin serve, with no impact for handedness of the server. In another study, Borgeaud and Abemethy (1987) asked subjects to recall the court position of all players following the viewing of a dynamic sequence of attack in volleyball. They provided both lateral and depth errors in their analysis. The results of the data analysis indicated that expert subjects demonstrated lower levels of errors in recalling player position, mainly due to a lower level of lateral, rather than depth, error. Borgeaud and Abernethy (1987) concluded that, since most attacks in volleyball originate at some point across the net, predicting the point of attack laterally is more crucial than making depth judgments. With the current trend in volleyball to make use of a back row attacker and to have the attack originate off the net, it would be interesting to discover if the perceptual demands of determining the attacking players' positions has changed. It may be that perception of depth has increased in importance for the defending volleyball player. The Cause of ExperUNovice Differences in Decision-Making As noted in the previous section, experts outperform novices on a variety of sport-specific perceptual tasks. Although some discrepancies exist, the data collected have indicated that experts make more accurate decisions based on earlier occurring information, they can recall larger chunks of structured information, and they exhibit greater speed in signal detection tasks. In this section we will consider hypotheses proposed to explain the source of these expert/novice differences in perceptual activiaes. As outlined in Starkes and Deakin (1984), these hypotheses can be grouped into one of two categories- hardware or software dimensions of analysis. Hardware Hypotheses The basic premise of the hardware hypotheses is that the expert performers' superiority on perceptual tasks is primarily due to the possession of a more highly developed mechanism,

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in particular the sensory-perceptual apparatus. In other words, the expert athlete has access to the mainframe computer while the novice must make use of the Commodore 64. A rather extensive research program was directed towards this question in the 1940's, 50's. and 60's. with the occasional smattering of research still appearing today. The normal approach taken was to compare athletes to non-athletes. athletes with expertise in a variety of sports, or athletes of different skill levels within a sport, on a variety of tests developed to measure particular aspects of the efficiency of the human mechanism for information processing. Studies have been directed at the functioning of the sensory receptors (e.g., visual acuity, peripheral vision), perceptual mechanism (e.g., depth perception), and the overall functioning of the system (e.g.. reaction time). In their paper, Starkes and Deakin (1984) reviewed the literature investigating stereoacuity (depth perception) and reaction time (RT). They concluded that the results of many years of extensive research have been that very few conclusive links between the expert performer and either of these underlying characteristics can be found (p. 119). That is, athletes do not demonstrate superior performance on measures of RT and depth perception. Although this conclusion is well taken, it must be pointed out that not all writers on this topic a g m with Starkes and Deakin. Sage (1971) concluded that "Numerous investigations over many years on RT and movement time (MT) indicate that the more skillful performers in physical education and athletics are superior to the less skillful in both of these components of response time...." (p. 246). Drowatzky (1981) stated that "The general consensus of research findings is that RTs of athletes in different categories are generally superior to those of nonparticipants." (p. 115). These writers go on to indicate that it cannot be determined from the research whether these differences exist because of self selection (i.e., individuals with superior RT ability experience more success so continue in the sport) or that sport participation helps foster the underlying hardware ability. To completely rule out hardware differences between experts and novices may be too harsh a conclusion. At best, the results from the research can be considered equivocal (Williams, Davids, Bunvitz, & Williams, 1992). It is apparent, however, that because the values from the correlations between hardware elements and superior performance are generally low, only a small proportion of the expednovice differences can be explained by a hardware hypothesis. Software Hypotheses A more productive approach to explaining expednovice differences in decision-making has been to investigate the software elements of performance. To continue our computer analogy,

the software hypothesis proposes that experts have more highly developed programs for operation that have access to a more extensively developed data base. In other words, the expert athlete has a more powerful software package to operate their computer, as opposed to a more powerful computer. A number of studies conducted within a variety of sport contexts, including baseball (Chiesi, Spilich, & Voss, 1979; Spilich, Vesonder, Chiesi, & Voss, 1979), basketball (French

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& Thomas, 1987), chess (Chi, 1978), and tennis (McPherson & Thomas, 1989), have clearly demonstrated the expert's advantage over the novice in terms of developed knowledge base and efficient use of that knowledge base to mediate both cognitive and motor responses. The study by McPherson and Thomas (1989) represents the most recent and comprehensive investigation of this nature and will be considered in more detail here.

McPherson and Thomas (1989) compared novice and expert tennis players on the relationship between their knowledge structure, decision-making, and skill performance. In the first phase of this study, subjects were measured on their tennis skill by use of a groundstroke (backhand and forehand) and serve test, and on their tennis knowledge by use of a pencil-andpaper test (measuring such items as knowledge of rules, player positions, stroke production, and scoring). Also, actual game performance was measured using video-tape analysis for components of tennis play (control, decision, and execution). In the second phase of the study, the relationship between the structure of a subject's knowledge base and decision-making ability was assessed by using verbal reports collected both during situation interviews (under controlled conditions) and during point interviews (collected during actual game play). The results of this study indicated that not only did the experts have a more extensive context-specific knowledge base, but that the structure of the expert's knowledge base allowed them to use this information more effectively during game play (p. 209). The superior decisionmaking capabilities of the experts appears to be due not only to a more extensive declarative knowledge structure (factual knowledge, consisting of "$-then" statements) but to a well developed procedural knowledge base (action plans, the "do" statements). The disadvantage for the novice, then, is the lack of a context-specific declarative and procedural knowledge base which leads to a more generalized approach to problem solving, resulting in slower access to information needed for arriving at accurate decisions. These conclusions can accommodate most of the results from the studies previously identified in this chapter. As a case in point, in general experts tend to have a speed, rather than accuracy, advantage in decision-making (e.g.. as evidenced by performance on signal detection tasks). The data from Allard and Starkes' (1980) study indicated that the speed advantage for experts on signal detection tasks is context-specific. When not in the particular domain of expertise, this advantage does not hold. Since novices take a more generalized (and slower) approach to problem solving, the nature of the context should not have as significant an impact on them as it does the experts. One other point that can be drawn from the McPherson and Thomas (1989) study is that the difference between novices and experts in the lack of procedural knowledge results in fewer complete couplings of information (if-then-do). If this is true, then significant differences between experts and novices should be found when subjects are compared on tasks which require the coupling of perception and action. As noted previously, rarely has the research investigating experthovice differences made use of such tasks.

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Methodological Issues from Decision-Making Research Although emerging themes can be identified from the research effort that has been directed towards decision-making in sport, a number of confounding methodological issues prevent the formulation of precise, theoretical statements. The intent of this section is to present several of these issues that need to be addressed before advancement of this area of investigation is to continue. Definition of Expertise One such problem comes from the lack of a clear definition of expertise. The vast majority of studies reviewed have made use of an experthovice paradigm. Normally, athletes competing at a higher level are considered to be the experts, while those who compete at a lower level are considered to be representative of novice performers. For example, individuals competing internationally, such as members of a national team, are considered to be more expert than individuals who compete at a university level, who are considered to be more expert than individuals who compete at a club or recreational level. This paradigm may be effective if the d e s k is to determine if athletes competing at a more advanced level make better decisions on average than athletes competing at a less advanced level. It would also be a viable approach if the concern is with motor performance capabilities. However, if the intent is to investigate differences on the perceptual attributes of decision-making that contribute to expert levels of performance, then a different approach may be needed. The case could be made that an athlete competing at a lower level might be an expert decision maker, but have a low level of motor capabilities. Conversely, an athlete competing at a higher level might be a novice at decisionmaking, but have a high level of motor performance. It would seem appropriate to classify experts and novices based on their decision-making capabilities, rather than on their current performance level which entails an interaction of perceptual and motor processes. To our knowledge, the only studies produced which attempted to provide a measun of decision-making capabilities in a sport setting were French and Thomas (1987) and McPherson and Thomas (1989). Although not expressly used in this way, it would appear to be useful to employ their decision-making measures as a pre-test screening instrument for the classification of subjects as expert or novice decision-makers. Once classification is accomplished, it would appear to be a more sound approach to attempt to determine what distinguishes an expert from a novice decision-maker, knowing that our two groups of subjects actually consist of expert and novice decision-makers, not just expert and novice sport performers. Perception-Action Link A second issue touched on earlier is the disassociation of the perception-action link in the majority of the experiments conducted. Especially in light of a direct perception view which proposes a close linkage of the perceptual/motor components of skilled performance, it would seem to be important that some type of linked motor response is required on the part of the subjects. The separation of perception and action would seem to create an artificial situation that may not accurately reflect a true measure of expertise in the sport context. (For a complete discussion of the direct versus indirect views of perception and the impact on decision-making in sport, see Williams et al., 1992.)

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Stimulus Display A third issue relates to the type of stimulus displays used in this research. Sport is, for the most part, characterized by a dynamic, changing environment. Much of the information used for decision-making is a result of the interplay of elements in the environment that cannot be conveyed by static depictions. Even in signal detection tasks, the movement of the players provide vital clues as to the location of the object. If anything, the use of static displays would serve to reduce the perceptual advantage of the expert performer. Similarly, most studies have presented dynamic displays using video technology. As will become cogent in the next section, issue can be taken with the capability of video-tape to provide a realistic enough depiction of the actual sport environment. It would seem prudent for research in this area to investigate decision-making in more realistic settings, perhaps developing analytical techniques that can be used during actual game competition. Another issue relating to the stimulus display is the removal of perceptual anticipation in the decision-making processes. Because the subject is naive to the identity of the individual or team portrayed in the stimulus display, decision-making is strictly based on receptor anticipation; that is, on information contained in the present sensory array. However, decisionmaking is a much more complex process that is the interaction of the present sensory array and previously acquired knowledge regarding event probability. For example, knowing that my opponent "always goes to the right" when dribbling the ball in basketball would significantly reduce the information load on my perceptual mechanism and allow for more rapid and, hopefully, accurate decision-making regarding event occurrence. Considerable evidence from the motor behaviour literature investigating anticipation timing has demonstrated the effect of prior knowledge regarding stimulus probability on the speed and accuracy of anticipatory responses (e.g., Adams 8c Xhingesse, 1960; Larish & Stelmach, 1982; Rosenbaum, 1983). It may be that the inability to demonstrate more accurate decisions in expert athletes might be connected to the removal of prior knowledge regarding probable performance characteristics of the stimulus display. Quantitative versus Qualitative Data Finally, issue can be taken with using objective data only when visual tracking is measured. Although identifying visual fixations and scan paths offers interesting, and much needed, information, it would seem that this information should be interpreted in light of more subjective, qualitative data. In other words, it would be useful to know not only where a subject is looking, but what they are actually seeing. As mentioned previously, comparisons of expert and novice subjects using visual tracking techniques have resulted in more similarities than differences. Because experts exhibit superior levels of decision-making, the information acquired from a particular fixation must be more appropriate for the decision being made. If we combine qualitative and quantitative information, we might learn not only where to fixate but also what information should be extracted from that stimulus display. For example, batters in baseball understand the need to "keep your eye on the ball". However, do they also understand that one source of critical information that is achieved by keeping their eye on the ball is the rotation of

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the ball so that flight path characteristics can be predicted? The current state of the research seems to have taken us to the "keep your eye on the ball" stage but has not gotten to the "ball rotation" information stage.

Training Decision-Making in Sport Despite the methodological limitations,it would seem safe to conclude from the literature cited in this chapter that experts exhibit a higher order decision-makingprocess when compared to novices, most liely due to a superior, context-specific knowledge base that consists of not only more information but more interconnections among the information. Although this finding is most interesting and offers some theoretical potential, from the practitioner's point of view the information is not particularly useful unless tied together with some notion of how to develop expert levels of decision-making.The research to date has provided a moderately rich descriptive base for the decision-making process. It would seem that some investigative effort should now be directed in a more applied manner so that information regarding the development of expert levels of decision-makingcan be made. In general, little effort has been expended in the development of perceptual capabilities in sport settings. As the quotes by Jones and Miles (1978), and Bard and Fleury (1981) that were cited at the beginning of this chapter indicate, the focus of most coaches' attention is on skill development from a motoric perspective. Despite the importance of a well-developed decisionmaking process for the overall success of performance in a sport setting, the development of this capability is usually left to "experience" and the occasional provision of hints, usually offered during the course of a game or scrimmage. Recently, the use of video-tape to enhance the decision-makingprocess has become more in vogue, with "film-sessions"being mandatory postgame analysis of game performance. It would seem interesting to investigate if a more systematic and efficient technique could be developed for enhancing decision-making in sport settings and if this might be achieved through the use of video technology. In other words, could an interactive video device be developed that places an athlete in a setting outside of the sport environment, but would promote skilled perception in the actual sport setting? If the amount of research directed towards understanding the decision-making process in sport can be described as modest, then the amount of research investigating the training of decision-making in sport can only be described as minuscule. Thiffault (1974, 1980) made a somewhat preliminary attempt at studying this question. Ice hockey players were rcquirtd to make tactical decisions (shoot, pass, or skate) following brief exposures to slides depicting game situations. As measured by vocal reaction times, subjects could be trained on this task to exhibit faster decision speeds. Also, performance on this task correlated significantly (r=.75) with speed of decision-makingin an artificially structured,on-ice perceptual task. However, due to a number of methodological limitations,including the lack of a connection to actual game decision-making, as Starkes and Deakin (1984) point out ".... the logical inference cannot be made.". That is, from Thiffault's studies we can conclude that visual perception, as measured by vocal RTs to a Tscope task, can be improved by training on that task. However, there is no evidence to indicate if this type of training will improve visual perception and decision-making in an actual

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game situation. Speculating on Training Decision-Making The remainder of this chapter will be directed towards speculating on the ability to train decision-making in sport. As is apparent from the previous paragraph, due to the lack of any directly applicable research evidence, consideration of this question must be speculative. We will consider whether perceptual training in an artificial environment can positively influence the perceptual process in the actual game setting. In particular, we will focus on the use of video technology as a technique for the training of decision-making.Speculation on the applicability of a video system for training decision-making can be based on information gleaned from two related areas of research- transfer of learning and simulators. Transfer of Lcaming In sport situations, it stands to reason that the effectiveness of a practice technique must be evaluated in light of the impact this training has on game performance. Therefore. any technique developed for perceptual training must be evaluated based on the gain in transfer of knowledge to decision-making during an actual contest. Achieving transfer of learning is the necessary goal for the practitioner. Although it seems rather straightforward to develop training techniques based on transfer of learning principles, the issue is complicated due to the theoretical basis of transfer not being well understood. It is not the intent of this chapter to provide a review of the considerable body of literature that has been directed towards gaining an understanding of the theoretical basis of transfer. Several excellent reviews already exist and the reader is directed towards those articles. (In particular, for transfer of motor skill learning. see Lee, 1988 or Schmidt & Young, 1987). Rather, a brief synopsis of transfer theory will be presented followed by a speculative discussion of the applicability of this information to perceptual training in sport. In general, transfer of learning can be considered from two theoretical perspectives (Magill, 1992). The earlier view evolved from Thomdike’s (1914) identical elements theory in which transfer was proposed to be a function of the degree of similarity between the elements involved in the performance of each task. Elements were later defined (Holding, 1976; Osgood, 1949) as the components of the tasks and the context in which the tasks were being performed. This approach to transfer fit well with the specificity notion of motor skill performance and, in fact, was a major contributor to the development of that notion (see Chamberlin & Lee, 1992). More recently, a second theoretical approach to transfer has emerged from the work of Bransford and his colleagues (Bransford, Franks, Moms, & Stein, 1979; Morris, Bransford, & Franks,1977) and has been termed transfer-appropriate-processing(see Lee, 1988). In this view, positive transfer results from increasing the degree of similarity of the cognitive processing requirements between the two tasks. The nature of the tasks is relatively unimportant; what is

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important is the type of cognitive processes invoked during task performance.' Since perception is a cognitive task, it would seem logical to assume that the transferappropriate-processing view would best provide a theoretical basis for developing pexeptud training devices for sport. The result would be that a perceptual training device would need to invoke the same cognitive processing scheme as would occur in an actual game situation. It would not be necessary to have the device match the response and context components of actual game performance. Therefore, an interactive video, in which segments of action that require a decision and a response are presented to an observer, appears to have some promise for enhancing the decision-making process in sport settings. It would be necessary, however, t o conduct a perceptual task analysis for a particular sport to determine what cognitive processes (such as timesharing, selective attention, and rapid visual search) underlie expert levels of performance. Simulators Since the training of decision-making in athletes outside of the actual game setting is being considered here, it would seem appropriate to examine the research that has investigated the ubiquity of simulators and simulation in skill development. Many examples of simulators and simulation in sport can be found; however, development of these devices has not k e n based on sound theoretical principles derived from empirical data. The only area for which a substantial body of data exists for the application of simulators to skill development has been from industrial settings, particularly dealing with the training of pilots. (For a more complete review of this work, see Chamberlin & Lee, 1992). In general, simulators have been found effective in promoting the development of knowledge regarding procedures to follow in response to specific stimulus events (Whiteside. 1983). Since the decision-making process can be considered procedural in nature, it would seem to follow that the simulation of a sport setting through the use of video technology might be effective for developing expert levels of decision-making. The question that remains would be what degree of realism (generally termed fidelity) would the simulator need for promoting knowledge development. The basic finding from the research has been that increasing physical fidelity (the degree to which the simulator looks like the actual device) results in greater transfer of procedural knowledge (Alessi, 1988). This seems to indicate that it would be necessary to provide a fairly realistic setting in which to conduct the perceptual training, a finding that appears to contradict the conclusion drawn from transfer-appropriate-processing theory. However, several factors intervene with this basic relationship.

First, fidelity is not a unitary construct as is often assumed. Lintern, Sheppard, Parker,

'It may be that both views invoke the same mechanism for transfer. That is. ensuring the similarity of task and context components as required in the identical elements approach would concomitantly result in similarity of cognitive processing requirements. The assumption is that transfer-appropriate-processing is the more veridical view.

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Yates. and Nolan (1989) contrast physical fidelity with functional equivalence (the degree to which the simulator feels like the actual device) and psychological fidelity (the degree to which the performer perceives the simulator to be realistic). Most of the research has been concerned with physical fidelity; little is known of the impact functional equivalence and psychological fidelity have on learning. Second, stage of learning is a concem when using simulators. Several researchers have identified that high levels of fidelity are more effective for positive transfer of learning in advanced, as compared to novice, performers (Alessi, 1988; Andttws, 1988; Lintem et al., 1989). In fact. high levels of fidelity may be detrimental to novices (Andrews, 1988). From this, we can conclude that the use of interactive video for perceptual learning might be more effective for novice performers, since this device is low in fidelity.

Third, the type of information provided the performer may be of a concern here. As . visual beings, presentation of information using video-tape should meet this humans a ~ highly need. However, decisions are often the results of the interactive processing of information from a variety of sensoIy modalities, such as auditory and tactile receptors. It would be necessary to determine if perceptual training in a single, visual modality would be sufficient for enhancing decision-making in sport settings, or if simulating the interaction of all modalities is required before this training becomes effective. Increasing the number of modalities represented in the simulator would, by nature, cause an increase in the fidelity of the device. It would also be necessary to determine to what degree prior knowledge regarding probable event occurrence impacts the decision-making process.

A Concluding Statement As can be deduced from the preceding discussion, theoretical developmentsfrom transfer of learning research, and investigations of simulator use in pilot training, give some indication that perceptual training in sport may be possible using interactive video technology. However, this research has also indicated a number of questions that need to be approached empirically before any conclusive statements can be made regarding the efficacy of interactive video for perceptual training in sport settings. The potential theoretical and practical benefits of such a research program would appear to argue strongly for the implementation of this endeavour. References Abemethy, B. (1989). Expert-novice differences in perception: How expert does the expert have to be? Canadian Journal of Sport Science, 14,27-30. Abemethy, B. (1990). Expertise, visual search, and information pick-up in squash. Perception, 19, 63-77. Abemethy, B., & Russell, D. B. (1984). Advance cue utilization by skilled cricket batsmen. The Australian Journal of Science and Medicine in Sport, 16, 2-10. Abemethy, B., & Russell, D. B. (1987a). Expert-novice differences in an applied selective attention task. Journal of Sport Psychology, 9, 326-345. Abemethy, B., & Russell, D. B. (1987b). The relationship between expertise and visual search

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strategy in a racquet sport. Hutnun Movement Science, 6, 283-319. Adam, J. A., & Xhingesse, L. V. (1960). Some determinants of two-dimensional visual tracking behavior. Journal of Experimental Psychology, 60, 391-403. Alessi, S. M. (1988). Fidelity in the design of instructional simulators. JournuI of ComputerBused Instruction, 15, 40-47. Allard, F., & Bumett, N. (1985). Skill in sport. Canudian Journal of Psychology, 39,294-312. Allard, F., Graham, S., & Paarsalu, M. E. (1980). Perception in sport: Basketball. Journul of Sport Psychology, 2. 14-21. Allard, F., & Starkes, J. L. (1980). Perception in sport: Volleyball. Journal ofSpon fsychology, 2, 22-33. Andrews, D. H. (1988). Relationship among simulators, training devices. and learning: A behavioral view. Educationul Technology, 28, 48-54. Bahill. A. T., & LaRitz, T. (1984). Why can’t batters keep their eyes on the ball? American Scientist, 72, 249-252. Bard, C., & Fleury, M. (1976). Analysis of visual search activity during sport problem situations. Journul of Hutnun Movement Studies, 3, 214-222. Bard, C., & Fleury, M. (1981). Considering eye movement as a predictor of attainment. In I. M. Cockerill & W. W. MacGillivary (Eds.), Vision and sport (pp. 28-41). Cheltenham, England: Stanley Thomes. Bard, C.. Fleury, M., & Carritre, L. (1975). La strategie perceptive et la performance motrice. Actes du 7eme Symposium canadien en apprentissage psychomoteur et psychologies du sport. Mouvement, 10, 163-183. Borgeaud, P., & Abemethy, B. (1987). Skilled perception in volleyball defense. Journul of Sport Psychology, 9, 400-406. Bransford, J. D., Franks, J. J., Moms, C. D., & Stein, B. S. (1979). Some general constraints on learning and memory research. In L. S. Cermak & F. I. M. CT& (Eds.), Levels of processing in human memory (pp. 331-354). Hillsdale, NJ: Erlbaum. Chamberlin, C. J., Coelho, A., & Reuter, J. (1992). [Decision making in volleyball]. Unpublished raw data. Chamberlin, C. J., & Lee, T. D. (1992). Arranging practice conditions and designing instruction. In R. N. Singer, M. Murphey, & L. K. Tennant (Eds.), Handbook of research on sport psychology (pp. 213-241). New York: Macmillan. Chamess, N. (1979). Components of skill in bridge. Canudian Journul offsychology,33,l-16. Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4 , 55-81. Chi, M. T. H. (1978). Knowledge structures and memory development. In R. Siegler (Ed.), Children’s thinking: What Develops? (pp. 73-105). Hillsdale, NJ: Erlbaum. Chiesi. H. L., Spilich, G. J., & Voss, J. F. (1979). Acquisition of domain related information in relation to high and low domain knowledge. Journal of Verbal Learning and Verbal Behavior. 18, 251-273. Coelho, A., & Chamberlin, C. J. (1991, June). Decision-making in volleyball as a function of expertise. Paper presented at the meeting of the North American Society for the Psychology of Sport and Physical Activity, Asilomar, CA.

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Drowatzky, J. N. (1981). Motor Learning: Principles and practices (2nd ed.). Minneapolis, MN:Burgess. Franks, I. M., Elliott, M., & Johnson, R. (1985, October). The effects of experience on the detection and location of performance differences in a gymnastic technique. Paper presented at the meeting of the Canadian Society for Psychomotor Learning and Sport Psychology, Montreal, PQ. French, K. E., & Thomas, J. R. (1987). The relation of knowledge development to children’s basketball performance. Journal of Sport Psychology, 9, 15-32. Garland, D. J., & Bany, J. R. (1991). Cognitive advantage i n sport: The nature of perceptual structures. American Journal of Psychology, 104, 21 1-228. Goulet, C.. Bard, C., & Fleury. M. (1989). Expertise differences in preparing to return a tennis serve: A visual information processing approach. Journal of Sport and Exercise PSyCholOgy, 11, 382-398. Holding, D. H. (1976). An approximate transfer surface. Journal of Motor Behavior, 8, 1-9. Hubbard, A. W., & Seng, C. N. (1954). Visual movements of batters. Research Quarterly, 25, 42-57. Isaacs, L. D., & Finch, A. E. (1983). Anticipatory timing of beginning and intermediate tennis players. Perceptual and Motor Skills, 57, 45 1-454. Jones, C. M., & Miles, T. R. (1978). Use of advance cues in predicting the flight of a lawn tennis ball. Journal of Human Movement Studies, 4 , 231-235. Larish, D. D., & Stelmach, G. E. (1982). Preprogramming, programming, and reprogramming of aimed hand movements as a function of age. Journal of Motor Behavior, 14.322-340. Lee, T. D. (1988) Testing for motor learning: A focus on transfer-appropriate-processing. In 0. G.Meijer & K. Roth (Eds.), Complex motor behaviour: ‘The’ motor-action confrovery (pp. 210-215). Lintem, G., Sheppard, D. J., Parker, D. L., Yates, K. E., & Nolan, M. D. (1989). Simulator design and instructional features for air-to-ground attack: A transfer study. Human Factors, 31, 87-99. Logan, G. D. (1985). Skill and automaticity: Relations, implications, and future directions. Canadian Journal of Psychology, 39, 367-386. Magill, R. A. (1992). Motor learning: Concepts and applications (4th ed.). Madison, WI: Brown and Benchmark. McPherson, S. L., & Thomas, J. R. (1989). Relation of knowledge and performance in boy’s tennis: Age and expertise. Journal of Experimental Child Psychology, 48, 190-211. Millslagle, D. G. (1988). Visual perception, recognition, recall and mode of visual search control in basketball involving novice and experienced basketball players. Journal of Sport Behavior, 11, 32-44. Moms, C. D., Bransford, J. D., & Franks, J. J. (1977). Levels of processing versus transfer appropriate processing. Journal of Verbal Learning and Verbal Behavior, 16, 5 19-533. Osgood. C. E. (1949). The similarity paradox in human learning: A resolution. Psychological Review, 56, 132-143. Prinz,W. (1977). Memory control of visual search. In S. Dornic (Ed.), Attention and

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performance VI (pp. 441-462). Hillsdale, NJ: Erlbaum. Reitman, J. (1976). Skilled perception in GO: Deducing memory structuns from inter-response times. Cognitive Psychology, 8, 336-356. Ripoll, H., & Fleurance. P. (1988). What does keeping one’s eye on the ball mean? Ergonomics, 31, 1647-1654. Rosenbaum, D. A. (1983). The movement precuing technique: Assumptions, applications, and extensions. In R. A. Magill (Ed.), Memory and control of action (pp. 251-274). Amsterdam: North-Holland. Sage, G. H. (1971). Introduction to motor behavior: A neuropsychological approach. Reading, MA: Addison-Wesley. Salmela, J. H., & Fiorito, P. (1979). Visual cues in ice hockey goaltending. C a d i a n Jourml of Applied Sport Science, 4 , 56-59. Schmidt, R. A., & Young, D. E. (1987). Transfer of movement control in motor skill learning. In S. M. C o d e r & J. D. Hagman (Eds.), Transfer of learning @p. 47-79). Orlando, FL: Academic Press. Spilich, G. J., Vesonder, G. T., Chiesi, H. L., & Voss, J. F. (1979). Text processing of individuals with high and low domain knowledge. Journul of Verbal Learning and Verbal Behavior, 18, 275-290. Starkes, J. L. (1987). Skill in field hockey: The nature of the cognitive advantage. Jourml of Sport Psychology, 9, 146-160. Starkes, J. L., & Deakin, J. (1984). Perception in sport: A cognitive approach to skilled performance. In W. F. Straub & J. M. Williams (Eds.), Cognitive sport psychology (pp. 115-128). Lansing, NY: Sport Science Assoc. Tenenbaum, B., & Bar-Eli, M. (1993). Decision making in sport: A cognitive perspective. In R. N. Singer, M. Murphey, & L. K. Tennant (Eds.), Handbook of Research on Sport Psychology (pp. 171-192). New York: Macmillan. Thiffault, C. (1974). Tachistoscopic training and its effect upon visual perceptual speed of ice hockey players. Proceedings of the Canadian Association of Sport Sciences, Edmonton, Alberta. Thiffault, C. (1980). Construction et validation d’une measure de la rapidit6 de la pansee tactique des joueurs de hockey sur glace. In C. H. Nadeau, W. R. Halliwell, K. M. Newell, & G. C. Roberts (Eds.), Psychology of motor behavior and sport (pp. 643-649). Champaign, IL:Human Kinetics. Thorndike, E. L. (1914). Educational psychology: Briefer course. New York: Columbia University Press. Whiteside, T. C. D. (1983). Simulators and realism. Quarterly Journul of Experimental Psychology, 35A, 3-15. Whiting, H. T . A., Gill, E. B., & Stephenson, J. M. (1970). Critical time intervals for taking inflight information in a ball-catching task. Ergonomics, 13. 265-272. Williams, A. M., Davids, K., Bunvitz, L., & Williams, J. G. (1992). Perception and action in sport. Journal of Human Movement Studies, 22, 147-204.

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CHAPTER 9 KNOWLEDGE REPRESENTATION AND DECISION-MAKING IN SPORT

SUE L.McPHERSON Department of Health, Physical Education and Recreation Western Carolina University, Cullowhee, NC 28723

Motor and cognitive skills are closely linked as both skills are necessary when modeling skilled performance in sport (for reviews see Allard and Burnett, 1985; Starkes & Deakin, 1984; Thomas, French, and Humphries, 1986). Researchers who employ sport performance paradigms during game play to examine players’ response selections (cognitive skills) and response executions (motor skills) indicate that both variables conmbute to the development of expertise (e.g., French & Thomas, 1987; McPherson & Thomas, 1989). As these researchers and other motor behaviorists understand more complex tasks in ecologically valid situations, they evince the need to examine areas of complex information processing such as problem solving in sport settings. Thomas et. a1 (1986) indicate that understanding the specific sport knowledge base is essential to the study of skilled sport behavior. They define sport performance as “a complex product of cognitive knowledge about the current situation and past events combined with a player’s ability to produce the sport skill(s) required’ @. 259). Most motor behaviorists would agree with this definition; however knowledge and understanding is limited particularly about the development of a player’s cognitive knowledge within the current situation and how this knowledge influences decision-making ability concerning response selections in sport.

In this chapter the author maintains that the representation of sport knowledge influences decision making-ability during sport performance. The purpose of this chapter will be to introduce a model of protocol structure which captures and examines players’ representation of sport knowledge when making decisions in the context of a sport situation. In this paper the author deliberately addresses only cognitive skill aspects of sport performance. Initial research will be presented to expand knowledge representationand decision-making theory in the domain of sport. The sport domains examined in this chapter, according to Schmidt’s Motor-Cognitive Skill Dimension, would reflect sport situations that require strong cognitive and decision-making components as well as response production components (see Schmidt, 1991, for a discussion).

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The intent is not to advocate that cognitive skill is an isolated aspect of sport performance but to examine an area that is limited in motor behavior theory building. The chapter proceeds in three sections: the first section offers a discussion of the role of knowledge representation within an information processing model. This section will review research on the properties of knowledge representation (content, structure, and cognitive strategies) and how this representation influences information processing in diverse domains. Also examined are methodologies employed by researchers to elicit verbal responses and the coding techniques used to capture and assess propositional-type knowledge representations. The second section presents a discussion of the models and limitations of two decisionmaking paradigms developed to model competent sport performance. Within these decisionmaking models, both perceptual and sport paradigms will be reviewed and limitations discussed. Perceptual paradigms examine the development of certain aspects of anticipation and perceptual strategies employed with expertise in sport. Valuable information has been gained from perceptual paradigms; however little understandinghas been gained of the knowledge underlying players’ decisions. In response to this lack of understanding, a sport paradigm emerged to examine the relationship of players’ knowledge representation and decision-making in sport (McPherson, 1987). This paradigm utilizes a protocol structure model to examine sport performers’ knowledge and use of this knowledge during sport performance. Section two concludes with a discussion of the evolution of this sport paradigm and presentation of the basic rules and coding scheme of the model. The third section presents some recent sport studies that employ the protocol structure model to examine knowledge representation and decision-making in the domains of tennis, volleyball, and baseball. These studies examine variations with expertise and age. Knowledge Representation and Decision-Making Few researchers have focused on understanding the nature of the representation of knowledge and its influence on decision-making in a sport situation. Theoretical frameworks concerning the influence of knowledge representation on the acquisition of decisionmaking skill in sport are limited. In the past two decades cognitive theorists have directed research to areas of complex information processing (e.g., problem solving and reasoning) in hopes of understanding the function and structure of higher mental processes. As a result, several theories have emerged related to competent performance (e.g, Chi, Glaser & Fan, 1988; Klahr & Kotovsky, 1989), learning (e.g., Anderson, 1987; Chi & Ceci, 1987; Chi, Hutchinson, & Robin 1989). and instruction (e.g., Glaser & Bassok, 1989; Voss, 1989). Researchers in a variety of content areas note that the acquisition of domain knowledge proceeds from a less sophisticated declarative form (e.g., knowledge of principles, specialized vocabulary, patterns, rules) to a more sophisticated procedural form or rule system consisting of condition-action sequences (if-then rules) that relate to the goals and subgoals of the problem solution (e.g., computer programming; Adelson & Soloway, 1988; Anderson, 1987; judicial decisions, Lawrence, 1988; social science decisions, Voss, Greene, Post, & Penner, 1983; problem solving

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in physics, Chi, Feltovich, & Glaser, 1981). The processes by which knowledge undergoes these transformations have been modeled by theorists interested in artificial intelligence (e.g., proceduralization,Anderson, 1987) or human informationprocessing (e.g., accretion, Rumelhardt & Noman, 1988) (for a review of other models see Gilhooly & Green, 1989). Propositional-typeanalysis of think-aloud protocols for both children and adults during problem solving and/or task performance have been used extensively to examine the representation of declarative and procedural knowledge and how this knowledge guides the solution process (for reviews see, Chi, Glaser, & Rees, 1982; Glaser & Bassok, 1989; Rumelhardt & Norman,1988; Siegler & Crowley, 1991; Voss, 1989). The use of verbal reports in cognitive science has not been without controversy, however. Most cognitive scientists agree that if used properly (Charness, 1989; Chi & Bassok, 1989; Ericsson & Simon. 1980; Nisbett & Wilson, 1977; White, 1988) they provide valuable insight into higher mental processes such as problem solving and reasoning. Others disagree (Evans, 1990). Gobbo and Chi (1986) conducted a propositional-type analysis of categorical knowledge to examine how children in certain domains with low and high levels of knowledge represented categories. Protocols were collected while children with high or low dinosaur knowledge sorted pictures of dinosaurs. Categorical knowledge concerningdinosaurs was represented declaratively in terms of the type of knowledge (termed content or nodes) and degree of organization (termed structure or links). High knowledge level children generated sortings based on protocols that contained explicit or pictorial features. In addition they elicited additional concepts derived from implicit knowledge associated with explicit features. Low knowledge level children sorted according to pictorial features of dinosaurs only. High knowledge children’s representation of dinosaur knowledge exhibited higher level categories and greater association of concepts (concepts mentioned contiguously) which in tun influenced their sophisticated sorting or categorizing of dinosaurs.

In other studies, the goal of the researcher has been to examine differences in conditionaction rules generated to solve problems during task performanSe or following presentation of a situation scenario. For example, Charness (1989) focused on adult expert bridge players and their generation of planning strategies used to play out a contract in bridge. Players were asked to think out loud while planning the best line of play. Propositions were coded as procedures (rules for planning) within a declarative representation. The condition sides represented the encoded concepts (card combinations)and the action sides represented the intended sequence of plays (e.g., a squeeze play). Chamess (1989) attributed planning differences to the level of sophistication of domain productions which controlled the recognition of sets or patterns of cards. Bridge players with the highest level of expertise recognized which conditions (and patterns of conditions) were most critical in the game situations. These patterns or conditions also evoked sophisticated plans of actions to achieve the goal.

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Interestingly, experts when presented with a problem had access to more sophisticated representations of procedural knowledge and more effectively monitored the input of information and remieval of information in order to plan and play out a contract in bridge. Players with less expertise generated primitive procedures based on limited conditions. Master players generated sophisticated productions based on chunked conditions (i.e., specific card patterns). In some cases, researchers may choose to examine the same set of protocols in both declarative representations of nodt-link structures and declarative representations of procedural structures (e.g., condition-action rules, if-then rules). Chi, Glaser, and Rees (1982, Study 6) provide an example by examining the different elaborations experts and novices produced when they were presented with physics concepts such as an inclined plane or angular motion. All subjects were asked to tell (in 3 minutes) everything they could think of about a concept (a total of 20 concepts were presented to each subject) and how a problem involving the concept might be solved. Protocols were represented declaratively as nodes which were key terms mentioned by subjects. These were obvious physics concepts and links or statements (mentioned contiguously) that linked the concepts. When presented the concept of an inclined plane, novices indicated they knew what key variables needed to be specified and what entities (i.e., friction) such a problem would entail. They also briefly mentioned one underlying physics principle (Conservation of Energy) without any, details on the conditions of applicability. Experts, however, generated more complex knowledge since they first delineated the incline plane concept into two principles or major physics laws each with conditions of applicability. Experts followed with descriptions of surface features or potential configurations, although the surface features generated by both p u p s were similar. Chi, Glaser, and Rees (1982, study 6) transformed the protocols into condition-action rules which revealed expert-novice differences in knowledge representation more distinctly. Condition-action structures revealed that novices' rules contained no actions as explicit solution procedures. For novices; actions were simply attempts to find specific unknowns such as "if block is resting on plane then find mass of block'. Conservation of energy was represented as a condition side of a procedure for novices and as an action side of a procedure for experts. Experts' Force Law productions included both explicit procedures and explicit conditions for when a specific procedure applies. Theoretically,declarativerepresentationsmay develop differently with expertise according to the domain under investigation (Glaser & Bassok, 1989). In some domains different attributes may become important within the same hierarchical goal structure. In others the hierarchical goal structure may change (for a review see Chi & Ceci, 1987). Productions may change similar to categorical knowledge, however, one or all of a variety of concepts in a domain may change (e.g., several conditions may become chunked or represent a pattern, old productions may become refined, or old productions may collapse into a new macro production) (for reviews see, Chi & Bassok, 1989; Gillhooly & Green, 1989; Glaser & Bassok, 1989). Some researchers have examined protocols for evidence of domain-related strategies.

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Protocol analysis revealed expert-novice differences in: (a) estimating the difficulty of a task, (b) stating what procedures did and did not work, and (c) self-regulating the time estimated to solve the problem (e.g.. Chi, Bassok, Lewis, & Glaser, 1989; Glaser & Bassock, 1989). Chi and Glaser (1988) reviewed strategies that emerge in a variety of domain studies and attributed the monitoring skills of experts to their representation of the problem. For example, accurately predicting problem solving difficulty depends on what features of the problem are considered important. Cognitive theorists have found novices to be arduous processors who must retrieve a less sophisticated (i.e., less associated and organized) network of declarative knowledge to arrive at an action in order to solve the problem This in turn places a high demand on working memory. Experts are presented as having access to a more sophisticated network of declarative and procedural knowledge that places less demand on working memory and in some cases allows automatic (effortless) problem solving. Knowledge representation, then, may influence decisionmaking in the following ways: (a) how the problem is solved (e.g., order of procedures employed in the solution process, defining the constraints of the task); (b) what concepts are monitored (e.g., literal or abstract features); (c) what concepts are interpreted or encoded (e.g., the degree of monitoring of a concept or chunks of concepts); and (d) how concepts are retrieved (e.g., a matching may occur between existing conditions and already stored situation prototypes). Knowledge Representation and Decision-Making in Sport As discussed by Schmidt (1991), in some sport situations a player’s response execution may rely heavily on the ability to engage in a variety of problem solving activities linked to response selection. Success may be determined by several factors. For example, prior to serving to an opponent in tennis, one may consider the opponent’s strengths in returning a deep serve, the score, and effectiveness of the serve. Based on these conditions, one decides the most appropriate response and attempts to execute it. The receiver, prior to returning the serve, also engages in a variety of problem solving activities such as; looking for a pattern in past serves, checking the scores, and recalling the opponent’s physical characteristics in connection with certain serves. The server has a self-imposed time constraint when selecting a response while the receiver must rapidly decide about the nature of the opponent’s serve and appropriate response selection. As a result, a player’s representation of the situation (e.g., problem representation and cognitive strategies) may affect the quality and speed of response. To determine how these and other factors contribute to players’ success, paradigms have evolved to examine players’ sport decisions and knowledge representation underlying these decisions.

Perceptual Paradigms Several researchers have developed paradigms to examine expertise and visual search strategies. Here the interest is in players’ perceptual skills used during simulated sport situations. This research examines players at various competitive levels when exposed to modified films of an opponent performing a sport skill. In some studies amount of information available to subjects varies; for example restricting time to view the opponent o r occluding

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opponent’s body parts. The research goal is to examine players’ perceptual differences as they make decisions about an opponents’ response selections (e.g., type of serve in tennis, landing location of the shuttle in badminton). Findings indicate that experts make faster and/or more accurate decisions when predicting an opponents’ response. Eye movement fixations and scan patterns (for a review see Abernethy, 1988) of experts suggest that they have access to sophisticated visual search strategies. Experts’ are better then because search patterns can more efficiently analyze the opponent (e.g., Abernethy & Russell, 1987; Goulet. Bard, & Fleury. 1989; Shank & Haywood, 1987). Recall tasks are based on perceptual models and examine the development of conceptual knowledge of game situations. These studies indicate experts have knowledge of highly structured patterns of game situations stored in their knowledge base. Thus they exhibit greater recall of player positions only in game suuctured situations (e.g., Borgeaud & Abernethy, 1987; Starkes, 1987). Situation prediction-tasks, such as predicting which spiker would receive a pass, indicate experts are more accurate in their predictions (e.g., Starkes, 1987; Wright, Pleasants, & GomezMeza, 1990). Again, eye movements suggest experts employ sophisticated visual search strategies to make these predictions (e.g., Ripoll, 1988). Some perceptual studies have examined expert-novice differences in response selection. Bard and Fleury (1976, 1981) examined decision time and eye-movement fixation patterns (frequency and location of fixations) of basketball players when asked to verbally select a response (i.e., pass, shoot, dribble, or stay). Subjects made decisions while viewing schematic slides depicting offensive positions. Decision times were similar for experts and novices. Experts exhibited a repeated scan pattern focusing on the relations of offensive-defensive players whereas novices ignored defensive players altogether. In an actual “on ice” game situation, Bard and Fleury (1981) examined ice hockey goalkeepers when asked to block an opponent’s slap shots and sweep shots. As expected, experts initiated faster blocks than novices. Experts adopted more tactical eye-movement fixation patterns but search did not vary based on type of shot. Novices used a more erratic search pattern. The stability of experts’ search patterns was linked to their ability to selectively attend to relevant opponent cue sources. Novices, however, relied on less relevant or irrelevant cues. Researchers continue to link knowledge base differences to performance differences. Still, no one has addressed how the representation of the underlying knowledge base guides visual search strategies. As a result the conceptual knowledge underlying decision-making in sport (e.g., keeping track of the count as a part of the process of deciding what type of pitch will be thrown) may not be revealed in the current perceptual paradigms. For example, studies that examine player differences in deciding an opponent’s response selection usually reduce or isolate the decision-making context. Often excluded are environmental variables (e.g., past pitches by the pitcher or server) and situational variables (e.g., the score or inning). In a true game situation this type of information is available and used.

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Researchersuse decision times and/or accuracy to directly measure overt decision-making behaviors (e.g., eye marks of a subjects’ scanning patterns measure their procedures or rules for searching the environment) not players’ awareness or representation of these procedures. Norman and Rumelhardt (1988) provide an excellent discussion of this distinction. In addition, perceptual differences (e.g., eye movements) are not necessarily linked to expert decision-making after a certain amount of experience is achieved. Abernethy and Russell (1987) indicate that it is not so much how the display is overtly searched in determining the landing location of an opponent’s shot but in how this information is used. They see a critical link between visual orientation and information pick-up and the knowledge base in terms of a players’ awareness and use of information. In training, they speculate that knowledge (e.g., what critical features are available in the environment and how they influence landing location) is a precursor to expert visual search strategies. In baseball, Hyllegard (1991) notes that a variety of conceptual information may contribute to players’ decisions in determining a pitcher’s response (e.g., game situation and count). Cognitive psychologists,such as Charness (1989) and Holding and Reynolds (1982) have questioned the effectiveness of using pattern recognition to predict chess players’ selections in actual game situations. These authors indicate that move selection continues to increase with playing experience; while recall performance after a certain level of expertise is not related to move selection. If visual search strategies, as researchers suggest, emerge from rules in the knowledge base it seems logical to examine how these rules are represented. As Abemethy (1987) implies, until the link or knowledge base underlying visual search strategies can be provided, training novices to search the environment in ways similar to experts is useless.

Sport Paradigms Allard and Bumett (1985) acknowledge that protocol analysis of expert athletes would be fascinating and productive. They have also suggested one way of doing it. They developed a sorting task in which players sorted pictures of basketball situations. Varsity players sorted according to strategic game situations whereas non-players sorted according to literal scenes (e.g., the number of players in the picture). However, protocol analysis was not used to examine the bases of their somng strategies. The paradigm introduced by Thomas, French, and Humphries (1986) encourages a more ecological approach to studying cognitive and motor skills of sport experts. Since the introduction of this paradigm, several studies have examined how sport knowledge and sport skill contribute to expertise (see French & Nevett, this volume). Interestingly, Thomas et al. (1986) first reviewed a variety of studies in cognitive psychology which employed protocol analysis to examine the nature of the representation of domain knowledge and its influence on problem solving (e.g., Adelson, 1984; Chi, Feltovich, & Glaser, 1981). According to Thomas et al. (1986) these findings could predict behavior in sport. The structuring of information used to recall text in reading situations may be similar to the structuring of information used to select responses in sport situations. However, in spite of

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an extensive literature review, no attempt was made to present methods of protocol analysis or examine the development of knowledge representation in sport and how it influences decisions in game situations. They did offer some predictions of how the knowledge representation of high knowledge individuals could influence decision-making in sport. They predicted that high knowledge individuals would: (1) possess more, larger, and interrelated chunks of information which would reduce processing time (i.e., greater and faster accessibility of appropriate information that would place less demand on their working memory and encoding of the most appropriate information); (2) use sport specific strategies to monitor important changes in game situations, plan for possible actions, and predict possible game actions; (3) monitor or use cues from the situation and attach probabilities to possible game actions; (4) monitor the most crucial sequences of actions based on their knowledge of abstract tactical offensive or defensive plays; and ( 5 ) generate expectancies concerning upcoming events and inferences of events occumng. The F i t series of experiments to examine the representation of sport knowledge were conducted by cognitive psychologists interested in text processing of adults (i.e.. Chiesi, Spilich, & Voss, 1979; Spilich, Vesonder, Chiesi, & Voss 1979, Walker, 1987). High baseball knowledge adults when compared to low baseball knowledge adults performed better on domainrelated tasks (recall and recognition of baseball text). Performance differences were attributed because high knowledge individuals exhibited (a) a greater understanding of lower levels of the hierarchical goal structure. of the game (e.g., to strike out), (b) a greater ability to relate specific game actions (i.e., activities occurring in a game) and states (i.e., any variable that influences the goal structure) to the goal structure, and (c) a greater ability to relate and integrate sequences of actions and changes in states to the game's goal structure. The authors speculated that high knowledge individuals used domain-related cognitive strategies such as monitoring and selective processing of input information during text processing. They indicated the more sophisticated knowledge structure of the high knowledge individuals guided both the interpretation of input information (including generations of expectancies concerning upcoming events and inferences from events occurring) and the retrieval of that information.

In these previous studies, performance differences were based on a variety of experimenter text processing tasks and related to a hypothetical game of baseball. One of the f i t studies to examine the representation of sport knowledge based on subject generated protocols during actual sport performance was conducted by Housner (1981). An adult expert and novice (based on level of competition) were interviewed prior to and during various phases of badminton play. Protocols were coded in a node-link structure and compared in terms of the subjects' type of knowledge and degree of structure. The expert generated more tactical gamerelated concepts (e.g., the opponent's weakness), more interconnections among concepts and more concepts linked together forming "if-then'' statements. Housner speculated the expert chunked pertinent information to infer and predict the opponent's play and to plan appropriate tactical response selections. A model of protocol structure for sport We have developed a protocol structure model for tennis based on previous studies in

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baseball and badminton and the protocol methods employed by cognitive scientists (e.g., Chi, Glaser, Rees, 1982; Gobbo & Chi, 1986). Originally, the model (McPherson, 1987) was developed to examine the conceptual development of children’s tennis knowledge and how this knowledge influenced decision-makingduring game play. Rather than using hypothetical models based on researchers idealized knowledge structure to analyze protocols (for a discussion concerning these models see Voss, 1988 and Chi & Bassok, 1989); this protocol structure was designed to capture subject generated knowledge. The protocols generated by each subject were coded according to rules of the protocol structure model; therefore, in this study, protocols were collected during situation scenarios (diagrams of on court situations) to analyze subjects’ planning strategies. Between points data was also collected to examine how this knowledge was applied during game play. An introduction of the protocol structure model will facilitate further discussion of the findings. First, verbal reports for each individual are transcribed verbatim and statements are classified according to the rules of the protocol structure model. A concept is defmed as a unit of information (or an idea or proposition) about response selection in the context of a game situation. The boundaries of concepts are placed on the basis of different concepts that can be separated by content when determining units of information. Long pauses, ends of sentences, or experimenter interjections are designated as one phrase. Phrases can be one or several concepts consisting of a few words to several lines. Each unit of infomation is classified according to three major concept categories: condition, action, or goal concepts (see Figure 9.1 for some possible examples of concepts in tennis, baseball, and volleyball).

Condition concepts are units of information that specify when or under what circumstances to apply the action or patterns of actions usually to achieve a goal. In baseball, condition concepts might consist of statements concerning the pitcher’s prior pitch, or the base runner’s position. In tennis and volleyball, condition concepts might consist of the opponent’s position on court, current status of the game or an opponent’s weakness. Condition concepts are usually related to an action or goal, however, as research will reveal these concepts may be generated as isolated statements. Action concepts are units of information which refer to the action selected or patterns of action which may produce goal related changes in the context of a game situation. Action statements might consist of a motor response statement such as hitting the ball deep or rushing the net; or a perceptual response statement such as watching the pitcher’s release point or setter’s hands. Similar to condition concepts, action concepts may be generated as an isolated statement without reference to conditions of applicability or changes in goal states of a game situation. When using the protocol structure model to examine knowledge representation during sport panicipation, individuals may generate some additional concepts within action statements. Statements that indicate if they were able to carry out the action would be coded as selfregulation concepts. Each self-regulation concept has a corresponding action concept. For example in tennis, a self-regulationstatement might consist of ”I was trying to hit a forehand

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Figure 9.1. A partial list of possible concept categories in tennis, baseball, and volleyball.

down-the-line but it did not work". This would be coded as a forehand action concept together with a regulation concept. Statements which describe how to perform or do the action selected would be coded as Do concepts corresponding to their respective action concept. For example, in tennis the statement "I am going to brush up on the tennis ball to put more topspin on my serve". The comment "brush up on the ball" would be labeled a Do statement for the action concept serve. Goal concepts are units of information which usually reflect the means by which the game is won, such as scoring runs in baseball or winning points in tennis. Goal concepts may also reflect the purpose of an action selected. For example, in tennis a certain action may be selected such as "hit it in the middle" (action) "to keep the ball in play" (goal). They may also reflect the purpose of specifying a condition such as "since she has a weak backhand (condition) I will win this point (goal)". Goal concepts may also be generated in isolation without any reference to condition or action concepts. Other statements are coded to reflect specialized strategy statements such as prediction or probability comments. In baseball, for example, statements that predict probability of a pitch (e.g., " I think he is going to throw a curveball now") would be coded as a probability concept.

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Once each concept is identified several measurements are designed to examine what information is stored (knowledge content) and how this information is structured (associations). Knowledge content is measured by the frequency of the total number and variety of concepts within each condition, action and goal concept category. Also, the fmquency of total number and alternative concepts for Do and self-regulation concepts for each action concept are measured. To examine knowledge content in more detail, coding rules were developed to examine the qualitative or hierarchical aspects of each condition, action, and goal concept. Rules for coding the quality of condition and action concepts and hierarchical goal level of goal concepts in baseball are presented in Figure 9.2. These baseball coding rules may be modified for other sports (e.g., tennis or volleyball). Once quality or hierarchical levels were determined for each concept the frequency totals for each these coding category levels were measured for each major condition, action, and goal concept categories.

Category

Code Decision Rule

Condition Quality

0-

Action Quality

I-

inappropriate or weak :enera1 condition without any characteristics

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appropriate and ha8 one characteristic

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appropriate and bas two or more characteristic: appropriate (no forceful quality. only action stated)

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0-

skill and himrrlf (execution, acttin: on b a r . put tho ball in play)

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himelf and tammam (keepin: the ball away. move bawrunncn over, avoid the double play)

2-

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innin:

Figure 9.2. Quality and characteristic of concepts.

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The frequency of connections between concepts and linkages of concepts were measures developed to indicate the structure or organization of content knowledge. Connections arc any word (if, when, or, and) or phrase (so that) connecting two concepts. Words linking qualities within a concept would not be considered a connection. For example, the statement "I'm going to volley and hit my volley deep with slice" is one action concept 'volley' and does not contain any connections. The linkage of concepts would be coded if two or more concepts were connected within one phrase. As previously defined, phrases can be one or several concepts consisting of a few words to several lines. Therefore, as in the previous example, the action concept, volley, would consist of a single linkage. Each linkage is coded as either a single, double, triple, or greater than triple linkage category. Frequency totals for these coding category levels were measured for each major condition, action, and goal concept categories. Reliability of the model is an important factor in protocol analysis; once the protocol structure model and analysis coding rules are established, an extensive amount of pilot work is required to refine the rules. A team of coders, (usually myself and 2 experts in the domain) code experts' and novices' protocols as they perform the experimental task. This step is essential to refine and clarify coding rules. After this pilot work, the reliability of the model is established. As Voss (1988) indicates the process is iterative both in developing a model that requires detailed analysis of structure and content and in sufficiently describing the rules for analyzing protocols when using such a model. Training and reliability are dependent on the effectiveness of the coding rules. After several weeks of training, interrater reliability estimates are conducted. Usually trained coders are familiar with the domain (collegiate coaches, players). A minimum of 2 coders is required. Interrater and intrarater reliabilities are established for all concept categories and measures previously described. Reliability is estimated by # of agreements/(# of agreements + disagreements) x 100 = % (Thomas & Nelson, 1990). Reliability is obtained on at least a quarter to a third of the protocols gathered in a study. The remaining protocols are randomly selected and coded by these coders.

Knowledge Representation Research in Sport Each subject in the following three studies arrived at a solution according to hisher representation of the problem when presented with a sport situation. All protocol analyses were model driven and served not as ends in themselves but as tools to address issues of interest to the investigators (see Voss, 1988, for guidelines in developing protocol analysis models). Each experiment examined how individuals: (1) perceived the problem situation; (2) constrained or guided solutions to the problem based upon the perception; and (3) employed specialized smtegies to solve the problem. The protocol structure model was modified according to the domains under investigation. This section will discuss expert-novice differences for each study using measures developed to examine content, structure, and cognitive strategies. TO clarify these differences, group mean frequency scores and examples of actual protocols are presented. In two of these experiments researchers examined expert-novice differences of decisions and

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executions during actual game play (video tape analyses); their findings are mentioned in brief and not the focus of this chapter. After considerable pilot work, McPherson (1987) designed interview techniques to elicit verbal reports in tennis which were similar in the following studies. Basically, subjects were questioned as quickly as possible, without cuing the appmpriate response, and as naturally as possible as to what they were thinking about while participating in a sport situation (or while looking at a diagram or videotape of a sport situation). When subjects appeared to be finished, they were asked "anything else?" to elicit any remaining responses. Subjects were questioned in a neutral way to avoid biasing responses. Also, subjects were not asked what were you trying to do, what strategies were you using, or how did you do that? The intent was to remain consistent in interview technique so that comparisons would be based on the same instructional set. A variety of interview techniques and problem solving tasks exist to elicit knowledge representation such as deeper probe techniques in conjunction with sorting tasks. In the following experiments, subjects were given an opportunity to define their own problem representation. This strategy more accurately reflects differences in problem representation and solution both in sport and in other domains (e.g., Charness, 1989; Noice, 1991; Swanson, O'Connor & Cooney. 1990; Voss, Green, Post & Penner, 1983).

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Study 1 Children's Knowledge Representation in Tennis McPherson and Thomas (1989) conducted the first study to utilize propositional-type protocol analysis to examine knowledge representation and decision-making in a natural sport setting. Several instruments were developed to examine components of tennis performance of children a40)in two age groups (10-11 and 12-13) and two levels of play experience (high skill levels and tournament experience versus low skill levels and no tournament experience). Experienced children regardless of age performed better than novices on tennis skill tests and multiple choice knowledge tests. This m n d was consistent during singles tennis game play as experts' made more tactical decisions and more forceful executions.

To capture how children approached problem solving situations in tennis, three open ended questions concerning service, backcourt, and net game situations were developed. Subjects were asked to respond to each question while viewing a diagram indicating their position and their opponent's position in the tennis court situation Each subject's verbal reports were transcribed and the protocol structure model was applied. For each subject verbal report codings of the situation interview were collapsed across questions. Players were asked between points to respond to the question "What were you thinking about while you were playing that point?". Responses were recorded by microphone. The function of these questions was to capture how children approached problem solving Situations during game play. Only one modification of singles play was required. Players were not allowed to change ends due to the recording techniques. Four games during set play were randomly selected for coding purposes for each subject. Verbal report codings were collapsed across "between point" responses for each subject.

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Frequency scores for measures of content and structure (see section 2) were analyzed by age and expenise level for the situation and point interviews. No significant age or interaction effects were noted for the situation or point interviews. Analyses of the situation interviews indicated that experts, as compared to novices, generated significantly more total conditions (7.7 vs. 3.7) and a greater variety of actions (4.2 vs. 2.5). Measures of total goals, alternative goals, total actions, and alternative conditions were similar for both groups. Novices when compared to experts produced more second level goals regarding their opponent (e.g., keep the ball away) (novices 0.8 vs. experts 0.3). Experts when compared to novices generated more conditions other than those regarding themselves with one specific characteristic' (3.9 vs 1.6). For example, one expert generated this phrase: "Keeping the ball deep and moving him from side to side and not hitting it right back to him and giving him an easy time'' which included two conditions (shot type and opponent's position) and one goal (keeping the ball away). Only experts generated the most sophisticated condition concepts regarding themselves or other conditions that included two or more specific characteristics (0.2) (e.g, "if he's slow and has a weak backhand'). Experts when compared to novices (2.4 vs. 1.3) produced twice as many actions with one forceful quality. Inappropriate actions were generated only by novices (0.8). Some actual protocols illustrate the qualitative differences when novices and experts generate action concepts. In response to a service question, one expert replied: "I concentrate on getting it in and placing it like in the corners or something sometimes I think like if I want to pull him out wide or something, I might put spin on it, and I just try to concentrate on the point". In contrast these comments were produced by a novice: "to get the ball in and hit it hard" (Anything else?) "try to hit it hard try to get a hard one in". The structure of concepts during situation interviews suggest that experts had access to more associated concepts. Experts generated more double (1.9) and triple linkages ( 0.8) when compared to novices (1.1 and 0.3, respectively). The number of connections between concepts were not significant. Thus, experts were more sophisticated than novices in knowing when or under what conditions to apply the actions or patterns of actions. When selected, these actions, were more appropriate and tactical. Between points experts and novices generated similar total and alternative condition and goal concepts. Total actions were similar. Experts generated more alternative actions than novices (2.6 vs. 1.0). Experts' statements were tactically more detailed and sophisticated. Experts generated four times as many conditions other than those regarding themselves with one specific characteristic as compared to novices (3.3 vs. 0.8). Experts exclusively generated conditions with two or more specific characteristics (0.4). Novices generated more conditions that were weak without any specifics (1.1 vs. 0.1). These trends were applied to actions, as experts generated twice as many forceful actions when compared to

'The four levels of quality of conditions in tennis were: (a) weak without any specific characteristics (coded 0); @) appropriate regarding only themselves with one specific characteristic (coded 1); (c) appropriateregarding conditions other than themselves with one specific characteristic(codcd 2); and (d) appropriate regarding any condition with two or more specific characteristics (coded 3).

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novices (1.8 vs. 0.9). While experts exclusively generated action concepts with two or more forceful qualities (0.6). none were generated by novices. For example, one expert stated these condition and action concepts: "Thinking about keeping him far back from the net" and "I was thinking about hitting a ball short with backspin". Experts generated more goal concepts at the top level of the hierarchy (e.g., winning the game or point) as compared to novices (1.1 vs. 0.6). Consistent with the situation interview, experts generated four times as many double linkages (1.2) as novices (0.3). In addition, experts generated more connections between concepts (3.7)as compared to novices (1.2). The findings seem to reflect experts' access to a more highly organized knowledge representation since the amount of information generated (i.e., frequency scores for total number of concepts) was not significantly different. Experts exhibited specializedself-regulatorystrategies between points as they made more statements concerning whether or not the action selected worked (2.4experts and 0.7 novices) or failed to work (1.6 and 0.6). Experts' self-regulatorycomments were interpreted as strategies for refining applicability of certain productions. In addition, experts' action concepts included statements of how to perform the skill (labeled Do concepts). An example of a self-regulatory and Do statements are provided in one phrase by an expert: "That time I was just wing to hit a little slice down the line but I didn't follow through and it went into the net". This suggests that the action sides of sport procedures may include action statements concerning "how" to perform or "do" the response selected and regulatory statements concerning whether or not the response selected was carried out. The protocol structure model provided profiles of players' declarative and procedural knowledge concerning singles tennis. Protocols of experienced children, regardless of age, revealed knowledge representations in which concepts were more complex (associated) and sophisticated (deeper more refined physical or abstract tactical features). Experts' concepts were more associated, reflecting causal or linked relations among concepts. Novices approached problem solving situations in a general manner. Novices indicated that they knew the goal structure of the game (generally, goals were similar); however in response to these goals they generated less associated and detailed condition and action concepts. During game play novices generated the same number of total concepts, however the tactical content and structure of these concepts was what separated experts from novices. The situation interview indicated that experts' simple production rules stored in the knowledge base were easily applied during game play. Study 2 - Knowledge Representation of Volleyball Blocking In the previous study children were exposed to any number of decision situations and contexts while playing singles tennis. In response to this limitation, Murray (1991) developed a paradigm to modify the actual game play situation so that all players would be exposed to a more similar context during a sport situation. In the first phase Murray was interested in examining decision and execution components of volleyball blocking. Subjects (I3=12)

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consisted of volleyball club players (novices) and NCAA Division I collegiate players (experts). The club players had no formal coaching and participated in an area women's level B volleyball club team. A testing contingent comprised of 3 attackers (spikers), a setter, and a ball tosser was assembled to serve as the surrogate opponent for all subjects. Five game play situations were randomly ordered to form 15 test mals. Thnx subjects of the same expertise level were tested simultaneously in which all players were exposed to five game play situations from each of the three front court positions. Subjects were instructed to forcefully block the attack back to the opponent's court. Game play analyses indicated experts selected more strategic initial positions to block, ma& better decisions regarding movements to a blocking position, and executed blocking skills more proficiently than novices. In addition, when experts were not able to participate in the blocking formation they selected more appropriate defensive responses. McPherson, Dovenmuehler, and Murray (1992) conducted Experiment 2 to examine representation of volleyball blocking knowledge. A trial interview was developed to examine how subjects in Experiment 1 approachedeach volleyball blocking situation. Subjects responded to the question, "What were you thinking about during that blocking situation?", following each of the 15 mals. Three tape recorders were placed off court (one outside the baseline and one outside each sideline). After each performance trial, players were instructed to go immediately to their designated tape recorder and respond to this question printed on a sheet of paper at each recorder location. To assess their status of knowledge, each subject participated in a situation interview. Subjects were asked to view a volleyball court diagram and instructed that they were the blocker on the opposing team. Each diagram corresponded with the same starting positions of one defensive blocker, three offensive hitters and one setter that had occurred in the modified blocking task trials. A circle designated their position on the diagram; three hitters and one setter were designated by the letters H and S. While viewing each diagram subjects were told "you are in this area- you see the opposing hitters line up like this. What are you thinking about?" The experimenter prompt, "anything else?" was also used. Individual verbal reports were coded using the model of protocol structure modified for the domain of volleyball (see Figure 9.1 for some possible examples). Subjects' action concepts were examined for Do and self-regulatoryconcepts. Verbal report codings were collapsed across each subject's responses for each situation and between trial interview. Frequency scores for measures of content and structure (see section 2) were analyzed by expertise levels for situation and between trial interviews. Coding rules were modified for quality of goals to examine characteristics of goals rather than hierarchical levels. Analyses of the interviews indicated that novices generated significantly more total goals (8.3 vs 3.5) and alternative goals (2.5 vs 1.2) when compared to experts. For example, one novice generated these alternative goal concepts during the situation interview, "to play defense", "to get off the net" and "trying to block". The same novice generated the concept "to play defense" three times during the situation interview. Although, total number of conditions and

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actions were similar, novices when compared to experts generated significantly more alternative condition (8.7 vs. 4.2) and action (3.5 vs. 2.8) concepts. Consider these examples: a novice in response to a diagram stated,"I'd be wondering which way she was going to set ...cause they're all pretty equal. I'd think she was going to go to the middle or to the far, to the strong side so I'd be trying to line up on which ever one I needed too for that, for the block". This protocol contained 4 conditions, 2 actions, and one goal. An expert in response to the same diagram generated only one condition concept that referred to the middle hitter and in response to this condition selected an action regarding a position move. These protocols were: "I'm going to go ahead and move out to where the middle hitter is lined up for a three". Trends were reversed in terms of sophistication of concepts as experts generated significantly more conditions (15.5) that contained two or more forceful qualities than novices (7.7). Novices generated significantlymore appropriateconditions without a forceful quality (14 vs. 4.3) and conditions with one forceful quality (11.3 vs. 4.8) than experts. Action concepts that included 2 or more forceful qualities were also significantly different, as experts generated twice as many tactical actions ( 5 ) compared to novices (2.2). Experts' protocols we= in the form of sophisticated condition-actionrules such as "basically looking at my hitter which is the right left side hitter coming in for a back five". This example of a condition-action rule specified an action to watch the hitter in front of her in anticipation of a specific offensive play pattern; and she indicates the setter will set a high backset to the right front area. Experts also denoted which hitter to watch based on possible play patterns their opponents might run such as, "Looking at the middle hitter, uh possible running a one or a three and I'm also looking at the setter just to see where the ball, where she is going to set it looking at her hand position". This phrase also included a detailed action concept that indicated what part of the setters' body to watch. Characteristics of goal concepts, indicated novices generated most goals in the category "to execute defense" compared to experts (4.8 vs. .03). Goals such as "to execute the skill", "to determine the set", and "to move according to position of ball and opponents" were similar for experts and novices. Novices generated three additional goal concepts: "to observe opponents' attack patterns", "to prevent offensive attack", and "to react to ball". Qualitative analysis of protocols indicated experts were more sophisticated or elaborate than novices in knowing when or under what conditions to apply the actions or patterns of actions; and when selected, these actions, were more appropriate and tactical. Both groups generated similar associations among concepts, as frequency scores for total connections and all linkage categories were not significantly different. Experts when presented with certain blocking situations made a more sophisticated diagnosis of the current situation based on less but more appropriate (detailed and/or relevant) information. The experts apparently framed the problem based on stored abstract offensive and defensive volleyball concepts or situation prototypes concerning blocking.

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Trial interviews indicated frequency scores for goal concepts were similar for both groups. Total and alternative condition concepts were similar for both levels of expertise; however differences emerged in terms of the level of sophistication of condition concepts. Similar to situation interviews, trial interviews indicated that novices: generated more appropriate conditions without any forceful qualities (11.8 novices vs. 6 experts), and experts generated significantly more conditions that contained two or more specific characteristics (2.4 experts vs. 0.8 novices). Experts also generated more and alternative action concepts than novices (5.7 vs. 1.3 and 2.3 vs. 1.2. respectively). Quality of action concepts indicated that experts exclusively produced action concepts denoting two or more forceful qualities (0.7). Novices exclusively generated action concepts that included Do concepts (1.33). These concepts usually indicated what was wrong with their parameter selections, for example, one novice commented: "feel like I need to go down and come straight up rather than just leaping across on that one" . Similarly, another novice stated: "I took too small of steps". No differences were noted for frequency scores of self-regulatory concepts (experts 3.2 and novices 2.8). Both groups produced comments reflecting their ability to carry out an action (visual or motor) such as "I did not get out in time", "missed the ball", "I was lined up right", and "I took my eyes off the ball and the spiker". Experts' abstract tactical condition and action concepts are demonstrated by one expert who indicated, "I watched her approach she was coming, kind of a one, it was more like a two set". This protocol included an action concept with one forceful quality and a condition concept with two specific characteristics. This action concept reflected a tactical volleyball offensive concept (i.e., reading an offensive player to see if she was making a certain position move to determine a high or quick set). Experts' position moves were also based on abstract offensive play patterns such as: "set a two so I moved into the middle" (indicates high set to the middle), and "five set so I moved off the net" (indicates high back set to the right front area). Experts, attempted to interpret (or read) conditions to actively choose a response. Novices, reacted to conditions as they occurred. For example, a novice stated: "So I'm basically confused, I'm jumping with the setter". Another novice remarked, "I was set to the middle (an appropriate condition) and I went up on it (an appropriate action). During game play experts produced more linking statements between concepts, although no differences were noted for linkage categories (e.g., double links). Both groups exhibited similar associations among their generated concepts when presented diagrams and participating in blocking situations. Differences emerged as novices were not as capable of utilizing perceptual information from the game environment to predict their opponents' actions and possible attack patterns associated with these actions. Novices' poor diagnoses of initial starting positions or probable sets hindered their ability to select the most appropriate visual (who to watch and when) and motor responses (what position move to make). Protocols revealed novices had a less sophisticated internal representation of volleyball

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blocking stored in their knowledge base. Experts had a highly sophisticated and tactical internal representation of volleyball blocking. During game play experts' sophisticated abstract productions were easily applied. Abstract productions guided experts' encoding of the most appropriate environmental information to predict their opponents' offensive tactics. In response to these predictions, they planned and canied out tactical moves and blocks.

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Study 3 Knowledge Representation of Batting Preparation Cumntly there is a lack of understanding about the representation of knowledge underlying overt decisions (e.g., decision time, accuracy of decisions) and eye-movements of players in simulated game situations such as baseball (seesection 2). Studies examining baseball knowledge (e.g., Chiesi et al. 1979) exposed subjects to scenarios of baseball games presented verbally or in written form. McPherson (1991) examined expert-novice differences in representationof baseball knowledge and how this representationinfluenced decisions concerning batting preparation during a simulated baseball game situation. Twelve experts (NCAADivision I collegiate baseball players) and 12 novices (college physical education students with no high school or college playing experience) were told to assume the role of the fourth batter while viewing a half-inning of a video taped collegiate baseball game. The collegiate players were also compared to their collegiate hitting coach. The edited video tape recording consisted of several g a m play sequences prior to the subject's attempt at bat in which a variety of environmental variables (e.g., the pitcher, infielders, outfielders, prior batters) and situational variables (e.g., batter performances, status of the game, pitch selections) were available to all subjects. The edited video consisted of one pitcher pitching a total of 15 pitches to three batters prior to their time at bat and 4 pitches to a player (the fourth batter) whom subjects were requiredto imagine as themselves. Pauses (blank video) were inserted between each of the first three batters, just prior to the subject's imaginary time at bat and between each of the four pitches during their simulated time at bat. Subjects were not allowed to look back or control the videotape. They were told that they would be asked to respond to the question, 'What are you thinking about?'' at different intervals (the blank video pauses) while viewing the tape. The experimenter prompt, "anything else?", was asked after each response during the designated question intervals. Individual verbal repons were coded using the model of protocol structm modified for the domain of baseball (see Figm 9.1 for some possible examples). Frequency scores for measures of content and structure (see section 2) were analyzed by expertise levels for situation and between aid interviews. Total number of lines was not significant for expertise level suggesting that all subjects were capable of verbalizing what they were thinking about. The total number of lines that experts generated ranged from 27 to 195 with a mean of 78.08. The lines ranged from 28 to 124 for novices with a mean of 48.75. Consistent with these findings, the coach generated a total of 88 lines. This was similar to experts' average number of lines, although less than some novices.

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Experts produced significantly more total condition (69.3 vs. 40.2) and action (19.4 vs.8.2) concepts than novices. Frequency of alternative concepts were similar. As a result total scores indicated experts were more a w m of the need to monitor or generate these concepts during the batting situation. Both groups generated similar total and variety of goal concepts during the simulated batting task. This was interpreted as evidence that all subjects were familiar with the goal structure of the game of baseball. Quality of goal concepts were not significant for expertise level. This suggests that novices and experts were generating similar hierarchical levels of goals inherent in the game of baseball and in the batting preparation task. Group differences were noted as experts generated significantly more sophisticated conditions than novices in terms of one specific characteristic (e.g., statements concerning type of pitch thrown by the pitcher) and two or more specific characteristics (e.g., statements concerning type of pitch thrown by the pitcher and location regarding the strike zone) (35.6 vs. 17.1;26.8 vs. 0.3 for experts and novices, respectively). Novices produced significantlymore low level conditions (e.g., statements concerning conditions regarding position of the catcher) (novices 19.8 and experts 8.3). With experts, the ability to interpret condition concepts increased. Also, the coach generated twice as many conditions with two characteristics (a total of 51) as expert players. Distributions of frequency scores were calculated to reveal in more detail the monitoring of condition concepts. Five condition categories were examined: the environment, coach, offense, defense, batter and pitcher. Experts generated more conditions regarding batter concepts and pitcher concepts. Novices generated twice as many defensive concepts. These distributions suggest novices were monitoring concepts regarding defensive positions while experts attended to more specific details of prior batters' performance and the pitcher's performance. Distributions were similar for conditions refemng to the environment, coach, and offense. A bias was evident for the coach, who focused entirely on batter and pitcher concepts (84 out of 86). Overall, the conditions of applicability underlying action(s) for experts were represented as an analyses of pitcher and prior batter performances. Experts exhibited a more tactical internal representation of conditions for interpreting batter characteristics (conditions of application). The internal representation used by experts for solving the problem was to interpret and determine how the pitcher behaved under certain conditions and then assign these behaviors to a probability of Occurrence for use during their time at bat. Initially, when experts were told they were going to be the fourth batter, they accessed stored procedures for gathering and collecting information about the pitcher's behavior under certain conditions. For example, one expert stated: "um, just watching' what kinda ball he's coming in with in what situation". Similarly, another stated: "right now urn, just trying to learn what I think he's gonna do to me from the previous hitters." These statements were similar for most experts suggesting the existence of stored rules for batting preparation.

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As the film progressed experts collapsed and updated certain batter-pitcher characteristics forming representations of externally presented events and internal models for these events. Some examples of this correspondence between past, present and future patterns of condition concepts were reflected by one expert as he continued: "right now if he gets ahead in the count, I know I can look possibly curve when he's ahead in the count 2-2 he came curve, there I think it was 1 & 2 he came curve, so I know if I'm behind in the count, I can maybe sit a little off speed, I don't think he's gonna really come at me fast ball right now".

Novices had a much more difficult time framing the batting situation which suggested they had a less developed internal representationof batting preparation. Consistent with novices' protocols, two novices in response to pitcher characteristics commented: "there's another sloppy error" and "pitcher's not as confident looking as he was earlier", respectively. These comments reflected incomplete statements that noted general non-tactical analyses of the pitcher. Novices, when summarizingpast performances,lacked the ability to tactically analyze conditions and use them for future reference. For example one novice remarked: "Trying to wonder if this guy's ever gonna give me a ball I can possibly try to hit. In the meantime, they're making a lot of errors in the field, so... I don't know. That's about it..just wait and see what happens". In addition, novices' had less sophisticatedinterpretationsof conditions other than pitcher concepts. Analyses of the level of sophisticationof action concepts revealed similar uends. Experts generated actions with one forceful quality and actions with two or more forceful qualities (9.6 vs. 1.2 and 2.0 vs. .1 for experts and novices, respectively). Novices produced only weak actions without any specific characteristics (1.2). Although, total number of actions generated by the coach were similar to mean actions generated by experts, the coach was capable of selecting four times as many actions (a total of 8) with two or more forceful characteristics. For example, the visual action comments of two novices consisted of: "you should just look for a hole" and "you gotta watch it. gotta keep your eye on the ball". In contrast, experts selected very forceful (tactical) visual actions. For example, one stated: "Right now if I'm the fourth hitter, I'm gonna be sitting over in the dugout looking at this pitches, picking up arm angle, where everybody's hitting the ball, checking velocity". Similar to experts, the coach also exhibited very forceful visual actions:" You would wanna not watch his body. You'd wanna focus on release point, get a good look at the ball". Action concepts referring to hitting were weak for novices. For example, a novice stated: "just stand up there and hit the ball". Consistent with most novices, one subject was not sure what to do: "so basically I need to be a hitter....p robably a grounder, cause it seems like their infield's playing pretty temble". Linked to their sophisticated visual actions; experts generated forceful hitting actions. These actions were varied and selected according to a variety of possible conditions that could occur. For instance, one expert stated: "The double play is pretty much out of order. Um I'm checking the infield right now to see if either all the infielders are in or just the comers. If just the comers are in I know I've got to drive the ball hard up the middle. If everybody is in, I just gotta drive the baseball to the out field but I'm

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looking fast ball.”

The action sides of procedures for experts consisted of tactical visual responses used to gather and interpret conditions necessary to build a profile of pitcher characteristics. As this profie developed, hitting actions were generated in response to these patterns of conditions. Visual actions were also planned during their time at bat to prepare or anticipate the pitcher’s anticipated response selection. Novices generated less specific visual actions to form a pitcher profile and employed less actions in response to the problem situation. Novices’ action concepts may be interpreted as an indication they lacked a sophisticated problem representation for batting preparation. Experts had greater associations between concepts in the knowledge base as they generated significantly more connections between concepts than did novices (22.8 vs. 12.3). Experts exhibited a more complex knowledge structure as they generated significantly more triple linkages and a trend towards mple or greater linkages (6.7 vs. 3.3 and 3.6 vs. 1.8. for experts and novices, respectively). These findings together with results of the analyses of condition and action concepts suggest the following about experts’ content and structure of baseball knowledge: (1) it provided more links and connections between stored knowledge and incoming information; (2) it facilitated more complete or extensive diagnoses of current situations; (3) assisted in planning the most appropriate response selections. Comparison of frequency of probability statements demonstrated that experts were predicting the type of pitch more frequently (7.25 vs. 1.08) than novices. The coach generated a total of 11 probability statements. Seven experts and the coach made note of whether their prediction worked or not. Only 1 of 7 novices who made a prediction explicitly stated whether their prediction worked or not. At the conclusion of their simulated batting, 6 experts made note of how they would use information gained from the pitcher for future situations. Novices did not make these notations. These findings suggest experts’ knowledge representation included self-regulatorystrategies to update, check, and modify their predictions of pitcher characteristics. Protocols revealed players’ condition and action concepts as compared to non-players were more tactical, refined, and associated. These concepts indicated players were different from non-players in what attributes were considered important to solving the problem. Experts and the coach were able to encode and update those cues and events most crucial to batting preparation. Theoretical Implications The research projects presented in this chapter represent an initial step in theory building about knowledge representation in sport. In a recent review, Chi and Bassok (1989) stated that properties of the knowledge structure such as type of knowledge, degree of structure and cognitive strategies may develop differently as a function of the knowledge domain. This new

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line of research presents noteworthy implications concerning the development of sport knowledge. What can we say about the representation of sport knowledge as expertise is acquired? Examination of goal concepts regarding batting preparation indicated collegiate players when compared to physical education majors exhibited a reorganization of what attributes were important within a similar hierarchical goal structure (see Chi, 1985). That is, expert-novice differences did not emerge from individuals' goal concepts, but were reflected in the characteristics (qualities and distributions) of condition and action concepts. In comparison to these findings, a more "goal driven" representation of blocking knowledge was noted for volleyball novices compared to experts. Novices generated a more extensive hierarchical goal structure (more total, more alternative, and additional goal concepts) when presented diagrams of volleyball blocking situations. However, differences were not noted when these subjects participated in actual blocking situations. Similar to the findings in baseball, volleyball novices knew what goals were required to accomplish the task but lacked the means or sophisticated condition-action rules to achieve these goals. In tennis, child experts and novices had similar goal concepts representing each hierarchical level (i.e., generations of similar total and alternative concepts). Analysis within each hierarchical level suggested that both groups generated most of their goals at the lowest level regarding themselves (e.g., to keep the ball in or execute a skill). During the situation interview novices generated more second level goals regarding their opponent (e.g., keeping the ball away). We postulated that novices knew enough to consider the opponent in order to accomplish the task and represented this knowledge as a subgoal concept. Experts also knew to consider the opponent; yet their condition concepts represented the opponent as a condition concept (e.g., their position on court or weakness). Examination of each level during game play indicated experts represented game situations at the highest level more frequently than novices (e.g., to win the point). Developmentally, both groups seemed to indicate early stages of hierarchical goal smcture; as most goal concepts were generated at the lowest possible level. In baseball text processing studies, low baseball knowledge adults exhibited less understanding of the hierarchical goal structure of the game (especially, at lower levels) as compared to high knowledge adults. The current studies do not support these trends? However, it is important to note that novices in these sport studies were not low knowledge individuals as they had played tennis or volleyball at low competitive levels. In the baseball study, they were physical education majors and could be considered fans as they watched an average of 1.7 baseball games a week.

?he goals of the researchers in the text processing studies were to examine how knowledge of a domain influences processing of text (e.g., recall of a baseball passage) in that domain. Goals of the researchers in the current studies were to examine how differences in levels of play experience influence problem solving of spon situations.

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Although more research is warranted, we postulate that the adult novices, child experts, and child novices in these studies formulate plans based on varying levels of goals within a hierarchical goal structure. For example, they m driven by goals as to which conditions to watch or why a certain action would be selected. With higher levels of expertise (i.e., years of formal coaching and higher levels of competition) goal driven decisions are replaced with condition-action rule decisions. In volleyball blocking, it appears experts' goal concepts are implicit and represented in the form of action plans during game play. This is further supported by protocols of the coach (with the most experience) during the baseball batting preparation who generated only one goal ("now I got a chance for two RBI's''). In all three sport studies, dramatic differences were noted in experts' generation of more sophisticated "condition sides" and "action sides" of procedures (e.g., condition-action rules, patterns of conditions). Players' declarative representations of content knowledge developed with expertise. This supports the findings of Chi and her colleagues (e.g., Chi, Feltovich, & Glaser, 1981; Chi, Hutchinson & Robin, 1989). Experts' condition and action concepts as compared to novices were more tactical (e.g., certain concepts were considered important), refined (e.g., concepts were analyzed at a deeper more tactical level), and associated (e.g., concepts emerged as patterns). Adult baseball and volleyball experts displayed differences in procedural knowledge consistent with studies in other domains which indicate that, as expertise is achieved, weak productions axe replaced with sophisticated productions (e.g., Anderson, 1983). For example, transitions noted across expertise levels in bridge planning were due to both quantity and quality of stored information (e.g., more sophisticated productions) (Charness, 1989). These sophisticated bridge plans were based on sets of suit combinations. This is similar to the baseball experts who based batting plans on the relation of patterns of conditions of past batters' and pitcher's responses. Baseball experts' productions suggested they mapped current information together with past events onto macroproduction(s) stored in the knowledge base for batting preparation. These macroproductions are rules for batting preparation and batting formed as a result of experience which could, according to Anderson (1983, 1987). be the result of a learning mechanism termed "composition". These macroproductions for batting preparation were easily applied to a simulated batting situation enabling subjects to modify and update situation specific procedures as a video progressed. In response to these patterns or updating of "condition sides" of procedures, experts also generated patterns of actions for monitoring present events (visual) and for planning and simulating their time at bat (visual and/or motor). Volleyball experts also displayed abstract macroproductions which included conditions of volleyball attack and set patterns along with actions for positioning and blocking. Child tennis experts did not exhibit macroproductions in comparison with adult volleyball and baseball experts. They seemed to be building procedures, as their condition and action concepts did not reach the same level of abstract tactical concepts generated by adults. Child experts also lacked extensive chunking or relationship between patterns of conditions when compared to adult experts.

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Building of "action sides" of procedures was further suggested by child experts' action statements which included statements concerning how to perform the sport skill selected (coded as Do concepts). Child novices seemed to represent their Do statements in the form of goal concepts, "to execute the skill" without any verbal labels denoting how to perform a response. Similar to child experts, adult volleyball novices also generated Do concepts during volleyball play. These concepts described how to block a volleyball or move to a defensive position. In contrast adult volleyball experts did not generate such concepts. "Do" comments may characterize individuals who recognize what to do in order to perform the motor skill but are not capable of consistently achieving it. Novices' Do concepts concerning volleyball blocking seemed to characterize emor detection labels which might be indicative of the second or associative stage of motor skill learning (Fitts & Posner, 1967). Child tennis novices lacked verbal labels of how to perform the skill learning which suggests they may be at the fmt or cognitive stage of motor skill learning (Fitts & Posner, 1967). How do problem representations influence solution processes? As Thomas et al. (1986) predicted, sport experts knew what information was important or relevant to monitor in order to achieve the goal. In the baseball study, all subjects were exposed to the same visual stimuli however the way these stimuli were represented and used by the experts and coach were very different from novices. Voss' (1989) information processing framework is consistent with experts' representation of batting preparation in that experts' internal representation of batting preparation enabled them to search through a highly restricted problem space (as evidenced by their high propomon of pitcher and batter condition concepts). The coach exhibited the most bias in framing the problem situation which was predicted and consistent with protocol analysis of problem solving in areas of banking (Yechovich, Thompson, & Walker, 1991), social sciences (Voss & Post, 1988), legal systems (Lawrence, 1988), and physics (Chi, Glaser & Rees, 1982). These trends were also true for volleyball collegiate players during the situation interviews as they focused on only the most relevant conditions. Novices in all three sport studies approached the problem as a more global sport situation, processing a variety of events important to the game, but not necessarily important to the task at hand. For example, baseball novices generated several unrelated conditions (e.g., stealing of bases) occumng in the simulated game suggesting a less effective and efficient use of their problem space and a less developed internal representation of batting preparation. Also, the degree of novices' interpretations such as low level diagnoses of condition concepts and less sophisticated actions in response to these conditions could be regarded as processing surface features of a problem rather than deeper more abstract tactical concepts (see Chi, Glaser & Rees, 1982). What domain-related strategies emerged? Planning and monitoring strategies emerged from experts' production rules and these influenced both how often certain condition concepts were monitored (total conditions and characteristics of conditions) and to what depth they were analyzed (quality of conditions). Analysis of action concepts indicated similar trends suggesting novices also lacked the "action sides" of procedures (e.g., in volleyball blocking or baseball

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batting preparation) and in some cases used a "wait and see" approach rather than planned actions. Glaser and Bassok (1989) note that self-regulatory skills are indicative of good problem solvers. As Thomas et al. (1986) predicted, baseball experts demonstrated self-regulatory strategies designed to test their accuracy of predicting the pitcher's pitch. Experts could oversee a procedure's applicability and modify this procedure, if necessary. If a prediction was not a correct pmxdure, players elaborated on why and added new parameters to this procedure. Selfregulatory swtegies were also exhibited by child tennis experts during game play. Selfregulatory strategies concerning position moves or blocking were similar for experts and novices during volleyball blocking situations; however novices in this study had the most extensive experience compared to the other studies. The use of self-regulatory skills could indicate building and refining of procedures for the task at hand. This is similar to Anderson's (1983, 1987) mechanism for building procedures (proceduralization). Sophisticated monitoring strategies were also tailored according to experts' problem representation. For example, in the baseball study, the expert approach to encoding strategies was to collect and gather information about characteristics of the pitcher (e.g., type of pitches he was capable of throwing and conditions under which these pitches occurred). Specialized strategies for monitoring these characteristics were evident in statements such as "watch for this" or "under this condition in the count, view a certain body part of the pitcher". Reuieval strategies were also used to continuously update (compare present cue with past) and summarize (actively recall patterns of past performances) condition characteristics. Novices may have been capable of using these strategies (although less sophisticated in this domain); however their problem npresentation did not constrain the problem requiring the use of these specialized encoding and remeval strategies.

Future Directions The studies reviewed in this chapter provide a limited demonstration of applications of propositional-typeanalysis when examining knowledge representation and sport expertise. One of the intentions of this chapter was to present a protocol structure model to capture sport performers' knowledge representations and use of their knowledge during decision-makingsport situations. The model is adaptable for a variety of sports as minimal coding scheme modifications were required to change from tennis to baseball or volleyball. The current studies were limited to group comparisons. A wealth of information remains to be investigated within these studies such as individual differences according to player position, ranking, or training experience. Also, analyses of the knowledge structures was limited to frequency counts according to any number of possible links or connections among conditions, actions, and goals. Modification of link measures, such as determining the characteristics of a single production and frequency of use might provide us with more information about production development.

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Sport domains may play a unique role in determining the influence of knowledge representation on task performance. Sophisticated decisions do not necessarily correlate with sophisticated responses (for a discussion see Thomas & Thomas, in press). Knowledge representation when examined in sport contexts, may interact with players’ perceptions of their motor as well as decision outcomes. More research is warranted to examine how knowledge representations (response selections and response executions) interact as expertise is acquired. The intent of this chapter was to specificallyreview research in a variety of domains that examined the conceptual knowledge underlying decision-making in sport. A research direction that could provide additional insight would be to combine the c w n t methodology in this paper with those methodologies previously reviewed (e.g., eye-marks, decision tim, components of game play) to investigate the interaction of knowledge representation with speed of processing. This research may move us closer to developing models of competent performance and ultimately toward examining instructional environments that may influence and expedite the development of expertise.

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CHAPTER 10 NEUROPSYCHOLOGICAL ANALYSES OF SURGICAL SKILL ARTHUR L. SCHUENEMAN AND JACK PICKLEMAN Loyola University Medical Center, Loyola University Chicago, Maywood, Illinois, 60153 Now a surgeon should be youthfirl or at any rate nearer youth than age; with a strong and steady hand which never trembles, and ready to use the lefl hand as well as the right; with vision sharp and clear, and spirit undaunted;filled with piry, so that he wishes to cure his patient, yet is not moved by his cries, to go too fast, or cut less than is necessary; but he does everything just as if the cries of pain cause him no emotion.

Celsus This quote from the first century emphasizes qualities of the competent surgeon which remain in the discipline’s folklore to the present day. The introduction of anesthesia (Wangensteen & Wangensteen, 1978) did not change the profession’s focus on speed as the surgeon’s most important tool. Surgeons today still judge themselves and their students in terms of ‘psychomotor skills’, and the halo effect from the perception that a resident’s level of technical ability in the operating mom is substandard (‘hands of stone’), is likely to overshadow other athibutes in his evaluation. Several years ago, we began a program to study operative skill as part of an ongoing educational concern with the selection and evaluation of residents in the Department of Surgery at Loyola University. Training in General Surgery begins only after successfully negotiating undergraduate studies, gaining entrance to medical school and completing four years of training leading to the M.D.degree. Residency training programs in surgery are highly competitive, generally screening only the top of each medical school’s graduating class. As a typical example, in 1991, the surgery department at Loyola received over 600 completed applications, chose to interview 130 physicians, and ultimately selected six to pursue training in a five-year program. Despite this multi-level screening process which results in a small cadre of students with

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uniformly impeccable academic credentials and character references, these same individuals often display a significant range of technical ability during training. Occasionally, the extremes of the range may not be identified until the later stages of training, at a time when the student is expected to assume major responsibilities in an unusually busy surgical service. For the unfortunate few, this may represent the first ”failure” in a long career of academic superachieving, and this becomes an emotional, educational and economic ordeal for both the individual and the program. Aware of the possible Kafkaesque dilemma of selecting residents by one criterion and evaluating them by another, we found that earlier efforts at prediction of postgraduate performance (Gough, Hall & Harris, 1964; Price, Taylor & Richards, 1971) had focussed on verbal-cognitiveindicators, typically reporting minimal relationships between academic variables and various criteria of clinical performance (Lazar,De Land & Tompkins, 1980). Other predictive studies had somewhat better success by including ‘noncognitive’ variables such as attitudes, personality and experience (Keck, Arnold, Willoughby & Calkins. 1979; Murden, Galloway, Reid & Colwill, 1978; Willoughby, Gammon & Jonas, 1979). Despite agreement as to the relative importance of component skills in surgical practice (Lawrence, et al., 1983; Spencer, 1983), the investigation of relevant psychomotor skills has suffered from the failure to distinguish between performance (the global efficiency with which a complex activity is completed), skills (the subcomponentsof a given performance representing the interaction of experience and task-relevant capacities of the performer), and abilities (the adaptive capacities combined in a given skill) which may be largely innate (Halstead. 1947). A second problem in this area of investigation has been the lack of an efficient and reliable index of surgical (technical) proficiency. Both of these issues were of concern in the design of our initial studies of surgical residents’ technical expertise (Schueneman, Pickleman, Hesslein, & Freeark, 1984). We felt that systematic study of nonverbal cognitive and motor abilities could identify important dimensions of variability among surgery residents and define precursors of superior technical ability. Accordingly, the four goals of this research program were: (1) to develop an effective criterion index of surgical performance, (2) to identify neuropsychologicmeasures of cognitive, perceptual and motor skills related to operative technique but unrelated to measures of academic achievement. (3) to evaluate the relative importance of these abilities in accounting for variation among residents, and (4) to compare the efficiency of neuropsychological variables with academic indices in the prediction of surgical performance. Following seminal work of Kopta (1971) in studies of orthopaedic surgeons, we devised a series of Likert rating scales having behaviorally-specific items suggested by experienced colleagues. The final group of items (Figure 10.1) were selected on the basis of a pilot study from a larger pool. To be included, an item had to demonstrate statistical discriminability among residents and correlate with other items in the scale. Initially, these scales were completed by all attending surgeons i n the department, immediately following an operative

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YEAR 1

2

3

4

5

I

I

I

I

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1.5

1.0

N=22 N40 I771

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Z SCORE

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- 0.5 - 1.0

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Nil9

N=9

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- 1.5 Figure 10.1. Standardized surgical skills ratings by training level.

procedure in which they first-assisted a resident. However, data from the pilot phase of the study indicated that some attendings completed few ratings or demonstrated a significant response bias, and these were eliminated from the rater pool. For an initial group of 42

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residents, 15 attendings completed 642 ratings. For 78 residents in a second phase of the study, 803 ratings were completed by six attending surgeons. Funher analyses of the surgical skills rating scale revealed significant "rater effects" due to differences in standards among attending surgeons. Systematic differences between 'toughminded' and 'soft-minded' raters were eliminated by converting the raw data to standardized scores based on the mean and standard deviation of all ratings completed by each rater. This allowed compilation of an average rating score for each resident across all raters who had observed himher during the course of a surgical procedure. As seen in Figure 10.1, level of training also had a significant effect (p<.OOl) on judged expertise, indicating (with considerable gratification) that practice during the five-year program does improve surgical skill. Since we were primarily interested in ability rather than practice, these mining effects were eliminated in subsequent statistical analyses with covariance techniques. Next, we turned to selection of potential measures which might predict operative skill. Scores on two measures of verbal abilities. the Medical College Admission Test (MCAT) and the National Board Examination (taken during the medical school years), were examined and found to have nearly zero-order correlations with our ratings of surgical skills. These two test scores. along with letters of reference and a brief interview with one or more attending surgeons, comprise the primary data used in selecting applicants to most surgical residency programs. Clearly, measures of other abilities would be needed if we were to better understand the technical element of surgical proficiency. Toward this end, we selected a battery of psychological and neuropsychological tests to index five mas of nonverbal abilities which might bear some relationship to operative skill:

1.

Psychomotor Abilities. These included measures of motor speed, fine motor coordination and rapid sequencing of bimanual movements. Although eye-hand coordination is an essential component of these tasks, they are very repetitive and higher-level visuoperceptualanalysis is of relatively minor importance. Examples are the Purdue Pegboard and two tasks from the Flanigan Aptitude Classification Tests ('Dots' and 'Circles').

2.

Perceptual Abilities. Tasks which require either the ability to detect a visual pattern, i.e. signal, in a 'noisy' background (Thurstone Concealed Figures Test) or logical analysis and synthesis of geometric relationships in mentally constructing a 'whole' from its component parts (Revised Minnesota Paper Fom Board). Although speeded, neither of these tests incorporate significant motor responses.

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3.

Perceptuomotor abilities. These tests are, conceptually, a hybrid of the first two categories in that they require both high-level perceptual abstraction and a significantmanipulatory component. Examples include the Block Designs subtest from the Wechsler Adult Intelligence Scale, the five most difficult Porteus Mazes and the Tactual Performance Test from the Halstead-Reitan Neuropsychological Test Battery.

4.

Spatial Memory. Measures of incidental memory for spatial relationships and spatial location are normally derived during the standard administration of the Tactual Performance Test (above). As implied, the primary component is memory; perceptual and motor abilities are certainly involved, but to a limited extent since the task is not specded.

5.

Stress Tolerance. Both characterological and situational anxiety can play a significant role in any motor performance, so monitoring of thii potentially confounding variable seemed essential. The Spielberger State-Trait Anxiety Inventory was chosen for this purpose.

The tests from each of these groupings were individually administered to the 141 surgical residents in our study, typically during the first or second year of mining. Test data were then analyzed in a series of multiple regression studies, using the standardized surgical skills rating data for the same residents as the criterion, or predicted, variable. These analyses produced a correlation coefficient of 0.68 between the tests and the criterion variable @<.001), with the neuropsychologicaltests accounting for 73% of the explained variance of the residents' surgical skills ratings. In a separate factor analysis of just the psychometric test data, we also found that the tests selected for our study could be grouped in thrtc factors, which we labelled Complex Visuo-Spatial Organization,Stress Tolerance and PsychomotorAbility. Only tests grouped in the Complex Visuo-Spatial Organization factor were found to be major predictors of surgical skill. This was the case with our initial group of 42 residents, as well as with the second group of 78 residents studied, thus providing some evidence of the reliability of our findings. In evaluating these findings, it appears that, contrary to surgical folklore, pure 'psychomotor skill' (manual dexterity) is not the major dimension distinguishing the proficient surgical performance from the mediocre. Rather, perceptual abilities involving the capacity to rapidly analyze and organize perceptions based on multisensory information and the ability to distinguish essential from nonessential detail, particularlywhen the "signal-to-noiseratio" is high, appear to be essential precursors of superior technique. This is not to imply that manual dexterity and verbal abilities are not important to surgeons in performing their activities - they obviously are quite significant; but that the distinguishing features of the superior practitioner are hisher ability to "see" the relevant anatomy of the operative site, even when this might not be immediatelyvisible; to quickly identify important "landmarks"in the incision; and to mentally organize multisensory data and actions at any given point of the procedure so as to allow a smooth and efficient sequence of responses.

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We next turned our attention to an analysis of specific ability patterns which might relate to strengths and weaknesses often noted by attending surgeons when evaluating their residents.

Neuropsychological studies of brain-behavior relationships in both clinical and nonclinical populations have suggested that spatial and organizational abilities "localize" to the right hemisphere, whereas verbal abilities are functionally related to the left hemisphere (Eccles, 1973; Jackson, 1878; Levy, 1974; Milner, 1974). This asymmetry is substantially more evident in males. who tend to be more spatially and motorically proficient than females, although less verbally proficient (McGee, 1979). Females tend to exhibit a more diffuse, or bilateral, representation of both verbal and perceptual abilities (Levy, 1973; Nottebohm, 1979). One theory proposes that gender-based differences in ability patterns have developed historically as the result of differential survival values: the greater degree of language localization in males ensuring adequate 'neurosynaptic room' for the spatial abilities necessary to hunting and defense (Witelson, 1977; Zurif & Carson, 1970). An alternate proposal suggests that these differences result from unequal maturational rates for verbal and perceptual-motor abilities in the two genders, since females experience more rapid physical maturation than males and their language abilities develop at a faster rate than perceptual and motor functions (Waber. 1976; Witelson, 1976). A second variable known to affect ability patterns is hand preference. Less functional asymmetry is found among the left-handed minority of the population (approximately 7%) than among right-handers (Coren & Porac, 1977; Hecaen & Sauget, 1972). Some studies have shown an association between left-handedness and less development of spatial skills, leading to the suggestion that the lack of functional asymmeny of this group is similar to that of females (Galaburda, Lemay, Kemper & Geschwind, 1978; Levy, 1973). Our observations of left-handed surgical residents over the years suggested that they experienced more difficulty in the operating room because they were working with right-handed attending surgeons across the table and because many surgical instruments are right-handed. Although a few of these individuals attempted to solve the problem by purchasing custom instruments, most eventually leamed to perform surgical tasks in a right-handed fashion, presumably to avoid the wrath of an attending with whom they were spending many hours each day. The question presented by these observations was whether left-handedresidents differed from their dextrous counterpans in innate abilities contributing to surgical skill, or whether the perceptions of their tonnent in the operating room were merely environmentally caused. Age is a third parameter known to influence cognitive ability patterns. Psychomotor and visuospatial capacities consistently appear to be more susceptible than verbal abilities, to deterioration caused by the aging process (Lezak, 1976). During normal aging, motor speed and manual dexterity begin to show some deterioration around the third decade, with decrement in spatial ability beginning a decade later (Botwinick, 1981; Reed & Reitan, 1963). Particularly because of the long hours involved in a surgical residency, concern is often expressed by attendings during the decision to admit an otherwise well-qualified, but older, candidate to training. Age-related decline in basic abilities versus endurance factors seemed to be a good

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differential question to ask of our data. Whatever the outcome of theoreticaldebate, these three parameters appear to be important variables in investigations of expertise in complex task performance involving nonverbal cognitive abilities. Based on earlier data, we expected that older residents would perform less well on psychomotor tasks than younger residents but that visuospatial abilities exhibited by the two groups would not differ. In accordance with the existing literature on gender differences. we also hypothesized that male residents might demonstrate a higher level of visuospatial ability, but lower verbal achievement than females. Finally, we tested the assumption that left-handed residents might be less proficient than right-handers in psychomotor and nonverbal cognitive abilities which we had already identified as central to surgical expertise. To examine these issues, we selected subgroups from the original pool of 141 general surgery residents involved in our training program between 1978 and 1984 (Schueneman, Pickleman & Freeark, 1985). The average age (27.1 years) was computed for this group, and 83 residents were assigned to a "younger" group (range=23 through 27 years) and 35 were assigned to an "older" group (range=28 to 42 years) on this basis. In order to compare genders, a modified matched groups design was necessary, since only 13 females (10%)were available for the study. Matching variables were age, level of training, year of entry into the program, and right-hand preference (none of the women were left-handed). Similar methods were employed in constructing matched groups by hand preference, using the same matching variables, since the total number of left-handed residents (all male) was 14, as determined by the Halstead-ReitanLateral Dominance Examination. Again, we utilized Medical College Admission Test and National Board scores as indices of verbal ability; scores from our battery of neuropsychologicaltests as measures of psychomotor and perceptual abilities; and ratings of surgical skill as indicators of technical expertise. Analysis of variance with repeated measures revealed that older residents, as expected, exhibited significantly less motor speed and bimanual coordination (p<.Ol) than the younger groups; but no significant decline on measures of perceptual, perceptuomotor, spatial memory or stress tolerance abilities. The only difference we did find (pe.05) between the two groups on perceptual or perceptuomotor task performance was on the error score for the Porteus Mazes, indicating that older residents tend to be more cautious in avoiding mistakes on this visuomotor planning task. Furthermore, older and younger residents exhibited no significant differences in patterns of verbal abilities, nor did their ratings of technical expertise differ. Gender differences were found consistent with theoretical expectation. Female residents were superior to male residents on measures of verbal achievement (pe.05); demonsrratedgreater proficiency than males on a visual signal detection task (Concealed Figures, p<.Ol); equalled males on pure motor measures; but performed less well than their male counterparts when the task included both visual analysis and motor components (Porteus Mazes, p<.05). Thus, in task situations requiring both visuoperceptual and psychomotor abilities, females appear

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to be at a disadvantage. It has been pncisely this type of task that has been found to be most sensitive to right hemisphere functioning in general (Sidtis & Gazzaniga, 1983) and to gender differences in functional asymmetry (Levy,1973). This is also the type of task we previously reported as most related to surgical proficiency (Schueneman, et al., 1984). This difference may be related to the greater degree of caution (p<.05) exhibited by females in performing these percepniomotor tasks. Although not statistically significant. female residents also tended to show slightly mon difficulty in spatial memory tests, stress tolerance and efficiency in performing bimanual tasks. Concordant with the above findings, rated surgical skill of female residents was found to be slightly, but consistently lower than that for matched male residents. The most significant difference between the groups (p<.OS), was seen on the item "Security in performance" from the surgical skills rating scale (Figure 10.2). Several other items, primarily involving task organization and bimanual coordination, approached significance. Left-handed and right-handed residents did not differ on any of the indices of verbal ability M on measures of psychomotor. perceptual or spatial memory abilities. Interestingly, lefthanded residents were clearly superior (pc.05) in the performance of a difficult spatial-motortask (Tactual Performance Test); but slightly less efficient (p=ns) on a somewhat similar task involving vision and motor planning (Porteus Mazes). Unlike females, who were also less efficient on this task as a result of cautiousness in avoiding errors, the left-handed men actually made more errors than their right-handed counterparts.

1. Precision in use of scissors. 2. Accuracy in placement of sutures.

3. Fine motor coordination during placement of difficult sutures. 4. Facility in following curve of needle when suturing (e.g., does the resident "skid the needle?). 5. Security in performance (e.g. general confidence in operating ability). 6. Avoidance of nonpurposeful movements. 7. Efficiency in use of traction and countenraction. 8. Knot tying ability. 9. Overall organization in the operating room. 10. Appropriate use of both hands when operating. 11. Overall technical ability. 12. Ability to plan sequences of different activities throughout the surgical procedure (e.g., resident acts as if aware of a sequence of steps or subprocedures and moves smoothly from one step to the next). Table I

.

Surgical skills rating scale.

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Of all of the subgroups studied, the left-handed residents exhibited the greatest degiee of improvement in rated surgical skill across the five years of the training program. Whether this phenomenon is something inherent in left-handedness or if our finding simply reflects a successful adaptation to a particular environment, is difficult to determine. The finding that lefthanded residents were superior to right-handed counteqarts on spatial perceptuomotor tasks clearly indicates a level of innate ability which we know to be the best general predictor of operative expertise; but the finding does not a d d n s s the nature-nurture problem insofar as skill changes observed as the result of practice. This dilemma also holds me for the female resident group. which was the least able to demonstrate improvement in rated skill proficiency during the five years of training.

Our older residents demonstrated the expected age-related decline in motor speed and manual dexterity; but did not differ in higher level perceptual and perceptuomotor abilities, verbal abilities or rated surgical skill (either overall or on a yearly basis) in comparison to the younger group. These results support our contention that pure motor ability is not the critical factor in surgical proficiency. Rather, relatively innate, nonverbal, perceptually-basedcognitions about complex spatial information appear to play a more central role in the operating room. This type of cognition is probably more difficult to describe and conceptualize because we do not routinely access it through verbal processing during our daily activity - it remains part of our “automatic pilot” which is only noticed when it is not functioning properly. The results of our multimethod-multitrait approach to the study of these phenomena as they interact with the surgical environment clearly suggest that these abilities are the product of higher cortical, integrative activity of the brain - not the hands. References Botwinick, J. (1981). Neuropsychology of Aging. In B.S. Fdskov & T.J. Boll (Eds.) Handbook of Clinical Neuropsychology. New York John Wdey & Sons, Inc. Coren, S. & Porac, C. (1977). Fifty Centuries of Right-Handedness: The Historical Record. Science, 198, 631-632. Eccles, J.C. (1973). The Understanding of rhe Brain. New York: McGraw-Hill Book Co. Galaburda, A.M., Lemay, M., Kemper, T.L.,& Geschwind, N. (1978) Right-Left Asymmetries in the Brain. Science, 199, 852-856. Cough, H.S., Hall, W.B. and Harris, R.E. (1964). Evaluation of Performance in Medical Training. Journal of Medical Education, 39, 679-692. Halstead, W.C. (1947) Brain and Inrelligence. Chicago: University of Chicago Press. Hecaen, H., & Sauget, J. (1972). Cerebral Dominance in Left-Handed Subjects. Correx, 8, 1948.

Jackson, H. (1878). On the Affections of Speech from Disease of the Brain. Brain, I, 304330.

Keck, J.W., Arnold, L., Willoughby, L. & Calkins, V. (1979). Efficacy of Cognitive/ Noncognitive Measures in Predicting ResidentPhysician Performance. Journal of Medical Education, 54,759-765. Kopta, J.A. (1971). An Approach to the Evaluation of Operative Skills. Surgery, 70,297-303.

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Lawrence, P.F.,Alexander, R.H., Bell, R.M., Folse, R., Guy. J.R.F., Haynes, J.L., Lauby, V.W., Stillman, R.M. & Cockayne, T.W. (1983). Determining the Content of a Surgical Curriculum. Surgery, 94, 309-317. Lazar, H.L., De Land, E.C. & Tompkins, R.K. (1980) Clinical Performance versus In-Training Examinations as Measures of Surgical Competence. Surgery, 357-362. Levy, J. (1973). Lateral Specialization of the Human Brian: Behavioral Manifestations and Possible Evolutionary Basis. In J. Kirger (Ed.) The Biology of Behavior. Corvallis: Oregon University Press. Levy, J. (1974). Psychological Implications of Bilateral Asymmetty. In S.J. Dimond & G. Beaumont (Eds.) Hemisphere Function in the Human Brain. London: Elek Scientific Books. Lezak. M. (1976). Neuropsychological Assessment. New York: Oxford University Press. McGee, M.G. (1979). Human Spatial Abilities: Psychomeuic Studies and Environmental Genetic and Hormonal and Neurological Factors. Psychology Bullerin, 86, 888-918. Milner. B. (1974). Hemispheric Specialization: Scope and Limits. In F.O. Schmitt & F.G. Worden (Eds.)The Neurosciences Third Snuly Program. Cambridge: MIT Press. Murden, R., Galloway. G.M., Reid, J.C. & Colwill. J.M. (1978). Academic and Personal Predictors of Clinical Success in Medical School. Journal of Medical Education, 53, 7 11-719. Nottebohm, F. (1979). Origins and Mechanisms in the Establishment of Cerebral Dominance. In M.S. Gazzaniga (Ed.) Handbook of Behavioral Neurobiology, Vol. 2. New York: Plenum Press. Price, P.B., Taylor, C.W. & Richards, J.M., Jr. (1971). Measurement and Predictors of Physician Performance. Salt Lake City: LLR Press. Reed, H.B.C. and Reitan, R.M. (1963). Changes in Psychological Test Performances Associated with Normal Aging Process. Journal of Gerontology, 18, 271-274. Schueneman, A.L., Pickleman, J., Hesslein, R. & Freeark, R.J. (1984). Neuropsychologic Predictors of Operative Skill Among General Surgery Residents. Surgery, 96, 2, 288295. Schueneman, A.L., Pickleman, J., & Freeark, R.J. (1985). Age. Gender, Lateral Dominance, and Prediction of Operative Skill Among General Surgery Residents. Surgery, 98, 3, 506514. Sidtis, J.J. & Gazzaniga, M.S. (1983). Competence versus Performance After Callosal Section: Looks can be Deceiving. In J.B. Hellige (Ed.) Cerebral Hemisphere Asymmetry. New York: Praeger Publishers. Spencer, F.C. (1983). Observations on the Teaching of Operative Technique. Bulletin American College of Surgeons, 68, 3-6. Waber, D.P. (1976). Sex Differences in Cognition: A Function of Maturation Rate? Science, 192, 572-514. Wangensteen, O.H. & Wangensteen, S.D. (1978). The Rise of Surgery. Minneapolis, MN: University of Minnesota Press. Willoughby, T.L., Gammon, L.C. & Jonas, H.S. (1979). Correlates of Clinical Performance During Medical School. Journal of Medical Education, 54, 453-460.

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Witelson, S.F. (1976). Sex and the Single Hemisphere: Specialization of the Right Hemisphere for Spatial Processing. Science, 193, 425-427. Witelson, S.F. (1977). Developmental Dyslexia: Two Right Hemispheres and None Left. Science, 195, 309-31 1. Zurif, E.G. & Carson, G. (1970). Dyslexia in Relation to Cerebral Dominance and Temporal Analysis. Neuropsychologia, 8, 351-361.

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CHAPTER 11 THE SKILL OF SPEECH PRODUCTION K.G. MUNHALL Department of Psychology Queen’s University, Kingston, Ontario, KIN 6N5 To a great extent, the field of skill research is devoted to the study of activities that are artificial. Skill is measured in the performance of activities that we acquire for employment, during education, and for experiments. As a result, the tasks that an studied are sometimes arbitrary and the performance level in the skill often has not reached asymptote. There exists, however, another class of skills that are naturally occurring and that better reflect the perceptual-motor demands on the species. For humans, speech production is one of this latter class of natural skills. Speech, like many other complex activities, can be described from a number of frames of references. It can be viewed as a purely cognitiveflinguistic activity in which skill is manifested in the representation and sequencing of linguistic primitives. Alternately, speech can be viewed as an acoustic skill in which the speaker acts so as to shape a complex patterning of sound frequencies and temporal intervals. Finally, speech can be seen in purely motoric terms where the skill is revealed in the speed and accuracy of the movements of the articulators. What makes speech a hvly remarkable skill is that the truth lies somewhere between all of these views. Unique aspects of the skill of speech can be seen within each of the cognitive, acoustic, and motoric domains. Most importantly, however, the skill of speech production lies between the acoustic and the movement levels and between both of these levels and the linguistic representation. As Fowler (1977) has pointed out the production of speech crosses the boundary between the realm of mental representation and the physical world. Linguistic units, defined in terms of informational dimensions, are mapped onto movements whose dimensions are force, spatial extent, and time. In the following pages I will briefly review some evidence for the separate acoustic, motoric, and linguistic levels of representation of the skill of speech. In addition I will begin to address the difficult problem of the mapping between these levels. Speech as Symbol Production Traditionally, the working assumption in the study of speech has been that a small number of basic sound units (phonemes) are combined to produce fluent speech. For example,

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the words "dog" and "bog" are distinguished by the initial sounds /b/ and /d/ and /b/ and /d/ are thus considered to be phonemes of English. Each language has an inventory of possible sounds or phonemes and a set of rules that prescribe how these sounds can be used in combinations. For example, in English the sequence "ps" as in "psychology' is not pronounced fully. Both sounds arc present in the inventory of English but the "p" is silent in this context because English does not allow /ps/ sequences at the beginning of a syllable. French, on the other hand, does allow this sequence and the /p/ is pronounced in "psychologie". These sequential constraints are called the phonotactics of a language. The inventory of phonemes and the phonotactics are part of the phonology and they represent a distinctly linguistic level of representation. The more than 5000 living languages differ widely in their phonological structure. The inventories of languages differ in size and complexity as does the phonotactic patterning. In Ian Maddieson's (1984) survey of 317 languages the smallest number of phonemes in an inventory was 11 while the largest number of phonemes was 141. Most languages in the database (70%) fell within a more moderate range between 20 and 37 phonemes. English lies above this group and has a relatively large number of both consonants and vowels. The modal number of vowels in Maddieson's database was 5. English, depending on how they are counted and the dialect of the speaker, has more than a dozen vowels. Maddieson has also made some estimates of phonotactic complexity using the number of possible syllables as a measure of this complexity. English, for example, allows syllables to be formed with 0,1, 2, and 3 consonants preceding or following the vowel. Other languages have different limitations on the types of syllables. In Bell's (1971) survey of syllable structures over 20% of the 114 languages in the database did not allow syllables which had consonants following the vowel. All the syllables used in these languages were open syllables with no final consonants. (In English words like "boy", "cow", and "see" are examples of open syllables.) As noted above, there are specific consonants that can occur in syllable sequences. For example, English allows syllables to be of the form CCVC and a large number of actual syllables fit this form (e.g., "stop", "trip", "spit", and "bliss" are acceptable English syllables while "vzop", "mbip", and "tfiss"are not.). The combination of the allowable syllable types and the phonemes that can occur in these types yields the total possible syllables. The total possible syllables varies widely in the languages Maddieson (1984) examined. Hawaiian allowed 162 possible syllables while Thai allowed 23,638 possible syllables. This variation was not simply a function of the number of phonemes in the language. This variation in the number and types of sounds and the complexity of their phonotactics indicates that the phonological level of representation is not strictly determined by the vocal tract. Each language community has a relatively arbitrary set of sounds and rules that can be realised by any vocal tract. Rather, this pattern of variation indicates that the phonology is a part of the skill that must be acquired by a speaker learning a language. Part of what it means to learn to speak French or Thai or Hawaiian is to learn the set of sounds and the acceptable arrangements of those sounds.

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Speech as Motor Control The production of speech involves a series of very rapid movements of many different parts of the oral motor system. The tongue tip, for example, produces distinct movements within approximately 50 ms (Kent & Moll, 1972). Consonant sequences such as /spV in the word "splash", involve a set of movements within a very short time span (Kent & Moll,1975). When we examine the timing of some of the movement events in this sequence we fmd that the interval between release of the /s/ and the closure of the lips for /p/ is in the range of 20 ms. The interval between the N closure and /p/ release has a similar timing range. This control of many different articulators at such rapid rates makes speech one of OUT most complex motor tasks. In the next sections I will outline some aspects of the motor skill involved in this behaviour. To begin I will describe the speech motor apparatus and then consider some of the characteristics of speech coordination.

The Vocal Tract In Figure 11.1, a midsagittal view of the vocal tract is shown. This is a magnetic resonance image of the vocal tract taken while the speaker is saying the vowel "aw" as in "hot". The approximate position of individual articulators are marked with numbers. The primary articulators are the lips (I), the jaw (2). the tongue (3), the soft palate or velum (4), the pharyngeal walls ( 5 ) and the larynx (6). Each of these articulators can contribute to the production of speech sounds by changing shape and/or position . The larynx is composed of a set of hyaline cartilages and the vocal folds. The vocal folds are the. primary source of sound in speech and are made up of muscles and a number of layers of connective tissue and epithelium. The folds have evolved primarily for protective purposes and can shut very quickly to prevent aspiration of any material into the lungs. During sound production the folds are approximated to produce the vibration during voicing and are pulled apart in some consonant productions. The pharyngeal walls move in a sphinctor-like action, narrowing the cavity called the pharynx and in some speakers they play a role in closing off the nasal cavity. The shape and size of the pharyngeal cavity are unique aspects of the adult vocal tract. In the human infant and in non-human primates the larynx has a much higher position and consequently the pharynx is reduced in size. This cavity substantially increases the vowel production repertoire of the adult speaker. The velum or soft palate plays the major role in opening and closing the velo-pharyngeal port which links the oral and nasal cavities. When this aperture is open sounds have a nasal quality such as "m" or "n". The velum raises and in some speakers the pharyngeal walls constrict to seal off the nasal passage for sounds that don't have a nasal quality. The jaw helps control the degree of openness of the vocal tract and also helps conml the position of the tongue. Two basic motions of the jaw produce these effects. The jaw rotates in the sagittal plane and also translates forward and down (Bateson & Ostry, 1992).

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Figure I 1 .I. A magnetic resonance image of the cross sectional view of the vocal tract. The approximate position of s o m of the articulators are marked. I . Lips 2 . Jaw 3. Tongue 4 . SOB Palate (Velum) 5 . Pharyngeal wall 6. Larynx

The lips control the size and shape of mouth opening but can also determine the effective length of the vocal tract. By protruding the lips, speakers can increase the length of the vocal tract. Linker (1982) has recently carried out a cross-linguistic study of lip shapes during vowel production. Using a principal components technique she has found that a variety of different parameters and combinations of parameters of lip shapes are used in languages such as Swedish, Cantonese, French, Finnish and English.

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The tongue is the most complicated but arguably the most important speech articulator. Like the lips it is a non-rigid body influenced by the actions of a large number of muscles. The tongue body is composed of the inainsic tongue muscles which combine to determine the shape and position of the tongue. In addition, a number of extrinsic tongue muscles exert forces on the tongue. When the tongue changes in shape and position, it valves the vocal tract in different locations. For example, when the vowel "ee" as in "feet" is produced the tongue is braced against the teeth and hard palate at the front of the mouth. When producing an "aw" as in "pot", the tongue is low and bunches back into the pharynx.

Speech Coordination The vocal tract shown in Figure 11.1 possesses a large number of degrees of freedom. By this I mean that the range of motions, the patterns of timing of motions and the number of independent directions of motions that can be produced by the speech articulatorsm quite large. During fluent speech each of these articulators can be in motion. To coordinate this motion the nervous system must control the timing and spatial accuracy of these movements. In striking contrast to the biophysically possible range of motion, the number of linguistically relevant units is usually portrayed as quite small for a given language. As noted above, the modal number of vowels in a language is five. A key question is how this multi-dimensional physiological system is coordinated to yield a phonetic message that has a lower dimensionality. A number of different kinds of evidence suggest that the nervous system controls its articulators in combination. By grouping the articulators the demands of control are reduced and the articulators can be linked for specific functions. The evidence supporting this view comes from two main sources: The study of response of the oral system to mechanical perturbation and the study of the relative timing of the movements of the speech articulators.

In speech, two types of perturbation procedures have been introduced: static and dynamic perturbations. Static perturbations involve a constant adjustment to the vocal tract of the kind that is quite frequently observed under natural conditions, c.g., when a person is speaking with a pen in the mouth. In such circumstances the vocal tract is either temporarily or permanently modified. The articulatory system must change in response to changes to the configuration of some of the articulators or to changes in the teeth or other hard boundaries of the tract. One group of studies called bite-block studies involve having speakers bite on an object so that the position of the jaw is frozen and so that the jaw posture can be manipulated by using bite blocks of various sizes. Studies of speech produced by speakers with a bite-block restricting jaw movements generally indicate that speakers can, and do, rapidly compensate (e.g., Lindblom, Lubker, & Gay, 1979; Lubker, 1979; Fowler & Turvey, 1980; Gay, Lindblom, & Lubker, 1981). These studies have revealed that speakers can modify their articulation so that the appropriate vowels are produced even during the fmt pitch period. To do this, speakers must modify their tongue articulation so as to compensate for the lack of motion of the jaw. Other types of alterations of the vocal tract that produce less rapid compensations have also been studied. Recently, Flege, Fletcher and Homiedan (1988) showed that there were articulatory, acoustic, and perceptual differences when dental consonants were produced under bite block

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conditions. In a study of more long term compensations,Hamlet and her colleagues (e.g., Hamlet & Stone, 1976,1978; Hamlet, Stone & McCarty.1978) have reported adaptation to an experimental prostheses for the hard palate. In a series of experiments, speakers were fitted with prostheses that thickened the palate in the alveolar region. Unlike some of the bite block studies compensation was not immediate. Speakers required several days to adapt to the prosthesis and fitting the speaker with a new prosthesis did not result in any greater ease in adaptation. It appeared that each prosthesis had to be learned individually. Also the removal of the prosthesis did not result in immediate return to the normal pattern of articulation. Hamlet et al. (1978) retested subjects who had previously worn the prosthesis at periods of months to a year following removal of the prosthesis. The subjects showed rapid compensation to the prosthesis with achievement of the previously adapted pattern of articulation being observed in minutes. One subject who had last worn the prosthesis a year earlier showed jaw adaptations similar to his original pattern of adaptation in the second repetition of the test utterances. A number of studies have used a dynamic perturbation methodology to study speech coordination (e.g.,Folkins& Abbs, 1975; Folkins & Zimmerman, 1982; Abbs & Gracco, 1984; Kelso, Tuller, Vatikiotis-Bateson,& Fowler, 1984; Gracco & Abbs, 1985.1988, 1989; Shaiman, 1989; Shaiman & Abbs, 1987). Most commonly, a sudden and unexpected load is applied during ongoing speech to one of the articulators such as the lower lip or jaw and the compensatory responses in other articulators such as the upper lip and the tongue are examined using kinematic records and/or EMG. The reported results of perturbation experiments have a number of common features (Usfqvist, 1990). 1.The compensations are observed on the first perturbed trial. 2. The compensations are rapid. 3. The compensations are effective. 4. The compensations we tailored to the articulation needs at the moment of perturbation. Each of these facets of the perturbation data reveals an aspect of the grouping or coupling process in speech coordination. Complete compensation on the first perturbation experienced by the subject has been reported frequently (e.g., Kelso et al., 1984). This finding indicates that no learning or experience is necessary for the subject to compensate. The importance of this is that it suggests that the kinds of compensations that are observed following perturbations are due to the coordination repertoire of the speaker. The potential for immediate compensation falls from something intrinsic to coordination and doesn’t have to be learned. The speed of the compensation has been assessed in studies in which simultaneous EMG and kinematic measures have been collected. Kelso, Tuller, Vatikiotis-Bateson, and Fowler (1984) found that increased activity in the genioglossus muscle occurs 20-30 ms after the onset if the jaw is loaded during the production of a dental fricative. Similarly, Abbs, Gracco. and Cole, (1984) observed an increase in upper lip activity 35-70 ms after the jaw was loaded during lip closure for a bilabial stop. The shon latencies have been interpreted as indicating that reaction time processes are not involved in the compensations.

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The functionality of the compensations has been demonstrated by showing different compensations as a function of context to the same perturbation. If the jaw is perturbed during the VC transition in the sequence /b;eb/, compensatoryresponses are seen in the lower and upper lips. If, on the other hand, the jaw is perturbed during the VC transition in the sequence /baed, a response is seen in the tongue (Kelso, Tuller, Vatikiotis-Bateson, & Fowler, 1984; Shaiman, 1989). This result suggests that something specific to the coordination which is involved in the particular speech act is at the mot of zhe compensations since the responses are not stereotypic. Finally, the compensations are said to yield perceptually effective productions. During speech production, one of the goals of the motor activity is to produce an acoustic signal that can transmit linguistic information. For this to be possible, the signal has to vary in the spectral and temporal domains according to the phonetics/phonology of the language being spoken. In the dynamic perturbation experiments, few perceptually salient acoustic deviations have been reported to date (Abbs, Gracco, & Cole, 1984). While the findings and conclusions of these perturbation studies of speech movements are important and appear to be robust, some of their Limitations should be noted. They have, with the exception of a few studies (Kollia, Gracco, & Harris, 1992; Munhall, Ufqvist, & Kelso, in press; Shaiman & Abbs, 1987) been dealing with articulators that are either mechanically linked (e.g., lower lip and tongue relative to the jaw) and/or functionally linked to produce a single constriction in the vocal tract (e.g., jaw-lips for bilabial constriction, jaw-tongue for tongue dorsum constriction). While EMG recordings suggest active muscular adjustments in compensations, it is difficult to assess articulatory movements from EMG recordings since simultaneous activation of several muscle groups commonly occurs while only a limited set of muscles can be recorded. In fact, changes in muscle activity inferred from EMG recordings do not necessarily comlate with kinematic measurements (Shaiman, 1989; Gracco & Abbs. 1989). The effectiveness of the observed compensations have generally been evaluated from kinematic records and casual ratings of the speech produced. Since the goal of speech production is a time-varying acoustic signal, it is desirable to verify the success of compensations by acoustic and perceptual analysis. While auditory analysis apparently has showed compensations to be successful, some acoustic differences appear to occur between perturbed and control trials in the published reports. These differences can be found in the temporal domain, since no spectral analyses have been reported. For example, Folkins and Abbs (1975) noted that cessation of voicing in a sequence of vowel and bilabial voiceless stop consonants was often delayed 15-25 ms when the jaw was loaded, even though the kinematic analysis showed that the upper and lower lips made spatial compensations. Similar results are reported by Shaiman and Abbs (1987). Munhall et al., (in press) have reported similar effects when the lower lip was perturbed. In addition, Munhall et al. found changes in the duration of the voice onset time of a /p/ consonant following perturbation. Even more drastic temporal differences between

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perturbed and unperturbed productions can be found in some recordings presented by Follcins and Zimmerman (1982). In their study, the perturbation consisted of elecdcal stimulation of the labial depressor muscles; the stimulation was applied during the transition from a vowel to a bilabial voiceless stop. Figure 3 in that study shows an increase in stop closure duration from approximately 300 ms to 500 ms in a normal and a perturbed trial. respectively. In the absence of more detailed acoustic analysis and more importantly. perceptual analysis, it may thus be difficult to evaluate the effectiveness of articulatory compensations. The study of interarticulator timing has revealed that the articulators show consistent timing relations across repetitions and across different speaking rates. Ufqvist and his colleagues (e.g., Ufqvist & Yoshioka. 1984) have shown that the larynx reaches its peak opening position very close to the time of stop consonant release by oral articulators such as the lips and tongue. This timing pattern is consistent across variations in speaking rate and linguistic smss level. Gracco has shown consistent patterns for the movements of the lips and jaw during the production of bilabial closures (e.g., Gracco, 1988; Gracco & Abbs, 1986). The subjects in these studies produced movements of the upper lip, lower lip and jaw such that the articulators reached peak velocity in a consistent order and the onsets of muscle activity in the different articulators also occurred in a consistent order. This patterning of the kinematics of individual articulators has been taken as evidence that the timing of movements by these articulators is controlled by a common mechanism. Sequencing in Speech The production of individual speech sounds is only part of the problem of speech coordination. In addition, the sequencing of movements must be controlled. Figure 11.2 shows kinematic data collected using the University of Wisconsin X-ray Microbeam system. This is a computerised x-ray tracking system which allows a large amount of kinematic data to be collected while exposing the subject to a limited x-ray dosage. Small gold pellets are attached to the articulators' surface and the positions of these pellets are tracked over time using a very small, computer-controlled, x-ray beam. Each of the pellet positions are recognised and future positions are predicted. The waveforms in Figure 11.2 show the vertical movement of the lips, tongue tip, tongue dorsum and jaw as a function of time. As can be seen, the articulators are all in motion simultaneously and there are no obvious phonemic boundaries.

One factor that enables such fluent articulation is that speech movements overlap in time. Movements and the phonemes they comprise aren't articulated individually in sequence but rather, movements of different articulators are "coarticulated'. The following examples illustrate anticipatory lip coarticulation. The vowels in the words "Sue" and "see" are produced with different lip shapes. The "00" sound in "Sue" is produced with rounded and protruded lips while the "ee" sound in "see" is produced with unrounded lips. It is natural in these sequences for the speaker to begin to round the lips for the "00" during the production of the "s". Overlap of this kind can be Seen in Figure 11.2; the lip movement for the /f/ in "finder" overlaps with the tongue tip movement for the /t/ in "fact".

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There is an extensive body of literature describing the articulatory and perceptual aspects of coarticulation (e. g., Stevens & House, 1963; Kozhevnikov & Chistovich. 1965; Stevens, House, & Paul, 1966; ohman, 1966; Daniloff & Moll, 1968; McNeilage & DeClerk, 1969; Benguerel & Cowan, 1974; Bell-Beni & Harris,1979,1982; Sussman & Westbury, 1981; Lubker, 1981; Lubker & Gay, 1982; Perkell, 1986). This context sensitivity of speech movements increases the complexity of the motor programming and yet it does not seem to limit the rate of output. Sequences of gestures can be produced at high rates and they are produced in a manner that maintains the integrity of the speech target. The maximum speaking rates that have been reported are truly remarkable. For example, John F. Kennedy, during a burst in a speech in December, 1961, spoke at a rate of over 300 words per minute. The Guiness Book of World Records reports that the John Moschitta spoke 545 words in 58.5 seconds in 1986. Speaking rates such as these are achievable only through the reduction of segment durations and through increasing the rate of sequencing. In a section of a comedy recording by John Moschina. he speaks some vowels with durations less than 30 ms. In one stretch he says the utterance, "Finally they get to California where Tom trips up the deputy and Jim kicks the deputy in the neck." in just over 2.5 sec. My production of this sentence at a normal speaking rate takes approximately twice that duration. The motor sequencing involved in producing speech is truly remarkable but the skill of speech is more complicated than just producing a rapid series of movements. To understand the full complexity of speech coordination, we must also consider the intervening medium between the motions of the axticulators and the perceptual units. I am referring to the acoustics of speech. The Vocal Tract as an Acoustic Tube During speech the vocal tract acts as an acoustic tube and the process of speaking can be viewed as the interaction of sources of sound with the filtering characteristicsof this acoustic tube (Fant, 1960). In Figure 11.3 a three dimensional view is shown of the airway itself. This figure was collected using magnetic resonance imaging during sustained vowel production by a single speaker. This view underlines the tube quality of the vocal tract and emphasises that the problem facing the speaker is a problem of tuning this exceedingly complex tube. For the past 30 years or more the field of speech production has worked within a theoretical framework called the acoustic theory of speech production (Fant. 1960) . In this approach, sound production in the vocal tract is approximated by a combination of a number of independent terms. This "source-filter model" of speech can be summarised by the equation shown below: P(f) = S(9 T(f) R(O where P(f) is the sound radiated from the mouth. S(f) is the acoustic spectrum of the sound source, T(f) is the transfer function of the vocal tract, and R(f) is the radiation characteristic. It is useful to consider the first two terms in this equation in some detail because the skill of speech can be independently manifested in these terms.

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Figure 11.3. 3-0 images of the vocal tract from magnetic resonance imaging. Thefigure shows 4 views of a single speaker's vocal tract during the production of a sustained "a"vowel as in "hat".

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Source Characteristics At the lower end of the vocal tract lies the larynx. The source of sound for vowels and many consonants is the vibration of the vocal folds in the larynx. The periodic acoustic energy that is created at the vocal folds is transmitted through the pharyngeal cavity into the upper vocal tract and is radiated out of the mouth. The sound characteristics of the laryngeal source are influenced by a number of mechanical and control variables. The tension on the folds and the mass of the folds alter fundamental frcquency while the shape of the spectrum is influenced by a number of factors such as medial compression of the folds. longitudinal tension and the presence of gaps between the vibrating folds. The source spectrum is composed of a number of harmonic partials of the fundamental frequency of the vibration and theoretically the amplitude of the partials should decrease at a rate of 12 dB/octave. With different loudness levels and pitches of the voice the source spectrum changes. For example, when people speak louder the pitch of voice usually goes up and the spectrum of the voice source envelope changes in shape. Generally, the higher harmonics increase more in amplitude than the lower harmonics. As a result the nonsinger produces distinctly different voice qualities under varying loudness and pitch conditions. Singers, on the other hand, must control the mechanical and aerodynamic factors that produce these acoustic effects in normal conversation, over a wide range of frequencies and loudness levels. One aim in skilled singing is to avoid sudden changes in voice quality when singing in different parts of the frequency and loudness ranges. Sundberg (1990) reports that singers producing loudness changes, maintain their characteristicmode of laryngeal vibration over a much wider range of singing frequencies and amplitudes than nonsingers. Filter Characteristics In response to the acoustic energy created at the larynx, the column of air above the larynx resonates. It is this complex pattern of resonance that gives speech its characteristic quality. The spectrogram in Figure 11.4 shows time plotted along the horizontal axis and frequency plotted along the vertical axis. The speaker is saying "the skill of speech production". This utterance is composed of a variety of different vowels and consonants and thus the spectrogram shows concentrations of acoustic energy in many different regions. One of the salient features of this plot is the presence of dark horizontal bands that can be seen throughout the utterance. These bands are commonly referred to as formants' and they correspond to the resonances of the vocal tract. As can be seen these patterns of resonances show a good deal of complexity over time. These patterns are produced by motion of the articulators. One determinant of the resonances in the vocal tract is its dimensions. The large vocal tracts of adults produce resonances that are lower in frequency than the resonances observed for children. Similarly, adult males tend to have lower resonances than females. As can be Seen in Figure 11.4, however, the pattern of resonances produced by a given individual

' Technically the term formant refers to the vocal tract resonances not the dark bands on the spectrogram. The position of spectrogram 'formants' could be influenced by a number of factors and an apparent shift in formant centre frequency will be observed.

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Figure 11.4. Acoustic displays of the utterance "the skill of speech production". The top panel shows the acollstic waveform with time on the abscissa and amplitude on the ordinate. The bottom display shows a speech spectrogram of the same utterance. Time is displayed on the abscissa and frequency is plotted on the ordinate. The distribution of energy is indicated by the darkness of the plot.

vary considerably. These changes are the result of motions of the articulators (Lindblom & Sundberg, 1971). The tongue, in particular, affects the pattern of acoustic resonances by forming constrictions within the vocal tract. The position and degree of constriction produce predictable patterns of formant change. When the tongue is positioned high in the mouth the

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first formant is low while for vowels in which the tongue is positioned low in the mouth the first formant is high. The second formant shows a relationship between the front-back positioning of the constriction in the vocal tract. The more.forward the tongue’s highest position is, the higher the second formant. These relationships are shown schematically in Figure 11.5.

Back

Front

“00“

High

a

“aw“ 0

Low

F2 (Hz)

Figure 115. The positions 4 of the author’s vowels are shown. The data are plotted with respect to the frequencies of the first and second formants. The relationship of the acoustic patern to the place of maximum constriction of the vocal tract is also indicated on the plot. For example vowels produced with the tongue high in the vocal tract can be seen to have low first formants.

We can picture the problem facing the speaker as similar to the task facing a craftsman constructing a fine horn. The horn maker must construct an instrument that has the correct overall dimensions but in the final analysis the truly great horns will require subtle adjustments

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to the cross-sectional area of the tube at different positions along its length. Speech production should be viewed as a similar process of acoustic tuning by vocal tract adjustments. Subtle positioning differences can produce differences in the radiated acoustics and speakers seem to control the vocal tract in ways that show some sensitivity to this fact. Recently, Perkell (1990) has presented evidence that speakers adjust the position of the tongue surface more precisely in acoustically relevant directions. For example, for the vowel "ee" as in the word "feed" the position of the tongue tip must be more precisely conaolled in the vertical plane than in the front-back direction. Singers Professional opera singers are sometimes required to produce remarkable acoustic effects with their vocal tracts. I will comment on two of these effects which demonstrate the tuning of the vocal tract to produce greater sound pressure level in singing. An effect called the singer's formant is a common atmbute of some types of singing. In particular, it is found in the bass, baritone, tenor, and alto ranges of opera singers. The singer's formant is a strong, relatively high frequency resonance of the vocal tract that serves the purpose of allowing the singer's voice to stand out from the background orchestra. Sundberg (1987, 1990) has argued that this formant is actually a clustering of formants that is achieve by adjustments in the lower pharynx and perhaps by lowering the larynx. The second vocal tract effect is found primarily in sopranos but also in some altos and tenors. At normal voice pitches, the fundamental frequency of the voice is lower than the first formant of the vocal tract. In females the first formant varies from approximately 300-900 Hz depending on the vowel and an average woman's speaking pitch is approximately 220 Hz. When sopranos sing however, the pitch of the voice can be much higher than the lowest vocal tract resonance. Frequencies above 800 Hz are not uncommon. It has been observed (Sundberg, 1990) that singers tend to tune the vocal tract so that the first resonance matches the pitch. The effect of this is to increase the overall sound pressure level of the sung note. The singer tunes the vocal tract by increasing the jaw opening. As was indicated in Figure 11.4, the lower the tongue and jaw the higher the first formant. Thus, as a soprano varies pitch at high frequencies the identity of the vowels is sacrificed in favour of acoustic power. The singer tracks the pitch of the voice with the first formant and so allows the vowels to be produced as relatively uniform in timbre. Mapping Between Levels As I said in the introduction, a key feature of speech production is that the units of the

linguistic representation map onto the units of the physical production of sound. In the last part of this paper I want to comment on the mapping between the levels of speech skill. I will mainly focus on the problems associated with mapping linguistic units onto movements but I will also briefly outline some of the problems associated with the articulatory/acoustic mapping. The production of an action is frequently described in terms of a series of sensorimotor transformations (Saltzman, 1979). Movements unfold from an initial goal or task level to a

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pattern of muscle activations and forces. The progression from goal to action is assumed to require a number of information processing stages or levels. If we view speech from a purely kinematic viewpoint, for example, then this process would involve stages in which geometrical transformations were required to relate the movement's vocal tract goals to the articulator and muscle geometries. One difticulty that must be overcome is that levels often do not map onto each other in a one-to-one fashion. This can be clearly seen when the relation between the acoustics and speech movements is examined. The relationship between the acoustic and articulatory aspects of speech is complicated by a number of factors. First, there is a non-uniqueness problem in the mapping between the acoustic patterns and vocal tract shapes. This means that more than a single tract shape can produce the same acoustics (Atal, Chang, Mathews, & Tukey, 1978). The vowel /u/, for example, can be produced with the tongue bunched at different locations near the velum. Second, Stevens (1989) has suggested that there are nonlinearities in the production of sound in the vocal tract. Some regions may be relatively stable acoustically in response to small changes in the articulator positions. Other regions of the vocal iract exhibit large changes acoustically in response to small articulator changes. Third, the vocal tract is a redundant system with excess degncs of freedom that can be used in different ways to achieve the same vocal tract shape. For example, the tongue and jaw, and lips and jaw can trade off their relative contribution to a vocal tract constriction (e.g., Sussman, MacNeilage & Hanson, 1973). This means that the individual articulators can have many positions for a given vocal tract shape and acoustic pattern.

Understanding how the linguistic primitives serve as action primitives is equally difficult. Traditional linguistic primitives underspecify details that we important for the control of a motor system. In flow charts that depict the stages of speech motor planning there is always a significant gap between the linguistic domain and that of action. For example, phonemic representation has traditionally not included an explicit representation of time. As a result, timing must be added by an independent motor control process in spite of the fact that timing seem to be important to many characteristics that define the inventory (Abramson, 1977). For the better part of this century (see Anderson, 1985 for a review) phonology has been discussed much in the manner I have described it in the preceding paragraphs. Sound units signifying our consonants and vowels have served as fundamental units i n phonological representations. These sound units are essentially timeless representations of sets of features that are concatenated to produce representations of words and morphemes. Since the mid 1970s, though, phonologists have been concerned about how well this approach captures the true representations of speech. Following John Goldsmith's (1976) thesis, many phonologists found the use of linear sequences of non-overlapping segments too resmctive to account for certain phenomena (e.g., vowel harmonies, tonal patterns following vowel deletion, etc.). As a result there have been a number of proposed reconceptualizations of the nature of the phonological primitives (e.g., dependency phonology (Ewen, 1982); autosegmental phonology (Goldsmith, 1976); CV phonology (Clements & Keyser, 1983); and metrical phonology, (Libeman &

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Prince, 1977). One of these phonologies. articulatory phonology (Browman & Goldstein, 1986; Fowler, 1977; Saltman & Munhall. 1989). has begun to use linguistic representations that directly reflect the organisational suuctures of movements. The linguistic units in this system have inherent dynamics and the features of the units reflect the organisation of the vocal tract physiology. New linguistic approaches such as this promise to reduce the mapping complexity. They also help focus attention on the representational requirements of motor planning. Recently, Jordan (in press) has summarised the need for internal models in carrying out complex movements such as speech. By internal models I am referring to mental representations of the biological plant (the vocal tract in this case) that are used by the speaker in the process of motor control. Such models could be useful, for example, in feedforward control. We encounter a variety of different dynamical conditions when performing actions and many of these environmental conditions are not easily predictable. Internal models of a skill may help us overcome these sudden changes in conditions and allow us to do so in a timely fashion. What is requirrd is that the speakers learn the characteristics of their own vocal tract and the biomechanical characteristics of their articulators. During development this means that children must create slowly changing models of their growing vocal tracts (Saltzrnan & Munhall, 1992). The vocal tract changes substantiallyin size and shape as the child matures and this has an impact on the acoustics of speech production as well as the dynamics of motor control . Hamlet’s study of vocal tract modifications summarised above suggests that we can modify or acquire new internal models as adults. If the phonological regularities of a language can be captured in a form such as this, that is more consistent with the needs of the motor system, then the mapping problem between phonology and articulation is considerably reduced.

Summary Being a natural skill has had a number of repercussions for the study of speech. First, there is almost universally a high level of skill. A vast majority of the population speaks at least one language at a high level of proficiency. Secondly, and perhaps as a result of speech’s universality, there is virtually no study of individual differences in speaking ability outside of research on special populations such as patients with communication disorders. We find no studies of expertise in speech and no evidence that people are concerned about individual variation between speakers. Finally, there is little study of skill acquisition in speech outside of child development. In spite of these differences in the study of speech as a skill, we do know a good deal about the nature of the skill of speech. It is clear that separate components of skill can be seen in quite distinct representations of speech. A major challenge facing researchers interested in spoken language is how to close the apparent gap between these representations.

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References Abbs, J., & Gracco, V. (1984). Control of complex motor gestures: Orofacial muscle responses to load perturbations of the lip during speech, Journal of Neurophysiology, 51, 705-723. Abbs, J., Gracco, V., & Cole, K. (1984). Control of multimovement coordination: Sensorimotor mechanisms in speech motor programming, Journal of Moror Behavior, 16, 195-232. Abramson, A. (1977). Laryngeal timing in consonant distinctions, Phonerica, 34, 295-303. Anderson, S.R. (1985). Phonology in the Twentieth Century. Chicago: University of Chicago Press. Atal, B., Chang, J., Mathews, M. & Tukey, J. (1978). Inversion of articulatory-to-acoustic transformation in the vocal tract by a computer-somng technique, Journal of rhe Acoustical Society of America, 63, 1535-1555. Bateson, E.V.& Ostry, D.J. (1992). Rigid body reconstruction of jaw motion in speech. Paper presented at the 124th meeting of the Acoustical Society of America, New Orleans. Bell, A. (1971). Some patterns of Occurrence and formation of syllable structures. Working papers on Language Universals. Language Universals Project, Stanford University. Bell-Berti. F., & Harris, K.S. (1979). Anticipatory coarticulation: Some implications from a study of lip rounding. J. Acousr. SOC. Am., 65, 1268-1270. Benguerel, A.-P., & Cowan. H. (1974). Coarticulation of upper lip protrusion in French. P honerica, 30, 9-20. Browman, C., & Goldstein, L. (1986). Towards an articulatory phonology. Phonology Yearbook, 3,219-252. Clements, G . & Keyser, S. (1983). CV Phonology: A generan've rheory of rhe syllable. Cambridge, MA: MIT Press. Daniloff, R. & Moll, K. (1968). Coarticulation of lip-rounding. Journal of Speech and Hearing Research, 1 1 , 707-721. Ewan, C. (1982). The internal structure of complex segments. In H. Van der Hulst & N. Smith (Eds.), The Srrucrure of Phonological Representarions, 2 , Dortecht: Foris. Fant, G. (1960). Acousric Theory of Speech Production. Mouton: The Hague. Flege, J., Fletcher, S., 8c Homiedan, A. (1988). Compensating for a bite block in /s/ and /tJ production: Palatographic, acoustic, and perceptual data. J . Acousr. SOC. Am., 71, 1225-1233. Folkins, J., & Abbs, J. (1975). Lip and jaw motor control: Responses to resistive loading of the jaw. Journal of Speech and Hearing Research, 18, 207-220. Folkins, J., & Zimmerman, G. (1982). Lip and jaw interaction during speech: Responses to perturbation of lower-lip movement prior to bilabial closure. J. Acoust. SOC.Am., 71, 1225-1233. Fowler, C. (1977). Timing Control in Speech Producrion. Bloomington: Indiana University Linguistics Club. Fowler, C., & Turvey, M. (1980). Immediate compensation in bite-block speech, Phonerica, 37, 306-326.

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Gay, T., Lindblom, B., & Lubker, J. (1981). Production of bite-block vowels: Acoustic equivalence by selective compensation, J. Acourt. SOC. Am., 69, 802-810. Goldsmith, J. (1976). Aurosegmental Phonology. Indiana University Linguistics Club. Gracco, V. (1988). Timing factors in the coordination of speech movements. Journal of Neuroscience, 8, 4629-4639. Gracco, V., & Abbs, J. (1985). Dynamic control of the perioral system during speech: Kinematic analyses of autogenic and nonautogenic sensorimotor processes, Journal of Neurophysiology, 54, 41 8-432. Gracco, V.L., & Abbs. J.H. (1988). Central patterning of speech movements, Experimental Brain Research, 71. 515-526. Gracco, V., & Abbs, J. (1989). Sensorimotor characteristics of speech motor sequences, Experimental Brain Research, 75, 586-598. Hamlet, S., & Stone, M. (1976). Compensatory vowel characteristics resulting from the presence of different types of experimental dental prostheses, Journal ofPhonetics, 4 , 199-218. Hamlet, S . , & Stone, M. (1978). Compenstory alveolar consonant production induced by wearing a dental prosthesis, Journal of Phonetics, 6 , 227-248. Hamlet, S., Stone, M.,& McCarty, T. (1978). Conditioning dentures viewed from the standpoint of speech adaptation. Jordan, M. (in press) Computational aspects of motor control and motor learning. In H. Heuer & S . Keele (eds.) Handbook of Perception and Action: Motor Skills. New York: Academic Press. Kelso, J.A.S., Tuller, B., Vatikiotis-Bateson, E., & Fowler, C. (1984). Functionally specific articulatory cooperation following jaw perturbations during specach: Evidence for oordinative structures. Journal of Experimental Psychology: Human Perception Performance, 10, 812-832. Kent, R., & Moll, K. (1972). Cinefluorgraphic analyses of selected lingual consonants. Journal of Speech and Hearing Research, 15, 453-473. Kent, R., & Moll, K. (1975). Articulatory timing in selected consonants. Brian and Language, 2 , 304-323. Kollia, E., Gracco, V., & Harris, K. (1992). Functional organization of velar movements following jaw perturbation. J. Acourt. SOC.Am., 91, 2474. Kozhevnikov, V., & Chistovich, L. (1965). Speech: Articulation and Perception. Joint Publications Research Service, 30, 543; U.S.Department of Commerce. Liberman, M., & Prince, A. (1977). On stress and linguistic rhythm. Linguistic Inquiry, 8, 249-336. Lindblom, B., Lubker, J., & Gay, T. (1979). Formant frequencies of some fixed-mandible vowels and a model of speech-motor programming by predictive simulation, Journal of Phonetics. 7. 147-162. Lindblom, B., & Sundberg, J. (1971). Acoustical consequences of lip, tongue, jaw and larynx movement. J . Acourt. SOC. Am., 50, 1166-1179. Linker, W. (1982). Articulatory correlates of labial activity in vowels: A cross-linguistic study. UCLA Working Papers in Phonetics 56.

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Lubker, J. (1979). The reorganization time of biteblock vowels, Phonetica, 36, 273-293. Lubker, J. (1981). Temporal aspects of speech production: Anticipatory labial coarticulation. Phonetica, 38. 51-65. Lubker. J.. & Gay. T. (1982). Anticipatory labial coarticulation: Experimental, bilogical, and linguistic variables. J. Acoust. SOC. Am., 71. 437-447. Ufqvist, A., & Yoshioka, H.(1984). Intrasegmental timing: Laryngeal-oral coordiantion in voiceless consonant production, Speech Communication, 3, 279-289. Maddieson, 1. (1984). Patterns of Sound. Cambridge: Cambridge University Press. McNeilage, P., & DeClerk, J. (1969). On the motor control of coarticulation in CVC monosyllables. J . Acoust. SOC. Am., 45, 1217-1233. Munhall, K.G., Ufqvist. A., & Kelso, J.A.S. (in press) Lip-larynx coordination in speech: Effects of mechanical perturbations to the lower lip. J. Acoust. SOC. Am.. Ohman, S . (1966). Coarticulation in VCV utterances: Spectrographic measurements. J . Acowt. SOC. Am., 41, 310-320. Perkell, J. (1986). Coarticulation strategies: Preliminary implications of a detailed analysis of lower lip protrusion movements. Speech Communication, 5 , 47-68. Perkell, J. (1990). Testing theories of speech production: Implications of some detailed analyses of variable articulatory data. In W. Hardcastle & A. Marchal (Eds.) Speech Production and Speech Modelling. Kluwer: Dordrecht. Saltzman, E. (1979). Levels of sensorimotor representation. Journal of Mathematical PSyCholOgy, 20, 91-163. Saltzman, E., & Munhall, K. (1989). A dynamical approach to gestural patterning in speech production. Ecological Psychology, I , 333-382. Saltnan, E., & Munhall, K. (1992). Skill acquisition and development: The roles of state-, parameter-, and graph-dynamics. Journal of Motor Behavior, 24, 49-57. Shaiman, S. (1989). Kinematic and electromyographic responses to perturbation of the jaw, J. Acowt. SOC. Am., 86, 78-88. Shaiman, S., & Abbs, J. (1987). Sensorimotor contributions to the temporal coordination of oral and laryngeal movements for speech, SMLC Preprints (Speech Motor Control Laboratories, University of Madison) Spring-Summer 1987, 185-202. Stevens, K. (1989). On the quanta1 nature of speech. Journal of Phonetics, 17, 3-46. Stevens, K., & House, A. (1963). Perturbations of vowel articulations by consonantal context: An acoustical study. Journal of Speech and Hearing Research, 6 , 111-128. Stevens, K., House, A., & Paul, A. (1966). Acoustic description of syllable nuclei: An interpretation in terms of a dynamic model of articulation. J . Acoust. SOC. Am., 40, 123-132. Sundberg, J. (1987). The Science of the Singing Voice. Dekalb. Ill.: Northern Illinois University Press. Sundberg, J. (1990). What’s so special about Singers? Journal of Voice, 4 . 107-119. Sussman, H., MacNeilage, P., & Hanson, R. (1973). Labial and mandibular dynamics during the production of bilabial consonants: Preliminary observations. Journal of Speech and Hearing Research, 16, 397-420.

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Sussman, H., & Westbury, J. (1981). The effects of antagonistic gestures on temporal and

amplitude parameters of anticipatory labial coarticulation. Journal of Speech and Hearing Research, 46, 16-24.

Acknowledgements I would like to thank E. Vatikiotis-Bateson and C. Gracco for providing me with MRI images. M. Kimelman and S. Shaiman for providing the spectrogram, and L. Goldstein for the John Moschitta tape. Thanks to P. Thompson for reading an earlier version of the manuscript. This work was supported by a grant from NSERC.

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Part 3 Acquisition and Development Aspects

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COGNITIVE ISSUES IN MOTOR EXPERTISE J.L.Starlces and F. Allard (Editors) 0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 12 A STITCH IN TIME: COGNITIVE ISSUES IN MICROSURGERY JANET L. STARKES*, IRENE PAYK**, PETER JE"EN***, DAVID LECLAIR*

* Department of Kinesiology McMaster University, Hamilton, Ontario, US 4Kl ** Microsurgery Lab McMaster University Medical C e w e Hamilton, Ontario, US 4KI

*** Faculty of Medicine, Universiv of Limburg Maastricht, The Netherlands Why study microsurgery as a skill and microsurgmns as experts? Microsurgery is an ideal venue for the sNdy of motor skill for a number of reasons. In general very little is known about the acquisition of movement skills performed at the microscopic level. This is true whether we talk about surgery, watchmaking, or the construction of electronic components. Even researchers in human factors have given little effort to the empirical study of micromovements. In microsurgery we know very little about the acquisiton or development of skill, or the transfer (positive and negative) of skill from macrosurgery. While there are relatively few microsurgeons in the population, it is a skill that is increasingly in demand. Because microsurgery is a cluster of skills that cross many specialties of medicine it impacts most areas. Some factions now suggest that all surgical residents should have some exposure and training in microsurgical techniques. Beyond these more academic reasons, microsurgery is a fascinating area - to think that you can transplant whole organ systems by grafting and/or reconnecting small vessels and nerves microscopically is amazing.From a researcher's point of view the medical community has been very supportive of our research in providing facilities, equipment, and volunteers as subjects. With the growing demand for microsurgeons, new questions are emerging about the best training methods, standardization of procedures, and varying training methods to meet prior levels of surgical experience.

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At the moment most courses in microsurgery are approximately one week in length so the entire learning curve of a student can be monitored and plotted. As the subject is operating, a second pair of eyepieces on the microscope allows the researcher to either monitor, photograph, or videotape performance. On the other hand to acquire “expertise”and efficiency in microsurgery requires many years experience in the clinical setting.

Historical Background While microsurgical techniques have a long history, the widespread use of microvascular surgery across a variety of subdisciplines of medicine is a relatively new phenomena. Really the history of microsurgery is linked to the development of better optical magnification of the surgical field and refinement of microinstruments. Early techniques using microscopes were developed primarily for the purposes of research. Alexis Carrel’s pioneering work on vascularized organ transplantation (Carrel, 1902) appears to have been the first report of microsurgical techniques being employed (Westbroek, 1988). Otolaryngologistswere the first to see the clinical benefits of microsurgery and operations on microstructures of the eyes and ears lead to the development of more sophisticated microscopes, equipment and techniques (Nylen, 1954; Pemtt, 1950). Jacobson (1960) first advocated the use of microsurgery in small blood vessel anastomoses, and over the last thirty years the use of loupes and microscopes has mushroomed. Today complex procedures are performed both on animals and humans. Most advanced techniques are first developed and trained using an animal model and later transferred to clinical use. Loupes or magnification spectacles arc used at lower levels of magnification (2X-8X) while operating microscopes work generally in the range of 9-4OX. In exceptional cases some operations require as high as 6OX. An interesting aspect of the development of microsurgery is that it did not develop as a separate subdiscipline of medicine. Techniques evolved for use in a huge variety of specialties. Two types of surgery have developed: one that is general involving tubal repairs such as blood vessels, vas deferens, fallopian tubes or nerves, the other branch deals with transplantations. Today microsurgery is used in areas such as plastic surgery, examples of which would be digit replantation or face reconstruction using free tissue flaps. In gynecology it might be used to reverse female steralization by fallopian tube anastomosis. In males a vasovasostomy may reconnect the vas deferens to reverse a vasectomy (Lee, 1985). From a research perspective it is used broadly in projects ranging from experimental colectomy on small animals, to experiments in new nerve grafting techniques. In view of this increased emphasis on microsurgery an International Society for Microsurgery was founded and several textbooks are now available on standardized procedures for microsurgery. As new procedures and instrumentation emerge the standardization of techniques becomes particularly important. Microsurgery: A Task Analysis It’s tempting to think that the primary skill involved in a task like microsurgery is simply

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controlling hand tremor. In reality this is quite controllable and very early in training students are taught ways to control and eliminate tremor. Simple biomechanically sound positions that reduce degrees of freedom of movement at the elbows and wrists and learning to hold and manipulate the instnments properly usually are sufficient. While most microsurgeons avoid heavy consumption of caffeine, alcohol, etc. most also report that not disrupting one’s routine of when they have a coffee is more important for tremor control. Things like not participating in heavy or repetitive manual work in close proximity to surgery are the kinds of simple precautions taken. What then makes microsurgery such a unique and difficult skill? To answer this we’ll look at body position, using the microscope, problems in working within a microscopic visual field, the kinds of manual skills required, and the normal stresses of the surgical environment.

Body Position Figure 12.1 illustrates the typical seated position of the surgeon when working. In the seated position the legs are spaced approximately shoulder width apart, which with the stool produces a three point stance. The back and neck are straight and relaxed to reduce physical strain. Note the arms are ”ramped”up to the surgical field. A pyramid-like structure of surgical linens is created to suppon the arms, although no body weight is canied by them. This places the hands in a relaxed position right at the surgical field under the microscope.

Figure 12.1. A microsurgeon at work. Reproduced from Manual of Microvascular Surgery, 1975, with the permission of Davis & Geck.

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Hand position is critical. The side of the hand and fourth and fifth finger rest against the ramp and provide support for the middle finger. Instruments are held in a "chuck grip between the thumb and first two fmgers, much like holding a pen. Having these three fingers always in contact reduces extraneous movement. Various kinds of high or low frequency tremors result if the hand or instruments are. not supported properly. For example, if an instrument is being overly supported by flexor carpi radialis instead of the middle finger, a high frequency tremor results.

The Microscope and Task Surgical microscopes normally operate through magnification ranges of 9X to 4OX. Three adjustable clamps allow positioning of the scope directly over the surgical field. The lenses are then calibrated for the surgeon. Visual acuity can be adjusted by diopter rings, interpupillary distance adjusted, and focus is checked throughout the entire range of magnification. Once calibrated the visual field stays in focus throughout the range of magnification. During surgery focus and magnification level is controlled by two double foot pedals. Although recent advances have produced a voice controlled microscope, the facilities in most operating rooms still requk surgeons to divide attention between the visual field, the manipulation of instruments with the hands, and controlling size of the visual field and focus with the feet.

In order to understand the task, let's examine one of the most basic procedures - a vein anastomosis. Figure 12.2 is a photograph of the femoral vein of a rat being sutured back together. The magnification level is 16X.

Figure 12.2. End-to-end anastomosis of the femoral vein of a rat. (16x)

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In this case the vein was clamped off (double clamp is visible), the vein cut, and adventitia was trimmed away from each end of the vein. The surgeon placed two "stay" sutures that support the vein by connecting it to the clamp, 5 intempted or single sutures were placed down one side of the vein to reconnect it, the clamp was turned over and she is about to complete 5 remaining sutures down this side of the vein. Surprisingly the actual vein is only 1.5 mm in diameter! This is the normal method for an end-toad anastomosis or reconnection of a vessel. Following the anastomosis the two stay sutures are cut free, the clamp is removed and w that blood flows back through it. An the functional patency of the vein is tested to make s artery is somewhat smaller (lmm)and would requirt only 9 sutures. If a vessel is grafted into the side of another the method is r e f e d to as a T-graftor end-to-side anastomosis. If the sutures run continuously (like a blanket stitch) without being tied off they are r e f e d to as running sutures. This terminology becomes important later when current research is presented. There are several difficult aspects of working within a small magnified visual field. First, since the field is under a ringer solution to protect it from the bright light and heat, from drying out, and to keep the lumen of the "flimsy" vein open, refractoriness occurs each time the needle and suture go underwater. The water also creates problems of surface tension that can cause the suture to stick to itself or the needle and the bright light can create glare. Second, depth of field changes occur each time magnification changes. For example to insert the needle in the tissue as seen in Figure 12.2 the magnification would be incleased to 4OX.At this level the total depth of the visual field is about 2mm. Any tiny tremor at this level looks like an earthquake. If the instrument touches the field it may depress it, putting everything out of focus. To pull the suture through the tissue and to tie the knot the magnification level is decreased to 16X. At this level about 1.5 cm of field are visible, and small movements are somewhat less destructive.

Research on the Training of Microsurgeons In the first research study of microsurgeons we were interested in following the performance of a skilled macrosurgeon as he first began to learn microsurgical techniques. Though he had all of the requisite declarative knowledge such as how to tie sutu~s,prevent thrombosis test patency etc. in place for surgery at the macro level, his procedural, or movement skill at the macro level was no longer of use. The learning curve of a macrosurgeon "turned micro" is really a study where "knowing" is intact and must be linked with entirely new ways of "doing". As such, one can plot the acquisition of procedural learning almost independently of declarative changes. In the first study (Starkes, 1990) suturing performance of an oral surgeon (M) was measured throughout a five day course in microsurgery. The basic skill learned was the interrupted suture technique and this was eventually used to perform end-to-end anastomosis of the femoral artery and vein of a rat. To complete the course M was required to successfully reconnect these vessels and restore blood flow in a live rat. Along the way there was a progression in task difficulty from suturing glove material, to an end-to-end anastomosis of an artery and finally a vein.

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Throughout the course the researcher monitored performance through the second pair of eyepieces of the microscope and timed how long it took M to complete a suture from insertion of the needle until it was tied and cut off. Speed in suturing is never a criteria in actual surgery. The surgeon does each move as slowly as necessary, and with as little trauma to the vessel as possible. Nevertheless the more efficient one becomes the less time it takes to perform each manoem. Figure 12.3 illustrates M's learning curve over the five day course and also recall when he returned on day 10.

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Several things are apparent in this graph. First, on day one M takes 45 and 36 minutes to complete each of two single sutures in glove material. By the afternoon he needs only 3.5 minutes and performance is less variable. On day 2 he spends the moming observing a videotape of an arterial anastomosis and later when he attempts his fmt s u m s in live tissue his time of 13 minutes shows retention from day 1, in spite of the more difficult task. Each day his performance time is reduced and variability decreases. in terms of accuracy he goes from only being able to do 3 sutures of the 6 required in a 1 mm space to placing 12 sutures m u n g a 1.5 mm vein. By day 5 he is able to achieve functional patency so that blood flow is restored. Clearly this case study shows a negative transfer effect on day 1. Wrist movements that were once a primary part of his surgical skill must now be inhibited and new "movement patterns" used. Once the new procedures are in place (day 1, pm) practice increases the efficiency both by economy of movement and less traumatic handling of the tissue. A unique aspect of microsurgery is that not everyone who learns it is a surgeon prior to taking the course. Very often graduate students or technicians learn the skills for laboratory and research purposes. This allows us to study more closely whether prior procedural knowledge really does hamper initial learning. The next study followed the performance of a macrosurgeon and a fourth year medical student (with no prior surgical experience) in their completion of the course. Figure 12.4 shows the learning curves for each subject. The upper graph is the data for the surgeon and the lower the student's.

In constrast to the first study the dependent measure was time from needle insertion to tie off and did not include the time to change instruments and cut the suture. This was done to reduce the variability associated with changing instruments. While this dependent measure reflects only one part of the suturing task, it is a more direct measure of efficiency of the one criterion movement sequence. Again the data point to negative transfer effects of prior surgical experience that influence performance early in learning. The finding is also in line with observations of the course instructor who reports that very often students with no prior surgical experience have an easier time learning movements in the early stages of the course. These results pose several questions about the best training methods for microsurgery. As demand for microsurgical skill increases there is a need for substantive empirical work comparing training systems. Alternative training systems are already available for surgical training at the macro level (Salvendy & Pilitsis, 1980) and perhaps these kinds of programs will eventually be available for microsurgery. Over the last ten years the demand for training in microsurgical techniques has surged. Whereas ten years ago there were only a few places one could go to study microsurgery, there are now over 55 centres in the U.S. alone (Goossens et al., 1990) While a microsurgery course is not compulsory for surgical interns many individuals are recommending that it should be

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(Westbrock, 1988). Naturally as the area develops there is intemst in standardizing training programs. To this end Goossens et al. (1990) surveyed 55 U.S. Centres and received responses from 70% of them. From their survey the majority of centres handle very few students each year. Thirty-five percent of the centres handle less than 20 students a year, most handle betwen 20 and 50 students annually (42%) and a few large centres train over 100 people a year (13%). Even with these numbers, only a small percentage of surgical residents receive microsurgical training. As the authors point out, using the AMA Directory of Residency Training P r o m (1986-87). in the specialties that could most benefit h r n training them wen 2460 Fesidcnt positions of which only 28.5% received microsurgical training.

Of the centres that do offer training only 60% an a c d t c d to offer Continuing Medical Education (CME)credit. Of these centres most of the courses were accndited as 40 hour courses. The students who currently pursue courses wen comprised of 50% residents, 39% private physicians, and 11% researchers or technicians. In terms of course content the centres offer a variety of courses the most c o m n of which includes basic skills of operating the microscope and instruments, tying knots,end-to-cndanastomoses and a block of instruction related to the student’s specialty. Some centres offer only basic skills instruction, and others vary the whole course based on the student’s specialty. Almost exclusively the courses teach vascular procedures and rarely include procedures for dealing with nerves. The skills taught include: end-wend anastomosis of U e s and veins, and grafting end-to-side (T-graft) techniques. Surprisingly all of the centres use live ram for instructional purposes. This is unusual since, particularly in Europe, thm has been a move toward teaching microsurgery without the use of live animals. Alternative materials include surgical glove material, silastic tube, sciatic nerves of chickens. coronary arteries of pigs (Frcys 8t Koob, 1988), and human placental vessels (McGregor et al., 1983). Most authors still believe the use of live animals in training is necessary (Goossens et al. 1990, Westbmek, 1986). Rats have been found most suitable because of their hardiness, similarity of anatomical structures. there is less chance of nansmitting diseases to man, and as Lee notes (1985) there is less public sentiment attached to rats. In this sense the course at McMaster University (where most of our research is done) is quite standard. Physicians,researchers,and techniciansan admittedas students, while physicians are the predominant clients. The basic course runs for 40 hours (8 hours a day for 5 days). Occasionally however, students will require additional hours to meet the standards required. A somewhat shorter (4 day) course may also be offered for certain specialties (eg.. Obsteaicsgynecology). Throughout the course video is used to introduce correct body position, use of the instruments and technique. An instructor works one on one with the student and monitors through a second pair of eyepieces on the micmscopc.

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Research with Microsurgery Experts The fist question posed with experts was simply to ask how good can someone can become with experience/ practice. We were also interested in whether the dependent measures used in the earlier case studies also reflect expertise and changes with experience. A second study (Starkes, 1990) compared the performance of three surgeons: M, who had just completed the course, an intermediate-level subject who had been performing operations for three years, and a world reknowned surgeon with many years experience. The dependent measure was time from needle insertion through tie off to the s u t m being cut. While M required 209 + 87 seconds to complete each suture, the intermediate surgeon r e q u i d 74 + 24, and the expert required 38 + 12.The moral of this story is that is you face microsurgery as a patient, get an experienced surgeon. Efficiency increases mean less tissue trauma during surgery and shorter time under anaesthetic. Our case studies of surgeons first learning microsurgery show that it is possible to separate "knowing" and "doing", and even possible to establish new links with alternative "doing". In the next study with experts (Allard & Starkes, 1991) we were interested in exploring these links further. We reasoned that if individuals have the ability to forge new links perhaps they also have the ability to construct skilled performance from currently existing elements even when the elements are unrelated and have never been performed together. To investigate this we recruited our intermediate -level surgeon and asked her to perform a well known task handwriting - but to do so under varying levels of magnification. She was asked to write both her name as well as several low frcquency words ("gentry", "dactyl", "ingot"). Although she had never "written" under the microscope she adapted tools to perform "microwriting".To do so she used microsurgical forceps to "write" on carbon paper at magnifications of 16X,2SX and 4OX. The words were then photographed and compared with her normal handwriting.

Figure 12.5 shows the surgeon's normal writing and her attempts at the various magnification levels. At 40X certain technical problems arose because the forceps sometimes became entangled on individual paper fibres. In spite of this, the writing at each level is distinctively hers. We should also note that while this surgeon had never attempted microwriting before, she had no doubt that she could do so. By recombining existing elements of knowing and doing the novel skill of microwriting was possible. In ow most recent study the issue again has been to determine how efficient the surgeon can becom, but in this case in a more ecologically valid surgical environment. For the purposes of this study the types and speed of movements of an expert microsurgeon were analyzed as he performed a total colectomy and hetemopic small bowel transplant on a rat. In lay terms the bowel was removed and replaced with another. More specifically: the superior mesenteric vein was moved from the pancreas; the liver reuacted superiorally; the gastric and splenic veins tied off; the aorta, renal, and lumbar arteries ligated; the portal vein cut; the proximal and distal aorta divided, and finally the small intestine and mesentery transplanted. With practice this surgical procedure has about a 90% success rate. The entire procedure was filmed on video

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Figure 12.5. Microwriting of high- and low-frequencywords by an intermediate microsurgeon (IP) at magnGcations of 16X,25X, and 40X. Top three are actual size (two normal followed by one at 40X). Magnification of top pair below them is 40X;that of next lower pair is 25X; that of bottom pair is 16X. The illustration itself has been reduced to 89 percent of its original size for this book. Reproducedfrom F. Allard and J.L. Starkes, "Motor skill experts in sports, dance, and other domains", in A . Ericsson & J . Smith (Eds.) Toward a general theon of exvertise, 1991,with permission of Cambridge Universiw Press.

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through the microscope for later use in research and teaching. Our use of the tape for research was proposed ex post facto, which has the advantage that the surgeon's performance was not influenced by the Hawthorne effect or any knowledge of our proposed use. A closer look at the types of SUMS and movements required revealed that in order to remove the bowel several vessels had to be tied off and cut. To perform the iliostomy several interrupted sutures were required. A T-graft of vessels was performed that required several running sutures and a couple of interrupted sutures, and an end-mend anastomosis and splint were performed. In general the task is an "open" skill requiring the surgeon to constantly adjust to changes in the surgical field and magnification levels, idiosyncracies of the animal, and events that may occur during the course of surgery. Figure 12.6 lists the types of procedures performed, the number of each, and several measures related to the time required and consistency of movements.

Mean time to perform

Range of time to perform

sd. of time to perfOI7I.l

N

(SeC)

(=)

Tie off vessels

27

18.4

13 - 29

2.8

Cut vessels

26

7.1

3 - 20

4.4

Interrupted sutures

13

22

13 - 45

8.4

cut s u m

9

4.7

3-8

1.6

Running suturts

9

18.71

8 - 25

5.0

procedure

s u m s to

Figure 12.6. Task analysis: An expert microsurgeon's performance during a heterotropic small bowel transplant of a rat. Types of procedures, number performed, and time to perform with range and sd. Several things are apparent from this Figure. This particular surgeon is a specialist with many years experience. In spite of great variability in the types of movements performed, the stage in surgery when required, and the difficulty in manipulating one type of tissue over another, each manoeuvre wass performed quickly, efficiently,and with exnemely little variability in performance time.

Recall that in the study of a novice, intermediate, and expert microsurgeon there was a large change in how long it took a surgeon to perform interrupted sutures. While a trainee required209 seconds per suture, an expert required only 38 seconds. In each case the task for

Cognitive issues in microsurgery

237

subjects was an end-to-end anastomosis of the f e d artery of a rat and the time recorded was how long on average it took the surgeon to complete and cut an interrupted suture. In comparison the bowel transplant study required both morc complex and more varied types of surgical manoeuvres. The most direct comparison of data between the studies can be made by taking data from Figure 12.1 and adding the time for the surgeon to both complete the interrupted suture and cut it. This means that for that portion of the bowel transplant that was most equivalent to the task required of subjects in the plevious study this expert surgeon required 26.7 seconds. What the present study reveals however, is that it is not just the most basic procedures of microsurgery that become efficient but all of the requisite skills. Efficiency in this case Seems to develop not simply because the surgeon moves morc quickly but because there is faster recognition, use of relevant information and an "economy" of movement that develops. To the observer it is this economy of movement which is most saiking. A morc commonplace example of the contrast might be if olie were to monitor the hand movements of a young child tying their shoes and contrast it with the hand movements of an adult. In essence the experienced microsurgeon tying sutures with instruments under a microscope at 4OX magnification might well be tying shoes.

Our most recent research has two directions. First, we arc interested in developing a standardized suturing task that can be used to assess daily changes in performance throughout the training course. It can also be used to compare performance across subjects, or novices with experts. The task is a simple interrupted suture in surgical glove material. To date we have collected normative data on 9 novice microsurgeons. Their performance on the task was assessed at the beginning and end of each day of the course. A second standardized task is also being developed to assess performance changes. The task is Fins tapping performed using forcepts on carbon paper targets. Index of difficulty ranges from 1.0 to 5.5 bits per second and is a l t e d by changing either size of the target or amplitude of movement. The task is performed p n and post training and at the beginning of each day of the course.

Each of these art attempts to develop more standardized tests of learning and transfer that will eventaully allow novice - expert group comparisons.

1.

2.

Areas for Future Research One of the curious aspects of motor performance under the microscope is why the motor system should be so adaptable as to be able to function efficiently at high levels of magnification that far exceed the normal range of vision. Since all sensory systems have an absolute range it is amazing that motor system capabilities should far ex& the limits imposed by the normal range of vision. The microwriting experiment (Allad & Starkes, 1991) demonstrated that even mimmovements have highly cognitive representation. This was our interpretation for why the overall structure, timing, and pattern of writing remained very similar for high

238

J.L. Starkes et a1 and low frequency words even at 4OX magnification. Others have suggested that handwriting at normal sizes may be driven by some invariant saucture (such as a motor program - Schmidt, 1982) OT the motor system taking advantage of inherent physical properties of the body (Vredenbregt & Koster, 1971) or by impulse-timing of forces (Hollerbach, 1978, 1981). Microwriting holds great potential for examining how a highly represented and practised skill may automatically be scaled and adapted when totally different procedural knowledge is suddenly required. The inherent nature of the task in microsurgery provides a way of testing the linkage between "knowing" and "doing". For cognitive scientists it is rare to find a skill where for a novice microsurgeon declarative knowledge may be well established and linked to an already established set of procedures. Suddenly when the surgical task becomes microscopic the motor parameters that once allowed expen performance are no longer valid. An entirely different set of procedures must be linked. This provides an ideal situation for the study of negative transfer effects. Research needs to be conducted on the normal learning progressions of those who are true novices vs. macrosurgeons with established declarative knowledge structures. Group differences could indicate that optimal training methods differ depending on prior surgical experience.

4.

During very long operations (e.g.. hand or face reconstruction) two microsurgeons will rotate with approximately 4 hours on and 4 hours off. Whether this is optimal in terms of fatigue effects is not known. Likewise whether iptimal time on changes with level of expertise has not been examined empirically. One might speculate that less expert microsurgeons whould tire more easily and perhaps should rotate at shorter intervals.

5.

For those individuals unable to acquire microsurgical skills, what are the limiting factors? Are they visual, speed or complexity of decisions or motoric in nature. For macrosurgery, Schuneman and Pickleman (this volume) suggest that relatively innate nonverbal, perceptually-based cognitions about complex spatial information are important for surgical success. Whether this holds true for microsurgery with its rather unique demands needs to be examined.

6.

As new technology emerges in medicine where the surgeon operates "one step removed"

from direct visual and motor manipulation several questions emerge, With endoscopes, arthroscopes and microscopes it becomes necessary to consider how difficult the translation process is for magnification, and manipulation in the surgical field by video. Certainly just learning to recognize where one is and what is being manipulated in the surgical field becomes an issue for the acquisition of skill and training programs.

Cognitive issues in m'crosurgery

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7.

A standardized test of suturing efficiency needs to be developed so performance changes both within and across subjects can be readily compared.

8.

Tasks such as Fitts tapping that have commonly been used to examine motor performance changes need to be adapted for use at the micro level. In so doing varying types of microscopic tasks (such as electrical component construction, surgery, watchmaking) might be compared, as well as increasing efficiency with practice in any one skill.

References Acland, R. (1972). A new needle for microvascular surgery. Surgery, 71, 130. Allard, F., & Starkes, J. (1991). Motor-skill experts in sports, dance and other domains. In K.A.Ericsson, & J. Smith (Eds.), Toward a general theory of expertise. (pp. 126152). Cambridge: Cambridge University Press. Carrel, A. (1902). La technique operatoire des anastomoses vasculaires et la transplantation des visceres. Lyon Med., 98, 859. Freys, S.M., & Koob, E. (1988). Ausbildung und training in der mikrochinugie ohne versuche am lebenden tier, Handchirurgie, 20, 11-16. Goossens, D.P., Gruel, S.M., & Rao, V.K. (1990). A survey of microsurgery training in the United States, Microsurgery, 11, 2-4. Hollerbach, J.M. (1978). A study of human motor control through analysis and synthesis of handwriting. Unpublished doctoral dissertation, Massachusetts Institute of Technology. Boston. Hollerbach, J.M. (1981). An oscillation theory of handwriting, Biological Cybernetics, 39, 139156. Jacobson J. H., Miller, D.B., & Suarez E. (1960). Microvascular surgery: a new horizon in coronary artery surgery, Circulation, 22, 767. Lee, S.(1985). Manual of microsurgery, Boca Raton, Florida: CRC Press Inc. McGregor, , J.C., Wyllie, F.J., & Grigor, K.M. (1983). Some anatomical observations on the human placenta as applied to microvascular surgical practice. British Journal of Plastic Surgery, 36, 387-391. Nylen, (2.0 (1954). . The microscope in oral surgery, its first use and later development. Actu Oto-laryngol.. 116. 226-240. Pemtt, R.A. (1950). Recent advances in coroneal surgery. American Academy of Opthalmology and Otolaryngology.

Salvendy, G., & Pilitsis, J. (1980). The development and validation of an analytical training program for medical suturing. Human Factors, 22(2), 153-170. Schmidt, R. (1982). Motor Control and Learning (pp. 324-334). Champaign, Illinois: Human Kinetics Publishers. Schueneman, A.L., Pickleman, J., Hesslein, R. & Freeark, R.J. (1984). Neuropsychologic predictors of operative skill among general surgery residents. Surgery, 96(2), 288-295. Schueneman, A.L., Pickleman J., & Freeark, R.J. (1985). Age, gender, lateral dominance, and prediction of operative skill among general surgery residents, Surgery, 98(3), 506-514.

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J.L.(1991). Eye-hand coordination in experts: from athletes to microsurgeons. In C. Bard, M.Fleury, & L.Hay (Eds.) Developmew of eye-hand coordination across the lift spun (pp. 309-326).Columbia, South Carolina: University of South Carolina Rcss. Vredcnbregt, J., & Koster, W.G. (1971). Analysis and synthesis of handwriting, Philips Technical Review, 32, 73-78. Westbmck. D.L.(1988). A training course in microsurgery: A must for all internes?. The Netherlands Journal of Surgery, 40, 3.62-63. Starkes,

Acknowledgement

The authors would like to thank Dr. Achilles Thoma of McMaster University for his assistance in the use of microsurgery facilities. We would also like to thank Dr. Robert Zhong of Univmity Hospital, London for use of his videotaped operations.

COGNITIVE ISSUES IN MOTOR EXPERTISE J.L.Stakes and F. AUard (Editon) 0 1993 Elsevier Science Publishers B.V. AU rights reserved.

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CHAPTER 13 MOTOR EXPERTISE AND AGING: THE RELEVANCE OF LIFESTYLE TO BALANCE MICHAEL J. STONES,BLAIR HONG, AND ALBERT KOZMA Gerontology Centre and Department of Psychology Memorial University of Newfoundland St. John's, Newfoundland, AIB 3x9 Several authors have distinguished between "types" of aging as a way to differentiate effects on performance due to age from those correlated with lifestyle and illness (Bimn & Cunningham, 1985; Rowe & Kahn, 1987; Stones, Kozma, & Hanah, 1990). A four-way classification is inclusive of the distinctions currently recognized: 1. Successful aping refers to age effects that are independent of lifestyle impediment and illness; 2. Usual aging includes losses in competence attributable to negative aspects of lifestyle (e.g., inactivity, poor nutrition, negative life attitudes, etc.); 3. Secondarv aging refers to the effects of disease and illness; 4. Tertiaw aeing encompassesan accelerateddeterioration often observed during the months preceding death. These distinctions connote categories of process that affect competence as people age. Although apparently deteriorating performance is frequently observed in cross-sectional data, cross-sectional and longitudinal stability, and even late-life gains in performance have been reported with a physically active lifestyle (Spirduso, 1980; Stones & KO1987) - thcrtby exemplifying the distinction between usual and successful aging. Our aim in this chapter is to use longitudinal and experimentaldata to demonstrate that motoric expertise is affected not only by lifestyle but also by life attitudes. We shall begin with a brief review of the applied measurement of competence, then discuss evidence on the utility of postural control to index motoric competence, and finally present new data on factors affecting unipedal balance. These data suggest that lifestyle orientation has a more powerful influence on motoric expertise than processes intrinsic to aging. The precedents for the applied measurement of competence in later life lie primarily in the fields of chronic care and rehabilitation. In chronic care, crude estimates of competence BIC obtained from the institutional classifications(Level 1,2,3.etc.) of ambulatory status and the level

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of nursing care required (Forbes,Jackson, & Kraus, 1987)Of greater psychometric sophistication are activities of daily living (ADL) instruments (Fillenbaum, 1984) and geriatric rating scales (Meer & Baker, 1966) However a limitation to such indexes is that their sensitivity is confined mainly to individual differences within the frail elderly. Potentially of wider applicability are functional age measures (Murray, 1951). Unfortunately these measures failed to meet their early promise for reasons that include inappropriate utilization in the workplace (Alvioli, B m t t , & Stems, 1984), problems stemming from reliance on an age-on-function multiple regression model, and a failure to take account of the effects on performance of lifestyle and illness (Costa & McCrae., 1980). Some investigators (Borkan & Norris, 1980 Stones & Kozma, 1988) developed functional indexes using a function-on-age regression model that permits standardized comparison with age (and gender) peers. Stones and Kozma (1988) used this approach in the context of a rigorous construct validity perspective on the measures appropriate for inclusion in a functional age battery. Aggregate forms of such indexes were shown to possess high internal consistency (coefficient alpha >.7), even after correction for age and gender, and sensitivity to lifestyle modification (Stones & Kozma, 1988; Stones et al., 1990) A novel innovation in the measurement of competence is the anempt to identify tasks for which the extent of departure from optimal performance can fairly be suggested to index the degree of acquired decrement (Stones et al., 1990). Although the variability in performance typically increases with cohort age on most tasks, Salthouse (1990) convincingly illustrates that comparison of scores within any particular cohort does not necessarily reflect the relative losses acqukd with age. However, Stones et al. (1990) gathered evidence to show that static balance measures (e.g., unipedal balance, quantitative body sway, clinically appraised sway) are p e r f o d with minimal error by almost all young people, with any subsequent decrement thereby reflecting the extent of impairment acquired during later life. Moreover, such tasks were shown to be reliable, to have generalized implications for other functions and to be sensitive to lifestyle characteristics and lifestyle modification.They appear to have utility for evaluating how successfully a person is aging and for measuring the extent to which lifestyle and illness factors impact on successful aging. Evidence on the construct validity of static balance as an index of motoric competence is now presented.

1.

Reliability. The reliability of static balance with eyes open has been shown to be >.85 within a session (Stones et al., 1990), .80 over a week (Potvin, Syndulko, Tourtellone, Lemon. & Potvin, 1980). .81 over two to six months (Lord, Clark, & Webster. 1991), and .67 over one year (Stones & Kozma, 1987).

2.

Age differences. Static balance indexes exhibit homogeneity in young adulthood, followed by progressively lower mean performance but higher variability across successively older cohorts (Era & Heikkinen, 1985; Hellebraun & Braun, 1939; Rikli & Busch, 1986; Stones et al., 1990, Vandewoort & Hill, 1990). The trend for unipedal balance was shown with data from the 755 subjects aged between 18-86 years (Stones et al., 1990) Almost all the young adults (i.e., 90% of persons aged 18-25 years ) balanced to the conventional time limit of 60 seconds per trial indicating that the postural control mechanisms were intact. At later cohort levels, mean performance decreased

Motor expertise and aging

243

and the variance increased progressively.

3.

Correlation with other functions. Static balance indexes have been shown to conclate with a variety of other physiological and performance measures (e.g., ankle strength, coding, forced vital capacity, proprioception, vibration sense, visual acuity, etc.) (Em& Heikkinen, 1985; Lord et al., 1991; Stones & Koma, 1987, 1988; Stones et al., 1990) and with ADL indexes (Myers & Huddy, 1985). The generaliability of balance to other measures of function is not surprising, given that the maintenance of postural control involves integrated contributions from the sensory apparatus, the central nervous system, and the musculature. Age decrement in postural control is attributed to "multiple, disseminated,subtle deficits" (Ochs, Newberry, Lenhardt, & Hawkins, 1985). that include diminished central processing of information from multiple sources.

4.

Correlation with Iifestvle, life attitude. and illness indexes. Stones et al. (1990) reported four studies showing negative relationships of unipedal balance with indexes relevant to physical illhealth (i.e., physical symptomatology, high blood pressure) and unhealthy behavior (i.e., smoking). Balance was shown to vary positively with positive life attitudes (i.e., happiness, psychological hardiness, low trait anxiety) and prohealth behavior (i.e., exercise, careful nutrition, social involvement).

5.

p . Unipedal balance was shown in several studies to be reactive to exercise intervention (Stacey, Kozma. & Stones, 1987; Stones & Kopna, 1987, 1988; Stones et al., 1990, Vanfraechem & Vanfraechem, 1977).

We report in this chapter on two studies of unipedal balance, The first is a longitudinal extension to four years of the Functional Age and Physical Activity (FAPA) study (Stones & Kozma, 1987, 1988; Stones et al., 1990). The present study is the fust we know of to examine factors related to the stability and reactivity of balance with an interval greater than one year. If balance can indeed be considered an index of motoric competence with implications for successful aging, retention of the skill can be anticipated to vary with baseline differences in stable prohealth behavior and life attitudes. Rowe and Kahn (1987) review evidence that personal control, autonomy, and effective coping styles all promote successful aging. A measure that encapsulates these dimensions is psychologicalhardiness, which was defined by McNeil, Stones, Kozma, and Hannah (1986) as "a personality based tendency to diminish the impact of stressful life events by optimistic cognitive appraisals and decisive coping actions." Evidence that psychological hardiness does discriminate groups differing in balance was previously obtained in three out of four studies, and a correlated measure of positive lifestyle behavior provided discrimination in the fourth (Stones et al., 1990). Consequently, the present data were used to test the hypothesis that positive life attitudes contribute to later-life expertise in balance. The second study takes as its point of departure the finding by Stones et al. (1990) that few persons aged over sixty years who smoked were able to perform a unipedal balance task to

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the 60 sec. criterion. Only one person in 30 (3%) who performed the task to criterion was a smoker, compared with 43 smokers out of 198 persons (22%)who fell below the criterion. That no such association was observed in persons younger than age sixty suggests either (or both) of two effects: the long-term consequences of smoking result in structural damage which affects balance; the immediate effects of smoking reduce balance skills among older persons in whom reserve capacity tends to be low. Possible mechanisms which underlie the immediate adverse effects of cigarette consumption include a high level of carboxyhemoglobin in the blood due to inhalation of carbon monoxide, and constriction of blood vessels due to nicotine. Both these consequences of smoking result in decreased availability of oxygen to bodily tissue (i.e., hypoxia), which has been suggested to contribute to low performance by older people on tests of cognitive and motor competence (Dustman, Ruhling, Russell, Shearer, Bonekat, Shigeoka, Wood,& Bradford, 1984).This study attempted to differentiatethe immediate effects of smoking on balance from longer-term influences by varying the time of day at which the test was performed. Specifically, it was hypothesized that any immediate effects of smoking on balance would be lower early in the morning, following several hours of cessation, than later during the day.

STUDY 1 The early aims of the FAPA study were to examine the effects on measures of function and life attitudes of participation in a community-based fitness program designed for persons aged over fifty. The effects of enrollment and drop-out during the first year of the study have previously been described for balance and “functional age” (Stones & Kozma, 1987, 1988). The present investigation added a third wave of data collection four years after the baseline measurements.Given evidence that balance retains stability but also shows responsiveness to life changes, we hypothesize that stable lifestyle orientations can have negative or positive prospective implications for balance depending on their potential for promoting either somatic decrement (e.g., smoking, stress) or meaningful activity (e.g., exercise, continued employment). Encompassed within our definitiion of lifestyle orientation are stable life attitude and personality measures with implications for both coping with stress and prohealth behavior (e.g., psychological hardiness, happiness, trait anxiety). The measure most strongly linked with effective coping is psychological hardiness.

METHODS The sample and methods of data collection in years 1 and 2 of the FAPA study have been described previously (Stones & Kozma, 1988; Stones, Kozma, & Stones, 1985). Of the 200 pdcipants in both those years, 168 (84%) were retested in year 4. The main reason for nonparticipation in year 4 was that the subject was not available at the home location during the period the assessments were conducted. Subiects The demographic profile for the participants in year 4 shows 50 to be male (30%). 120 to be married (71%).and the mean age to be 66 (+/- 6) years. The majority of participants were middle class, as indicated by skilled, managerial, or professional occupations (or former

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245

occupations) of the participant or the spouse of the participant. Fifty-four participants (32%)were working in year 1 of the study, with 11 having retired after year 2 but before year 4. Twenty percent were smokers, and 48% were participants in an exercise program during year 1. Materials The measures taken in years 1 and 2 can be divided into lifestyle and life attitude variables (i.e., includingpersonality measures shown to possess long-term stability),demographic indexes, and function variables. Measurement of balance and ratings of change in behavior during the preceding two years were obtained at year 4. DemomaDhic indexes The demographic indexes include age, gender, employment status, and marital status. Lifestyle and life attitude measures The lifestyle measms include habitual physical exercise and smoking. Exercise was indexed as a categorical variable (participation or nonparticipation in an exercise program) and on a continuous scale combining exercise frequency and intensity items derived from the Standardized Test of Fitness (1981). Smoking was assessed both categorically and by the frequency of cigarette consumption.The life attitude measures include the Memorial University of Newfoundland Scale of Happiness (Kozma & Stones, 1980); trait anxiety (Spielberger. Gorsuch, & Lushene, 1970). and psychological hardiness (McNeil et al., 1986). All h e questionnaires possess high reliability and have been shown to be valid indexes of their respective constructs. Function measures d The function measures include balance with eyes open (Stones & K o m , 1987), f vital capacity, trunk forward flexion (Standardized Test of Fitness, 1981). reaction time (Jalavisto, 1965), Digit Symbol coding (Weschler, 1958), hearing loss (i.e.. in the better ear at 4,000 Hz), and systolic and diastolic blood pressures. Body mass was also estimated. Life chanee indexes These indexes were obtained in year 4 and refer to life changes during the two years since the preceding assessment. The life domains for which change was indexed include health (i.e., a composite score derived from ratings on 30 illness categories). exercise (i.e.. a composite score derived from ratings on 20 physical activity categories), and employment status. PrOcedUre Participants in year 4 were contacted fmt by mail then by telephone to arrange an appointment for testing. As in year 1 and 2, all assessments were conducted at the participant’s home location. The measures included forced vital capacity, trunk forward flexion, balance, and the life change indexes. Balance with eyes open was measured by the time a subject could stand on one foot with the other positioned midway up the calf of the supporting leg and the arms held

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s h g h t by the sides. The task was performed without shoes on a f m surface. Timing was to a 60 sec. criterion per mal over four mals (i.e.. alternating between right and left as the supporting leg). The 60 sec. criterion is a conventional one for unipedal balance, with shorter periods suggested to be insensitive to the range of individual differences among older people and longer intervals reflecting mainly the effects of muscle fatique rather than differences in balancing skill (Stones & Kozma, 1987).

RESULTS AND DISCUSSION Preliminary analyses were computed to assess the concurrent relationships of balance to the other functions measured in year 4. The findings of significant positive relationships of balance with the forced vital capacity, trunk forward flexion, and Digit Symbol measures (all p<.O1) confirm the generalizability of the former to other domains of function. The main intent of data analysis was to identify those variables which most reliably discriminated experts at balance at year 4 - those persons able to balance to the 60 sec. criterion. These experts are people for whom the balance task provides no evidence of any acquired decrement in competence. Twenty persons met this criterion on all four mals in year 4 (i.e., the EXPERT group) with 148 persons showing a decrement on one or more mals (i.e., the NONEXPERT group). The mean time balanced per mal across all subjects that year was 18.6 (+/- 8) sec. Four sets of data analyses were computed to discriminate the EXPERT and NONEXPERT groups: zero-order analyses with independent variables comprising (1) all the demographic indexes, the lifestyle and life attitude measures, and the function measures obtained in year 1. plus the life change indexes from year 4; and (2) all the demographic indexes, the lifestyle and life attitude measures, and the function measures pertaining to year 2, plus the life change indexes from year 4: covariance analyses with respect to (3) all the measures described under (1) as independent variable, with year 1 balance as the covariate; and (4) all measures described under (2) as independent variables, with year 2 balance as the covariate. MANOVA and MANCOVA statistics showed the independent variables to significantly discriminate the EXPERT and NONEXPERT groups in all four analyses (all p<.Ol). Prior balance was the strongest predictor of year 4 balance in the zero-order analyses, with year 1 balance explaining 23% of the variance (p<.oOOl) and year 2 balance explaining 31% of variance (p<.oOOl). The other variables with significant zero-order associations with expertise in balance were a younger age (p<.03), nonsmoking (p<.05 in years 1 and 2), high psychological hardiness (p<.Ol in year 1; p<.OOl in year 2), paid employment (p<.02 in year 1; p<.05 in year 2), high Digit Symbol coding (p<.05 in year 1). and a high level of exercise (pc.03 in year I). Only three variables had significant associations with year 4 balance with prior balance covaried out. These variables were nonsmoking (p<.05 in years 1 and 2). psychological hardiness (pc.05 in year 1; p<.OOI in year 2). and exercise level (p<.05 in year 1). The corresponding means and standard deviations are shown in Figure 13.1. The findings extend upon earlier research showing an impact of health-relevant behavior and life attitudes on balance. But whereas the earlier research studied only concurrent

Motor expertise and aging

247

Variable Balance' (year 1)

EXPERT CIWD

NONEXPERT Group

47.5 (18.4)

27.4 (19.6)

Balance' (year 2)

54.1 (7.9)

27.9 (20.5)

Smoke? (year 1)

0 (0) 0 (0)

.2 (.4)

Hardiness3 (year 1)

14.9 (5.8)

18.7 (5.8)

Hardiness3 (year 2)

13.2 (4.4)

19.4 (6.4)

Exercise (year 1)

15.1 (14.3)

9.3 (10.3)

Smoke? (year 2)

.2

(.4)

' Time per aial (secs.) This variable is categorical with nonsmoker = 0 and smoker = 1. Psychological hardiness is keyed (i.e., lower scores denote higher hardiness).

Figure 13.1. Means (and Standard Devianons on Variables Reliably Discriminating the EXPERT and NONEXPERT groups.

relationships, the present study examined the impact on subsequent balance of measures taken three and four years previously. Of particular significance are the results of the covariance analyses in which the effects of prior balance were partialled out. The findings show a stability to balance over four years, with additional variance explained by baseline measures of psychological hardiness, nonsmoking, and level of exercise, but not age. Those persons classified as balance experts in year 4 were assessed as psychologically hardy, nonsmoking, exercisers three and/or four years previously. Because these three independent variables are all stable characteristics (i.e., the stability correlations all exceeded r=.6 over years 1 and 2), the implications are that aspects of motor expertise are controlled more by a person's life orientation than by age.

STUDY 2 The negative lagged association between smoking and postural control in Study 1 confirms the negative concurrent associations that were reported previously (Stones et al., 1990). McKim and Mshara (1987) indicate that the most harmful effects of smoking are caused by inhaling carbon monoxide and ingesting nicotine. In vivo estimates are that hemoglobin in the blood is two hundred times more likely to combine with carbon monoxide to produce carboxyhemoglobin than to combine with oxygen to produce oxyhemoglobin (Bruchner, 1967), with each cigarette estimated to elevate carboxyhemoglobin levels by a figure approaching one percent (Shephard, 1983). The result of elevated carboxyhemoglobin as with the vasoconstriction

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MJ. Stones et a1

effects of nicotine, can be a state of hypoxia. The detrimental effect of hypoxia on measures of competence has been proposed by several researchers (Dustman et al., 1984; Mcfarland, 1963; Spirduso, 1980). Because decrement in a postural control measure such as balance has been suggested to result from "multiple, disseminated, subtle deficits" (Ochs et al., 1985) - which include diminished central processing capacity - it is conceivable that the failure by smokers to perfonn balance tests to criterion could be due to hypoxic effects contingent on cigarette consumption. Study 2 examined balance by older smokers and nonsmokers both early in the morning and later during the day. Because the half-time for the elimination of carbon monoxide from the blood is estimated to vary around three to four hours for a resting subject under normal atmospheric conditions, the prior effects of cigarette consumption can fairly be assumed to have dissipated after a night's sleep. Consequently,the effects of smoking on balance are hypothesized to be greater for a smoker later during a normal day than early in the morning.

METHODS Subiects The subjects were 40 volunteer community - dwellers aged over 60 years with a mean age of 64.5 (+/- 5.7) years. In a balanced design, they were divided into four groups with respect to gender and smoking status. The smokers reported themselves to consume an average of 16.5 (+/- 4.7) cigarettes daily. Materials In additon to unipedal balance, each subject was measured on three additional self-report instruments. One was the psychological hardiness measure (McNeil et al.. 1986). with the others being the Activity Propensity and Activity Limitation scales from the SENOTS battery (Stones & Kozma, 1989). The latter scales possess high internal consistency (alpha >.8) and respectively measure social, familial, and household activity and limitations to physical activity. Procedure The procedure was explained to each subject during a preliminary telephone interview, which also served as a request for participation. Times and a date for the assessment sessions were agrecd upon with all subjects willing to participate. A condition of participation was that the subject consumed no cigarettes prior to the morning session and refrained from consuming alcohol until after the afternoon session. The assessments took place at the subject's home location, with the initial session taking place early in the morning. Four trials on the eyes open balance task (alternating between right and left supporting legs) were conducted first. The procedure for this task was identical to that described in Study 1. with the subject performing the task barefoot on either a tile or linoleum floor.The criterion limit for timing was again set at 60 sec. Subsequent to performance on the balance task, the self-report instrumentation was completed. The subject was then told the

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Motor expertise and aging

experimenter would return for the afternoon session eight hours later, and to behave throughout the intervening period in a customary manner. Only the balance task was performed in the afternoon session, after which time the purpose of the experiment was thoroughly explained.

RESULTS AND DISCUSSION Preliminary analysis of data included a 2 (gender) by 2 (smoking status) ANOVA with age as the dependent variable. The mean ages within groups varied between 65.1 - 68 years, with no sigificant main effects nor interaction. The main analyses were computed on the balance scores shown in Figure 13.2.

Smoking

Status

Gender

Morning Balance

Afiernoon Balance

Male

16.2 (13.5)

14.0 (10.8)

Female

15.1 (14.9)

15.0 (10.1)

Male

46.7 (14.7)

50.3 (11.1)

Female

32.6 (16.7)

33.3 (18.1)

Smoker

Nonsmoker

Figure 13.2. Means (and Standard Deviations) on Morning and Afrernoon Balance Trials by Gender and Smoking Status Groups.

The overall mean time balanced per trial was 28 (+/- 9.7) sec. A stepwise multiple regression was computed to identify influences on the morning balance scorns. The first variable to enter was smoking status (beta = -.75, p<.OOOl), followed by age (beta = -.67, pc.OOO1). followed by the Activity Propensity measure (beta = -.27, p<.05), giving a total explained variance of 81%. Gender, psychological hardiness, and the Activity Limitation scale failed to enter. Afternoon balance was analyzed first in a 2 (gender) by 2 (smoking status) ANCOVA with morning balance as the covariate. The results showed only a significant effect of smoking status (F[1,35]=8.28,p<.Ol) and significant variation explained by the covariate (F[135]=191.24, p<.OOOl). These findings were confmed in a stepwise multiple regression with afternoon balance as the dependent variable. Morning balance entered first (beta = 37, p<.OOOl) followed by smoking status (beta = -.14, pc.02). with age gender, psychological

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hardiness, and the Activity Limitation and Activity Propensity scales failing to enter. The explained variance was 93%. The findings support the hypothesis that afternoon balance was affected by the immediate effects of smoking, probably through mediation by hypoxia. Had the effects of smoking been limited to longer-tern influences on the body (e.g., pulmonary damage or structural changes within the cardio-vascular system), smoking would have explained the same variance in balance at both times of measurement, with no funher contribution to the explained variance in afternoon balance expected after covarying out the effects of morning balance. That smoking was significantly related to afternoon balance in both the ANCOVA and the multiple regression indicates that smoking does have immediate consequences for balance. But from a broader perspective, the major impact of smoking on moming balance suggests the longer-term influences to be of greater magnitude than the immediate effects. That the life attitude and activity measures contributed minimally to explained variance in Study 2 is probably due to the strategy used to recruit subjects. The subjects were specifically chosen to contrast between smoking status, whereas in the FAPA study they were initially selected to contrast between participation or nonparticipation in a fitness program. Because people with more positive life attitudes show the stronger adherence to exercising (Stacey, Kozma, & Stones, 1985), the FAPA sample exhibits considerable heterogeneity on lifestyle and life attitude dimensions. The sample in Study 2 were relatively homogenous on the self-report instrumentation, with a corresponding limitation imposed on these measures to explain variance in the dependent variable.

GENERAL DISCUSSION Stones and Kozma (1988) proposed a "tonic and overpractice effects" (TOPE) model to explain why function measures are differentially reactive to lifestyle parameters. Sensitivity is thought to be affected by the level of practice, the extent to which variability is determined by stable systems properties, and the specificity of the functional domain. Although a measure such as trunk forward flexion may show sensitivity to lifestyle change, its reactivity is specific to change in physical activity, with its variability limited by body type (e.g., gender). Similarly, the variability in Digit Symbol coding is largely determined by the systems property of intelligence. The components of balance are not specific to a single domain but derive from the sensory systems, the central nervous system, and the musculature. It is for this reason that balance has generalized implications for other functions, which makes it more globally reactive to indexes of lifestyle orientation. A case was made in this chapter that a failure to balance to criterion represents an a c q u i d decrement in function (see Stones et al., 1990). By this token, the EXPERT group in Study 1 retained motoric expertise whereas the NONEXPERT group had lost a measure of expertise. Those persons who retained expertise were psychologically hardy exercisers who didn't smoke. The findings from Study 2 indicate that cigarette consumption throughout the day affected balance adversely, although the magnitude of this effect was secondary to the

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differences between smokers and nonsmokers early in the morning. It is probable that the consquences of smoking are mediated by hypoxia largely due to long-term changes in the pulmonary and cardiovascular systems, with further decrement in oxygen transport resulting from daily cigarette consumption. We conclude from this research that the retention of motoric expertise in later life can be affected by life attitudes and lifestyle. The findings were obtained using a balance task possessing generalized implications for other functions, and for which any loss in expertise is thought to represent an acquired decrement. It is probable that if motoric expertise w m measured by more circumscribed functions, or those with variability limited by the effects of prior overpractice or pre-existing systems properties, those indexes may be reactive to specific intervention but less to global life orientation parameters. Balance may be considered a more useful index of motor expertise because it possesses both generalized implications for other functions and sensitivity to a more global range of lifestyle and life attitude parameters.

References Alvioli, B., Barren, G., & Stems, H. (1984). Alternatives to age for assessing occupational performance capacity. Experimental Aging Research, 10, 101- 105. Birren, J.E., & Cunnigham, W. (1985). Research on the psychology of aging: Rinciples, concepts and theory. In J.E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging (2nd Edition). N.Y.: Van Nostrand Reinhold. Borkan, G.A., & Norris, A.H. (1980). Biological age in adulthood: Comparison of active and inactive US.males. Human Biology, 52, 787-802. Brucher, J.M. (1967). Neuropathological problems posed by carbon monoxide poisoning and anoxia. In H. Bour & I. McA. Ledingham (Eds.), Progress in Brain Research, 24 Carbon Monoxide Poisoning. Amsterdam: Elsevier. Costa, P.T. Jr., & McCrae, R.R. (1980). Functional age: A conceptual and empirical Critique. In S.G. Haynes & M. Feinleib (Eds.), Proceedings of the Second Conference on the Epidemiology of Aging. Bethesda, Md.: National Institute on Aging. Dustman, R.E., Ruhling. R.O., Russell, E.M., Shearer, D.E., Bonekat, H.W., Shigmka, J.W., Wood, J.S.,& Bradfod, D.C., (1984). Aerobic exercise training and improved neuropsychological function of older individuals. Neurobiology of Aging, 5, 35-42. Era, P., & Heikkinen, E. (1985). Postural sway during standing and unexpected disturbance of balance in random samples of men of different ages. Journal of Gerontology, 40, 287295. Fillenbaum, G.G. (1984). The well being of rhe elderly: Approaches to Multidimensional assessment. Geneva: World Health Organization. Forbes, W.F., Jackson, J.A., & Kraus, AS. (1987). Institutionalization of the elderly in Canada. Toronto: Butterworths. Hellebraun, F.A., & Braun, G.L. (1939). The influence of sex and age on the postural sway of man. American Journal of Physical Anthropology, 24, 347-360. Jalavisto, E. (1965). The role of simple tests measuring speed of performance in the assessment

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of biological vigour. In A.T. Welford & J.E. Birren (Eds.), Behavior, Aging and the nervous system. Springfield, Ill.: C.T.Thomas. Kozma, J.A., & Stones, M.J. (1980). The measurement of happiness: Development of the Memorial University of Newfoundland Scale of Happiness (MUNSCH). Journal of Gerontology, 35, 906-916. Lord, S.R., Clark, P.D., & Webster, I.W. (1991). Postural stability and associated physiological factors in a population of aged persons. Journal of Gerontology: Medical Sciences, 46, 69-76. McFarland, R. (1963). Hwnan factors in air transportation: Occupational health and safety. N.Y.: McGraw Hill. McKim, W.A., & Mishara, B.L. (1987). Drugs and Aging. Toronto: Butterworths. McNeil, K. Stones, M.J., Kozma, A., & Hannah, A.E. (1986). Measurement of psychological hardiness in older adults. Canadian Journal on Aging, 5 , 43-48. Meer, B., & Baker, J. (1966). The Stockton Geriatric Rating Scale. Journal of Gerontology, 41, 85-90. Murray, I.M. (1951). Assessment of physiologic age by combination of several criteria - vision, learning, blood pressure and muscle force. Joural of Gerontology, 6, 120-126. Myers, A.M., & Huddy, L. (1985). Evaluating physical capabilities in the elderly: The relationship between ADL self-assessments and basic abilities. Canadian Journal on Aging, 4, 189-200. Ochs, A.L., Newbeny, J. Lenhardt, M.L., & Hawkins, S.W. (1985). Neural and vestibular aging associated with falls. In J.E. Birren & K.W.Schaie (Eds.),Handbook of psychology of aging (2nd Edition). N.Y.: Van Nostrand Reinhold. Potvin, A.R., Syndulko, K.. Tourtellotte, W.W., Lemon, J.A., & Potvin, J.H. (1980). Human neurologic functions and the aging process. Journal of the American Geriatrics Society, 29, 1-9. Rikli, R., & Busch, S. (1986). Motor performance of women as a function of age and physical acticity level. Journal of Gerontology, 41, 645-649. Rowe, J.W., & Kahn, R.L. (1987). Human aging: Usual and successful. Science, 237, 143-149. Salthouse, T.A. (1990). Cognition, motor behavior, and the assessment of atypical aging. In M.L. Howe, M.J. Stones, & C.J. Brainerd (Eds.), Cognitive development in adulthood: Progress in cognitive development research. N.Y.: Springer-Verlag. Shephard, R.J. (1983). Carbon Monoxide: the Silent Killer. Springfield, Ill.: Thomas. Spielberger, C.D., Gorsuch, R.L., & Lushene, R.E. (1970). Manual for the State-Trait Anriefy Inventory. Palo Alto, Ca.: Consulting Psychologists Press. Spirduso, W.W. (1980). Physical fitness, aging, and psychomotor speed. Journal of Gerontology, 35, 850-865. Stacey, C. Kozma, A., & Stones, M.J. (1985). Simple cognitive and behavioral changes resulting from improved physical fitness in persons over 50 years of age. Canadian Journal on Aging, 4 , 67-73. Stones, M.J., & Kozma, A. (1987). Balance and age in the sighted and blind. Archives of Physical Medicine and Rehabilitation, 66, 85-89.

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Stones, M.J., & Kozma, A. (1988). Physical activity, age, and cognitive / motor performance. In M.L.Howe, M.J. Stones, & C.J. Brainerd (Eds.) Cognitive development in adulthood: Progress in cognitive development research. N.Y.: Springer-Verlag. Stones, M.J., & Kozma, A. (1989). Age, exercise, and coding performance. Psychology and aging, 4 , 190-194. Stones, M.J., Kozma, A., & Hannah, T.E. (1990). Measurement of individual differences in aging: the distinction between usual and successful aging. In M.L. Howe, M.J. Stones, & C.J. Brainerd (Eds.) Cognitive and Behavioral Performance Factors in Atypical Aging. N.Y.: Springer-Verlag. Stones, M.J. & Kozma, A,, & Stones, L. (1985). Preliminary findings on the effects of exercise program participation in older adults. Canadian Journal of Public Health, 76, 272-273. Vandervoort, A.A., & Hill, K.M. (1990). Neuromuscular performance of the aged. In M.L.Howe, M.J. Stones, & C.J.Brained (Eds.), Cognitive development in adulthood: Progress in cognitive development research. N.Y .: Springer-Verlag. Vanfraechem, A,, & Vanfraechem, R. (1977). Studies of the effect of a short training period on aged subjects. Journal of Sports Medicine and Physical Fitness, 17, 373-380. Weschler, D. (1958). The measurement and appraisal of adult intelligence, (4th Edition). Baltimore: Williams and Wikins.

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CHAPTER 14 THE DEVELOPMENT OF EXPERTISE IN YOUTH SPORT KAREN E. FRENCH AND MICHAEL E. NEVE'IT Department of Physical Education, University of South Carolina, Columbia, SC 29208 The Development of Expertise in Youth Sport Every year millions of children enter into youth sport programs in a variety of sports. Because children highly value physical competence at an early age, it is important to understand the underlying processes associated with skilled sport performance from a developmental perspective. Adult sport experts possess both advanced motor skills and sport specific cognitive skills which allow them to select sport appropriate responses and successfully execute the sport specific skills in game situations. Young children entering youth sport can often be consided universal novices because they

possess limited sport specific skills and sport specific knowledge. As children mature and learn the cognitive sport strategies, knowledge, and skills, their performance in game situations improves (French & Thomas, 1987; McPherson & Thomas, 1989). The purpose of this chapter is to review research regarding the development of expertise in youth sport. Our focus is primarily on the development of cognitive skills and their relation to sport performance. Much research has been conducted in the cognitive (Chase & Simon, 1973; Chi, 1978; Chi, Feltovich, & Glaser, 1981; Chiesi, Spilich, & Voss, 1979; Spilich, Vesonder, Chiesi, & Voss, 1979) and sport domains (Abemethy & Russell, 1987; Allard & Burnett, 1985; Allard, Graham,& Paarsalu, 1980 Bard & Fleury, 1976; Garland, 1989; Jones & Miles, 1976; Starkes, 1987) comparing adult experts and novices. However, fewer studies have been conducted using child subjects in cognitive and sport domains. Our intent is to present the paradigms and findings from various areas of research on sport expertise in children and adults. We emphasize the studies with a developmental focus, point out m a s in which developmental research is limited, and suggest future research directions.

In the first section, the knowledge base is defined and developmentalprocesses associated with working memory and long term memory are summarized. Subsequent sections focus

specifically on the sport knowledge base and its relation to performance on simulated sport tasks and in actual sports. The final section highlights the constraints on the development of cognitive and motor expertise in youth sport.

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Knowledge base The term knowledge base is frequently used to describe the interactions of working memory and long term memory. Discussions regarding long term memory are associated with working memory processes because it is generally believed that information must be active in working memory in order to be used. Many recent models of memory refer to working memory in terms of activation or the portion of long term memory that is currently activated (Anderson, 1976, 1982). Deficits in performance can occur due to working memory e m , lack of information in long term memory, and ineffective processes for storing and retrieving information from long term memory. Thus, the term knowledge base is used to describe both working and long term memory processes. Considerable research in cognitive development and motor development has documented developmental changes in working memory processes. In the next section, we summarize the major developmental changes in working memory processes. Workine memory Research in cognitive and motor development (Belmont & Butterfield, 1971; Chi, 1976; Naus & Omstein. 1983; cognitive; Gallagher & Thomas, 1984,1986; Thomas, 1980 Thomas, Thomas, Lee, Testerman, & Ashy, 1983; Winther & Thomas, 1981, motor development) have identified developmental changes in the use of control processes in working memory (e.g. labeling, encoding, rehearsal, organization, retrieval). Young children exhibit both production and mediation deficits in rehearsal. Under the age of seven, children often do not use a rehearsal strategy unless they are cued to do so. Near seven years of age, children begin to rehearse spontaneously and their ability to mediate cognitive (Naus & Orstein, 1983) and motor performance (Gallagher & Thomas, 1984) increases with age. Intervention with younger children to force them to use adultlike rehearsal strategies increases the accuracy of children’s movements but the variability remains larger than for adults (Thomas, 1984). The development of organizational strategies is similar for cognitive and motor performance tasks (Thomas, 1984). Young children may attempt to organize information. However, young children typically organize on perceptual characteristics whereas older children and adults organize on semantic characteristics. Younger children’s performance can be enhanced by imposing adultlike organization strategies. However, mature organizational strategies do not transfer across task situations until about 12 or 13 years of age. The use of verbal labels also enhances children’s performance. Winther and Thomas (1981) found that younger children are less likely to label movement responses efficiently, making it more difficult to recall the movement correctly. The performance of younger children was enhanced when forced to use an adultlike labeling strategy. Studies examining modeling have also shown that verbal strategies are particularly helpful for enhancing performance in young children. A series of three studies (McCullagh. Stiehl, & Weiss, 1990, Weiss, 1983; Weiss & Klint, 1987) found either verbal cues or verbal rehearsal important to the movement reproduction of a modeled sequence of motor skills in young children.

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These findings clearly indicate that verbdcognitive strategies or control processes of working memory mediate young children's motor performance. When children are cued to use efficient strategies, their performance improves. Specd of Drocessing The speed of processing generally increases linearly with age (Thomas, 1980). Chi and Gallagher (1982) suggest that a portion of the deficit in processing speed of young children is associated with lack of knowledge concerning the task and inefficient mnemonic strategies. When tasks are simple enough to reduce the effects of strategies and lack of knowledge, childrcn are still slower in selection and organization of responses. Furthmm, as tasks become more complex, knowledge and strategies contribute to slower encoding and manipulation processes in young children.

Long term memory A discussion concerning the types of knowledge is wananted to understand how domain knowledge in long term memory influences performance. Chi (1981) defined three types of knowledge. Declarative knowledge is factual information or lexical knowledge. Procedural knowledge is defined as a set of rules or procedures concerning how to or when to accomplish a specific task. Declarative and procedural knowledge are domain specific. Strategic knowledge is viewed as heuristic rules (i.e. rehearsal, organization, control processes, etc.) which can be applied across several domains. Distinguishing between types of knowledge is salient because in some theoretical frameworks each type of knowledge is represented in memory in different ways. See Anderson (1976,1982), Chi and Rees, (1983), and Rummelhart and Norman,(1978) for further discussion.

In the verbal literature, declarative knowledge has been modeled as propositional networks or semantic networks consisting of nodes, features, and links. Nodes represent concepts; features are characteristics which define given concepts; and links are associations between and among concepts. The state of knowledge in a given domain would be conceptualized by the density of the knowledge structure, i.e. the number of concepts, the number of features defining each concept, the number of multiple links associating concepts, and the hiermhial organization of concepts (Chi, Hutchinson, & Robin, 1989; Chiesi et al., 1979). Chi (1978) was the first to clearly demonstrate that knowledge of the stimuli conmbute to developmental differences in memory performance. She compand the recall performance of chess positions by child expert and adult novice chess players. Child experts exhibited superior recall of chess positions, required fewer trials to recall a chess pattern, and retrieved a larger number of chunks on the first trial. However, recall of digits by adult novices was superior to children. Thus, when children possessed more knowledge of the stimuli, their performance was superior to adults. Chi and colleagues (Chi & Ceci, 1987; Chi, Hutchinson, & Robin, 1989; Chi & Koeske, 1983) conducted a series of experiments to study the relationship between the structure of

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knowledge and how it is used by young expert children. The structure of knowledge of experts on dinosaurs was hierarchically organized and coherent within a level of the hierarchy. Greater hierarchial organization allowed the expert children to s o n dinosaurs into well-defined family types, use categorical reasoning. generate mox accurate causal explanations regarding attributes of novel dinosaurs, and exhibit superior recall. A number of other verbal learning studies have shown that domain knowledge influences children’s performance in a variety of ways. For example, domain knowledge facilitates recall performance (Chi, 1978), reduces developmental differences in the speed of processing (Roth, 1983), improves children’s inferences about novel stimuli (Chi et al., 1989) and conmbutes to the development of strategic knowledge (Chi, 1981; Omstein & Naus, 1984, organization; Siegler. 1989, backup strategies; Starkes, Deakin, Lindley, & Crisp, 1987, rehearsal).

Declarative SDOR knowledee Definitions of declarative sport knowledge have been vague at best. Thomas, French, and Humphries (1986) defmed declarative sport knowledge as knowledge of sport rules, player positions, basic offensive and defensive strategies. Declarative sport knowledge could also be defined in terms of movement concepts (movements common in a given sport, or dance activity), offensive concepts (give and go, screens in basketball, pass blocking schemes in football, overlapping in soccer) and defensive concepts (one on one. zone). Few studies have attempted to elicit the structure of propositional networks for declarative sport knowledge. Allard and Bumett (1985) and Garland (1989) used a sorting task with adult subjects to examine the structure of a propositional network of sport declarative knowledge in expert and novice athletes. Their results suggest that experts possessed a more organized structured network. No studies have examined the structure of declarative sport knowledge in children using similar tasks. Measurement of declarative knowledge in children has generally been done with multiple choice paper-pencil tests (French & Thomas, 1987; McPherson & Thomas, 1989). Paper-pencil tests measure recognition and only examine the contents of knowledge, not the structure or organization. More research is necessary to examine the structure of children’s sport knowledge and its influence on performance.

Recall Many differences in the structure and content of sport declarative knowledge have been inferred from comparing the recall of structured and unstructured game information by expert and novice athletes. Several studies (Allard, Graham, & Paarsalu, 1980; Allard & Burnett, 1985; Garland, 1989; Starkes, 1987) have been conducted with adult subjects. Subjects were briefly presented pictures or slides of game structured or unstructured situations. Subjects were asked to either recall the position of players as accurately as possible on a scaled playing board or draw the position of the players. Skilled athletes were more accurate in recalling player positions for game structured information. However, no differences were found for recall of unstructured game information. Thus,the content and structure of the experts’ sport knowledge allowed them to recall game information much more accurately. These findings are very similar to those in

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verbal learning (Chi, 1978; Chiesi, et al.. 1979; Spilich, et al., 1979). Experts exhibit superior recall of game related information. No studies have used similar paradigms to examine recall of sport declarative knowledge in children. French and Thomas (1987) used one other measm of sport declarative knowledge. Children were asked to generate as many offensive alternatives and out-of-bounds plays as possible. Expert children regardless of age recalled more offensive alternatives and morc out-ofbounds plays and their answers were more organized.

Several studies have used a different approach to compare the recall of experts and novices in closed skills (Starkes, Deakin, Lmdley. & Crisp, 1987; Vickers, 1986). The paradigms used either verbal and motor recall of movement sequences (Starkeset al., 1987) or a pictorial resequencing task of a previously presented movement (Vickers, 1986). The findings generally support superior performance for experts in both tasks. The study by Starkes et al. (1987) illustrates this point quite nicely. They compared 11year-old experts and novices in ballet. Experience in ballet was equated in the two groups of subjects and the experts were selected from an elite group of ballet students. Subjects were visually presented videotaped sequences of professionally choreographed ballet movements (structured trials). Each sequence consisted of eight elements in serial order. A second series of ballet movements was constructed with the order of the movements randomized so that elements appeared in an unstructured manner. The subjects viewed a sequence twice and immediately recalled the sequence either motorically or verbally. The results indicated that experts exhibited superior motor and verbal recall for structured sequences, whereas no group differtnces were observed for unstructured sequences. The expert-novice differences wen greater for elements that occurred later in the movement sequence than for the elements occurring early. An interesting observation was noted. Novice subjects tended to rush to recall the sequence motorically and verbally immediately after it was presented. Experts asked if they could think about the structured sequences prior to verbal or motor recall. During this time, the experts rehearsed using both motoric and verbal rehearsal. Experts often used both hand and foot movements and verbalizationprior to verbal recall and motor recall. However, the rehearsal strategies were not used consistently for unstructured trials. These findings strongly support Chi’s (1981) suggestions that strategic behavior may develop first as task specific strategies. Only one study, using adults subjects, has attempted to map the declarative structure of movement concepts or movement elements in closed skills (Deakin & A l l d , 1991). No studies have been conducted with children. The structw of the movement concepts or movement elements may also contribute to superior recall by expert performers. This may be a fruitful area for future research. ResDonse selection Different sports require certain types of cognitive processing. For example. in open skills (team

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sports, net games, etc.), motor responses axe contingent upon the context or game situation. Performers in open skills rely heavily upon both response selection and response execution for successful performance. Athletes must make accurate and rapid decisions within the context of the game and execute the motor skill to carry out the chosen response. Closed skills (i.e., dance, figure skating), where the motor skills are largely performed with little change in the environmental context, require fewer response selection skills. Several studies have examined response selection processes in complex open sports. Some studies have investigated perceptual processing prior to response selection (Abernethy, 1988; Abernethy & Russell, 1987, badminton; Buckoltz, Rapavesis, & Fairs, 1988; Jones & Miles, 1978, tennis; Starkes, 1987, field hockey). Other authors have examined response selection in laboratory simulations (Bard & Fleury, 1976; Fleury, Bard, & Carrikre, 1982, basketball; Helsen & Bard, 1989; Johnson, 1991, soccer, Starkes. 1987, field hockey). In the next two sections, the paradigms and findings of research on perceptual and response selection processes axe summarized. Perceptual DrOcesses

Two types of paradigms have been used to investigate the perceptual abilities of experts and novices. First, a ball detection method (Allard & Starkes, 1980 Starkes, 1987) was used to examine the perceptual skill of detecting an object. The second paradigm used was a film occlusion method (Abernethy, 1988; Abernethy & Russell, 1987; Buckoltz, Rapavesis, & Fairs, 1988; Jones & Miles, 1978; Starkes, 1987) which explored the ability to predict the flight of an object.

In some sports (volleyball, baseball) focusing on ball information, while ignoring other visual cues. may be an advantageous strategy. In detection studies, subjects had to respond as quickly and accurately as possible to the presence or absence of a ball. Allard and Starkes (1980) found that volleyball players were much faster, but not more accurate than non-players in detecting the presence of a volleyball. Thus, skilled volleyball players acquired a rapid visual search strategy for monitoring the location of the ball. In other sports (Allard & Starkes, 1980, basketball; Starkes, 1987, field hockey), this saategy may not be salient to successful performance. For example, Starkes (1987) compared ball detection performance of elite field hockey players with intermediate level players and non-players. No differences were found in ball detection. To date, no studies have investigated these abilities in children. Several studies have used a temporal film occlusion method to study the speed of information processing by manipulating the exposure duration of the visual display. Exposure time has typically varied from a pre implementlobject contact to a post implementlobject contact. Accuracy of the flight of the object has been used as the dependent variable. Most results reveal the superior perceptual anticipatory skills for the expert athlete in a variety of sports, including tennis (Buckolz et al., 1988; Jones et al., 1978), badminton (Abernethy et al., 1987) and field hockey (Starkes, 1987). Experts were able to process information prior to implementlobject contact to accurately predict the object’s destination. The accuracy exhibited by novices was at

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or below chance levels for pre-contact visual displays. Abemethy and Russell (1987) included an experimentalcondition which occluded specific events in the action sequence. The authors attempted to isolate the visual cues which subjects used to make their predictions of shuttlecocklanding. Experts and novices both used racket cues as a source of visual information. However, experts were also capable of using information provided by the arm of the opponent. Thus, experts were able to extract and use additional perceptual information from the movement to facilitate accuracy. In one of the few studies conducted to examine perceptual abilities from a developmental perspective, Abernethy (1988) compared expert and novice badminton players at various ages (12-, 15-, and 18-year-olds)using the same temporal and event occlusion task used in previous work with adults (Abernethy & Russell, 1987). The results of the two studies showed adult experts were better at predicting shuttlecock flight than all other expert age groups when the film was temporally occluded. There were no differences between the novice age groups. On the temporal occlusion task, no expert-novice differences were found at ages 12, 15, and 18. The event occlusion trials revealed experts of all ages used racket and arm cues. Novices of all ages used only racket information to predict shuttlecock flight. Similar results have been reported for young hockey goaltenders (Bard & Fleury, 1981). Eye movements of expert and novice players were recorded while subjects viewed various shots on goal. Both experts and novices fixated on the stick and puck. However, novices tended to have more fixations on the puck. Thus, even as young as 12 years, experts are able to use perceptually higher order variables within the visual field. Decision making Several laboratory studies (Bard & Fleury, 1976; Fleury. Bani, & Canike, 1982, basketball, Starkes, 1987, field hockey) have investigated expert-novice differences in complex decision making. In these studies, subjects viewed slides of game situations and were asked to select an appropriate response as fast and accurately as possible. Experts made more accurate response selections but speed of processing was similar for experts and novices. Helsen and Bard (1989) compared the response selection of adult expert and novice soccer players while viewing filmed sequences of soccer play. All game situations were presented from the point of view in which the subject was in possession of the ball. The subject viewed the sequence and decided whether to shoot on goal, dribble around an opponent, or pass to an open teammate. Experts were faster in responding and more accurate than novices. Eye movements recorded during the task indicated that experts had fewer fixations than novices and focused more on open space and the free back player. Johnson (1991) examined decisions regarding where to run when not in possession of the ball in experiencedsoccer players age 11-15. and college players. Twelve short videotaped game situations were selected from World Cup matches. The subject viewed sequence, waited

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for identification of the specific player on the videotape, and was asked to determine as quickly and accurately as possible where the player would run in this game situation. Decision time and accuracy of response were measured. In all situations, varsity college players responded faster than 15-year-oldexperiencedplayers who responded faster than 1I-year-old players. Differences in accuracy were situationally specific. For situations which involved complex chunks of game play (overlap, take over) or where long passes were inherent to the game situation, college players and 15-year-oldplayers were more accurate than younger players. However, when the run was directed straight ahead toward the goal into open space, 11-year-old subjects were slightly more accurate than older players. Johnson (1991) also interviewed subjects using the same game SiNatiOnS and asked subjects where they would run to and why they made this choice. A short summary of the important mnds from the interviews provided insight into the errors players were making in response selection. First, 11-year-oldsdid not appear to understand the offside rule well enough to translate the rule into appropriatemovement. Fifteen-year-olds were beginning to develop this awareness but many were still choosing offside runs. Varsity college players eliminated some alternative runs due to the offside rule and justified their movements based on this premise. Second, 11-year-oldsdid not recognize chunks of player movements. Younger players had fewer solution strategies. Most often they selected a straight run toward the goal to receive a pass. Older players were more likely to recognize game patterns and label them as such in their verbal accounts. Third, younger players had greater difficulty encoding the situation. They ignored and did not mention teammates other than the one with the ball. They were more likely to run into positions already occupied by teammates or defenders. The 15-year-old players could more accurately encode the situation but still ran into positions already occupied more than college varsity players. These differences are largely due to developing knowledge of soccer. One other important vend in the data was evident in a game situation which required defending a long cross or pass across the field. Few 11-year-olds considered this a possibility. However, 15-year-old and college players immediately recognized the switch and were very accurate in their response. Skill tests for kicking distance clearly indicated this distance was well beyond the skill level of the 11-year-olds. One possible reason for their poor response selection in this situation may be that they had never been exposed to it because response execution is not a possibility. The 11-year-olds chose a response that was most frequently encountered during their play. Specifically, they chose to move toward the middle of the field to prevent a pass or stop the dribbler. Research in naturalistic settings One group of researchers (French & Thomas, 1987; McPherson & Thomas 1989; Thomas, French, & Humphries, 1986) have attempted to study developing expertise in naturalistic sport environments. Thomas, French, and Humphries (1986) provided detailed rationale for research on developing sport expertise in natural environments and suggested a paradigm to conduct research.

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They proposed that researchers develop procedurcs to measure sport declarative knowledge, procedural sport knowledge, sport specific skills, and the cognitive (decision making or response selection) and motor (execution of sport skills) components of actual game performance. The cognitive component of game performance and the motor component could be determined by observing the accuracy of responses during game play. Often this would require videotaping game performance for subsequent observational analysis. In the studies that we report using this approach,response selection during game play has been modeled as procedural knowledge repsented 89 production systems (Anderson, 1982). Productions are generalized stimulus-responsepairs, (if-then), stored in memory. For example, if the goal is to perform this task, and these conditions exist, then execute a given action. Productions contain a task goal, a set of conditions, and an action to be performed or carried out. Productions are likely oversimplified models of behavior. However, the model is useful to examine response selection, motor execution, as well as linkages between response selection and response execution. Several authors (French & Thomas, 1987; McPherson & Thomas, 1989; French, Spurgeon, Nevett, Rink, & Graham, in preparation) have used production models and Anderson’s (1982) framework for the acquisition of cognitive skill outlining the development of productions to guide research on development of sport expertise in children. The Fist study to use this framework was conducted by French and Thomas (1987). In Experiment one, they examined the relationship of sport specific knowledge to children’s performance in basketball. The hypothesis for the study was that sport knowledge would be related to decision making during actual games, whereas sport skill execution would be related to actual skill execution during games. Measures of basketball knowledge (paper-pencil), dribbling skill, and shooting skill were obtained on expert and novice male basketball players age 8-10 and 11-12. The levels of expertise were operationally defined as the best and poorest players on each team within a given league. Actual game performance of each team was videotaped and subsequently analyzed using an observational instrument designed to assess the accuracy of controlling the basketball and skill execution during game play (motor components of performance) and the accuracy of decisions made during game play (cognitive response selection). Results substantiated two components of performance in basketball, a cognitive decision making component and a motor skill execution component. Basketball knowledge was related to decision making during game play, whereas dribbling skill and shooting skill were related to the motor component (control and execution). Age was a significant factor in discriminating knowledge. Older players possessed more knowledge. However, age did not discriminate skill tests or game performance. Experts of both ages differed significantly from novices in basketball knowledge, shooting skill, and the decision component of performance. The component of performance which maximally distinguished experts from novices was the decision component. Thus, experts were making better decisions during the game than novices. The knowledge test used in this study contained declarative knowledge questions

K.E. French and M.E. Neven regarding player positions, rules, basic strategies. A few of the questions could have been considered masures of productions or condition/action decisions. Decision making during the games was a measure of cognitive procedural knowledge. It measured the subject’s ability to match game conditions with the selection of appropriate responses in the context of the game (if-then). The positive relation of the knowledge test with the decision making component provided some support that sufficient declarative knowledge was necessary for the development of productions (Anderson, 1982) used in response selection during games. French and Thomas (1987, Experiment 2) investigated changes in the decision making and skill execution components of performance of game play for 8- to 10-year old basketball players over the length of a season. Basketball knowledge, dribbling and shooting skill, and game play measures were collected at the beginning and end of the season. Both knowledge and the accuracy of game decisions improved across the season, whereas dribbling, shooting, and skill execution during games remained constant. Thus, children were learning what decisions were appropriate during the game faster than they were acquiring skill. These results may have been due to greater emphasis by coaches on cognitive skills during practices and games. The second study investigating knowledge and sport performance was conducted by McPherson and Thomas (1989). Expert and novice male tennis players age 10, 11, 12, and 13 were measured on tennis knowledge, tennis skill, and game performance. Serve decisions were positively related to both serve and tennis knowledge. Both tennis knowledge and serve skill influenced decisions about where and how to serve, but not the execution of the serve. Game decisions were positively related to tennis knowledge and groundstroke skill. During game play, subjects who made better decisions had greater tennis knowledge and possessed higher groundstroke skills. In the second phase of their study, McPherson and Thomas (1989) examined the procedural knowledge structures of child expert and novice tennis players. Two types of interviews were conducted. The situation interview consisted of three categories of open ended questions pertaining to what players were thinking during service, backcourt and net game situations. The point by point interview required subjects to answer a probe question (what were you thinking about during that point) between points during actual game play. This interview assessed what and how knowledge was being used during game play. Transcripts of the verbal protocols from each interview were coded to determine the content and structure of knowledge. Content was assessed by the number of concepts (goal, condition, action), the number of different concepts, and the quality of each concept. The structure of knowledge was measured by the number of connections between concepts and the linkage of concepts. In both interviews, experts had more condition concepts and more alternative actions that were associated with the goal structure of tennis. The variety of action concepts maximally discriminated expertise. These results support Anderson’s (1982) contention that proceduralization of condition concepts is easier to acquire than proceduralization of action concepts. Anderson suggests that considerable practice and experience is necessary to

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proceduralize action concepts. Experts also possessed a greater number of connections and linkages between goal, condition, and action concepts. Thus, the structure of the experts' p d u r a l knowledge was much more advanced than novices. The number of agreements and disagreements between the action selected and the ability to execute the action motorically was determined from game play and by comparing point interview ptocols with actual game play. Experts. with p t e r knowledge and skill, could execute the responses they selected more. often than novices. However. forceful decisions were not always related to forceful execution even for the experts. The protocols also suggested that experts were more likely to include elaboration of action concepts that included parameterization of the action concept such as spin, direction, placement. These statementsreflect further discriminationof response selection and was r e f e d to by McPherson and Thomas as "do". The "do" component should not be confused with execution of the response, rather it reflects a more. detailed representation of response selection. Experts were also more likely to verbalize errors made on previous points, i.e., I hit it too hard. Thus, experts were monitoring their performance and using self-regulation strategies to detcct errors. These are largely cognitive processes used to monitor ongoing performance. The preliminary results of one of our studies provides additional insight into proceduralization processes in youth sport athletes. We recently collected baseball situation interviews with 7-, 8-, 9-, and 10-year-oldplayers in a local Little League (French, Spurgeon, Nevett, Rink, 8i Graham, in preparation). In one of the baseball situations, subjects were asked to assume the role of the third baseman and were instructed to select the appropriate response when a ball is hit to third with a runner on second base and one out. The p b e question asked subjects to state what the third baseman should do with the ball and why. The children's e m provide some interesting results. The two most common errors were throwing the ball to second base or running with the ball to tag third base. These m r s at least reflect a sensitivity to a higher order goal (preoccupation with the lead runner). Many of the children who chose to run the ball to third base, when asked why they made this choice. changed their answer to the correct response. Once prompted, most answers were similar to the following: "oh, he doesn't have to run, ok, he doesn't have to run so you would throw the ball to frst." The c m t MSWW was present in the subject's knowledge base but was not automatically retrieved until the subject explained their answer. The game situation (runner on second only) does not occur frequently in game play. Subjects seemed to have proceduralized some responses to game decisions that o c c d frequently in games and these were easily retrieved. However, subjects a p p e d to automaticallyretrieve a general response to this situation that was not appropriate. More general productions that can be used in a larger variety of game situations may likely develop first and gain strength with repeated use (Anderson, 1982). Thus, when novices and children are developing decision making productions, their errors may often be due to failure to ncognize or monitor game states in order to detect, select, or create more situation specific productions.

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M m work is needed using similar protocols to determine how and what proceduralization processes are occurring as children leam various sports. It is important to understand the errors that occur in children’s thinking as expertise progresses. Anderson’s (1982) description of proceduralization, tuning, and strengthening processes maybe a valuable theoretical framework in this regard. Constraints on cognitive and motor exuertise Many other factors influence motor skill development, i.e. physical growth, maturation of the nervous system and musculature. A 13-ycar-oldexpert is likely to possess superior sport skill in relation to a 10-year-old expert. Even though practice and skill learning are major contributors to cognitive and motor performance across childhood, motor skill performance is constrained by maturational factors. A more thorough review of the constraints on motor performance for the developing child is given in Newel1 (1986). Because these constraints impact or l i t skill development, they are likely to indirectly limit the development of cognitive sport skills in some instances. One example was discussed earlier (Johnson, 1991, soccer). Mort research is needed to address how constraints on skill development impact the development of cognitive sport skills. Most youth sports modify the rules to accommodate the developmental level of the child. An example is the propssion from tee ball, to coaches pitch, to batting a pitched ball in baseball. Soccer provides a youth sport coaches manual (Rees, 1987) with suggestions for the types of skills and skill/strategy combinations that are appropriate at a given age level. Thus, many sports modify the rules based upon the developmental level of the child. Such modifications constrain the introduction of specific skill and strategy combinations due to the skill and developmental level of the child. Children are capable of learning these strategies before they are capable of executing the sport skills necessary to use the strategies in game play. However, highly developed productions may not develop until children can practice and use these strategy/skill combinations in actual game play. Skill development may also constrain response selection in other ways. Research needs to examine whether perceptions of ability to execute a skill impacts a player’s choice to use the skill in game performance. Clearly, if one does not possess a skill, one is less likely to be able to use the skill. However, definition of successful skill performance is subject to personal interpretation. A player may indeed possess sufficient skill but perceive their skill ability as low (underestimaters) or perceive their skill to be more advanced than it is (overestimators). Thus, perceived competence of one’s ability to execute the skill effectively in performance situations or perception of a teammate’s ability may be related to response selection. Some preliminary data suggests that both cases do impact response selection. Several

of Johnson’s (1991) adult subjects stated what the best choice in a situation would be but they did not have the ability to actually execute that skill in a game, Also, several 9- and 10-year old baseball players stated answers in a baseball situational interview (French, et al., in preparation) that suggest their actions would be dependent on the ability of their teammate. No 7- or 8-year-old gave similar responses. For example, one subject said that he would throw the

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ball to first base (correct answer was throw to second to start a double play). When probed on why he chose f i t base, he responded “that the second baseman could not catch the ball so I don’t throw it to him” Several subjects stated that where he positioned himself as a cutoff was dependent on if the teammate could throw far enough. Position specificity also tends to occur in some sports. The cognitive and sport skills for specific positions differ, i.e. point guard or post position in basketball. Often these positions and their related skill and cognitive requirementsare differentiatedvery early in youth sport. General observationsof children’s basketball suggests that young children (I-year+lds) are willing to try a variety of sport skills during game play (French & Thomas, 1987). Yet, older childrcn Stem to develop more specific skill related roles. For example, all eight-year-olds will dribble during a game. Fewer lo-, 11-, and 12-year-oldswill dribble unless they are the point guard or have adequate dribbling skills. Future research is needed to determine if response selections by children are influenced by position specificity of skills taught by coaches or if response selection is influenced by individual perceptions of ability and that of others. This becomes an inkresting developmental question because it is well documented that children‘s perception of competence becomes more accurate with age (Harter, 1981; Horn & Weiss, 1991). Future directions We believe that longitudinal research and research on instructional influences offcrs the greatest promise. Experience alone does not translate into expertise. Research examining changes in knowledge, skill, and performance over short periods of time provide limited pieces of information. Sport expertise develops over considerable time and practice using the knowledge and skills. Many sport experts begin participation in childhood. Only by measuring changes in procedural knowledge and skill development over extended periods of time,can we expect to uncover the changes in knowledge structures, the interaction of cognitive and motor processes, constraints on cognitive and motor performance, relations to improved p e r f m e , and instructional practices which facilitate performance.

References Abernethy, B. (1988). The effects of age and expertise upon perceptual skill development in a racket sport. Research Quarterly for Exercise and Sport, 59, 210-221. Abernethy, B, Russell, D. G.(1987). Expert-novice differences in an applied selective attention task. Journal of Sport Psychology, 9, 326-345. Allard, F., & Bumett, N. (1985). Skill in sport. Canadian Journal of Psychology, 2 , 14-21. Allant, F., Graham, S., & Paarsalu, M. E. (1980). Perception in sport: Basketball. Journal of Sport Psychology, 2 , 15-21. Allard, F., & Starkes, J. L. (1980). Perception in sport: Volleyball. Journal of Sport Psychology, 2, 22-33. Anderson, J. R. (1976). Language, memory, and thought. Hillsdale, N.J.: Erlbaum. Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369-406. Bard, C., & Fleury, M. (1976). Analysis of visual search activity during sport problem situations. Journal of Human Movement Studies, 3, 214-222.

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Bard, C., & Fleury, M. (1981). Considering eye movement as a predictor of attainment. In I. M. Cockerill, & W. W. MacGillivary (Eds.), Vision and sport. (pp.28-41). Cheltonham, England Stanley Thomes. Belmont, J. M., & Butterfield, E. C. (1971). What the development of short term memory is. Human Development, 14,236-248. Buckolz, E.. Rapavesis, H., & Fairs, J. (1988). Advance cues and their use in predicting tennis passing shots. Canadian Journal of Spon Science, 13,20-30. Chase, W . G.,& Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4 , 55-81. Chi, M.T.H. (1976). Short term memory limitations in children: Capacity of processing deficits? Memory and cognition, 4 , 559-572. Chi, M.T.H. (1978). Knowledge structures and memory development. In R.S. Siegler (Ed.), Children’s rhinking: What develops? (pp.73-105). Hillsdale, N.J.: Erlbaum. Chi, M.T.H. (1981). Knowledge development and memory performance. In M.P. Friedman, J.P. Das, & N. O’Connor (Eds.), Intelligence and learning. (pp.221-229). New York: Plenum Press. Chi, M.T.H., & Ceci, S.J. (1987). Content knowledge: Its representation and restructuring in memory development. In H.W. Reese (Ed.), Advances in child development and behaviour (vol. 20, pp.91-142). New York: Academic Press. Chi, M.T.H., Feltovich, P.J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognirive Science, 5, 121-152. Chi, M.T.H., & Gallagher, J.D. (1982). Speed of processing: A developmental source of limitation. Topics of Learning and Learning Disabilities, 2, 23-32. Chi, M.T.H., Hutchinson, J.E., & Robin, A.F. (1989). How inferences about novel domainrelated concepts can be constrained by structured knowledge. Merrill-Palmer Quarterly, 35, 27-62. Chi, M.T.H.. & Koeske, R.D. (1983) Network representation of a child‘s dinosaur knowledge. Developmental Psychology, 19, 29-39. Chi, M.T.H., & Rees, E.T. (1983). A learning framework for development. In M.T.H. Chi (Ed.), Contributions in human development (Vol. 9, pp.71-107). Basel: S. Karger. Chiesi, H.L., Spilich, G.J., & Voss, J.F. (1979). Acquisition of domain related information in relation to high and low domain knowledge. Journal of Verbal Learning and Verbal Behaviour, 18, 257-273. Deakin,J. & Allard, F. (1991). Skilled memory in expert figure skaters. Memory and m p h , 19, 79-86. Fleury, M., Bard, C., & Carrikre, L. (1982). Effects of reduction of processing time and level of expertise in a multiple-choice decision task. Perceptual and Motor Skills, 55, 12791288. French, K.E.,& Thomas, J.R. (1987). The relation of knowledge development to children’s basketball performance. Journal of Sport Psychology, 9, 15-32. French, K.E.,Spurgeon, J.H., Nevett, M., Rink, J.E., & Graham. K.G. (in preparation). Longirudinal changes in procedural baseball knowledge. Unpublished manuscript, University of South Carolina, Columbia. Gallagher. J.D., & Thomas, J.R. (1984). Rehearsal strategy effects on developmental differences

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for recall of a movement series. Research Quarterlyfor Exercise and Sport, 55,123-128. Gallagher, J.D., & Thomas, J.R. (1986). Developmental effects of grouping and recoding on learning a movement series. Research Quarterlyfor Exercise and Sport, 57, 117-127. Garland, D.J. (1989). The nature of chunking in recall of schematic sport diagrams. Perceptual chunking or conceptual chunking. Unpublished doctoral dissertation, University of Georgia, Athens. H m r , S. (1981). Effectance motivation reconsidered Toward a developmental model. Human Development, 21, 34-64. Helsen, W., & Bard, C. (1989) The relation between expertise and visual information processing in sport. A paper presented at the Itdernational Conference on Youth, Leisure, Physical Activity, and Kinanthropometry N,Brussels, Belgium. Horn, T.S.,& Weiss, M.R. (1991). A developmental analysis of children’s self-ability judgements in the physical domain. Pediatric Exercise Science, 3, 310-326. Johnson, D.L. (1991). Off the bull decision making in soccer. Unpublished master’s thesis, University of South Carolina, Columbia. Jones, C.M., & Miles, T.R. (1976). Use of advance cues in predicting the flight of a lawn tennis ball. Journal of H m n Movement Studies, 4,231-235. McCullagh, P, Stiehl, J., & Weiss, M.R. (1990). Developmental modeling effects on the quantitative and qualitative aspects of motor performance. Research Quarterly for Exercise and Sport, 61, 344-350. McPherson, S.L., & Thomas, J.R. (1989). Relation of knowledge and performance in boy’s tennis: Age and expertise. Journal of Experimental Child Psychology, 48, 190-211. Naus, M.J., & Omstein, P.A. (1983). Development of memory strategies: Analysis, questions, and issues. In M.T.H. Chi (Ed.), Contributions to human development: vol. 9. Trends in memory development research @p. 1-30). Basel: S Karger. Newell, K.M. (1986). Constraints on the development of coordination. In M.G. Wade & H. T.A. Whiting (Eds.). Motor development in children: Aspects of coordination and control. (pp. 341-360). Dordrecht, Netherlands: Martinus Nijhoff Publishers. Omstein, P.A., & Naus, M.J. (1984). Effects of the knowledge base on children’s processing. Unpublished manuscript, University of North Carolina, Chapel Hill. Rees, R. (1987). The manual of soccer couching. Spring, TX: Annbon, Inc. Rummelhart, D.E., & Norman, D.A. (1978) Accretion, tuning, and restructuring: Three modes of learning. In J.W. Cottor. & R. Klatzky (Eds.), Semantic factors in cognition (pp. 3753). Hillsdale, NJ: Erlbaum. Roth, C. (1983). Factors affecting developmental changes in the speed of processing. Journal of Experimental Child Psychology, 35,509-528. Siegler, R. S. (1989). How domain-general and domain-specific knowledge interact KIproduce strategy choices. Merrill-Palmer Quarterly, 35, 1-25. Starkes, J. L. (1987). Skill in field hockey: The nature of the cognitive advantage. Journal of Sport Psychology, 9, 146-160. Starkes, J. L., Deakin, J. M., Lindley, S., & Crisp, F. (1987). Motor versus verbal recall of ballet sequences by young expert dancers. Journal of Sport Psychology, 9, 222-230.

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Spilich, G. J., Vesonder, G. T., Chiesi, H. L., & Voss, J. F. (1979). Text processing of individuals with high and low domain knowledge. Journal of Verbal Learning and Verbal Behavior, 18, 275-290. Thomas, J. R. (1980). Acquisition of motor skills: Information processing differences between children and adults. Research Quarterly for Exercise and Sport, 51, 158-173. Thomas, J. R. (1984). Children's motor skill development. In J. R. Thomas (Ed.),Motor development during childhood and adolescence (pp. 91- 104). Minneapolis: Burgess. Thomas,J. R., French, K. E., & Humphries, C. A. (1986). Knowledge development and spon skill performance: Directions for motor behavior research. Journal of Sport Psychology, 8, 259-272.

Thomas, J. R., Thomas, K. T., Lee. A. M., Testerman, E.. & Ashy, M. (1983). Age differences in use of a strategy for recall of movement in a large scale environment. Research Quarterly for Exercise and Sport, 54, 264-272. Vickers, J. N. (1986). The resquencing task Dttermining expert-novice differences in the organization of a movement sequence. Research Quarterly for Exercise and Sport, 57, 260-264.

Weiss, M. R. (1983). Modeling and motor performance: A developmental perspective. Research Quarterly for Exercise and Sport, 54. 190-197. Weiss, M. R., & Klint, K. A. (1987). "Show and tell" in the gymnasium: An investigation of developmental differences in modeling and verbal rehearsal of motor skills. Research Quarterly for Exercise and Sport, 58. 234-241. Winther, K. T., & Thomas, J. R. (1981). Developmental differences in children's labeling of movement. Journal of Motor Behavior, 13, 77-90.

Part 4 Theoretical Considersations and Evaluations of the Approach

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CHAPTER 15 A MODULAR APPROACH TO INDIVIDUAL DIFFERENCES IN SKILL AND COORDINATION STEVEN K. JONES Department of Psychology. University of Oregon Eugene, Oregon 97403-1227 Much of the field of motor learning has developed as a reaction to the rather appealing idea that individual differences in the performance of complex skills are due to differences in simpler, underlying abilities. In essence, this idea suggests that there are a small number of general abilities which support a wide variety of complex activities. Individual differences in these few abilities, then, contribute to the differences we see in a vast array of complex tasks. Those who have spent time on playgrounds are surely familiar with the concept of the "all-around athlete," a person who is believed to have a high degree of general athletic ability. As a result of his or her general ability, the all-around athlete is able to excel in many different types of sporting activities. There are several advantages to the idea that complex motor performance is determined by a small number of underlying abilities. First, it is theontically elegant. One of the major goals of developing scientific theory is to reduce the number of consmcts required to explain a variety of phenomena (Underwood, 1975). If one is able to explain differences in seemingly diverse motor behaviors by appealing to a few underlying abilities, one can develop a rather powerfill theory. The notion of general underlying abilities is of practical sigmfkance as well. If such general abilities exist, there should be a high correlation between an individual's performance on a complex task and his or her performance on a simpler one that relies on the samc underlying ability. This being the case, one could use performance on the simpler task to predict performance on the more complex one. Clearly, this would be of use to those involved in fields such as personnel selection, where prediction of future task perfonnance is of the utmost importance. In addition, to the extent that future perfonnance of complex skills is dependent upon general underlying abilities, one could argue for the development of teaching techniques that attempted to foster these abilities, especially in children (Fleishman, 1967). Despite the intuitive appeal of the "abilities" approach, much of the work in the area of motor control suggests that individual differences in task performance cannot be explained by

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appealing to a small number of general abilities. This literature extends back at least 70 years, and has a very rich history. While it is not appropriate to include a thorough review here, several of the key studies are discussed briefly below. Mort thorough reviews are available elsewhere (e.g., Adams, 1987; Marteniuk, 1974; Schmidt, 1988). Correlational Studies If individual differences in motor skills could be explained by differences in "general ability," then one would expect high positive correlations between an individual's performance on different tasks. Those individuals who have the ability necessary to perform well on one motor task should also perform well on a separate motor task. However, numerous studies show that high correlations are difficult to find. For example, Pemn (1921) tested subjects on 17 different motor tasks, ranging from stacking blocks to a choice reaction time task. While most of the correlations between these tasks were positive, they were very low. often near zero. Based on these results, Pemn argued that there cannot be a single "general motor ability." More recently, similar studies have been conducted by Henry and his colleagues (Henry, 1961; Henry & Whitley, 1960). In these studies, near-zero correlations were found between movement time and reaction time. If individual differences between even these two seemingly similar tasks are undated, it is difficult to argue that a general motor ability underlies all of motor behavior. To account for these types of results, Henry (1958; cited in Schmidt, 1988) proposed that motor skill is specific to a particular task. According to this "specificity hypothesis." there need not be high positive correlations between individuals' performances on different tasks. Factor Analytic Studies Given the findings described above, it is apparent that motor skill cannot be predicted by a single underlying ability. Therefore, one may wish to look for a somewhat larger set of more specific underlying abilities. This task was undertaken by Fleishman and his colleagues, who relied on factor analytic techniques in an attempt to describe a taxonomy of motor abilities (see Fleishman. 1967, for a review). Over the course of several studies, Fleishman tested thousands of subjects on several batteries of motor tests. By examining the patterns of correlations between these tests, he attempted to outline a relatively small number of abilities that could describe subjects' performance. While this approach was reasonably successful, Fleishman still lists no fewer than 19 factors, ranging from "aiming" to "trunk strength", that are important in describing differences in motor behavior. In addition to developing a taxonomy of basic abilities, Fleishman was also interested in examining the importance of these different factors at different stages of learning a complex skill (see Fleishman, 1967, for a review). Interestingly, he found that the pattern of abilities which contribute to overall task performance systematically changes with practice. While his proposed set of general abilities accounts for a fairly large percentage of variance in the early phases of practice, their total contribution tends to diminish with practice. In the later stages of practice, the largest percentage of variance in task performance is accounted for by a factor specific to the task itself. This suggests that, as subjects become proficient at a particular task, the role of general abilities may decrease, and the importance of task-specific knowledge becomes more

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important. Taken together, the results described above deal a serious blow to the "abilities" approach described earlier, at least in its simplest form. These results suggest that motor expertise is largely specific to a particular task. Therefore, outstanding performance on one task will not necessarily imply outstanding performance on a different task. As an example, world-class gymnasts may be highly skilled at gymnastics, but they may not necessarily be able to excel at other motor activities. The idea that expertise is largely specific to a particular task is not unique to the field of motor learning. Similar conclusions have been drawn by Chase and Simon (1973) in their well-known study of chess experts. In this study, chess masters were found to be superior to weaker players in their ability to reproduce a pattern of chess pieces on a board after a few seconds of viewing. Importantly, the superiority of the chess masters' memories was limited to the domain of chess; they did not generally have better memories. This provides further confirmation that a large component of expertise involves skills specific to a particular task. It is undeniable that much of what we consider to be expertise within a domain involves extensive knowledge or experience in that domain. However, this does not preclude the possibility that more general abilities play an important role in determining the level of individual performance. After all, Fleishman's (1967) general factors did account for approximately one-third of the variance in subjects' performance, even after many trials of practice. Rather than claiming that general abilities play no role in determining the level of task performance, it may be more accurate to assume that some kind of general abilities serve as limiting factors of that performance. Certainly, one cannot become an "expert" at some task without a large body of task-specific experience. However, the degree to which an individual possesses the requisite underlying abilities may constrain the level of expertise to which one can aspire. The challenge, then, is to identify the basic building blocks that may constmin task performance. The early work by Pemn (1921) and Henry (1961) suggests that we should not be searching for a single "athletic"ability. Instead, it is likely that "ability" can be broken down into several components. Fleishman (1967) has proposed several candidate components, but his list may not be entirely sufficient. For one, Fleishman's factors were determined as a result of posr hoc analysis of task performance; they were not derived on the basis of a systematic theoretical approach. Second, a reliance on Fleishman's techniques would not take full advantage of our current understanding of cognitive neuroscience. It seems that any taxonomy of underlying abilities must be based on knowledge concerning the ways in which the central nervous system controls movement.

In this paper, I will introduce a different approach to uncovering the basic abilities that may constrain complex motor skills. This approach is based on the idea that motor skills can be broken down into a small number of separable components, each of which is controlled by

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a different module in the brain. In the next section, I will describe this modular view in more detail, and use it to develop a general approach to the study of motor skill and coordination. In

the last section, I will discuss how this approach can be applied to the study of individual differences. In particular, I will describe recent applications of this view to the study of clumsiness in children. The Modular View A fundamental assumption in the area of motor control is that movements are organized

centrally in the form of a motor program. Evidence for this idea comes from studies that show that entire movements can be executed in the absence of peripheral feedback. These findings undermined early S-Rlearning theories which posited that each component of a given movement was elicited by the feedback caused by the component movement immediately preceding it. Instead, these results suggested that the central nervous system creates a sort of "master plan" for each coordinated sequence of movements it wishes to perform. This "master plan," then. can be implemented, often without a great deal of on-line control (see Keele, 1981 for a complete discussion). Ivry and Keele (1989) liken the motor program to the software used by computers. It is a detailed description of the job the system is to perform in a particular situation.. Given this metaphor, one might be interested in examining the specific functions and procedures that make up the motor program. These elementary components may serve as basic abilities, the building blocks of more complex task performance.

The modular view of motor control, developed by Keele and Ivry (e.g., Keele & Ivry, 1987) attempts to clarify the nature of the elementary components that make up the motor program. According to the modular view, the brain is organized by function rather than by task. Therefore, two very different tasks may call upon the same module to perform a given function. Take, for instance, bicycle riding and piano playing. These are clearly very different tasks, but their performance is dependent upon many of the same functions. In both activities, the brain must calculate each of the following: the sequence of movements that must take place and where they will occur, the relative times at which each movement should occur; and the force with which the movement should be performed. Keele and Ivry postulate that these three functions -- sequencing, timing, and force regulation -- are separate modules in the brain.' These modules, then, are utilized regardless of the specific task (bicycle riding, piano playing, etc.) that needs to be performed.

'The reader should be aware that this is certainly not an exhaustive list of functions used in the construction of motor programs. Additional computations surely must be accomplished. In addition, it is likely that these functions can be further broken down into separable sub-components. For example, the module of sequential spatial specification probably involves several component operations. Still, an examination of the modules proposed here should Serve as a useful starting point for funher discussion.

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The sense in which the term "module" is used here is somewhat different from other descriptions of mental modularity (e.g., Fodor, 1983). Basically, I am proposing that a "module" represents an anatomically distinct neural computation. One important feature of this view was alluded to above: the same computation may be called upon in a number of different tasks. Therefore, one might expect similar tasks to be influenced by at least some of the same modules. This idea is supported by Kosslyn (1987). who has argued that many diverse perceptual tasks. such as object recognition, navigation, and imagery, all rely on common modular processing systems. A second feature of the view described here is that separate modules m c o n s i d e d to be functionally independent. Therefore, the performance of one module may be unrelated to the performance of a separate module. Consistent with this view are the results of Favilla. Hening, and Ghez (1989) suggesting that the amplitude and k t i o n of voluntary movements are specified independently. Recent work by Keele, Ivry, and their colleagues has provided further evidence consistent with this modular view. For instance, a series of correlational analyses has provided support for the proposed module of timing (see Keele & Ivry, 1987). In one study, subjects were asked to synchronize key-presses with a regularly-occumng auditory signal. After synchronization the tone stopped, and the subject was required to continue tapping the key at the same speed. In some trials, subjects were asked to tap the key by moving only their index finger. In other trials, subjects were to tap the key by moving the entire forearm. Variability of inter-tap intervals serves as a measure of timing ability. To the extent that subjects' timing abilities correlate across the different effectors, one could say that timing is a general-purpose module and not one that is dependent upon the effector system used to make the response. In the above experiment. the correlation was .90. Therefore, it appears as if central control of timing is independent of the effector system used to perform the movement. One possible criticism of the key tapping experiment reported above is that the two tasks (tapping with finger and tapping with arm) are too similar to serve as a true test of the modular view. If timing is a general-purpose module that is used in many diverse skills, one should be able to see its effects in other tasks as well. In a separate study, Keele, Pokomy, Cmos, and Ivry (1985) examined the correlation between tapping accuracy (using the finger tapping task described above) and perceptual acuity. In the perceptual task, subjects heard two sets of two tones. The first pair was always separated by 400 msec.. while the second pair was separated by a variable amount of time. The subject's task was to indicate whether the interval separating the second pair of tones was longer or shorter than the fmt interval. Thresholds were calculated for each subject, indicating each subject's ability to distinguish small differences in the two intervals. The perception task, like the tapping one, requires the subject to make a computation of time. However, the structure of the task is very different; it is purely perceptual, rather than being based upon production. Still, Keele et al. reported a significant positive correlation (-53) between these two tasks. This result provides further evidence that timing is a rather general ability that is independent of the particular task that subjects are asked to perform.

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To demonstrate that force control may be its own separate module, a similar strategy was

used (Keele, Ivry, & Pokomy, 1987). Subjects were to press on a force uansducer and try to match a target force. In some trials, this was done only with the index finger, while in other trials it was done by moving the entire forearm. Variability of responses gave an index of force control. To the extent that subjects’ force control abilities correlate across the different effectors, one could say that force regulation is also a general-purpose module. In this experiment, the correlation was .76. Similar correlations were found between finger and foot, further suggesting that force control is independent of the particular effector system used to respond. The above results suggest that both timing and force control correlate very highly across different effectors. This suppons the modular view. The more important result, however, is that the correlations between timing ability and force control are rather low (ranging from . I 8 to .34; see Figure 15.1). This is m e even

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Figure 15.1. Swnmary of Correlations between Timing Ability and Force Control (from Keele CG Ivry, 1987).

when responses are made with the same effector (i.e., when both the timing task and the force control task are done with the finger, or both are done with the arm). This suggests that the modules that regulate timing and force are separable components. That is, they are independent of each other. Recent work by Keele and his colleagues has begun to investigate the modular nature of sequencing (e.g., Keele, Cohen, & Ivry, 1990). This work has used a simple key-pressing task (based on Nissen & Bullemer, 1987). in which subjects are asked to respond to x-marks presented on a computer screen. The x-marks can appear in one of three different locations on the screen, and subjects are asked to press a key corresponding to the location of the mark as quickly as possible. Unbeknownst to subjects, the x-marks appear in a repeating sequence in

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some blocks of trials, while in other blocks they appear in random order. Learning of the sequence can be inferred by decreasingreaction times in the sequence blocks (relative to random blocks) over the course of the experiment (see top panel of Figure 15.1). One critical test of the modular view is whether learning a sequence of key presses is independent of the effector used to make the response. Therefore, some of the subjects switched effectors halfway through the experiment. For example, subjects responded during the first half of the experiment with their index fmger and during the second half of the experiment with their arm. Other subjects continued responding with the same effector throughout the entire experiment. Results indicated that there is no difference between the performance of those subjects who switched effectors (bottom panel of Figure 15.1) and those who used the same effector throughout (top panel). Therefore, knowledge of the sequence acquired during performance with a particular effector is independent of that effector system. This supports the view that sequence representation is a general-purpose module that is not dependent upon the specific task performed. A more powerful test of the modular view asks if sequence knowledge transfers to responses of a completely different kind, rather than just between effectors. In a recent study (Keele, Jennings, Jones, & Cohen, IWl), subjects performed the sequence learning task described above, either by making manual responses (i.e., pressing keys corresponding to target location) or verbal responses (i.e., indicating the location of the x-mark by speaking into a microphone). Half of the subjects performed the entire experiment by making verbal responses, while the other half of the subjects switched from manual to verbal response midway through the experiment. If sequencing is a central processing module, one would expect that sequence knowledge would transfer between different response conditions. The results demonstrated partial support for this prediction. Subjects in the manuaverbal condition did show a significant sequence learning effect, even after transfering to a different response modality (see Figure 15.2). However, it should be noted that the sequence learning effect in this condition was smaller than the sequence learning effect shown by the group who gave verbal responses throughout.

Given that timing, force, and sequence representation appear to be separate modules, it may be reasonable to assume that different mas of the brain perform each of these computations. This indeed appears to be the case. Timing seems to be controlled by the cerebellum (Ivry & Keele, 1989; Ivry, Keele, & Diener, 1988). Ivry and Keele compared the performance of Parkinson, cerebellar, cortical, and peripheral neuropathy patients with age-matched controls on both the time production and time perception tasks described earlier. Only the cerebellar patients were impaired on both timing tasks. They showed increased variability in the tapping task, and they were less accurate on the perceptual task. Importantly. the latter result is not a function of any perceptual difficulties, since these patients were not impaired in a control task measuring the perception of loudness.

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SAME EFFECTOR DURING TRANSFER 400 T TRAINING TRANSFER

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Force regulation seems to be a function of the basal ganglia. Patients with Parkinson’s disease (which affects the basal ganglia) are impaired, relative to normal controls and other types of patients, in force regulation tasks like those described in Keele, Ivry. and Pokorny (1987)

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(Ivry, 1989; cited in Lundy-Ekman, 1990). Parkinson's patients have also shown impaired performance in a number of other similar force regulation tasks (e.g., Stelmach Br Woningham, 1988; Wing, 1988).

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Figure 152. Reaction time to visual signals appearing on a computer screen. In blocks 1, 2. 11, 12, and 14, the signals are presented randomly. In blocks 3-10 an 13, the signals are presented in a repeating sequence. The effect of sequence learning is represented by the difference between reaction times in block 13 and the random blocks surrounding it. Subjects in one condition made verbal responses throughout the entire expenentent (open circles). In the other condition, subjects learned the sequence by making manual responses (blocks 3-10), but were tested in blocks 11-14 using verbal responses.

The neural underpinnings of sequence representation art not as clear-cut. One problem is that the idea of a single "sequencing" module is probably too general. It is likely that sequence representation can be broken down into several separable sub-components. For instance, one component may be the identification of places in space where movements should occur. This is similar to the idea described by Hogan (1984), who proposes that the central nervous system defines a series of equilibriumpoints through which a given limb is programmed to pass. Hogan called this sequence of equilibrium points the "virtual trajectory." The idea is

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that part of the brain creates an abstract spatial image of the forthcoming sequence of movements, and uses this image to coordinate the effectors required to cany out the action (see also Berkenblit & Feldman, 1988; Bizzi & Mussa-Ivaldi, 1989). Given the spatial nature of this computation, the posterior parietal cortex is a possible neural substrate. This region of the brain is known to play a critical role in the visual localization of objects (Mishkin, Ungerleider, & Macko, 1983). Also, Roland and his colleagues have used various imaging techniques to implicate the parietal lobe in movement planning (Roland, Skinhoj, Lassen, & Larsen, 1980). Increases in regional blood flow were found in this region when subjects were asked to move their hands toward a target in space. but not when movements were not directed at external targets. Roland et al. concluded that parietal regions were important in providing motor neurons with information about the spatial configuration of the prescribed movement. Thus far, I have discussed three proposed modules -- timing control, force control, and sequence representation -- that may form the foundation for complex motor skills. In particular, I have proposed that these three modules contribute basic computations that are used in forming a motor program. While the evidence reviewed here does suggest that there are reliable individual differences in the performance of these modules, it is unlikely that these three modules, by themselves, account for all of the underlying abilities that constrain complex task performance. In the paragraphs that follow, I will propose two other modular computations that may play a role in determining the quality of motor performance.

Coordinating Information from Multiple Sources The approach proposed above suggests that motor programs are made up of several elementary components. That is, when constructing a particular motor program, an individual must specify where, when, and with how much force each movement will occur. Before implementing this plan, however, the central nervous system must integrate the information that is given by the timing, force, and sequencing modules. Recent research by Hunt and his colleagues (see Hunt, 1991) suggests that reliable individual differences exist in the ability to integrate information from multiple domains in performing a task. “Coordination”ability was most noticeable when subjects were required to integrate linguistic information (i.e., in the form of a question) and perceptual information (i.e., a visual display on a computer screen). If a coordination ability exists, then performance on the dual task should not be predicted by performance on the two tasks (linguistic or perceptual) separately; an additional component should be needed to explain inter-subject variation in task performance. Results of these studies suggest that a coordination ability does exist. An important concept to consider is that Hunt’s results showed an ability to coordinate information from two external domains. Both linguistic and perceptual information was presented to the subject within the context of the experiment. The ability to coordinate these types of information may or may not be the same as the ability to coordinate different

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computationswithin the motor system. Therefore, while the proposal of a general "coordinating" ability is not far-fetched in light of Hunt's results, one should interpret Hunt's evidence as merely suggestive. Clearly, more research could be done in this m a to enlighten the issue.

Attention Switching When performing complex skills, individuals often have to pay attention to perceptual input corning from several different locations in space. For example, when you ride your bicycle down a crowded street, you must pay attention to the contours of the road,the movements of people on the nearby sidewalk, and the movements of passing cars. If you are unsuccessful in monitoring information from any of these channels, your chances of an unsafe mp gnatly increase. Within psychology, the study of attention is based on the simple assumption that people are limited in their ability to process incoming information (Posner, 1982). In other words, people can pay attention to only so much information at one time. Therefore, in instances where the information load exceeds normal capacity, an individual will be forced to switch his or her attention back and forth between the multiple sources of input. To the extent that there are individual differences in the ability to switch attention between different sources, then these differences may be predictive of success or failure in complex task performance. Several studies have shown that individual differences exist in the ability to switch attention from one source to another (Gopher & Kahneman, 1971; Keele & Hawkins, 1982). The Gopher and Kahneman study was based on a dichotic listening task, in which subjects monitored a cued ear for the occurrence of digits among strings of words. Midway through each trial, subjects received a second cue indicating which ear they were to monitor. In some trials, then, subjects were required to switch their attention from one ear to the other. Results showed that more emrs, particularly intrusions, occurred after the second attentional cue. This suggests that subjects found it relatively difficult to rapidly re-orient their attention. The ability to do so, however, was predictive of success in a flight school training program in which the subjccts were enrolled. In a separate study, the ability to switch attention was also negatively correlated with the number of traffic accidents among Israeli bus drivers (Kahneman, Ben-Ishai, & Lotan, 1973). Taken together, the results discussed above suggest that attention switching may be a fairly general ability that is necessary for complex task performance. This is consistent with the work of Posner and his colleagues (e.g.. Posner, Inhoff, Friedrich, & Cohen. 1987). who have described a modular view of the attentional system. Generally, this view suggests that a general attentional system is used as a "command system" to orient and re-orient attention between different sources of perceptual input. To the extent that differences exist in the performance of this command system, one might expect them to have important behavioral consequences. I have outlined five modular abilities that may play a role in the performance of complex

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skills. The proposal is that each of these basic modules perform separable computations within the brain, and that these modules are called upon in the performance of many different skilled behaviors. That is. each module serves a particular function, regardless of the specific task that is being performed. This modular view provides an alternative to the view that expertise at a certain task is entirely task-specific. Rather than saying that expert skill relies completely on task-specific experience, it is proposed that some general underlying abilities also contribute to task performance. The modules of timing, force control, and sequence representation are proposed to serve as "building blocks" in the consnuction of motor programs. Before the motor program can be implemented, these basic parameters must be integrated. perhaps by a "coordination" module. Specific task knowledge may have an effect on the performance of tasks only after these elementary computations are in place. Finally, the ability to switch attention between competing sources of information may also contribute to success or failure in complex real-world activities. According to the modular view presented here, individual differences in the performance of each module should be predictive of ultimate task performance. That is, people with high levels of these basic abilities should be able to achieve high performance in a number of tasks (assuming, of course, that they practice enough to gain necessary task-specific knowledge). In contrast, people with low levels of one or more of these basic abilities are likely to be limited in how well they can perform certain tasks. A critical test of the modular view, then, is an examination of individuals with relatively low levels of one or more of these abilities. The modular view predicts that these people should be in the low end of the continuum of motor skill performance. Especially among children, they should be characterized as "clumsy." It is to the topic of clumsiness that I now turn. Clumsiness While most children have very little difficulty executing motor movements, a small percentage do. These are children whom we typically call "clumsy." Clumsy children are often slower and more awkward in their movements than normal children. As a result, they may have troubk with many common childhood tasks, like writing, hopping, or catching a ball (Dellen & Geuze, 1988). The problems that clumsiness can cause for a child, both socially and in terms of physical development, are obvious.

Past research has suggested several possible causes of clumsiness. These have included perceptual difficulties (Dare & Gordon, 1970), problems in response selection (Dellen & Geuze, 1988). and deficits in response planning (Cermak, 1985). While it is likely that clumsiness may be caused by failures with any of these aspects of information processing, the modular view focuses our attention on the aspect of response planning. Specifically, clumsiness may result from relatively poor functioning of one or more of the basic computational modules that constitute the motor program. For example, failures in the cerebellum may lead to poor timing, which, in tum, could lead to clumsy behavior. If problems exist in the functioning of the basal

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ganglia, force regulation may be imperfect; and so on. Two important points need to be made with respect to the above suggestion. First, imperfect processing in any of the modules does not necessarily imply problems as severe as those experienced by, for example. stroke patients. Neurological patients have more seven problems than those discussed here; clumsy children typically have no detectable signs of brain damage. I am merely suggesting that clumsy children lie in the lower extreme of the normal distribution. Second, it should be remembered that the modules proposed earlier axt thought to be independent of one another. Therefore, an individual who has problems in one module may not necessarily be impaired in any of the other modules. With this in mind, it should be possible to isolate the source of a clumsy child's dysfunction empirically. This would be very beneficial, both in terms of testing the proposed modular approach and in tern of clinical assessment. Recent studies have begun to address the issue of whether clumsy children suffer from impaired functioning of one of the proposed modules. For example. Williams, Woollacott. and Ivry (1989) tested the timing abilities of clumsy subjects and normal conmls using a paradigm identical to the one reported in Ivry and Keele (1989). That is. subjects were asked to (i) produce regularly-spacedtemporal intervals by tapping a key;(ii) discriminate between tones that vary in their duration; and (iii) discriminate between tones that vary in their loudness. Results indicated that clumsy subjects were significantly poorer than normal children at both the production task and the perception of duration task. No differences existed between &roupson the perception of loudness task. The results of the Williams et al. paper are important in two respects. First, they confirm that clumsy children may have difficulties representing time. Importantly, these subjects were impaired at both a time-based motor task and a time-based perceptual one. Therefore, the problems experienced by these children may involve a higher-oder process of temporal representation. Second, notice that the results of the clumsy children are very similar to the results of the cerebellar patients reported in Ivry and Keele (1989). While it is impossible to make direct comparisons between these two groups (since subjects were of different ages, etc.), it is interesting that the same pattern of results emerged. perhaps the clumsy subjects are experiencing impaired functioning of the "cerebellar module." This, in turn. could be an explanation of their clumsiness. One thing missing from the Williams et al. paper is a demonstration that other modules of the motor system are independent of the timing module. The modular approach suggests that the functioning of the force module, for example, is independent of the timing one. Therefore, clumsy subjects with timing difficulties may perform perfectly fine in tasks that require force regulation. Conversely,clumsy children who experiencedifficultiesin force regulation may have normal timing abilities. A demonstration of this double dissociation is needed to provide further evidence for the modular approach.

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A m e n t paper (Lundy-Ekman, Ivry, Keele, & Woollacott, 1991) provides evidence for this type of double dissociation. Lundy-Ekman et al. tested the idea that clumsy children with basal ganglia soft signs differed from those with cerebellar soft signs in timing and force regulation tasks. A "soft sign" is a milder version of a "hard sign," which typically indicates nervous system disorder. Examples of basal ganglia soft signs are choreiform movements (jerky, incgular). athetotiform movements (small, slow, writhing), and associated movements (extranous, nonrandom motions which accompany purposeful movements). Examples of soft signs associated with the cerebellum are dysmetria (inability to control the distances of movement), dysdiadochokinesia(inability to perform rapid, alternating movements), and intention tremor (shaking during intended movements).

If a soft sign is indicative of disorders localized in a particular brain region. one might expect that different soft signs would predict difficulties in different modules of the motor system. Specifically, those children with basal ganglia soft signs may have difficulties regulating force, but may have no trouble representing time. Subjects with cerebellar soft signs may have trouble representing time, but may perform normally on force regulation tasks. Lundy-Ekman et al. (1991) provided tests of these hypotheses. They tested two groups of 7-8 year-old clumsy children, one group with basal ganglia soft signs and one group with cerebellar soft signs. None of them had unequivocal evidence of neurological damage (i.e.. in the form of a hard sign). The performance of these two groups on both timing and force regulation tasks was compared with a group of age-matched controls. The force regulation task was similar to that reported by Keele, Ivry, & Pokomy (1987). Subjects were to press a force transducer with a target amount of force. Variability around the target force is taken as a measure of force regulation ability. As expected, the basal ganglia children performed significantly worse than both cerebellar subjects and controls on this task. Timing tasks were similar to those presented in Ivry and Keele (1989). In the production task, subjects hied to tap a key at regular intervals of 550 msec. Variability of inter-tap intervals provides a measure of timing ability. As expected, cerebellar subjects performed significantly worse than both basal ganglia subjects and controls on this task? In the perception of duration task, subjects were asked to discriminate between tones that vary in their duration. A control task of perception of loudness was also included. As expected, cerebellar subjects perfonned significantly worse than both basal ganglia subjects and controls in the perception of duration task. There were no differences between any of the groups in the

*Funher analysis shows that differences between children are isolated to central mechanisms of timing. "his is shown by partitioning the total variance of inter-tap intervals into two independent components: Clock variance (which is due to central mechanisms) and Motor variance (which is due to peripheral mechanisms); see Wing and Kristofferson (1973) for details of this partitioning. The timing deficits of clumsy children are evident by the large repolted differences in clock variance.

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control task of perception of loudness. The results of the timing task provide a nice dissociation between the timing module and the force module. Cerebellar subjects appear to have difficulty performing the tasks requiring control of timing, while basal ganglia subjects do not. Taken together with the results of the force regulation task, this suggests a functional independence between these two modules, each of which may contribute equally to clumsiness. A third module that might contribute to clumsiness is that of sequence representation. Recall that Hogan (1984) introduced the idea of a virtual trajectory, a spatial image of the forthcoming sequence of movements. Hogan suggested that the underlying principle behind the creation of a virtual trajectory is to minimize jerk (the rate of change of acceleration) in a movement. In other words, the goal of planning a sequence of movements is to make them as smooth as possible. hsumably, problems in refining one's virtual trajectory or in matching one's movements to the planned trajectory will reduce one's ability to perform optimally smooth movements.

The idea that clumsy children should produce ''jerkier" movements than normal childrtn is hardly profound; it is one common notion of what typifies clumsiness. It is, however, a testable prediction. For instance, clumsy children often produce relatively poor handwriting (Cermak. 1985). Poor writing is characterized by movements that are much less smooth than those produced by good writers (wann, 1987). Wann posits that this is because poor writem have not yet made the transition to effectively producing "planned trajectories." This type of failure (i.e., inability to plan trajectories for a forthcoming sequence of movements) may be a general characteristic of at least some clumsy children. Research has not yet examined the possible links between clumsiness and the performance of a "coordination" module. However, it is possible that such a link may exist. Hunt (1991) found a relationship between the ability to integrate different sources of perceptual information and expertise in competitiveorienteering. Specifically,expen orienteers were better able to link together a succession of visual scenes, giving them the ability to form what Hunt called a "surveyor's form" of their surroundings. Perhaps an important component of high-level motor performance is the ability to link together information from different sources. If true, some clumsy people may be characterized by relatively low levels of this ability. Therefore, they may be less able to integrate information from their surmundings, as well as less able to coordinate the modular computations that make up their motor programs. It is also unclear whether clumsiness is related to an "attention-switching''ability. Again, however, previous research is at least suggestive that it may be. Recall that Gopher and Kahneman (1971) found the ability to switch attention from one source to another to predict success in an air force training program. This is in agreement with the intuition that expert flyers are better able to monitor information coming from several different channels simultaneouslythan are lesser pilots. Perhaps the general ability to switch attention is predictive

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in other motor tasks as well. This would suggest that some forms of clumsiness are due to a relative inability to switch back and forth between several sources of information.

In summary. I have suggested that clumsiness may be caused, at least in some children, by imperfect functioning of one or more general-purpose modules. If true, this would suggest several important insights into clumsiness. First, it suggests that at least some clumsy children can be associated with an identifiable problem. For instance, the cerebellar subjects in Lundy-Ekman et al. (1991) may be characterized as having problems in timing. Importantly, this single problem may be the root cause of many of these children’s motor problems. Second, this approach suggests that clumsiness is a multidimensional phenomenon. Not all clumsy children should be charactenzed, or treated, in the same way. Indeed, the underlying problem (e.g., timing) associated with some children may be very different than the underlying problem (e.g., attention switching) associated with other children. Finally, this approach suggests a theoretical impetus for the development of remediation techniques. If the underlying problems associated with clumsiness can be limited to a relatively small number of candidate modules, then improvement in the functioning of these modules may lead to general improvement in motor functioning. It is to this final topic of remediation that I now turn.

Remediation Of course, the ultimate goal of research dealing with the topic of clumsiness is to develop

intervention techniques that may help clumsy children successfully participate in normal motor activities. Unfortunately, past research in this area does not offer much insight into the development of these techniques (Sugden & Keogh, 1990). The main problem is that therapy is often not based on theoretical grounds. Rather than base treatment on what is considered to be the underlying cause(s) of clumsiness, many practitioners focus their remediation techniques on individual motor tasks that the child has been shown to be unable to perform (Laszlo & Bairstow, 1989). While extensive practice on a particular task may improve performance on that task, it is not clear that this approach is the best way to improve the general motor performance of clumsy children. A more appropriate approach to remediation may be one in which therapists attack the cause(s) of the problem. According to the modular view presented here, clumsiness may be caused (at least in some cases) by imperfect functioning of a general motor module. The best way to treat this type. of clumsiness may be to attempt to improve the the functioning of the module (or modules) in question. The idea that general motor performance can be improved by training the basic abilities that underlie them flies in the face of traditional views of motor control. It is generally believed that abilities represent stable traits that cannot be easily modified (see Schmidt, 1988). However, it is ultimately an empirical question whether the modular abilities proposed here can be trained. In a recent unpublished study, we have begun to examine whether the module of timing can be trained. A small group of normal subjects (n=6) came to the lab on ten different days, over the course of a two-week period. On the first day, they were administered a long series of tests

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designed to assess their timing abilities. These tasks included a time production task similar to that described above and in Ivry 8c Keele (1989). Rather than always tapping the same interval, however, subjects tapped three different intervals: 400 msec., 550 msec., and 700 msec. In addition, subjects performed a perception of time task, and a perception of loudness task. each of which was identical to that described previously. The former test is used to assess timing independent of motor systems, and the latter task is used as a control. On each of the next eight sessions, each subject practiced the tapping task, but only by tapping the 550 msec. interval. Each session was filled with enough trials to last approximately half an hour. Finally, on the tenth day, each subject repeated the tests performed during the f r s t session. If the general ability of timing can be trained, we would expect performance on all of the timing tasks to improve from the first session to the last one. In other words, the variance of subjects’ tapping should be lower on the tenth day than on the fmt, even for the intervals (400 msec. and 700 msec.) that the subject did not practice. Performance on the time perception task should also improve. That is, subjects should improve their ability to discriminate small differences in the durations of two tones. This would be evident by finding smaller thresholds on the tenth day than on the first. Finally, because the perception of loudness task is unrelated to timing, we would expect no improvement on the performance of this task over time. Results of this experiment are summarized in Figure 15.5. Generally, practice in the key tapping task did lead to improvements in that task. Importantly, this improvement was not limited to the interval that subjects actually practiced, they improved at all three intervals. This is in agreement with the predictions outlined above. However, subjects did not improve at the perception of time task. This presents a challenge for the modular view, since it predicts that improvements in the tapping task would be linked to improvements in the perceptual one. Apparently, the improvement gained in the tapping task does not transfer to the perceptual task.

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Cell entries for each of the three tapping tasks represent estimates of clock variance, based on the techniques described in Wing and Kristofferson (1973). Clock variance represents the portion of variance in intertap intervals that is due to central mechanisms. Enmes in the perception tasks represent estimates of discrimination thresholds. Larger numbers represent larger thresholds (i.e., subjects less able to distinguish between two similar tones). In order to further examine this issue, a second study was run, in which six different subjects performed the same pre- and post-test described above, but received practice at the perception of time task in the interim. Analogous results were obtained. Subjects showed significant improvement on the perceptual task, but, in general, they showed no significant improvement in the tapping tasks. The benefit of training on the perceptual task does not appear to transfer to the tapping task. Several possible explanations exist for these perplexing results. First, it may be that, as Schmidt (1988) suggested, basic abilities cannot be trained. While training may give subjects enough task-specific knowledge to improve at a particular task, that training may not transfer to related tasks. Second, these results suggest that timing may not be a singular ability. Perhaps the production and perception tasks involve slightly different neural computations, even though both rely on similar substrates and reflect correlated abilities. For instance, the perceptual task may put greater emphasis on the representation of time, while the production task may emphasize the implementation of specific intervals. Finally. these results may be explained, in part, because of the subject population used. It is possible that training of the sort described here is only useful for individuals with extreme timing difficulties, which normal college students tested in these studies are not likely to have. Our future research in this area will attempt to address these general issues.

Summary and Conclusions A long-standing debate in the field of motor control concerns the role of general underlying abilities in constraining task performance. While a great deal of research suggests that motor expertise is largely task-specific, we have argued that a great deal can be learned by looking for more general abilities as well. By examining individual differences in the performance of these various modules, it is hoped that we can gain further insight into important topics such as clumsiness and motor expertise. References Adams, J.A. (1987). Historical review and appraisal of research on learning. retention, and transfer of human motor skills. Psychological Bulletin, 12. 41-74. Berkenblit, M.B., & Feldman, A.G. (1988). Some problems of motor control. Journal of Motor Behavior, 20, 369-373. Bizzi, E., & Mussa-Ivaldi, F.A. (1989). Geomemcal and mechanical issues in movement planning and control. In M. Posner (Ed.), Foundations of Cognitive Science. Cambridge, MA: MITRess.

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Cermak, S. (1985). Developmental dyspraxia. In E.A. Roy (Ed.), Neuropsychological Srudies of Apraxia and Related Disorders (pp. 225-248). Amstedam: Elsevier Science Publishers. Chase, W.G., & Simon, H.A. (1973). Perception in chess. Cognitive Psychology, 4.55-81. Dare, M.T., & Gordon, N. (1970). Clumsy children: A disorder of perception and motor organisation. Developmental Medicine and Child Neurology, 12, 178-185. Dellen, T. van, & Geuze, R.H. (1988). Motor response processing in clumsy children. Journal of Child Psychology and Psychiatry, 29,489400. Favilla, M., Hening, W., & Ghez, C. (1989). Trajectory control in targeted force impulses: VI. Independent specification of response amplitude and direction. Experimental Brain Research, 75, 280-294. Fleishman, E.A. (1967). Individual differences and motor learning. In R.M. Gagne (Ed.), Learning and individual differences @p. 165-191). Columbus. OH: Memll. Fodor, J.A. (1983). The modularity of mind. Cambridge, MA: MIT Ress. Gopher, D., & Kahneman, D. (1971) Individual differences in attention and the prediction of flight criteria. Perceptual and Motor Skills, 33, 1335-1342. Henry, F.M. (1961). Reaction time-movement time correlations. Perceptual and Motor Skills, 12, 63-66. Henry, F.M., & Whitley, J.D. (1960). Relationships between individual differences in strength, speed, and mass in arm movement. Research Quarrerly, 31, 24-33. Hogan, N. (1984). An organizing principle for a class of voluntary movements. The Journal of Neuroscience, 4, 2745-2754. Hunt, E. (1991). Computerized assessment of individual differences (ID No. N00014-86-C-0065). Alexandria, VA: m i c e of Naval Research. Ivry, R.I., & Keele, S.W.(1989). Timing function of the cerebellum. Journal of Cognitive Neuroscience, I, 136-152. Ivry, R.I., Keele, S.W., & Diener, H. (1988). Dissociation of the lateral and medial cerebellum in movement timing and movement execution. Experimental Brian Research, 73, 167-180. Kahneman, D., Ben-Ishai, R., & Lotan, M. (1973). Relation of a test of attention to road accidents. Journal of Applied Psychology, 58, 113-115. Keele. S.W. (1981). Behavioral analysis of movement. In V. Brooks (Ed.),Handbook of Physiology. Section 1: The Nervous System, Vol. 2: Motor Control. (pp. 1391-1414). Baltimore: Williams & Williams. Keele. S.W., Cohen, A., & Ivry, R. (1990). Motor programs: Concepts and issues. In M. Jeannerod (Ed.), Attention and Performance XIII: Motor Representation and Control. (pp. 77-110). Lawrence Erlbaum Associates. Keele. S.W., & Hawkins, H.L. (1982). Explorations of individual differences relevant to high level skill. Journal of Motor Behavior, 14, 3-23. Keele. S.W., & Ivry, R.I. (1987). Timing and force control: A modular analysis. Paper presented at IREX meeting, Moscow, USSR. Keele, S., Ivry, R., & Pokomy, R. (1987). Force control and its relation to timing. Journal of Motor Behavior, 19, 96-1 14.

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Keele. S.W., Jennings, P., Jones, S., & Cohen. A. (1991). On the modularity of sequence representation. Paper submitted for publication. Keele, S., Pokomy, R., Cmos, D.. & Ivry, R. (1985). Do perception and motor production share common timing mechanisms: A correlation analysis. Acra Psychologica, 60, 173-191. Kosslyn, S.M. (1987). Seeing and imaging in the cerebral hemispheres: A computational approach. Psychological Review, 95, 148-175. Laszlo, J.I., & Bairstow, P.J. (1989). Process-oriented assessment and treatment of children with perceptuo-motor dysfunction. In P. Lovibond & P. Wilson (Eds.), Clinical and Abnormul Psychology. (pp. 31 1-318) Amsterdam: Elsevier Science Publishers. Lundy-Ekman, L. (1990). Soft neurological signs as indicators of timing and force Unpublished doctoral dissertation, University of Oregon, Eugene, Oregon. Lundy-Ekman, L., Ivry R., Keele, S., & Woollacott, M. (1991). Timing and force control deficits in clumsy children. Journal of Cognitive Neuroscience, 3, 367-376. Marteniuk, R.G. (1974). Individual differences in motor performance and learning. In J .H . Wilmore (Ed.), Exercise and sports sciences reviews, Vol. 2. (pp. 103-130) New York Academic Press. Mishkin, M.,Ungerleider, L.G., & Macko, K.A. (1983). Object vision and spatial vision: two cortical pathways. Trends in Neuroscience, 6, 414-417. Nissen, M.J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from performance measures. Cognirive Psychology, 19, 1-32. Perrin, F.A.C. (1921). An experimental study of motor ability. Journul of Experimental P~chology,4,24-56. Posner, M.I. (1982). Cumulative development of attentional theory. American Psychologisr, 37, 168-179. Posner, M.I., Inhoff, A.W., Friedrich, F.J., & Cohen, A. (1987). Isolating attentional systems: A cognitive-anatomical analysis. Psychobiology, 15, 107-121. Roland, P.E., Skinhoj, E., Lassen, N.A., & Larsen, B. (1980). Different cortical areas in man in organization of voluntary movements in extrapersonal space. Journal of Neurophysiology, 43, 137-150. Schmidt, R.A. (1988). Motor control and learning. Champaign, L Human Kinetics Publishers. Stelmach, G.E., & Womngham. C.J. (1988). The preparation and production of isometric force in Parkinson’s disease. Neuropsychologia, 26, 93- 103. Sugden, D.A., & Keogh, J.F. (1990). Problems in movement skill development. Columbia, S C University of South Carolina Press. Underwood, B.J. (1975). Individual differences as a crucible of theory construction. American Psychologist, 30, 128-134. Wann, J.P. (1987). Trends in the refinement and optimization of fine-motor trajectories: Observations from an analysis of the handwriting of primary school children. J o u r ~ l of Motor Behavior, 19, 13-37. Williams, H., Woollacott, M., & Ivry, R. (1989). Perceptual-motor timing problems in clumsy children. Paper presented at the 19th annual meeting of the Society for Neuroscience,

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Phoenix, Arizona. Wing, A.M. (1988). A comparison of the rate of pinch grip force increases and decreases in Parkinson bradykinesia. Neuropsycho~ogia,26, 479-482. Wing, A.M., & Kristofferson, A.B. (1973). Response delays and the timing of discrete motor response. Perception & Psychophysics, 14, 5-12.

Acknowledgements Much of the research reported in this chapter was supported by the Pew Memorial Trust to the Center for the Cognitive Neuroscience of Attention, University of Oregon. The author gratefully acknowledges the assistance of Steve Keele, Mike Posner, Kris Jones, and Marjorie Woollacott, each of whom offered helpful comments on earlier drafts of this chapter. Requests for reprints should be sent to Steven K. Jones, Department of Psychology, University of Oregon, Eugene, Oregon, 97403.

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CHAPTER 16 THREE LEGACIES OF BRYAN AND HARTER: AUTOMATICITY, VARIABILITY AND CHANGE IN SKILLED PERFORMANCE

TIMOTHY D. LEE* AND STEPHAN P. SWINNEN** *Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada, U S 4Kl **Department of Kinanthropology, Catholic University of Leuven, 3001 Heverlee, Belgium Nearly a century ago, Bryan and Harter published two seminal papers on the skill of telegraphy (Bryan & Harter, 1897, 1899). We call these "seminal" papcrs since they laid much of the ground work for the current interest in expertise. To begin this chapter we discuss the research strategies, results, and theoretical ideas that were presented by Bryan and Harter. Then, we attempt to trace how three of their ideas evolved in motor learning theory and research during the ensuing 100 years.

The Bryan and Harter Papers William Lowe Bryan was a psychology professor. Noble Harter was an ex-telegrapher and graduate student in psychology (Keller, 1958). Their interests were not specifically in perceptual or motor learning. They were interested in telegraphic language; how the language was acquired, and how novices and experts differed when using the language. Nevertheless, their study of telegraphy provided many insights into the acquisition and nature of expertise for a perceptual-motor task. Sending and receiving both have perceptual and motor characteristics. The perceptual component of sending involves reading numbers and letters from text, and does not involve new learning. The motor component involves producing timed auditory signals with a key, and must be learned. The perceptual component of receiving involves interpreting the incoming auditory patterns, which is a new and complex skill that must be learned. The motor component is copying down the message, and involves no new learning. As a result, the study of telegraphy involves the independent study of two skills, with diverse perceptual and motor subcomponents. The telegraphy skill studied by Bryan and Harter was American Morse code, which

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involves a series of timed auditory signals and timed periods of no signal. The "dot" is equal to one unit of auditory signal. The "dash" is equal to 3 units of continuous signal (or more simply, the dash is to be three times longer than the dot). Letters and numbers are comprised of a series of dots and dashes (e.g., the letter S is three dots; the letter C is two dashes followed by a dot). The period of time of no auditory signal also is important. There should be one unit of no signal between dotddashes within a letter, thret units between letters, and six units between wads. All of these units of time arc theoretical goal proportions that make up a telegrapher's "signature" for sending. The signature is in relative time. The study of expertise and the processes involved in acquiring expertise were the primary interests of Bryan and Harter. In these papers, Bryan and Harter use considerable narrative introspections to describe expert-novice differences. Regarding the skill of sending, they remarked that "young operators have a peculiar way of grouping the letters of words, which gives the impression of some one walking unsteadily as when partially intoxicated" (Bryan & Harter, 1897, p. 35). For the skill of receiving, they observed that "when a considerable degree of speed in receiving is reached, the space between the letters of a word become so small that one ceases to recognize it consciously, the letters seem to blend together, and the word is recognized as a sound whole. Thus, expert operators read words from their instruments; and (these words) group themselves into larger wholes, so that the entire sentence becomes the conscious unit, much as in the reading of printed matter" (Bryan & Harter, 1897, p. 28). While these introspectionswere often quite interesting, what stands out about Bryan and Haner's efforts was their empirical investigations, using both a learning paradigm and an expert-novice paradigm as tools.

The Empirical Studies In their 1897 paper, Bryan and Harter reported two experiments; one that used telegraphers of varying years of experience, and one that examined two subjects during the acquisition of telegraphy skill. The second paper continued this focus on skill acquisition by examining a subject who learned to receive different types of messages. We will present some of the results of this research. Their initial investigation (Bryan & Harter, 1897) involved a comparison of 16 telegraphers, selected from a larger pool of subjects, to represent a range of experience and diverse levels of expertise. Six of these telegraphers had relatively low levels of experience (5 years or less), whereas the remaining ten subjects had many more years of experience (10 years or more). Their task was to send the following message a dozen times in succession: SHIP 364 WAGONS VIA ERIE QUICK. Eight repetitions of the message were selected from each of the 16 subjects for analysis. Measurements of time elements that comprised the message were made to the nearest hundredth of a second. A quantitative assessment of expertise in sending was made by examining the relative time that a telegrapher used to express the various periods of signals and no signals. Bryan and Harter made this assessment by first calculating an individual's average time to make a dot.

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Using this as the defmition of one unit of time for that individual, the timing for the other elemcnts was then expressed as a ratio,relative to this unit of time. Several key features of the data are noteworthy here as they highlight differences and similarities of the subjects in relation to years of experience. We have used the data from their Table 1 (Bryan & Harter, 1897, p. 39) and calculated the deviation of each subject's element of sending relative to the goal for each of the units of time mentioned above. m e means for the subjects with 5 years of experience or less and the means for the subjects with 10 or more years of experience are plotted in Figure 16.1. From this figure it is clear that the telegraphers with more experience were better at producing the goal element times, especially so for the space between letters.

2'4

rn

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< 5 years experience (n

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0.9

Dash

Within letter

Between letter

Between word

Figure 16.1. lnfluence of years experience on accuracy of sending (adaptedfrom data presented in Bryan & Harter, 1897, p . 39, Table I ) .

Expertise in sending was also examined by comparing two different types of variation in producing dots. There were 69 dots spaced throughout the message. Each dot was sent eight times (i.e., once per repetition). Bryan and Harter (1897) defined heteroraxic variation as the variability in time to produce the dots within a single message. Homoraxic variation provided a measure of the timing consistency for one specific dot over the eight repetitions of the

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message. As shown in Figure 16.2, the telegraphers with more than 10 years of experience had less homotaxic variation, but slightly more heterotaxic variation than the telegraphers with less than 5 years of experience. The increase in heterotaxic variation was explained to be due to variation for "inflection". The difference in homotaxic variation was interpreted as revealing the true difference in skill.' The examination of expertise in terms of systematic differences in performance variation is one of the legacies of Bryan and Harter's work. Later, we will consider in more detail how the examination of performance variation reveals specific insights regarding skilled motor control.

.-

L

a

>

.05

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Hrtrrotaxic variation

Figure 162. Influence of years experience on homotaxic and heterotaxic variation in sending (adapted from data presented in Bryan & Harter, I897,p . 44, Table V).

A similar finding was reported by Gentner (1988) regarding the differences in novice and expert typists. Novices and experts were no different in what Genmer called "task-based variability" (interstroke interval variance between different letter pairs. or heterotaxic variation in Bryan & Harter's terms). However, there were large differences in "repetition variability" between experts and novices (interstroke interval variance across performances of the same letter pair, which was called homotaxic variability by Bryan & Hatter). This similarity is curious given that homotaxic variability has functional significance for the telegrapher (in that consistent relative timing is critical for a telegrapher), but not necessarily so for h e typist.

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Bryan and Harter made it very clear that they interpreted the differences between experts and novices as primarily one of qualitative change. For example, they noted that experts received messages by lagging behind the message -- waiting until many letters had been sent before copying down the message in order to get an idea of the content. In contrast, novice telegraphers often tried to copy a message as fast as possible without interpreting the meaning of the message. Improvements in the speed of using telegraphy skill was brought about by both quantitative and qualitative change. This emphasis on the name of change during learning represents the second legacy of Bryan and Harter. We will discuss theories of motor learning in terms of the nature of changes that are proposed to accompany the acquisition of expertise. Bryan and Harter also noted that novice telegraphers were better at sending than at receiving, but that experts were better at receiving than sending. This specific improvement in the skill at receiving became the clear focus of their subsequent research. In one study, they examined two novice telegraphers over 36 and 40 weeks of learning to send and receive Morse code. The curve of improvement for sending was "textbook -- a rapid change relatively early in practice followed by slow growth that reached an asymptote. In contrast, the curve for receiving was irregular, showing periods of rapid growth, then little or no improvements, then rapid change once again. Bryan and Harter found this very intriguing and concentrated on this finding in the next paper (Bryan & Harter, 1899). Interest in the motor aspects of telegraphy in their second paper was minimal. The attainment of expertise depended upon the development of a series of higher-ordcr habits according to Bryan and Haner. The use of a higher order habit was facilitated when the performance of a lower habit was automated. In their words, "Only when all the necessary habits, high and low, have become automatic, does one rise into the freedom and the speed of the expert ... There is no freedom except through automatism" (Bryan & Harter, 1899, pp. 357 & 369). Although they did not stress the motor aspects of automaticity, this has become an important area of research interest in recent years. The emphasis on the conmbutions of automaticity in the development of expert performance represents the final legacy of Bryan and Harter that we will consider in this paper. The Bryan and Harter papers are important in many regards and most textbooks written about skill acknowledge these early conmbutions as providing a foundation upon which later research was conducted and interpreted. This is not to say however, that the conclusions made by Bryan and Harter have been accepted by all. Indeed, some of their theoretical views were challenged quite vehemently (e.g., Keller, 1958). Nevertheless, we believe that their contributions laid the groundwork for three distinct research areas regarding motor skill expertise: 1) performance variation, and 2) the nature of change in the acquisition of expertise, and 3) automaticity. Our goal is to sketch out a brief (and admittedly, incomplete) retrospective

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account of these legacies of Bryan and Harter as seen in "current" theoretical positions.'

1) Performance Variation The type of variability described by Bryan and Harter (1 897) referred to inconsistencies in performance outcomes. However, the underlying reasons for variability in performance outcomes can be quite different, and these have been the focus of many theoretical discussions since the writings of Bryan and Harter. What these discussions revolve around is the problem of describing the nature of skill. During the early to middle part of the century the discussion of skill usually included reference to the quality and quantity of motor performance (see Adams, 1987, for review). But, definitions of skill at the output level are incomplete when the factors that produce excellence in performance are not considered. Reference to the mechanisms underlying skill were inferred from Guthrie (1952) however, who noted that skill involved a "minimum outlay of energy" (see also Basmajian. 1977). Bemstein (1967) provided another notable exception to the considerationof skill at the output level, noting that skilled performance demands the unlocking of additional degrees of freedom of movement and the exploitation of nonmuscular forces (Turvey, Fitch, & Tuller, 1982). Gentile (1972) suggested that the later stages of skill acquisition required somewhat opposing processes. Fixarion involved the refinement of a consistent motor plan, whereas diversification suggested that the motor repertoire be quite flexible. With Gentile's distinction between fixation and diversification in mind, we tum now to a more expanded discussion of research that has addressed invariance and plusriciry in skilled motor performance.

Invariances in Skilled Performance An impressive argument for the existence of invariances in motor performance has been forwarded by Schmidt (e.g., Schmidt, 1985, 1988). He argues that the control of movement is regulated by two central mechanisms. Classes of action are under the regulation of generalized motor programmes, and are instantiated by adding parameter values as determined by movement schemata (Schmidt, 1975). The search for invariances involves the search for what constitutes a "class of action". Presumably, what remains invariant over many different performances defines a class of action. What changes on each performance are the parameters for action according to this view. Schmidt has gathered evidence from a wide range of experiments to support the notion that certain features of movement remain invariant while others are free to vary. One example that the reader can perform is to print the capital letter "E". Some people draw the vertical stroke first, either from top to bottom or bottom to top, then add the three horizontal strokes, from the vertical line outward, adding these either from top to bottom or bottom to top. Other people (both of us, in fact), first draw one continuous line in the block form of a capital "C",

* Our review is limited. for the most part, to motor learning theory since the 1950's. Prior theories of learning either did not specifically consider the acquisitionof motor skills, or did so parenthetically.

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starting at the top horizontal line going right to left, then down the vertical line, then out the bottom horizontal line, and adding the middle line last. The point is that although there are differences between people, one tends to order the production of these individual elements the same regardless of whether the capital "E"is being printed with a pen held by the dominant hand, with a piece of chalk held in the non-dominant arm. with a paint brush held by the teeth, or by drawing the letter in the sand on a beach using a toe. In this example, the ordering of the individual elements comprising the letter remains invariant across the many different possible muscle groupings used to parameterize the action (see also the work of Raibert. described by Schmidt 1988, p. 241). Another invariant feature of motor behaviour is relative time, or phasing. One experiment that has been argued to support the concept of invariant relative timing was reported by Tenuolo and Viviani (1979, 1980). The interkeystroke timing performance of expert typists revealed that while repeated performances of any particular digraph differed considerably in absolute time, the interkeystroke time relative to the total time to produce the entire word remained the same across different performances. The argument was that relative time for word production is an invariant feature of motor performance for expert typists. The concept of invariances in motor performance remains a debated issue. One key question is how much variation can occur while retaining the view that the feanues of the pattern are invariant? There appears to be no evidence for absolute invariance (that we know of). So, what constitutes invariance from a practical viewpoint? Gentner (1987) argued that invariance should be addressed statistically -- for example, by using analysis of variance (ANOVA) methods. Gentner's reanalysis of some of the data cited by Schmidt revealed instances where arguments in favour of invariance failed to be supported by statistical test. The issue becomes even more complex however, given Heuer's (1988) arguments that the absence of invariance at the peripheral level (where performance is measured) does not invalidate the possibility of central invariance (e.g., at the motor programme level). During the evolution of a movement there are many factors that add variability to the output (such as neuromuscular noise) that could mask underlying regularities (Heuer, 1988). Plasticity in Skilled Performance Consider how you brush your teeth. Most adults fixate the head and move the edge of the tooth brush up and down (well maybe not most adults, but rather the ones that heed the advice of their dentists). However, one could also accomplish the same goal by fixating the arm and moving the head. This is a simple example of the plasticity in movement control -- very different actions can be used to attain the same goal. The concept of motor equivalence refers also to situations where goal directed actions are accomplished using very different patterns of activity -- presumably not under the control of the same generalized motor programme. Consider Rosenbaum's (1991) example of turning on a light switch with your chin while holding two bags of groceries. This illustrates the plasticity of "expert" motor systems -- the same end result can be achieved in many ways. Expertise, in

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this sense, is the plasticity of the motor system to solve a goal in an increasing variety of ways. A study by Cole and Abbs (1986) provided an empirical demonstration of motor equivalence. Subjects were asked to make a rapid pinch using the thumb and index finger. Over trials this was attained by keeping constant the position on the thumb that was contacted by the finger. However, in doing so, the subjects showed considerable variability in a number of kinematic variables regarding the movements of the fmger and thumb. In other words, the same end result was achieved efficiently, by means of a variety of different movements. Motor equivalence has also been demonstrated for the actions of the mouth during speaking, where the same vocal sound can be made despite perturbations to the lip (e.g.. Abbs & Gracco. 1984). and for reaching and grasping movements, where disks of varying sizes were picked up efficiently using a variety of different relations between the reach and grasp components of the movement (Marteniuk & MacKenzie, 1990).

The concepts of motor invariance and motor plasticity attack the issue of expertise in distinct but compatible ways. Both theoretical positions view expertise in terms of efficiency and flexibility in performance. For the sake of efficiency, motor invariance suggests that certain features of performance do not need to be specified separately on each new parameterization. Motor plasticity permits a performer to achieve efficiency by specifying goal attainment as the key, rather than by specifying the means to attain the goal. Invariances in motor performance allow many different outcomes to occur through different parameterizations. Plasticity in motor performance emphasizes that the same goal can be attained through a variety of relations between the component parts of the movement. Although these views represent the concept of variability in different ways, the message that expertise can be characterized by efficiency and flexibility is preserved. 2) The Nature of Change in the Acquisition of Expertise The nature of change in learning to receive telegraphic code was probably the most contentious issue to emerge from Bryan and Harter’s work. Each of the subjects examined while learning the language showed periods of time where no improvement in receiving was noted.

These plarealcr were interpreted as evidence for stages of qualitative change in the progress of learning. But, the existence of plateaus in performance curves and what they suggest about the learning process are complex issues.

First, the existence of plateaus has been difficult to replicate (see Adams, 1987). Second, even when plateaus are evident in data, their interpretation is open to challenge (KeUer, 1958). And third, the plateaus discussed by Bryan and Harter were for receiving; they found no evidence for plateaus in acquiring skill at sending (although plateaus in typewriting were found by Book, 1925). Despite these complexities, the legacy that remains for our purposes is that the acquisition of skill involves qualitative as well as quantitative change, and this was the major focus of Bryan and Harter’s view (see also Namikas, 1983 and Patrick, 1992). Quantitative change reflects a

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relatively continuous and incremental process. Characteristics of expert performance can be traced back to characteristics seen in the early stages of performance. In contrast, qualitative changes are not incremental, but reflect periods of little change and periods of rapid change. More importantly, the characteristicsof expert performanceoften have little in common with the characteristics of performance by a novice (Namikas, 1983; Patrick, 1992). Theories of motor learning can be characterized by their emphasis on quantitative and qualitative changes. These are not explicii distinctions in mast theories, but do provide for interesting comparisons regarding how skill acquisition is conceptualized.

Quantitative Change Theories Motor learning theories that have emphasizedquantitative change have had two important influences. The first was the power law ofpractice (Crossman, 1959; Fitts, 1964, 1965; Snoddy. 1926). which is a description of performance change during skill acquisition that is purely quantitative (linear, in fact). When performance on a motor skill is plotted over time (or trials) the typical result is a negatively accelerated curve. However, when performance is plotted on a log-log graph, the result is a linear improvement of remarkably good fit (Fitts & Posner, 1967). One interpretation that is drawn from a log-log graph is that motor skill improves in a quantitative fashion that has clearly definable mathematical properties. The other important influence for quantitative change in motor learning theory was the early S-R theories of reinforcement learning (e.g., Hull, 1943; Thomdike, 1927). By Thorndike’s view, for example, learning involved the strengthening of bonds or connections between the stimulus and the response. The emphasis on strengthening was a critical one, as it implied a gradual process whereby learning was an accumulation of skill as a function of practice. The impact of the theories of Thomdike, Hull, and others on motor learning theories is unmistakeable, as many have retained these quantitative consmcts. From the perspective of Fitts (1964, 1965) and Adams (1971), skill acquisition involved the reduction of verbally mediated actions. In Adams (1971) theory, control of the movement sequence was the responsibility of the perceptual trace. However, this capability did not imply a fundamentally different process in the control of movement as a function of skill acquisition. The process of movement control remained fundamentally the same. Learning occurred by strengtheningthe correct perceptual trace. Strengtheningwas a process of accumulating sensory feedback from produced movements. The perceptual trace that was strengthened most often (the mode of this dismbution of sensations) became the dominant one. Motor learning according to schema rheory also depended on a quantitative process (Schmidt, 1975). By this theory, generalized motor programmes are parameterized by adding value specifications derived from a movement schema. The schema is learned from prior experiences of parameterizing the programme. The greater the variety of prior experiences, the more breadth there is to the schema. Zelaznik, Shapiro and Newell (1978) consided the variability of practice feature of schema theory as analogous to a regression line. The regression

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line expressed the relation between a parameter value used to instantiate the generalized motor programme on one axis, and the outcome on the other axis. The variance accounted for by the

regression equation was determined by the variability of practice: more variability providing for a greater m g e of data points upon which to generate the equation, leading to greater variance accounted for when projecting new values. Thus, the key quantitative aspect of the theory was the variance accounted for by the schema. Transfer was the critical prediction, and a well developed schema was predicted to provide for morc accurate transfer, especially so for transfer attempts that exceeded the range of values experienced during practice. Schema theory could be considered in terms of qualitative changes if the development of generalized motor programmes were specified. The assumption of schema theory is that these generalized motor programmes already exist. A qualitative change would involve a shift in control from the absence to the presence of a generalized motor programme. To this d e p , schema theory does not account for the very early stages of skill acquisition when a m l y new movement pattern is formed. Next we will consider two theories that do attempt to specify how qualitative change occurs.

Theories of Quantitative plus Qualitative Change An important early idea that, unfortunately, has gained relatively little attention in the motor learning area, is the progresssion-regression hypothesis. The original version of this hypothesis was proposed to explain movement tracking behaviour (Fitts, Bahrick, Noble, & Briggs, 1959). The emphasis was on the nature of perceptual information that a subject uses during movement control, and how the complexity of that information changes with learning and during periods of smss. For tracking tasks, there is evidence to suggest that during the course of learning a subject uses higher order derivatives of the perceptual information -- from position to velocity to acceleration information (e.g., Fuchs, 1962; see Jagacinski & Hah, 1988 for a review). Using higher order derivatives implicates qualitative change, or progression. However, under conditions of srress, such as increased attentional demands or due to periods of forgetting, the performer regresses to a previous stage in performance. Although this hypothesis loads heavily on the perceptual aspects of tracking performance, there is evidence to suggest that movement control also follows progression and regression changes (Jagacinski & Hah, 1988). However, that evidence is minimal at present.

The recent dynumical perspective regarding the acquisition of movement skill may be also

be considered in terms of both quantitative and qualitative changes. To illustrate one aspect of this theory, the reader can try performing the following. Hold both of your hands in front of you with the index fingers pointing outward, like two pistols. Now, move these fingers rhythmically back and forth such that they point towards each other at maximal flexion and away at maximal extension (this is called moving "in-phase"). Now try specding up these oscillations. You will probably have no difficulty remaining in-phase while doing this. Now try moving the fingers as the windshield wipers do on a car (this is called moving in "anti-phase"). You will have no problem in doing this when the oscillations a n made slowly. But. at some frequency when the movements are sped up, the relative phase between the fingers switches, and you find

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yourself moving in-phase. From the dynamical perspective, this occurs because the in-phase coordination pattern has more inherent stability than the anti-phase pattern (Kelso, 1984; Kelso. Scholz, & Schbner, 1986). Inherently stable states of movement are modes of Preferred coordinations to which the motor system is attracted during performance. The discovery of these so-called "attractors" provides a basis upon which to interpret changes during leaming. New coordination patterns arc not developed "from scratch". Rather, new patterns emerge as qualitatively different modes of coordination from old patterns. In this way, learning is seen as a process of cooperation and competition (Zanone & Kelso, 1992% 1992b). There is cooperation when the new pattern of coordination closely resembles an attractor (i.e., an intrinsically stable state). However, competition arises when the new pattern does not conform to an existing attractor. This competition emerges as a tendency to regress back to an attractor state while learning the new pattern of coordination. We noticed some very clear examples of competition and cooperation in a recent learning study (Lee, Swinnen, & Verschueren. 1993). Ten subjects were observed over threc days of practice in learning to produce a 90 out-of-phase mode of coordinating forearm movements. The actions were performed rhythmically for 15 seconds at 1 Hz. resulting in over 3000 individual cycles during the three days of practice. The pattern was made more difficult by requiring a larger amplitude for the right arm than for the left arm. Comct p e r f o m c e resulted in the production of a relative motion plot (or Lissajous figure) that had the form of an ellipse. An oscilloscope provided real time visual feedback of the relative motion as it was being produced by a subject. For many subjects, the initial bias in performing this task was to move in an anti-phase mode (6 out of 10 subjects). Of the four remaining subjects, the initial bias was towards inphase for two of them, and the other two subjects showed no clear bias. An example of this bias is illustrated in the upper left panel of Figure 16.3 for the fmt trial for one of o w subjects. The straight line denotes an anti-phase pattern of coordination. The dotted line is the elliptical pattern to be learned. This trial illustrates the bias resulting from the dominance of the intrinsic anti-phase pattern. To learn this task subjects had to break away from this preferred mode of coordination (Swinnen, Walter, Lee, & Semen, in press; Walter, Swinnen & Franz, in press). This competitive process resulted in the subject searching for new patterns of coordination, and this is revealed in the next two panels in Figure 16.3 (after 20 and 40 trials of practice on Day 1). By the end of Day 1 this subject had achieved a rudimentary "idea" of the movement pattern (Gentile, 1972). However, the initial trial on the next day saw a regression back to the antiphase mode (reminiscent of a "warm-up decrement", Schmidt, 1988). From this point until the end of the experiment the subject appeared to be fine tuning the 90 coordination pattern. One way to view these results is to consider learning as involving a shift from a preferred mode of coordination to a new mode of coordination (qualitativechange), plus increased stability in the new coordination mode (quantitative change). Viewed in this way, the dynamical

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-5

Day 1 Pretest

t

Dav 1 (After 2 0 trials1

c

c

C

t

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-60.0 -30.0 .DO0 30.0

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Figure 16.3. Changes in the Lissajou performances of a bimanual coordination task across three days of practice (pornLee et al., 1993).

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perspective is fundamentally similar to the progression-regression hypothesis. Progression involves the development of a new mode of coordinated activity. Regression is seen when the motor system reverts to a more stable mode of coordination under certain stnssors (such as a period of no practice or with an increase in perfomance speed). The attraction of combining these views is that, together, they provide a strong theoretical basis upon which to describe how learning evolves (regardless of initial starting point), and how expertise is developed.

3) Automaticity The concept of automaticity was one of the most important in Bryan and Harter's theoj), and has remained a consistent theme in motor learning theory. The concept of automaticity lost popularity during the influence of behavowism, but has seen a revival since the publication of Plans and the Snucture ofsehavior (Miller, Galanter, & Ribram, 1960). In their book, Miller et al. described how a series of closed-loop operations could be used to govern behaviour. With respect to motor behaviour, they defined habits and skills as plans that have become automatic (p. 82). Learning was a process of moving through three phases according to Fitts (1964, 1965). The first two phases (termed the "cognitive" and "fixation" phases by Fitts, 1965) involved the development of the movement pattern and elimination of errors. The third, or "autonomous" phase according to Fitts, was characterized by two changes: a) increased speed (for tasks that demand both speed and accuracy), and b) increased resistance to interference caused by other tasks when performed at the same time. The developmentof automatic processes played a different role in the closed-loopthof learning proposed by Adams (1971). Although he also adopted a progression with learning, the shift was for a different reason. For Adams, the shift from a "verbal-motor'' stage of movement production to a purely "motor" stage was the shift from conscious to automatic control of a movement sequence @. 125). At this advanced stage of learning the performer had the capability to detect and correct errors based upon an internalized referent for correctness (the "perceptual trace", in Adams' terms), without the need for mediation by thought or augmented feedback. Motor expertise, as characterized by automaticity, can be examined in terms of two distinct, but related lines of reasoning. These are each captured nicely by comparing the development of automaticity in Adams' and Fitts' theories (although the distinction was noted long ago by James, 1890, see Adams, 1981). We will refer to this as the distinction between sequential and parallel operations. For Adams, automaticity referred to processes involved in

In fact, the concept of automaticity in motor skill predates Bryan and Hatter. Adams (1981, 1987) traced the popularity of the concept to James (1890), and noted that the idea goes back even further (Bain, 1868; Spencer. 1881).

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the sequential regulation of movement. For Fitts, automaticity meant that the learner has become morc adept at parallel processing. We will consider the roles of each of these concepts of automaticity as revealed in different views related to motor performance. a) Automatic Control of Movement Sequences Closed-loop accounts of movement control (such as Adams' theory) and open-loop theories appear to converge towards a similar line of reasoning when considering the development of automaticity for movement sequences. Open-loop theuries often attribute the ongoing regulation of movement to a motor p r o g r a m . Kecle (1968) described an extreme version of the motor programme concept. The idea was that stored within the motor programme am all of the necessary movement commands necessary for the regulation of the entire action. These am recalled and invoked prior to movement initiation. Once the movement has begun, it is "run off automatically.

The similarity of a motor programme account to Adams' view is the non-involvement of cognitive activity to regulate a movement sequence. For Adams, automaticity meant the dropping out of verbally mediated corrections during movement -- these corrections being regulated at a non-cognitive level by the perceptual trace. By an open-loop account, automaticity meant that an increasing number of the sequence of activities that comprise a movement were programmed prior to movement onset. Automaticity meant that the motor programme became increasingly responsible for a greater number of the sequential parts of a movement.

Early Practice

Middle Practice

Figure 16.4. Hypothetical changes in the operations under the connol of a generalized motor programme for shifting gears (from Schmidt. 1988, Figure 14-6, p . 477).

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Although few researchers, if any, put their must in the extreme version of the open-loop view, variations still remain. One prominentvariantis the generalized motor programme concept (see Schmidt, 1988 for an overview and refer to section 1 above). For our purposes, a view that arises from this programming notion is that learning involves the development of larger motor programmes. This idea, attributed to Keele (and described by Schmidt, 1988). is capturcd nicely by the analogy to learning to shift gears in a car. As illusanted in Figure 16.4. shifting from one gear to another is hypothesized to involve a sequence of seven actions. Each of these actions is controlled by a separate motor programme early in practice. With learning, the programme is expanded such that a single programme is responsible for control of a greater number of the (initially) separate actions in the movement sequence. The expert is hypothesized to shift gears using just one motor programme. The automatic regulation of a movement sequence, either by closed- or by open-loop conml, has appeal in that it reduces the burden of cognitive mediation during movement. One must be cautioned however, by the w e n t conclusions of Adams (1987) that in spite of the popularity of this view there is "no evidence of a shift from controlled to automatic processing for motor behavior" (p. 66). This view is echoed by Annett (1985), and suggests that these assumptions and theoretical views be given careful reconsideration.

b) Parallel Processing and Movement Organization Expert performers differ from novices in the capability to do more than one thing at a time, referred to as time-sharing. We marvel at the ease with which professional dancers coordinate their limbs, and by the complicated arm and finger movements demonstrated by highly trained typists and pianists. The distinction between novices and experts may result from differences in experience with the component tasks, although a distinct time-sharing ability may also be involved (Ackerman, Schneider, 8c Wickens, 1984). Experimental psychology and the more applied professions (such as ergonomicshuman factors) have devoted considerable efforts to the study of attention and limitations in the division of attention. On the one hand, there is a profound theoretical interest in assessing the human performers (in)capabilityto do various tasks simultaneously. On the other hand, from a practical perspective, many industrial activities require the operator to monitor or control more than one task at a time. Failure to do so may cause serious accidents. Structural and capacity models have been advanced to account for decrements in performance under multiple-task conditions (Kahneman, 1973). Saucnual models explain such decrements mainly in terms of competition for specific information-processingmechanisms or structures that both tasks call upon (e.g.. listening to two auditory messages simultaneously or performing different movements with each arm). Alternately, the many variants of capacity or resource theories predict decrements whenever the total available capacity is exceeded. Performance is related to the task's demand for processing capacity (resources) in these models. An experienced driver does not have any difficulty in communicating with his colleague while scanning the road at the same time. However, when the freeway becomes too crowded, the

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driver concentrates on the task of driving, omitting all other activities. Task difficulty is an important determinant of interference among tasks within the capacity model. Alternately, decrements within a structural perspective depend mainly on the degree to which concurrent tasks simultaneously elicit incompatible responses or impose simultaneous demands on specific perceptual and motor mechanisms (Kahneman, 1973). Finally, some have advocated a hybrid model that combines the suengths of both approaches into one theoretical construct (Wickens, 1980). Although there is still considerable debate about the plausibility of these theoretical constructs and their degree of applicability, each perspective poses a distinct focus on the changes that occur in multiple-task performance as a result of practice and experience. From a resources perspective, Wickens (1989) contends that automaticity may be defmed in part by the reduction in resource demands that occurs with practice. Extensive practice may result in automatic processing of the task information (Wickens, 1984). This will most likely occur when dealimg with stimuli consistently over many trials (automatic processing). In contrast, when novel or inconsistent information is presented. processing will remain more effortful and capacitydcmanding. even after large amounts of practice (controlled processing) (Schneider 8c Fisk, 1982). Secondly, Wickens (1989) points to the development of a skill as the optimal allocation of resources. That is, one should not allocate more resources than needed in each of the component tasks to achieve optimal performance. Sometimes performers spend more effort in a task than required, at the expense of the other task(s). Thirdly, subjects can learn to use different strategies to perform the task and this may have an effect on the landscape of resource demands. Thus, the very efficient time-sharing performance that the expert shows is not only a result of the more automated performance of the component tasks, but also from a true time-sharing skill that evolves with practice (Wickens, 1984). When two different motor tasks are performed together, the interference that often emerges is structural in nature. Given the existence of preferred modes of coordination between the limbs (see previous discussion on in-phase and anti-phase movements), it is not striking that the motor control system encounters difficulties when a course of action deviates from these "comfort" modes. Therefore, difficulties in doing different things simultaneously should be viewed against the backdrop of these intrinsic coordination patterns. In the past few years, a research paradigm has been developed to better understand the limitations in performing different discrete motor activities in both upper limbs together and the problems that arise when attempting to do so (Swinnen, Walter, & Shapiro, 1988). The standard task required subjects to perform a horizontal forearm flexion in one limb together with a flexion-extension-flexion movement in the other limb, both in the time of 600 ms. These movements differed in their overall forms or topologies as well as in their intensity specifications: The flexion-extension-flexion movement had a higher degree of spatiotemporal complexity and also required more muscle activity. A series of experiments revealed that many subjectsexperienceddifficulties in simultaneouslygeneratingthese distinct movement topologies. In addition, problems were also encountered in assigning different intensity specifications to the

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limbs simultaneously (Swinnen, Walter, Beirinckx, & Meugens, 1991). In most cases, the e m that were observed in the left limb movement resembled the pattern of activity of the right limb movement, and vice versa, a case of neural crosstalk (Swinnen & Walter, 1991). In addition, increased speed or torque requinments (increased level of task difficulty) led to increased structural interference (Swinnen, Walter, Semen, & Vandendriessche, 1992; Walter & Swinnen, 1990). These findings are congruent with Navon's "outcome conflict" hypothesis, which accounts for difficulties in dual-task performance in terms of inter-task confusions, similar to confusions between two telephone conversations on a party line (Navon, 1985; Navon & Miller, 1987).

Practice enables learners to act against these natural tendencies for interlimb synchronization and to improve task dissociation (Swinnen & Walter, 1988). Whereas naive subjects and beginning musicians have difficulty in producing rhythms that are not integer multiples of each other (3/2, 5/3), trained pianists can generate complex polyrhythms in which both limbs produce seemingly independent movements (Shaffer, 1981; Summers & Kennedy, 1992). In the experimental paradigm described previously, we found that various forms of kinematic information feedback (displacement, velocity) enabled subjects to produce the tasks more successfully (Swinnen, Walter, Pauwels, Meugens, & Beirinckx, 1990, Swinnen & Walter, 1991). In addition, performing the tasks more slowly in the initial stages of learning (i.e., with lower force requirements), followed by a gradual speeding up, resulted in more successful interlimb dissociation (Walter & Swinnen. 1992). This corresponds with the novices' natural tendency to slow down the movements when first confronted with the task. A final observation that was made is that there are large differences among learners in the degree to which the tasks can be performed successfully, pointing to individual differences in time-sharing capabilities. Other studies have also shown improved time-sharing performance as a result of practice (Klapp, 1979; Klapp, Kelly, & Netick, 1987). On the basis of these observations, it is evident that the human performer can learn to perform different movements in the limbs simultaneously. Success in performance appears mainly dependent on the capability to insulate both activity patterns from each other and to act against a natural tendency for interlimb coupling (Marteniuk, Mackenzie, & Baba, 1984; Swinnen & Walter, 1988). Neural crosstalk is predominant in the beginning performer as shown by converging patterns of electrical activity in the major muscles involved as well as an excess of activity (Swinnen. Young, Walter, & Semen, 1991). With increasing practice, the expert performer has learned to produce a differentiated pattern of activity where the forms and the intensity specifications of the limb actions become &synchronized. In summary, automaticity represents a diverse yet commonly used construct in skilled motor behaviour. The non-cognitive regulation of an ongoing movement is one view, and the ability to time-share without performance decrement is a second view of automaticity. Both assume important roles in theoretical views of skilled motor performance.

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Concluding Comment Bryan and Harter’s investigations gave recognition and legitimacy to the study of the perceptual and motor processes of skilled behaviour. as well as to the acquisition of these processes. Their contributions left us with three legacies that remain, either implicitly or explicitly, in most analyses of skilled behaviow today. It has been argued that the field of motor skill is without a founding parent. It would be difficult to consider Bryan and H m r as founding parents given that their interest was not in motor skill but in the language of telegraphy. However, we agree with Namikas (1983) that they should at least be considered founding uncles.

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CHAPTER 17 STRATEGIES FOR IMPROVING UNDERSTANDING OF MOTOR EXPERTISE [OR MISTAKES WE HAVE MADE AND THINGS WE HAVE LEARNED!!] BRUCE ABERNETHY*, KATHERINE T. THOMAS**, AND JERRY T. THOMAS** *Department of Human Movement Studies University of Queensland,St. Lucia. Qld 4072, Australia **Department of Exercise Science and Physical Education Arizona State University, Tempe, AZ 85287 This chapter has been written in response to requests, initially made separately to us, by the editors for commentaries on the limitations of past research on motor experts, and for predictions and suggestions regarding prospective directions for the field. Since the requests coincided with us all being in the same place at the same time (Thomas and Thomas visiting Abemethy at The University of Queensland), we decided to combine our efforts to produce an integrated viewpoint. Given our differences in paradigmatic commitment (Abernethy increasingly adopting an ecological perception-action viewpoint e.g., Abernethy, 1991 and Thomas and Thomas working largely from within a knowledge-based approach e.g., Thomas & Thomas, in press; Thomas, Thomas & Gallagher, in press), the undertaking was not a straightforward one. Nevertheless writing the chapter afforded us a unique opportunity to explore the relative merits of contrasting theoretical approaches to motor expertise, as well as isolate common methodological problems in the existing body of research on motor experts which we believe act to constrain the advancement of understanding of motor expertise. This chapter represents our consensus on a number of issues related to motor expertise research. From the outset we should make it clear that we view motor expertise as a vital area of research focus, not only worthy of study for its intrinsic interest but also for its potential instrumental value in the improvement and maximization of human movement potential.

In line with the brief provided by the editors, our purpose in writing this chapter was to both assess, as objectively as we could, the progress that has been made thus far in understanding motor expertise and to attempt to identify common problems in existing approaches to the study of motor expertise which may act to constrain further understanding.

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In each case where a problem was identified, we have attempted to elucidate potential solutions and this results in the discussion throughout the chapter of a range of strategies for improving paradigmatic, methodological, and measurement approaches to the study of motor expertise. Given that the orientation of this particular text, like the majority of existing research on motor expertise, is cognitive in nature, we focus in some detail on some of the inherent assumptions of the cognitive approach to motor expertise; assumptions of which researchers operating within this framework may be unaware. However at no point, importantly, do we deny the role of cognition in the expert performance of motor tasks nor do we attempt to bclittle the contribution to knowledge made. by existing cognitive studies of motor expertise. Rather OUT intention is simply to draw the anention of readers to some theoretical alternatives to the cognitive approach and to suggest conditions under which these alternatives may and may not facilitate knowledge advancement. In addition to a focus on paradigmatic issues we also direct detailed attention to, what we regard as, pivotal issues in research design and measurement in motor expertise research. The chapter is divided into four major sections. The first section provides a brief overview of the existing state of knowledge on motor expertise through discussion of the historical origins of motor expertise research, identification of contemporary areas of research focus, description of the prototypic motor expertise studies, and assessment of the degree of generalizability possible from the existing knowledge base. The second section examines strategies for improving/modifying paradigmatic approaches to the study of motor expertise by discussing the necessity to recognize the limitations within the use of recipient paradigms, to value situation specificity and ecological validity, and to link studies of motor expertise to contemporary theories of motor control and learning. In the third section alternatives to traditional cross-sectional research designs and to "one-off research studies are described as strategies for improving methodological approaches to the study of motor expertise. The final section of the chapter deals with strategies for improving the measurement of motor expertise, highlighting existing anomalies in the definition of experts, in the formation of control groups, and in the use of uni-dimensional forms of performance measurement.

The Existing State of Knowledge on Motor Expertise Historical Development and Origins of Motor Expertise Research Although there exist some fine historical precedents to the modern study of motor expertise (e.g., Bryan & Haner's 1897, 1899 studies of telegraphic skills and Book's 1925 study of typing skills), a concerted research focus upon motor experts, especially sport applications, is a relatively recent phenomena. The relative dearth of studies on motor expertise is surprising given the practical problems the motor learning and control field purports to try to understand (Glencross, Whiting, & Abernethy, in press) but is explicable in terms of the long-standing relationship that the motor learning and control field has had with cognitive psychology. Cognitive psychology has frequently used motor tasks simply as a means of addressing cognitive issues whereas motor learning and control has generally

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relied historically upon cognitive psychology for its principal theoretical and methodological models. With the information-processing model dominating both cognitive and motor psychology from the 1950s onwards, the traditional emphasis in motor learning and control research has been very much upon reductionism with laboratory-based approaches being favoured as the means of examining fundamental processes and their capacities and limitations. Such laboratory-based experiments typically used novel tasks to neutralise experience or practice effects and therefore provided no data of direct relevance to the question of expertise. Only with the emergence of the classical studies on cognitive expertise conducted in the mid-1960s and early-1970s on chess players (e.g. Chase & Simon, 1973; de Groot, 1966) has interest in motor expertise re-emerged (e.g.. Allad, Graham & Paarsalu, 1980; Allard & Starkes, 1980; Jones & Miles, 1978), albeit almost a decade later. The study of expertise in the motor learning and control field has also lagged well behind interest in expert subjects by other branches of the exercise sciences. Experts have traditionally been subjects in biomechanical and physiological research and considerable information is now known about expertise, in sport tasks in particular, from the physical conditioning and efficiency of movement perspectives. An interesting parallel is to consider how far the field of exercise physiology might have progressed had it not compared trained with unnained subjects (Glencross, Whiting & Abemethy. in press)! The resurgence of interest in expertise in the motor learning and control field now opens the doors for multidisciplinary and interdisciplinary research on motor expertise, yet work of this type has not yet emerged to our knowledge. The limited focus on expertise as a priority interest for the motor learning and control field is also a specific reflection of the broader historical bias in motor research for the issues of control to the detriment of studies of learning (Adams, 1987; Newell, 1985, 1991; Whiting, 1980). Fortunately there is clear evidence of a resurgence of interest in the issues of motor learning even though such interest may be largely arising because such issues are increasingly being seen as the ‘acid test’ for competing theories of control (Abemethy & Sparrow, 1992). The increasing use of expert subjects within studies of motor control (e.g., Beek, 1989; Beek & van Santvood, 1992; 1989; Bootsma, 1988; Fischman & Schneider, 1985) is a direct reflection of the growing interest in control. The renewed interest in expertise over the past two decades has also been fuelled substantially by a growing dissatisfaction with the relevance and applicability of the majority of existing motor learning and control research to the acquisition of natural skills (Singer, 1990) and the proliferation of expert systems from within cognitive science (e.g., Duda & Shonliffe, 1983; Stillings, 1987). Cognitive science is the modem hybrid of cognitive psychology and computer science. The advent of cognitive science has seen a shift in emphasis away from information processes per se to knowledge structures, placing expert subjects in new demand. Motor control and learning , being under the prevailing influence of cognitive psychology, has made a similar shift, increasingly incorporating the skill level of the subjects as an independent variable within experimental designs.

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One effect of the delayed interest in the study of motor experts is the existence currently of a surprisingly limited empirical data base on motor experts. Although quite a lot is now known about the nature of expertise in cognitive activities as diverse as the playing of board games like chess (e.g., Chamess, 1976; Chase & Simon, 1973; Holding, 1985), bridge (e.g, Charness, 1979; Engle & Bukstel, 1978). Othello (Wolff, Mitchell & Frey, 1984) and GO (Reitman, 1976), solving physics problems (Larkin, McDermott, Simon & Simon, 1980). writing and interpreting computer programs (Adelson, 1981; Bateson, Alexander, & Murphy, 1987; Soloway, Adelson & Ehrlich, 1988; Weiser & Shcrtz, 1983). diagnosing medical conditions (Clancey, 1988; G m n & Patel, 1988). making legal decisions (Lawrence, 1988) and memorising architectural plans (Akin, 1980) and restaurant orders (Ericsson & Polson, 1988), relatively little is known empirically' about motor experts.

Areas of Focus in Motor Expertise Research The majority of published research on motor expertise has been concerned either with the pragmatic issues of enhancing either safety or productivity or with the more theoretical issues of describing the expert performer or describing the development of expertise. Expertise (and experience) has also becn examined as a moderating variable in a number of studies primarily directed at understanding phenomena other than expertise. Studies concerned with safety and productivity issues have, not surprisingly, drawn their motor experts almost exclusively from the workplace; expert and novice pilots (e.g., Stem & Bynum, 1970; Szafran, 1970). automobile drivers (e.g., Mourant & Rockwell, 1972) and mine workers (e.g., Blignaut, 1979) being examined with regard to safety issues and typists (e.g., Gentner, 1988; Larochelle, 1983; Rumelhart & Norman, 1982), machinists and mechanics (e.g., Murrell, Powcsland, & Forsaith, 1962) being examined on productivity issues. The studies which describe expertise (e.g., Allard & Burnett, 1985; Starkes & Deakin, 1984; Vickers, 1986, 1988) and examine its development (e.g., Davis, Thomas. & Thomas, 1991; French & Thomas, 1987; McPherson & Thomas, 1989) have focussed largely on motor experts drawn from the sport domain, although the work on typists and on musicians (e.g., Shaffer, 1980, 1984; Sloboda, 1976) has also been of this nature. Despite the impressive breadth of different motor tasks from which expert subjects have, at one time or other, been studied, the existing database on motor experts generally lacks depth either in terms of persistent studies on experts from the same motor task or in terms of persistent study on the same characteristic across a range of different motor experts. At this point, consequently, the existing empirical database is inadequate to support generalisations about the nature of motor expertise.

'The term empirical is important here as significant influential commentary exists in the cognitive psychology literature (e.g., Bartlett. 1947; Knapp. 1963) about the nature. of motor expertise but this commenlary is generally based on observation and intuition rather than methodically collected data.

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The Mediating Influence of Task Type The temptation, in the absence of a generalizable empirical database on motor expertise, has been to assume that much of what characterises the expert in a range of cognitive tasks will also characterise expertise in motor tasks. The accuracy of this assumption will obviously vary substantially depending on the nature of the specific motor task being examined. In motor tasks which clearly have a high cognitive component it is reasonable to expect that many of the same characteristics which discriminate experts from novices in cognitive tasks will also be reliable discriminators of the motor expen and novice. Motor tasks which place great importance upon conscious. intentional decision-making might be logically expected to be of this type. For instance, experts in high strategy ball sports (e.g., the point guard in basketball), have been shown to exhibit many of the same perceptual (e.g., Allard, Graham, & Paarsalu, 1980) and knowledge (e.g., French & Thomas, 1987) characteristics as experts in purely cognitive activities. In contrast experts in motor tasks in which strategic decision-making is relatively unimportant and in which level of performance is appmntly determined primarily by the quality of the execution of overlearned, automated movement patterns (e.g., as in gymnastics, archery etc.) might be expected to be discriminated from novices by characteristics quite different from those emerging from studies of cognitive expertise.

In a similar vein the respective utility of investigative paradigms from cognitive psychology to the study of the motor expert will also undoubtedly vary with the nature of the task. While the paradigms of cognitive psychology may be most appropriate to the study of high strategy motor tasks, low strategy motor tasks, especially those which exploit repetitive, overlearned phylogenetic skills, may require a quite different set of investigative tools in order to unearth the fundamental aspects of motor expertise? This is an important consideration given that the majority of existing studies of motor expertise have relied, almost exclusively, on variants of paradigms initially developed to study expertise in cognitive task settings. Characterking the ‘Typical’ Approach to the Study of Motor Expertise The ‘typical’ approach which has been used thus far to study motor expertise involves exposing two groups to a single experimental task using a method adopted directly from the cognitive psychology literature. These groups are nominally an group, consisting of the most skilled subjects an experimenter can access on the specific task, and a group, frequently consisting of untrained University undergraduate students. The task to be examined is typically administered in a laboratory setting, involves the derivation of a single dependent measure, and results in the description of expert-novice differences on the particular parameter under study.

2Under specific circumstances even phylogenetic skills may incorporate a substantial cognitive component. Competitive sport settings, in particular, can turn phylogenetic motor acts, such as distance running and swimming, into sirategic activities.

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In the remaining sections of this chapter we criticize a number of aspects of this ‘typical’ approach to the study of motor expertise. It is important to state ‘up front’ that our criticisms should not be misinterpreted as a suggestion that either gJl motor expertise research has been of this type or that the findings from all studies of this typical type are not important. Rather, we believe making explicit the assumptions and constraints within the existing approach is an essential step to recognising the diminishing returns to original knowledge that such approaches offer and to highlighting the necessity to address the l i t a t i o n s of current approaches in order to advance our understanding of motor expertise to a new level.

Strategies For Improving Paradigmatic Approaches to the Study of Motor Expertise Recognizing the Limitations in the Use of Recipient Paradigms Recipient paradigms (Wilberg, 1972) or imported paradigms (Bunge, 1967) are those theories and associated normative methods of study which have been borrowed directly from other fields of study. In the study of motor experts the recipient paradigms are those which have been imported, with little or no modification, from the study of experts and expertise in other domains. Recipient paradigms are typically vital in providing a conceptual suucture for new fields of study, in facilitating the cross-fedisation of ideas between areas, and in demonstrating and assessing the explanatory scope of particular ideas. Nevertheless a transition away from recipient paradigms to increased reliance on either intrinsicallydeveloped paradigms or paradigms sufficiently modified from the original imported idea to assume their own identity is generally indicative of a maturing field of study. The most obvious recipient paradigms in the motor expertise field are the pattemrecognition paradigm used to study perceptual expertise, first introduced in the chess studies of de Groot (1966) and Chase and Simon (1973) and since applied to a range of motor experts (e.g., Allard, Graham & Paarsalu, 1980; Imwold Kc Hoffman, 1983; Starkes, 1987; Starkes. Deakin, Lindley, & Crisp, 1987), and the knowledge-based paradigm inuoduced into the study of cognition by Anderson and others (e.g., Anderson, 1982) and since applied to some selected sport situations (e.g., French & Thomas, 1987; McPherson & Thomas, 1989; Wall, 1986). Given the prevalence of these two recipient paradigms in existing studies of motor expertise, a detailed examination briefly overviewing the paradigm and highlighting some of its potential limitations is appropriate. Attention will be paid in particular to the difficulties that may be created when each of these paradigms from cognitive psychology is applied too literally to the study of motor experts and how future advancement of the motor expertise field may require “breaking away“ from reliance upon paradigms from cognitive psychology. The Pattern Recognition Paradim. The pattern recognition paradigm of de Groot (1965, 1966) and Chase and Simon (1973) is used to highlight the differential recall and recognition performance of experts and novices for perceptual material varying in its degree

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of task structure. Experts are typically shown to be able to recall more stimulus items than novices when the stimulus display contains structure with which they are familiar but to perform equal to novices when the characteristic patterns or structures within the display arc removed. In the original work this involved presenting chess players of different skill levels with brief viewings of chess boards containing pieces either in typical game positions or in random positions although, in more recent applications of the paradigm, slide presentations are typically used with stimulus exposure time controlled. The presence of an expert advantage only on the structured stimulus conditions is used as evidence that expertise is a product of encoding strategies rather than overall memory capacity. The experts improved their recall of structured material by using pattern information to increase the size of the chunks of information stored in memory (Simon & Gilmartin, 1973). The paradigm has been widely used to examine expertise in team sports with slides of structured game offences or defences and slides of unstructured player movements used as direct analogues to the normal and random placement of pieces on a chess board. The same expert advantage is typically revealed as found on the cognitive task. Two key concerns with the direct application of the pattern recognition paradigm to the study of motor experts warrant consideration however. The first concern d a t e s to the use of static display presentations (typically slides) when the paradigm is applied to motor tasks (e.g., Allard et al., 1980). Although static slides may have been appropriate in the original context where the chess board and its pieces were themselves stationary, it is also true that many tasks in which motor experts operate do not involve static displays. Static displays cannot capture the essential dynamism of displays containing motion, as classically illustrated in the ‘point light’ demonstrations of Johansson (1973, 1975), and motion in many tasks may be an integral component of the pattern recognition process. Motion is necessary for the perception of natural events (Warren& Shaw, 1985) and this needs to be a pressing consideration in application of this paradigm in many motor task settings (especially ‘open’ skills). Static ‘snap shots’ do not form an adequate base for natural event perception (Michaels & Carello, 1983) and adding motion into the display of motor experts (by substituting video for slides, for example) can produce expert-novice differences in instances where an expert advantage is not otherwise evident (e.g., Borgeaud & Abernethy, 1987). The second concern, is that the expert advantage within this paradigm may be an epiphenomenon, being a by-product of the expert’s experience rather than a direct cause of the expert’s advantage (Holding, 1985). Evidence from studies of chess, for instance, indicates that, contrary to the pattern recognition notion of expertise, players at the same skill level can vary in their efficiency of pattern encoding (Chamess, 1981) and that skill-related differences in the quality of decision making exist even in the absence of familiar patterns (Holding & Reynolds, 1982). Pfau and Murphy’s (1988) recent demonstration that general chess knowledge is a much better predictor of chess skill than performance on chess pattern recognition tests has further fuelled scepticism about the utility of the pattern recognition paradigm as a means of understanding expertise and has helped re-direct research endeavours in the direction of understanding the knowledge-bases of experts.

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Knowledee-Based Paradigms. The knowledge-based approaches to expertise emanating from the writings of Anderson (e.g., Anderson, 1980, 1982) and Chi (e.g., Chi, 1981) in particular are based on the common premise that although experts and novices have similar ‘cognitive architecture’ (or ‘hardware’ to use Starkes & Dcakin’s. 1984 term) experts they utilise that knowledge. A differ from novices in terms of what they know and number of taxonomies of types of knowledge now exist in the literature, although almost all of these draw a distinction between factual knowledge and procedural knowledge. Knowledge of facts relevant to a specific task is generally r e f e d to as declarative knowledee or prowsitional knowledge, the latter term deriving from the favoured conceptualisation that such facts are organised in the form of a propositional network. The term procedural knowledee is generally reserved for domain-specific knowledge about how to do something (i.e., knowledge of the rules and concepts used to select particular patterns of action), although there appear to be discrepancies in the literature as to whether the ability to state or the ability to actually enact procedures should be the minimal requirement for knowledge to be regarded as procedural rather than declarative (e.g., Evans, in press). The latter debate obviously takes on greater significance in the study of motor expertise (where the action production is generally complex) than in the study of cognitive expertise (where the action to be produced is generally simple and its quality is a non-determinant of the performance level attained). The description of procedural knowledge is complicated in motor expertise because knowledge of ‘how to’ could refer to either the selection of a movement or its execution. In high strategy motor tasks decision-making could have procedures (e.g., when the opponent is at the net in tennis, an error could be forced if the offensive player plays deep to the receiver’s weak side) just as skill execution could also have procedures (e.g., step, turn, accelerate, release for a throw in a pitching task). In low strategy motor tasks the procedural knowledge may be related entirely to skill execution (and once learned, is likely produced, stored and altered without conscious effort), while in a high strategy sport context, the procedural knowledge may relate to both movement response selection and execution. In the early stages of learning, skill execution is probably the primary focus of procedural knowledge development, but as the execution of the action becomes more automatic, strategies pertaining to response selection may become the primary site for procedural knowledge development.

In addition to procedural and declarative knowledge occasional reference has also been made in the literature to strategic knowledge (knowledge of rules, concepts and strategies which are generalizable across a number of different domains; Chi, 1981) and meta-cognitive knowledge (‘knowing about knowing’; Brown, 1975), although these terms have had less impact on the study of motor expertise than the declarative and procedural knowledge terms. These terms have become a primary focus of research using expertise as a vehicle for studying other issues such as age-experience accommodations.

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The knowledge bases of subjects of differing degrees of expertise have been tapped through a variety of methods such as questionnaire, strucctlved interview, think-aloud protocols, etc.; all of which rely on the fidelity of subject’s self-reporting on their own cognitive processes. A number of clear differences in the n a m of the knowledge bases available to experts and novices have been reported in the literam. In both cognitive tasks (e.g., Chi, 1978; Chi, Feltovich & Glaser, 1981; Chiesi, Spilich & Voss, 1979) and motor tasks (e.g., French & Thomas, 1987; McPherson & Thomas, 1989) experts have been shown to possess a more complete and highly differentiated store of both declarative and procedural knowledge than novices. Experts have been consistently reported to be able to see and represent problems at a deeper, more principled level than novices, solving problems through the use of concepts, semantics and abstract principles rather than relying upon superficial, literal features of the problem (Glaser & Chi, 1988; Chi et al., 1981). Indeed, given the apparent relationship between knowledge and skill, some authors have even chosen to define expertise using knowledge as the criterion. For example Gilhooly and Green (1988, p. 379) define expertise as the possession of a large body of knowledge and procedural skill’. I...

As was the case with the pattern recognition paradigm, there are a number of concerns with the knowledge-based paradigms that motor researchers should be aware of before accepting the paradigm without question as paradigm for advancing understanding of the nature of motor expertise. Given our earlier discussions on the mediating effect of task type, these concerns relate most strongly to the application of knowledge-based approaches to tasks involving movements produced with a minimum of conscious control and strategy formation. There are at least four concerndassumptions with the knowledge-based paradigm which the motor researcher should be aware. The first concern is that the knowledge-based approaches, being designed for the understanding of expertise in cognitive tasks, pay little attention to the action production element of expertise. Action production, as Thomas and Thomas (in press) point out, is essentially regarded as automatic and error-free with the cause of performance errors and expert-novice differences being entirely knowledge-based. It is precisely this inadequacy in the original knowledge production taxonomies that has caused the confusion noted previously about whether being able to state how to solve a particular problem but not necessarily enact the solution is indicative of procedural or only declarative knowledge. A second concern is that the knowledge structures proposed by Chi (1981), Anderson (1982) and others (e.g., Brown, 1975) are quite arbitrary; the range of different knowledgebased models and taxonomies currently in existence bearing testimony to this. The different types of knowledge which are proposed are derived from the theorists’ representations of the problem rather than from empirical observation or natural law (Dennett, 1983). Although the knowledge taxonomies may be a useful heuristic for examining the problem of expertise, they

may be little more than that and care must be taken to avoid assigning a level of reality to these taxonomies which is unjustifiable.

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Many will recognise the concern expressed here with knowledge-based paradigms as a re-statement of some of the more general assumptions and limitations that exist within all traditional cognitive models of human performance. The knowledge-based approaches, perhaps more than any other paradigm used to examine expertise, are firmly grounded on a cognitive model of mind and its attendant assumption of representation. Alternatives to the The assumption of aaditional cognitive models of skill acquisition are possible. representation and the arbitrariness of concept labelling and formation are cornerstones of the critique of cognitive psychology made by ecological psychologists (e.g., Beek & Bingham, 1991; Carello. Turvey, Kugler. & Shaw. 1984; Kelso, 1986). Much of the existing study of motor experts, which makes recipient use of knowledge-based paradigms, is therefore based on a cognitive model, the validity of which has been the subject of substantial contemporary debate (e.g.. Colley & Beech, 1988; Meijer & Roth, 1988). A third concern relates specifically to the concept of procedural knowledge and the common tendency to link growing expertise directly with an increased knowledge of the rules and concepts underlying performance in the domain of expertise. Dreyfus and Drcyfus (1986a. 1986b) have demonstrated in a range of activities as diverse as flying a plane, driving a car, playing chess, and learning a second language, that while rule-based behaviour may characterise the performance of the advanced beginner and competent performer, the decision-making behaviour of the true expert is intuitive’ rather than analytical and cann~t be described through procedural rules. Dreyfus and Dreyfus note that it is precisely for this reason that the so-called ‘expert systems’, designed by cognitive scientists and operated on rule-based principles, are unable to mimic well the skills of human experts. Somewhat ironically this insight also suggests that knowledge-based approaches may be more suitable for describing novices than experts. A fourth concern is the reliance the knowledge-based paradigm places upon verbalisation and self-repon as the fundamental source of data. The fidelity of self-report is controversial and problematic for a number of reasons. There has been significant dispute throughout the history of experimental psychology (e.g., Boring, 1957) as to how much direct verbal access performers have to their own cognitive processes. Proponents for the use of behavioural rather than self-report data (e.g., Le Plat & Hoc, 1981; Nisbett & Wilson, 1977) argue that self-report data are necessarily unreliable and should only be used in situations where its fidelity can be substantiated by concurrent behavioural data. In the case of studying movement control. especially for well learned actions, self-report data are, in all probability, fallible because control occurs below the level of consciousness ( e g , gait

’We could argue that the definition of intuitive behaviour is actually the ability to make rapid progress through decision making steps. This progress is made automatically, without conscious awareness and as a result of considerable practice. The traditional veiw of intuition is that correct jumps are made, but are more reliant upon luck. The former is supported by the observation that novices demonstrate very few intuitive behaviours. If intuition was inherent and luck based it should be observed in novices (who will become experts), rather than just in experts.

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control resides primarily at the spinal rather than the cortical level; Shik & Orlovskii, 1976), modulated by special purpose devices which are ‘informationally encapsulated’ podor, 1983). As a consequence the self-repon provided by performers in such instances will be little more than a rational reconstruction of events made not from a position of privileged fmt person introspective insight but rather from a third person perspective. varying little in vantage point from that occupied by the experimenter. Although self-report data may be reliable in some instances (White, 1980, 1982), especially those where the cognition involved is very conscious, serial, deliberate and effortful (controlled to use Schneider & Shiffrin’s 1977 term), verbalisation data is likely to be far less reliable when subjects are required to introspect on cognitive processes which are fast, parallel, non-volitional and non-attention-demanding (automatic to use Schneider & Shiffrin’s terminology). As increased automatic processing is a characteristic, arguably a cause, of growing expertise (Abernethy, in press-a; Fitts, 1964, Gentile, 1972), self-report data on skill execution is also likely to be less valid for experts than for novices. Examples in support of this contention are the ability of beginning drivers to reliably self-report on each of the sequence of steps involved in changing gears and of beginning typists to accurately describe the position of all keys on the keyboard, information that experts have long since forgotten or automated to the point of non-awareness. Self-report data is less likely to be useful for understanding movement execution processes than movement selection ones and a host of examples can be readily supplied to support such a contention. For instance (i) improvements in performance on tasks like bicycle riding frequently occur without any parallel improvement in knowledge of the underlying control theory (Polayni, 1958; Runeson, 1977), (ii) improvements in tracking repetitive sequences have been documented without the subjects being aware of the repetitions being present (Pew, 1974). (iii) subjects are able to reflexively bring hand-held unstable mechanical systems into stability while perceiving they are doing nothing (Henry, 1953; Neilson & Neilson, 1978), (iv) increasing consciousness for many acts typically results in poorer performance (the ‘paralysis by analysis’ problem; Eccles, 1972). (v) expert-novice differences in the location of anticipatory information pick-up in the sport of squash have been objectively demonstrated even though these differences are not apparent in the selfreports of the subjects (Abemethy, 1990) and (vi) expert baseball players report ‘watching the ball hit the bat’ (echoing the coaching cliche of ‘watch the ball’) even though it is demonstrably clear from both theoretical data on the angular velocity limitations of eye movements and empirical data from eye movement recording studies that ocular tracking of the approaching ball finishes well before the point of bat-ball contact (Bahill & La Ritz, 1984). It needs to be restated here that there is nevertheless considerable value in using knowledge-based approaches to understand the decision-making elements of high strategy motor tasks. The acquisition of greater knowledge, for example, has been shown to explain the improved job performance often attributed to greater experience (Schmidt et. al, 1986)

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and, in children, to explain seasonal improvements in sports like tennis (McPherson & Thomas, 1989) and basketball (French & Thomas, 1987). Therefore although the application of the knowledge-based paradigm may be potentially misleading and inappropriate for studying some aspects of motor expertise, it nevertheless remains a powerful paradigm for studying the strategic elements of response selection which act as the limiting factors to performance in many types of motor skills. Valuing Situation Specificity and Ecological Validity In importing methods, theories and knowledge from cognitive psychology the study of motor expertise gains not only the strengths of the cognitive approach but also the attendant biases, assumptions and paradigmatic weaknesses. The historical bias within cognitive psychology has been for a laboratory-based, reductionist approach, where control over the experimental variables of interest is given priority despite the concomitant losses in naturalness, authenticity and representativeness that such a pursuit for maximised control generally necessitates (Gibbs, 1979). This bias against, what Neisser (1976) termed. ecological validity has been similarly reflected in the majority of motor learning and control research (e.g., Christina, 1989; Whiting, 1982). The predominance of simple, contrived. laboratory tasks, such as linear positioning and pursuit tracking, in the study of motor learning has been roundly criticised in recent years by proponents of ecological psychology on the grounds that the movement simplicity does not allow the problem of co-ordination to be meaningfully addressed (Newell, 1985) and the tasks are such that the essential linkages between perception and action, which are fundamental to skill acquisition in 'real world' tasks, are broken (Hofsten. 1987; Turvey & Carello, 1986). Newell (1991, p. 218), for example, suggests that I... the examination of tasks with a richer perceptual-motor environment than provided by the traditional single-degree-of-freedom tasks (such as linear positioning) is likely to open the door to a more ecologically relevant description and explanation of motor skill acquisition.'

Arguments, such as these for increased ecological validity in the investigative paradigm, are increasingly familiar to students of motor learning and control.

Poor ecological validity within the paradigm used to examine motor expertise may have a quite dramatic retarding effect on the capability of the paradigm to contribute usefully to knowledge. The available evidence indicates quite clearly that the more closely the experimental protocol mimics the natural task, the more probable the expert advantage is to appear. A range of reviews (e.g., Abernethy, 1987; Starkes & Deakin, 1984) and empirical studies (e.g., Starkes, 1987) of sport experts clearly indicate that expert-novice differences do not emerge systematically on general tests with non-task-specific stimuli but do emerge when situation-specific stimuli are provided. (The classic Chase & Simon finding is indeed a reflection of this non-general nature of expertise). To state something of a truism, situation specificity is necessary in any paradigm to examine expertise because expertise itself is so

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situation-specific - a point on which both cognitive psychologists (e.g., Glaser & Chi, 1988) and ecological psychologists (e.g., Fowler & Turvey, 1978) concur. Simplistic/contrived tasks may largely negate the expert’s advantage by (a) removing from the task the experiential basis for the expert’s advantage, (b) i n d u c i n g potential floor (or ceiling) artefacts in measurement, and (c) causing experts to function inefficiently by either denying them access to the dedicated special-purpose processors developed with experience (Fodor, 1983; McLeod, Mchughlin, & Nimmo-Smith, 1985), or causing them to use such processors for tasks other than those for which they were developed. This latter point is elegantly demonstrated by way of analogy by Runeson (1977) in his paper on ‘smart’ perceptual mechanisms. A tangible motor example of the expertise-suppressing nature of contrived laboratory tasks is the ability of experts in high speed catching and hitting skills to successfully perform these interceptive skills within time windows as short as 4-5 ms (Alderson, Sully & Sully, 1974; Bootsma, 1988; McLeod et al., 1985), yet performance errors by experts on laboratory analogues of coincidence-timing (e.g., the Bassin coincidencetiming task) are typically more in the range of 30-40 ms (Hofsten, 1987). Ecological validity in the study of motor experts needs to be retained not simply in terms of the use of realistic stimuli but also in terms of the requirement for the production of natural responses. Continued technological developments should allow increasing measurement of variables of interest in natural settings without sacrificing experimental control, although clearly in studying motor expertise such developments are going to require the experimenter to become increasingly familiar with the movement pattern measurement tools of the biomechanist and the physiologist. Fortunately there are clear signs both in studies of motor learning arising from an ecological perspective (e.g., Beek, 1989; Bootsma, 1988; Emmerik, Den Brinker, Vereijken, & Whiting, 1989) and in studies of sport expertise which adopt classical cognitive approaches (e.g., Davis et al., 1991; French & Thomas, 1987; McLeod, 1987) of a growing predominance of natural tasks and a reduced utilization of contrived laboratory tasks. The continuation of this trend can only have positive effects on our capability to understand more about the nature of motor expertise. Linking Studies of Motor Expertise to Contemporary Theories of Motor Learning and Control Understanding and application are the ultimate twin goals of all scientific endeavours. Describing the phenomenon of interest is a vital stepping stone toward understanding but cannot and should not be seen as a substitute for understanding. Understanding is a step beyond description and requires the development, verification and, where necessary, modification of theoretical explanations of the phenomenon. While the existing data on motor experts provide a reasonable description of some of the many characteristics of expert performance, it is fair to suggest that little progress has been made towards the development of a strong conceptualhheoretical framework for the understanding (explanation and prediction) of motor expertise. This problem of emphasising description to the detriment of explanation is reminiscent of the limitations many have

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identified in the motor development field (cf. Connolly, 1970; Kelso & Clark, 1982; Wade, 1976). The problem is symptomatic of most ‘new’ fields of study and should not be misconstrued as a denigration of the contribution of existing studies. However, now is an appropriate time to make more concerted efforts to link the existing descriptive work to theoretical premises, especially those existing in the motor learning and control field. This challenge is examined in greater detail in the sections that follow. Although theory development in the motor learning and conml field provides a logical starting point for the development of theories of motor expertise, only very limited links have been made thus far between the studies of expertise and the study of movement control and learning. Forging such links may help the desired progression from description of motor experts to the explanation of motor expertise. Issues of motor control and motor learning are necessarily linked, and although any worthwhile theory of control must be able to account for learning and vice versa, historically there has been far from equal research interest in the two topics. The past t h n e decades of motor learning and control research has been characterised by an almost singular focus on the issues of control to the obvious detriment of knowledge acquisition about learning (Adams, 1987; Schmidt, 1988; Whiting, 1980). There appears, nevertheless, to be indications of a renewed interest in issues of movement skill acquisition. This interest has been fuelled by a range of factors including a growing dissatisfaction by motor skills practitioners with the lack of applicability of existing motor control literature (e.g., Christina, 1989; Singer, 1990) and the convergence of theoretical debates regarding control to questions of learning so that learning issues are seen as ‘acid tests’ of the contrasting theoretical views of motor control (Abernethy & Sparrow, 1992). As the major contemporary theories of control offer some useful, albeit differing perspectives on the question of learning, the next section of this chapter briefly examines the perspectives on expertise which can be derived from these contemporary motor learning and control propositions. The motor learning and control field has been characterised by the rise and fall of a limited number of different theoretical premises, most notably the closed-loop (feedbackbased) and open-loop (motor programming) theories, derived from information-processing premises, and the dynamical theories of self-organisation, derived from biological physics. The field is currently in a paradigm conflict between the traditional information processingkognitive theory based on generalised motor programs and the ecological theories based on notions of self-organisation and non-linear dynamics (Abemethy & Sparrow, 1992; Dickinson & Goodman, 1986; Meijer & Roth, 1988). Each theoretical position results in different questions being asked and different orientations being presented to the nature of skill acquisition and expertise. Feedback-based Learnine Theories. Adams’ (1971) closed-loop theory of motor learning posited continuous feedback monitoring as the basis for movement control with well-practised subjects being hypothesised to control movement through comparison between

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current sensory feedback and a cenually stored representation of the feedback from previously successful movements. With skill acquisition a transition from reliance on externally-provided feedback to internally-derived feedback was predicted. When exuapolated to the study of expertise, such a theory might predict superior self-monitoring skills to be a characteristic of expert performance. Such a characteristic is known to be true of experts in cognitive tasks (e.g., Glaser & Chi, 1988; Larkin, 1983) and there is some limited evidence from the motor domain of error-detection capabilities improving with practice (Schmidt & White, 1972). Expert-novice differences in the skill of cycling in effort perception have also been recently shown, with experts more attuned to internal sources of information than novices (Lynagh, 1987). The value of Adams' theory has been shown to be limited because its explanatory power is restricted to a narrow class of one degree-of-freedom slow positioning movements and evidence is available of accurate movement control on some rapid and well-learned movements in the absence of sensory (feedback) information (e.g., Kelso & Stelmach, 1976). The predictions with respect to expertise differences in sensory monitoring and error detection nevertheless remain potentially useful ones for the development of models of expertise in some motor activities. Motor Promamming Theories. Rogramming explanations of movement control are based on the premise that desired movement characteristics are represented centrally by some form of plan or program that allows the necessary neural commands D be prescribed and fully organised prior to the commencement of movement. The original concept of the motor program as a set of specific neural commands, structured in advance of the movement, which allowed the movement to be completed in the absence of sensory feedback (e.g., Keele, 1973) have been modified over the years to current, more generalised versions (e.g., Schmidt, 1985), including forms which prescribe an important role for sensory feedback (e.g., Schmidt, 1975). In its contemporary form the generalised program theory of movement control argues that movement is the product of generalised features which remain invariant from one execution to the next of a particular type of action and situation-specific parameters which give each movement unique rate and topographic properties.

Both theorising and data collection on motor programming has focussed on the issue of control with the question of how the nature of programmed control might alter with learning (and the acquisition of expertise) being largely neglected. While there appears to be general consensus and indeed some data (e.g., Schmidt & McCabe, 1976) to support the view that increased programmed control occurs with practice, what has not been explored, at either the macro or micro level, is how this increase in programmed control might occur (Abernethy, in press-b; Glencross et al, in press). For example, at the macro level, does the development of expertise involve the development of a larger range of more differentiated programs, the development of "bigger and better" programs, achieved by combining smaller

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progrms together (e.g., Book, 1908; Pew, 1966r or some combination of the two? Likewise, at the micro level, does expertise result in clearer establishment of the invariant features, more appropriate situational specification of the modifiable parameters or a reduced time for mapping the situation-specific parameters into the response specification process? In a preliminary examination of these latter issues Abemethy, Neal, Moran and Parker (1990) and Neal, Abemethy, Moran and Parker (1990) were unable to find any expert-novice differences in the preservation of the putative invariant feature of relative timing (cf. Schmidt, 1985) in either the kinematic or electromyographic patterns of golfers. This occurs despite markedly lower trial-to-trial variability in the experts’ actions. There is nothing to prevent leaming issues being addressed in the natural setting, yet relatively little applied learning research has been done in the past because of the traditional preoccupation with laboratory-based research. DvnamicaUEcoloeical Theories. The traditional programming theories of motor control have been increasingly subjected to criticism as a number of the inherent assumptions and limitations of this perspective have become apparent. In particular criticism has been directed at the assumption and arbitrary nature of cognitive representation (e.g., Carello et al., 1984). the literal reliance on the information processing metaphor and the associated notion of hierarchical control (e.g., Turvey, 1977). the computational problems posed by contextconditioned variability and large degrees of freedom (e.g., Turvey. Fitch & Tuller, 1982), the assumption of performer-environment independence rather than mutuality (Fitch & Turvey, 1978; Lombardo, 1987). the general disregard for the level of control inherent in the dynamics of the musculo-skeletal system (Kugler & Turvey, 1987; Kelso, 1981, 1986). and the narrow range of contrived motor tasks rather than natural actions being examined (Newell, 1985, 1991; Turvey & Carello, 1988). In an attempt to overcome these concerns, the work of Gibson on perception (e.g. Gibson, 1979) and Bemstein on action (e.g, Bernstein, 1967) has been used by Turvey and others (e.g, Kugler & Turvey, 1987; Kugler, Kelso, & Turvey. 1982; Turvey, 1977) to develop an ecologicaVdynamical theory of motor learning and connol which operates on quite different grounds to the traditional cognitive models. This theory, among other things, seeks an explanation of control within the natural laws of non-linear dynamics (Kugler & Turvey, 1987). invests control in heterarchical, coordinative structures (Turvey, 1977). and assumes performer-environment mutuality based on perception-action cycles (Kugler & Turvey, 1987; Turvey, Carello & Kim, 1990). The latter assumption is a particularly important one in the current context as it suggests that one needs to understand the performer, the environment and their relationship rather than simply know the characteristics of the performer in order to understand expertise (Rach, Lintern & Larish, 1990, Fowler & Turvey, 1978). Such a view makes the examination of expertise in its natural setting imperative and suggests that the base unit of analysis should be the event

?his question is essentially the motor equivalent of the cognitive issues of whether the development of expertise results in more chunks being stored in memory or the number of chunks remaining relatively fixed but the chunk size increasing.

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rather than the performer (Fowler & Turvey, 1978; Warren & Shaw, 1985). The perspective taken by the ecologicUdynamica1 theorists naturally raises a quite different set of questions about skill acquisition and expertise than are raised by motor programming theorists. The development of expertise from an ecological/dynamical perspective has been suggested to involve progressively increased attunement to environment and response structure (Flach et al., 1990) and outimisation of the values assigned to control variables within a given coordinative structure (Kugler, Kelso & Turvey, 1980; Newell. 1985). Improving attunement to task structure has baen proposed to involve perceptual differentiation and filtering (Gibson, 1969). the differentiation of essential from nonessential control variables (Fowler & Turvey, 1978; Warren & Kelso, 1985) and the discovering and development of direct links between perceptual (informational) variables and action system variables (Turvey et al., 1990; Warren, 1988). In a useful matrix view of the performer (actor) - environment linkage which underpins the ecological model. Owen (1990) and Flach et al. (1990) have conceptualised the acquisition of expertise as the selective migration of information within the action-event structure matrix such that non-functional sfructures became functional (e.g., the action progresses from non-attunement to attunement to relevant information) and dysfunctional structures become afunctional (i.e., the actor progresses from attunement to non-attunement to irrelevant information). Optimising control involves the formation of coordinative structures (Kugler, Kelso, & Turvey, 1980) and shifts in movement topology due to improved hamessing of reactive forces (Fowler & Turvey, 1978), improved efficiency (Newell, 1985) and independent control over increasingly more d e w s of freedom (Fowler & Turvey, 1978; Newell, Kugler, van Emmerik. & McDonald, 1989). Mapping the search strategies through which learners attempt to optimise the linkage between perceptual informational invariants and movement kinetic invariants has been proposed as a fruitful avenue for understanding skill acquisition (Fowler & Turvey, 1980; Newell, 1991; Newell et al., 1989). The ecologicaVdynamical approach to motor skill acquisition is still very much in its infancy, so to what extent it can be successful in extending our understanding of motor skill acquisition beyond that provided by the traditional cognitive approach is yet to be determined. Given the stated commitment to the study of natural actions, studies of motor experts may well be an integral part of the development and assessment of the ecological theories. A receni special issue of the Journal of Motor Behavior (Newell, 1992) focussing on dynamical approaches to motor skill acquisition provides some examples of the use of motor task experts in studies of skill acquisition. Undoubtedly, as we have noted at a number of points earlier, the respective utility of the cognitive and dynamical theories of motor learning and control will vary somewhat depending on the specific nature of the task one seeks to explain. At this stage the tools for dynamical analysis restrict the dynamical approach to the study of repetitive cyclic activities, of the type prevalent in low strategy, overlearned motor tasks. The dynamical models therefore seem more adept, at this time, in accounting for expertise in tasks of this type than

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in high swtegy motor tasks where response selection rather than execution serves as the limiting factor to performance. In contrast, cognitive models, appear to offer more in explaining expertise in high strategy motor tasks than do the emerging alternatives. Regardless of which theoretical view ultimately proves to have greatest explanatory power, it is obviously paramount that future studies of motor expertise be tied more closely to motor learning and control theory than has been the case in the past. Not only may studies of expertise contribute meaningfully to the developmen4 verification and refinement of motor learning and control theories but, in a reciprocal vein, such theories may also provide the foundation for a conceptual understanding of the motor expert. A conceptual basis for studying the motor expert is needed to move the field beyond description and towards explanation and prediction. Strategies for Improving Methodological Approaches to the Study of Motor Expertise In addition to carefully examining the suitability of currently dominant paradigmatic approaches to the study of motor expertise, the case can also be made for re-assessing a number of the methodological decisions typically made in existing studies of motor expertise. In this section we examine the case for greater use of longitudinal-style research and programatic research in the study of motor expertise. Use of Alternatives to Cross-Sectional Designs Although the majority of existing research on motor expertise uses cross-sectional designs, a more desirable, though logistically more difficult, approach is to use longitudinal research -- research in which one or a number of individual subjects arc mced continually through their transition from novice toward expert. The case for increasing the use of longitudinal research in expertise studies is identical to the case which has been presented by a number of prominent figures in the motor development field (e.g., Newell & Barclay, 1982; Sugden, in press) but, as with the motor development case, such research is infrequent, perhaps even non-existent for expertise studies, because of a number of logistical difficulties. These difficulties include: (i)

the possibility/probability that very few, if any, of the subjects first followed at the novice level will ever become experts (this is certainly true of most sport tasks but may be less true of ergonomic tasks where the sheer weight of imposed practice may ensure a proportionally high translation to expert status);

(ii)

new or altered theoretical views cannot be easily accommodated once the project is in progress;

(iii)

researchers are reliant on delayed gratification - insights into expertise may not be gained until years after the onset of data recording;

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researchers have difficulty attracting the level of continuous funding needed to support longitudinal research; and the sheer number of trials needed to become a motor expert (the millions of trials and thousands of hours needed to become an expert far exceed the amount of practice ever given in laboratory-based learning paradigms; e.g., Chase & Simon, 1973; Newell, 1985). The limited longitudinal data which exists in the expertise field (e.g, Bloom's 1985 work on talented individuals) has been collected on a retrospective basis. Although such an approach is efficient (in the sense that the majority of nonexperts do not have to be traced), the retrospective approach is fraught with difficulties such as (i) the inaccuracy of retrospective reports -- recollection being sullied by more recent experiences, (ii) the incompleteness of retrospective data, and (iii) the loss of cohort data (which makes it impossible to partial out individual from cohort causes/facilitators of expertise). Given these concerns with retrospective data, the clear need for prospective longitudinal research into expertise remains. The difficulties associated with prospective longitudinal research m not necessarily insurmountable. One approach to quasi-longitudinal research into expertise is to follow expert and novice performers over the course of a season. This is a particularly useful strategy with children where both novices and experts are expected to improve. (For an example see French & Thomas, 1987.) Since it seems unlikely that any true longitudinal study from novice to exceptional expert will ever be done (what are the odds of picking the correct subject? ; if we knew how we would be rich and not writing chapters like this!!), taking cross-sectional snapshots of periods of longitudinal development Seems the best alternative. Another possible solution to the longitudinal -- cross sectional problem is the microgenetic technique (Siegler & Crowley, 1991). The technique takes frequent measurement during periods of change and less frequent measurements during stable periods. Clearly the problem is predicting when periods of change will occur in the development of motor expertise. However if this can be achieved with reasonable accuracy rather than having to following an entire "career", researchers could simply look at time windows which mark significant improvements in each aspect of the skilled performance. This might be particularly useful with children who participate in seasonal sports (e.g., basketball, baseball) where children can be observed regularly during a season and experts followed across seasons. Greater Use of Programatic Research The existing research on motor expertise is characterised by 'one-off studies rather than programs of research, to the point where the existing data-base appears to reflect a 'shot-gun' rather than a systematic approach. In this regard the motor expertise field is reminiscent of the sport personology research of the 1960s and 1970s (e.g., Ryan, 1968).

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With some exceptions (e.g., Starkes, 1987) the majority of research has tended to seek knowledge advance by applying common paradigms to expert groups drawn from different motor tasks (therefore seeking knowledge advance through assessing the generality of narrow characteristics of expertise) rather than by applying a range of paradigms to the same groups (or at least groups drawn from the same activity). Given that task-specificity appears to be an essential element of expertise and that understanding expertise requires a detailed understanding of the specific task requirements, configurations and performer-environment interactions and transactions (e.g., Fitch & Turvey, 1978; Fowler & Turvey, 1978; Newell, 1985). a more detailed analysis of expertise in a particular activity through the applications of multiple paradigms to the same group or groups would appear a more desirable future direction. The programatic approach to research advocated by McGrath (1964) would appear to offer some advantage in this regard. McGrath suggests that a logical progression toward theory development in emerging areas of study is from field-based exploratory studies (Stage l), to field and laboratory experiments as follow-ups (Stage 2). to computer simulations for theory elaboration and refinement (Stage 3), with a return to further laboratory and field experiments for theory validation (Stage 4) and then back to field studies for final theory cross-validation (Stage 5). (See Landers, 1983 for an advocacy and elaboration of this approach within the sport psychology field ). Some of our own research work on expertise in fast ball sports, especially racquet sports (Abernethy, 1992). provides something of the flavour of research continuity and methodological pluralism that we consider to be important for the advancement of our field. For example, Abemethy’s (1992) use of video-tape to capture perceptual dynamics of expertise (experts were in laboratory) could be extended to video recordings of experts as they make decisions and performer actions in real game settings. This allows the perception-action connections important to an ecological model or the decision-making links of ‘if-then do’ procedures (cognitive models). The movement from laboratories to field settings and back to laboratories (and even computer simulations) must maintain the ecological validity of the strategies and skills involved in expert’s performance. Strategies for Improving Measurement in the Study of Motor Expertise

In this section we examine the question of how the independent variable of expertise and the dependent variables of expert performance might best be defined and measured in order to improve understanding of motor expertise. Selecting and Measuring the Independent Variable: Defining the Expert Group

A fundamental step in any scientific inquiry is the unambiguous operational definition of the subject matter. In the study of expertise defining who (or what, if one includes machines, robots and computerised automata based on expert systems technology) is an expert is problematic (Thomas & Thomas, in press). Dictionary definitions which suggest that an expert is ‘a person who has special skill or knowledge in some particular field’ (Macquarie Dictionary. 1988, p. 330) reflect well the lay sense of the word but are generally

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too vague to be of use in scientific work. The selection of expert subjects clearly needs to be directly related to the research question one is addressing. with different expert group selections being needed if the purpose is to trace the development of expertise rather than describe the characteristics of expertise in adult performers.

In practice, exceptional levels of perfomwce on the task of intmst arc usually adopted as the criterion for the identification of expertise, at least in studies of adult experts. While such an approach has obvious face validity, it is not without drawbacks. For example, linking the classification of expertise to demonstrated levels of task performance subtly creates a mindsct for experimentation that focuses upon what experts can do that novices cannot. While such an approach is undoubtedly valuable, it remains true that some equally important insights into expertise might be gained by also examining what experts cannot do and especially what causes experts, as opposed to novices, to make particular typcs of m. Reason (Reason, 1979; Reason & Mycielsh, 1982) and Norman (1981) have demonstrated the value of analyses of ‘slips of action’ in understanding aspects of the control of natural day-to-day actions; them may be considerable new insight to be gained into the nature of expertise by applying this kind of analysis to motor experts. Some of the most intensting insights may be gained into expertise by observing the varying levels of skill execution competency among expert performers. For example. McPherson and Thomas (1989) have shown that considerable variability exists in experts’ ability to perform the motor response they have selected. This area of variability is of coursc unique to motor experts--variability in the performance of cognitive experts being traceable almost completely to the connections between ‘if and then’ statements. The ‘if-then’ connection which determines response selection may be perfect in a group of motor experts, yet their motor performances may vary greatly, due either to diffennces in the execution and control of skills, or differences in the connection made between ‘if-then’ and ‘do’ parts of performance. As noted previously, the “do“ aspects of performance may be consided from either a motor program (e.g., Schmidt, 1985) or a dynamical systems (e.g., Clark & Kelso, 1982) perspective. In the majority of cases the study of motor experts, like the study of experts in cognitive tasks, has proceeded through the use of expert-novice comparisons to &scribe the characteristics of experts. The ‘typical’ approach of comparing expert and novice p u p s as a means of gaining insight into the nature of the expert advantage is based on the assumptions, often implicitly held, that: (i)

experts have reached the pinnacle of skill development on their particular task;

(ii)

experts are universally and unambiguously rccognisable; and

(iii)

novices an true controls, being naive to all elements of skill (thmby have effectively no learning on the task under study).

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All three of these frequently held assumptions can be shown to be palpably incorrect. The first assumption, that experts have reached the upper limit of skill development on their particular task of specialisation, is untrue because learning can be shown to be an ongoing process even for subjects with tens of millions of trials of practice on the very simplest of tasks (Crossman, 1959). Although performance asymptotes often create the impression of learning plateaus for highly practised subjects, sensitive measures, such as secondary task performance within dual-task paradigms, can demonstrate continued learning even when improvements in performance on the primary task of specialisation are no longer apparent (e.g., see Parker, 1981). Innovative practice methods can facilitate improvements in performance even by expert performers to the extent that, after exceptional amounts of practice, levels of performance are often achieved which appear super-human (e.g., see Spelke, Hirst & Neisser, 1976, and Ericsson & Chase, 1982 for some examples).

The second assumption, that experts are universally and unambiguously recognisable, can also be shown to be untrue by the simplest comparison of the categorisation of expertise from one study to another. As Thomas and Thomas (in press) have noted in studies of sports expertise, what one researcher classifies as expert may be classified as intermediate or even novice by another researcher. In the sports expertise literature, for instance, some studies select college level playing experience as the minimal criterion for inclusion in the expert group while others reserve the term expert for use only with intemational-calibre athletes. Only a brief perusal of the sport expertise literature is necessary to reveal that the classification of an expert, at least by researchers on expertise, appears to be quite arbitrary and that the criterion used to define expertise varies substantially from researcher to researcher and from study to study. This issue is particularly problematic in developmental studies of expertise. As size, strength, coordination. practice, and experience increase across childhood and adolescence, the motor expert of 12 years of age may differ considerably from the motor expert of 14 years of age. Nevertheless studying the development of expertise across childhood and adolescence is essential to understanding skill acquisition as these are the times when the "literally thousands of trials", often mentioned but seldom studied in motor behaviour, occur. The talent identification programs used by Olympic programs in many counmes are clearly based on the ,belief that potential motor experts must be identified prior to 10 years of age. The continuous nature of skill development and the arbitrary defining of the expert collectively reinforce the need to recognise expert and novice as points on a skill development continuum rather than as discrete states. The recognition that there are more than two phases in the skill acquisition process is of course not a revelation - Fins (1964) description of cognitive, associative and autonomous phases of learning being one of the

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more enduringly influential works in the motor learning literature? More recently Dreyfus and Dreyfus (1986a. 1986b) have described five stages in the transition from novice to expert as the learner moves from being an analytical, detached decision-maker to one who is both involved and intuitive. Regardless of the specific labels one chooses to attach to the different phases of skill acquisition, the important observation is that very different conclusions will be reached about the nature of expertise if a subject classified as an expert in one study is only considered as the equivalent of a competent or proficient performer (to use the labels of Dreyfus and Dreyfus for the thud and fourth skill levels in their schema) in another study. The lack of consistency in the standards used to define the expert group makes it examely difficult to combine data or observation across studies in order to derive generalisations about the development of expertise. The problem in clearly and consistently defining the subject matter therefore acts to exacerbate the generalizability problem which arises from the limited size of the existing data base.

An additional problem arises if a very stringent criterion is imposed for the classification of a subject as an expert. True experts, by definition, constitute a very small section of the population making it frequently difficult to assemble a group of experts without some trade-off being necessary between group size (and hence experimental wwer) and the minimal criterion used to qualify as an expert. Increasing the sample size in adcr to improve experimental power inevitably necessitates some diminution of the expertise criterion. Although the predominant decision in existing research on motor expertise has been to attempt to optimise experimental power wherever possible (generally by forming expert and novice groups of reasonably large and approximately equal size), a sound argument can be made for the increased use of case studies and small subject designs in order to maintain expert group quality (e.g., Smith, 1988). The third assumption regarding the novice group’s role as naive controls with essentially no task learning is untrue because all subjects, however apparently untrained they may be, bring to the experimental setting skills. strategies, and knowledge which can be applied to the task. The novice group can never be regarded as a tabla m a . In studies of anticipatory skill in fast ball sports, for instance, although experts systematically outperform novices, the novices’ anticipatory performance is still usually well above chance levels, indicating more than a rudimentary understanding and utilisation of the existing task structure (Abemethy, 1990; Abemethy & Russell, 1987). The problems posed by the selection of an appropriate control group for studies of motor expertise are sufficient to warrant separate and more detailed consideration.

’Indeed Glencross et al. (in press) have argued that the continued use of Fitts’ taxonomy in modem motor control and learning texts is indicative not only of the quality of the original work but also the little progress that has been made over the past three decades with respect to understanding skill acquisition.

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Sdccting and Measuring the Independent Variable: Defining the ConW Group The prototypic study of motor expertise involves the use of a novice group typically assembled from undergraduate students in a University population who are untrained and demonstrably paor performers on the task of interest. The validity of such a control group is rarely, if ever, questioned even though a logical argument can be advanced to suggest that a more appropriate control group might involve subjects who arc highly practiced and experienced yet whose task performance remains at a below average level. The typical expert group differs from the novice p u p both in terms of attained level of task performance and in task familiarity (due to differences in the mount of practice undcrtakcn on the task). Differences which arise in the typical expert-novice comparison, and which arc inevitably atuibuted causal status with respect to expertise, can never in fact be reliably distinguished from epiphenomena caused by familiarity differences (see Holding, 1985). For this mson a control group with q u a l practice but poorer performance than the expert group may provide a superior design alternative to the usual novice group, at least in the sense of W i g able to control for any differences in task performance that arise between experts and novices which are due to familiarity effects alone. Such wined novice control groups arc of course much morc difficult to find than the aaditional untrained novice control group and precise control over practice levels is difficult. if not impossible. to achieve because persistence and the quantity and quality of practice are generally linked directly to performance level - attaining top level performance causing an increase in practice just as much as the more widely documented converse effect. An example of this type of control is provided in a recent study by Thomas and Lec (1992) using two groups of mame women golfers equal in experience but markedly different in skill level. The subjects in this study had all played golf for at least 7 years, and were currently playing at least 3 days per week including regular competition. While the handicaps for the two p u p s differed substantially (low handicap M=13.8, high handicap M=30).the average years of experience for the two groups actually favoured the less capable players (23 years as compared to 21.6 years playing experience). One other characteristic which was critical to the separation of these two groups was that players in the high handicap group had never qualifd for the low handicap group. This control was essential as skill could have deteriorated due to many factors, including aging, since these players averaged ncarly 62 years of age. A true control group was also formed composed of women of similar age and experience, but drawn from a different activity, tennis. The major question addressed in the Thomas and L a study was why women would continue to participate and not improve their skill. A similar control for experience was used in a study of ballet dancers by Starkes et al.. (1987). Expert dancers were members of the national company, novices danced for the same number of years (and were the same ages) but in local companies. The studies examined m a l l of dance sequences comparing the two levels of expertise. The concerns expressed in this section with regard to the potentially confounding effect of experience on expertise parallel concerns expnssed in the aging literature regarding

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the potential confounding of age effects by experience effects. Salthouse (1990). has suggested that a major pitfall of research addressing the ageexperience issue is failure to quantify both the amount of experience and overlap between the content arc~sused for training and. testing. For example, memory tasks presented within a specific experimental context for both training and testing for experience. (e.g.. remember thc news or a set of instructions for medications). may also be facilitated by practice outside the specific context (c.g., remembering a gmcery list may be beneficial practice). Since memory is a general skill, which can be practiced within and outside of the research context, results may be misleading. Using a context where practice CM be better conmllcd, and experience quantitiad, will potentially yield results which am easier to inmpret and have greater generalizability. Of course not only must the amount of experience be controlled but also the quality of practice. In natural tasks this is somewhat mum problematic to resolve.

In sum, although groups controlling for experience may be difficult to form, they may provide important insights into expertise that the pmtotypic approach with its single control group is unable to provide. Furthermore, given the earlier discussion on the essential view of expertise as a point on the skill learning continuum, a strong case can also be made to extend the typical two group (expert-novice) comparison to comparison of multiple skill groups. Only through the use of such groups may the transitional stages in the development of expertise suggested by authors such as Dreyfus and Drcyfus (1986a) be fully and adequately tested

Selecting and Measuring the Dependent Variablds): Recognizing the Multi-Deminsiod Nature of Expertise A common problem in measuring expertise is failure to m g n i s c the multiplicity of attributes that compose expert performance. While the predominant cumnt approach of using single dependent mcasurcs may be enlightening in somc instances. such appmach is necessarily limited in its utility. Given the large range of component skills that go togetha to produce expert motor performance, it is probable that like experts may differ substantially with respect to their performance on tests of component skills. In high snategy motor tasks. for instance. performance is a function of not only an individual’s competence in skill execution but also of their physical and perceptual attributes (size, speed, acuity, etc.). their physical conditioning, their knowledge. their psychological characteristics and their intuition. To accurately characterise expert motor performance. t h m f m , task demands and individual these l component parts. In rwasuring expert performances must be described in terms of & performance dependent variables should be selected which represent as many of these performance components as am appropriate and, wherever possible, confmtory variables should also be m a s d (e.g.. electromyography to c o n f m when or which muscles IUC in action; electroencephalography to confirm when--or perhaps whm-- cognitive attention is demanded, game performance to confirm level of expertise, etc.). Experts are. frequently found to be surprisingly poor (or certainly not above average) on some component skills but am able to comptnsate for any such weaknesses with

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exceptional capability on one or more of the other skill components (cf. Clarke, 1971). As the pattern of specific strengths and weaknesses may vary substantially from individual-toindividual and even from expert-to-expert, expertise may only be meaningfully interpretable in terms of the total profile of performance on tests of the component skills. The results of expert-novice comparisons on tests of one component of a total skill must therefore be interpreted with caution. A further reason for caution in placing too much weight on the results of expertnovice comparisons involving single dependent measures arises from the well established wisdom that the whole is typically more than the sum of the parts (to draw on an old clichb) and that the whole is frequently not able to be predicted from the behaviour of the component parts because the parts interact in non-linear ways (this being one of the fundamental premises of the physics of self-organising systems e.g., Garfinkel, 1987). Although, for instance, expert-novice differences may not exist on a particular skill component when that component is measured in isolation, it may nevertheless, through its interaction with other skill components, be an important locus of the expert advantage

u.

For example, knowledge, skill execution proficiency, and experience all combine in some way to produce expertise. However, the combinations, or interactions, of these components are likely to vary depending upon at least two factors -- the age of the experts and the type of skill being performed (e.g.. is it high or low strategy). Figure 17.1 presents some hypothetical data on how the relative contribution of knowledge, skill execution competence and experience to performance might vary depending upon the age of expert performers involved in high strategy motor tasks. In young experts, knowledge probably plays a pivotal role in performance while experience, being limited, probably has minimal influence. Skill execution proficiency is likely to be moderately important. From late adolescence through adult years, skill execution competence and experience are probably most important with knowledge playing a lesser role. As experts age and neuromuscular control (and thus the ability to execute skills as desired) declines, knowledge and experience enable the older expert to maintain high levels of performance. The importance of these three components is likely to be considerably different if the skill is a low strategy one -- for example, throwing darts (Figure 17.2). In such activities the execution of the required movement pattern is always important, even though experience may be helpful, particularly later in the lifespan. However, beyond learning basic rules, form, and scoring, knowledge would be unlikely to play a major role in the quality of the expert’s performance in this type of motor task. Of course we have hypothesised the relationship in Figures 17.1 and 17.2 from our knowledge of the expertise literature, experiences with expert subjects across the lifespan, and help from Salthouse (1990) and Fitts (1964). It is unlikely (unfortunately) that the actual relationships are ever exactly as we have proposed. Our hope here is simply that these hypothetical plots may cause scholars to recognize that interactions among components of the behaviour of motor experts will likely depend on mediating variables such as the nature of the motor task(s) and the age of the expert.

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Figure 17.1. Hypothetical Changes Across Age in Relative Importance of Knowledge, Experience and Skill for Expert Performers in High Strategy Tasks. Collectively the concerns addressed above about the use of single dependent measures argue strongly for the necessity to derive macro level observations on experts in the natural setting and for collecting multiple dependent measures rather than single dependent measures on all subjects under study. Given the current rhetoric about ‘perceiving in order to act and acting in order to perceive’ (e.g., Hofsten, 1987; Turvey & Carello. 1986, 1988) and ‘knowing in order to act and acting in order to know’ (e.g., Norman & Shallice, 1985; Weimer, 1977), which highlight the essential mutual interactions between perception, cognition and action, there is clearly a case for incorporating measures of all of these elements6 in studies of expertise. In order to generate an ensemble of measures of all elements of expertise in a given skill it will be necessary for researchers of motor expertise to become conversant with measurement approaches not typically utilised by the cognitive

6Supponers of ecological psychology may object to use of terms such as element and component as implicit indicators of the treatment of mutually-dependent pmesses as if they are logically separable and independent. Such an inference is not intended.

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Figure 17.2. Hypothetical Changes Across Age in Relative Importance of Knowledge, Experience and Skill for Performers on Low Strategy Movements.

psychologist. Measures of kinematics and kinetics (typically the domain of the biomechanist) and of elecaophysiology and efficiency (typically the domain of the physiologist) appear increasingly necessary to describe the performance (expert or otherwise) of 'real world' actions. A positive upshot of addressing the multi-dimensional nature of expertise is the increasing emergence of multi- and inter-disciplinary approaches to the study of human skill (e.g., sec A h e t h y & Neal. 1992). The multi-ateibutional nature of expert performance also suggests the concomitant need f a researchers to move away from reliance upon the, typically used. standard univariate analysis methods to more routine usage of morc appropriate multivariate methods. The difficulty such a transition poses to the expertise researcher is that while multivariate statistical approaches such as MANOVA, canonical comlation, structural modelling, and discriminant analysis ~n clearly the most suitable for the subject matter. the necessity for large sample sizes to make such analyses valid is at odds with the small number of experts who exist on most tasks. The options faced by the researcher include (i) studying expertise in tasks (such as car driving) which arc deliberately designed to make competent performance

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relatively easy to achieve and hence in which sufficient expert subjects exist to make multivariate analyses possible, (ii) collecting multivariate measures with small subject numbers but either abandoning formal statistical analyses (e.g., via case studies) or using the statistical analysis as a guide only or (iii) continuing the ‘tried and me’ approach of using univariate analytical methods even though such approaches are demonstrably limited.

SummaryKonclusions The past decade has seen a substantial resurgence of interest in the study of motor expertise. Typical of most emerging fields of study, the development of a systematic body of knowledge on motor expertise has proceeded largely through the dinct application of the paradigms, methods and measurement approaches of a already established field of study; in this case that field of cognitive psychology related to the study of expertise. Also typically the existing studies on motor expertise have produced more questions than they have provided answers. In this chapter we have sought to assess the current state of knowledge on motor expertise and to identify constraints within the dominant existing approaches to motor expertise which may act to limit further understanding of the fundamental and applied nature of motor expertise. While it is indisputable that significant advances have been made in the past decade our concern is that, in many cases, research into motor expertise (including our own, we hasten to add) has only ‘scratched the surface’ with respect to understanding motor expertise, using designs, methods and theoretical assumptions which may have been less than optimal. With the considerable benefit of hindsight we have discussed in this chapter a range of paradigmatic, methodological, and measurement constraints which we believe warrant serious consideration by both practising and prospective researchers of motor expertise dike. In particular. in order to make the essential transition from description to explanation, it is our view that future research on motor expertise needs to consider, among other things. the limitations and assumptions in the use of recipient paradigms from cognitive psychology, the mediating effect of variables such as task type and developmental age, the imperative nature of situation specificity and ecological validity, and the importance of linking motor expertise studies to contemporary theoretical developments in the motor control and learning field. In the latter regard we believe studying motor expertise has the potential to shed considerable light on many of the fundamental issues in motor learning and control which have thus far proven problematic because of the field’s traditional preference for laboratory-based studies of untrained subjects. Clearer definition of the independent variable of expertise (and its unconfounding from the level of experience), more suitable measurement of the multidimensional nature of expert performance and the development of a research mentality which favours programs of research over ‘one-off‘ studies, are also part of our view of the ‘way forward’. The strategies proposed in this chapter are an attempt to suggest prospective directions for the progressive understanding of what we regard as one of the singly most fascinating aspects of all human behaviour - the development of motor skill expertise.

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Acknowledgement Reduction of this manuscript was made possible by research support to the first author by the Australian Research Council and the Australian Sports Commission and a University of Queensland Travel Grant awarded to the third author. The support of these bodies is p a t l y appreciated.

Part 5 Editors’ Epilogue: Where are we now?

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EDITORS' EPILOGUE: WHERE ARE WE NOW? We are now in a position to evaluate what the expertise approach has brought to the study of motor learning and control. Clearly, this approach is fundamentally different from previous approaches to the area; approaches such as closed loop theory, schema theory, dynamical systems theory, or direct perception. Each of these approaches has started with a theory and developed laboratory tests of predictions derived from the theory being evaluated. The expertise approach starts with a description of expert-novice differences, as shown by many of the chapters in this book. Can there be any generalizations made from the domain-particular observations shown in this work? The first important lesson from these chapters is that there are cognitive differences between "motor" experts and novices, and as shown by this volume, these differences extend over a wide range of skills. Theories of motor learning that seek to explain skill acquisition without invoking cognitive factors must come to grips with these findings. What do these chapters tell us about what it takes to become a motor expert? As in cognitive skills, experience in the particular domain is critical for the development of expertise in motor skills. In addition, the differences between basketball coaches, players, and referees shown in the Allard, Deakin, Parker, and Rodgers chapter show that how the performer must interact with the environment is an important determinant of what skill will develop. Starkes, Payk, Jennen, and LeClair show that negative transfer can occur when a skilled macrosurgeon first learns microsurgery. This observation would seem to indicate that skilled performance develops as automatic condition-action links are forged in a particular environment. However, the rapidity with which the macrosurgeon is able to divest himself of a careers' worth of habit - note that the negative transfer is observed only for the first morning of the course in the Starkes et al. data - reveals that automaticity cannot adequately describe the surgeon's learning. As well, the ability of a skilled surgeon to perform novel movements, to microwrite, shows that the microsurgeon's real world size skill can generalize to the microenvironment. It must be that the intention of the individual drives the performance, and intention is surely a cognitive consuuct. Thus the message of this volume is that skilled motor performers have learned to overcome the limitations posed by their own nervous systems. Consequently, limitations in the speed of processing visual information are no banier to the skilled batter, and the limits to the acuity of the visual systems may be overcome by learning how to move under a microscope. Motor experts are anything but tied to the environment. This message is important because any viable theory of motor controMeaming must have

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the potential to explain all forms of motor performance. Early theories were criticised for being too cognitive, for proposing central control where none was needed, wanted, or even possible. However, the balance has shifted way too far in the opposite direction: to motor control without a mind. Such theories might do an excellent job of describing initial attempts at performing a skill, and correctly point out difficulties which come when people attempt to perform, for example, out of phase movements. But a theory of control must also explain the ability of the skilled percussionist to play polyrhythms - or does such an individual have a different set of oscillators than the rest of us? By not considering the conmbution made by cognitive factors, contributions which are described in this book, we risk accepting overconstrained theories; theories which fly in the face of the skilled performances we see and hear from experts every day.

Author Index &

Subject Index

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Motor Eapertise Abbs. J.H., 49, 206. 207, 208, 3M Abernethy, B.. 24, 25. 26, 111. 112, 113, 114, 116, 117, 118, 121, 124, 137. 138. 139. 140, 141, 142, 143, 144. 145. 146. 164, 165. 255, 260, 261, 317. 318. 319, 323, 327, 328, 330, 331, 332, 336, 339, 344 Abramson, A., 216 Ackennan. P.L., 6, 120. 309 Adams. J.A., 150, 274. 302. 303, 308. 309, 319, 330 Adams, J.J., 6 Adelson. B.. 18, 160, 165. 320 Adesman P., 113 Akin, 0.. 320 Alderson. G.J.K., 329 Alessi. S.M., 153, 154 Alexander, R.A.. 320 Alexander, R.H.. 190 Mard, F., 6, 35. 36.40. 41. 62, 70. 96, 99. 103. 110. 112. 137. 138. 139, 148. 159. 165, 234, 235. 237. 255, 258, 259,260, 319. 320, 321. 322. 323 AUport. D.A.. 70 Alvioli, B., 242 Anderson, S.R., 216 Anderson, J.R.. 12. 70. 72. 73. 109. 123. 124. 126. 160, 161, 182, 184, 256, 257, 263. 264. 265. 266, 322, 324. 325 Andrews. D.H., 154 Annett. J.. 309 Anzai, Y.. 18 Armstrong. C.W., 75. 79. 116 Arnold, L.. 190 Ashy, M.. 256 Atal, B., 216 Athenes, S. 46. 50 Atkeson, C.G.. 46 Austin, M.. 112 Baba. D.M.. 44.57. 31 1 Bahill, A.T., 141. 327 Bahrick, H.P.,304 Bairstow, P.J., 288 Baker, J.. 242 Bar-Eli, M.. 135 Barclay. C.R.. 334 Bard, C., 111, 112. 113, 116, 117, 123, 124, 125. 126. 136. 139, 140. 141, 146, 151. 164, 255, 260. 261

363

Bmnfeld, M..113 Barnes, M.J.. 96, 104 Barrette, G.T..75. 242 Bany, J.R..137 Banhles, 79 Bartlea, F.C., 320 Basmajian, J.V.. 300 Bassok. M.,77, 160, 161. 162, 163, 167, 180. 184 Bateson, A.G., 320 Bateson, E.V.. 203. 320 Beavers, Ch.,112 Beech, J.R.. 326 Beek, P.J.. 319, 326, 329 Beirinckx, M.B., 44. 31 I Bell, A., 202 Bell, R.M., 190 Bell-Berti, F., 210 BelmMt, J.M.. 256 Ben-Ishai, R.,283 Benguerel. A.P., 210 Berkenblit, M.B., 282 Berliner, DC.76 Bemstein, N.,50, 300, 332 Bingham. G.P.. 326 Biolsi. K.J., 11, 96 Birren, J.E., 241 Biscan. D.,116 Bianer. A.C., 58 Biui, E.,282 Bjork, R.. 78 Blakemore, C., 10 Blignaut. C.J.H.,320 Bloom, B.S.. 7, 102. 335 Bomhoff. G.T., 112 Bonekat. H.W., 244. 248 Bonnell, A-M., 20, 25 Book, W.F.. 302. 318, 332 Bootsma, R.J., 21, 24, 25, 112. 319, 329 Borgeaud. P., 112, 137, 138, 146, 164. 323 Boring, E.G.,326 Borkan, G.A., 242 Boswell, T., 31 Botwinick, J., 194 Bouffard. M.,110 Boutmans. I.. 112 Bradford, D.C., 244, 248 Bransford, J.D.,152 Braun, G.L.,242 Breen,J.L., 28 Briggs. G.E.,304 Browman, ., 217

..

364

Brown, A.L., 324. 325 Brown. L.. 49 Brucher. J.M.. 247 Bryan. W.L.. 295, 296. 297. 298,299. 300. 302,307,318 Buckob E.. 113.260 Buekers, M..112 Bukstel. L., 320 Bullemer. P., 278 Bunge. M.,322 Bumett, N.,35. 70. 96, 110, 112, 137. 138, 159. 165,255, 258.320 Bumughs. W.A., 112. 127 Bunvitz. L., 147, 149 Busch. S..242 Butterfield, E.C.,256 Bynum. LA., 320 Caldenvood, R., 77,79 calkins, v., 190 Campbell. J.I.D.. 7 Carello. C., 323, 326. 328. 332, 333. 343 Carey, D.P., 45 C d a n , H.. 8.43 Carrel, A,. 226 C d b r e , L.. 112. 113. 115, 116. 123. 139. 260.261 Carson, G.,194 Ceci, S.J., 160. 162. 257 Cennak, S.. 284. 286 Chambedin, C.J.. 35, 143, 145, 152. 153 Chang, I., 216 Chariton, J.L.. 45 Chamess, N.. 7, 18. 35, 62, 113. 137, 161, 165, 171, 182. 320, 323 Chase,W.G.. 7, 12, 17, 18, 62.78.79, 111. 113. 137, 255. 275, 319, 322, 335. 338 Chi. M.T.H., 10. 18, 62, 77, 78, 79, 87. 89, 90.91.96, 102, 109, 110, 148. 160. 161, 162, 163, 165, 167. 180, 181. 182. 183. 255. 256, 257, 258, 259. 324, 325. 329. 331 Chiesi. H.L.. 112. 147. 166, 177, 257. 259, 325 Chistovich. L., 210 Christina, R.W.. 79, 328.330 Chua, R.. 40 Clancey, W.J.. 320 Clark,J.E., 330, 337 Clark. P.D., 242.243

Clarke. H.H., 342 Cockape, T.W.. 190 Coelho. A.. 143. 145 Cohen. A., 279,280.283 Cole. K.J.,49. 206. 207, 302 Colley. A.M., 326 Collins. I., 5 Colwill. J.M., 190 Colwin. C.M., 81, 82 Condon A., 113 Connolly, K.J.. 330 Cook. M., 112, 143 Cooney, J.B., 171 Coppert. L.. 29. 30. 32 c o r n s , D., 79. 277 Coren, S., 1W Corkin. S..10 Costa, P.T. Jr., 242 Costill, D.L.. 76, 81. 82 Cousy, B.. 104 Cowan, H., 210 Crisp, F., 110, 113. 258. 259, 322. 340 Crossman, E.R.F.W., 303, 338 Crowley, K.,161. 335 CNSe, H., 50 Crutcher, R.J.. 7 Cummings, A.L.. 79 Cunnigham. W.. 241 Daniloff. R., 210 Dare, M.T., 284 D a m P.W., 75 Davids, K., 147, 149 Davis, C.M., 320. 329 Day, ., 113 de G m t . A.D., 12,49.79. 113, 319, 322 de Leeuw. N., 91. de Koning, J.J.. 49 De Land, E.C.. 190 Deakin, J., 7. 19, 35. 103, 109, 110, 124, 125, 126. 112. 113, 136. 146. 147. 151. 159, 258, 259, 320. 322, 324, 328, 340 Deary, I.J., 6, 20 &Clerk, J., 210 Den Brinker, B.P.L.M., 329 Dennett, D.C., 325 Deridder. M., 112 Dewan, I.. 30 DiCicco, G.L.. 79 Dickinson. J.. 330

Motor Expertise Dickson, P.. 17. 31 Diener, H., 279 M y . S.G., 112 Dovenmuehler, A., 174 Dreyfus. H.L., 8, 11. 326. 339. 341 Dreyfus. S.E.. 8. 11. 326, 339, 341 Drowatzky, J.N.,147 Duda. R.O.. 319 Dugas. C., 5.45. 50 Dustman, R.E., 244, 248 Eccles, J.C., 194, 327 EMich, K..319 Eickmeier, B., 46 Eisenstadt. M.. 62 Eldar, E., 76 Elliott, D.,5 Elliott, M.. 140 Ellis, S.H., 113 Elstein, AS.. 79 Enberg, M.L.. 113 Engle. R.W., 320 Enoka. R.M., 39 Era, P.. 242. 243 Ericsson, K.A., 7, 8. 11, 17, 18, 79, 161, 320, 338 Evans, G.,324 Evans, 1. St B.T. 161 Ewan. ., 216 Fairs, J., 113, 260 Faloon, S.. 79 Fant. G.,210 Fan, M.J., 18, 160 Favilla, M., 277 Feigenbaum. E., 11, 12 Feingold, R.S.. 75 Feldman. A.G., 282 Feltovich, P.J., 79, 85. 89, 90. 91, 96, 102, 161, 165. 182. 255. 325 FIBA. 103, 104 Fillenbarn, G.G..242 Finch, A.E., 113, 141. 142, 144. 145 Fmdlay. H.. 110 Fmdlay J.M., 115 Fiorito, P., 112, 141. 142, 145 Fischman. M.G.. 5, 319 Fischoff, B., 79 Fisk. A.D., 310 Fisk, J.D., 45

365

Fitch H.L.. 300,332,336 Fitts. P.M.. 20. 183, 303. 304, 307. 308, 327, 338, 342 Flach. J.M.. 332,333 Flanagan, J.C.. 79 Flege. J.. 205 Fleishman, E.A., 5 , 6. 19. 58, 72. 273. 274. 275 Fletcher, S.. 205 Fleurance, P., 139, 140 Fleury. M.. 112, 113, 115. 116, 117, 123, 124. 125. 126. 136, 139. 140. 141, 142. 146, 151. 164, 255.260.261 Fodor. J.A. 124,327.329 Folse. R.. 190 Forbes. W.F.,242. 277 Forsaith, B., 320 Fowler, C.A.. 201,205, 206,217, 329, 332, 333. 336 Fowlkes. E.B.. 96 Franks. I.M.. 40 Franks, J.J., 152 Franz. E.A.. 305 Freeark. R.J., 190, 196 French. K.E.. 110. 135. 147. 149. 159, 165, 183. 184. 255. 258, 259, 262, 263, 264. 265. 266. 267. 320, 321. 322, 325, 328, 329, 335 FRY,P.W., 113. 320 Freys, S.M..233 Friedrich, F.J.,283 Fuchs. A.H., 305 Galaburda, A.M., 194 Galanter, E., 307 Gale, 1.. 79 Gallagher. J.D.. 256, 257, 317 Galloway. G.M.. 190 Gammon, L.C., 190 Gardner. H., 10 Garfinkel. A.. 342 Garland, D.J., 137, 255, 258 Gay, T., 205, 210 Gazzaniga, M.S., 196 Gentile, A.M., 35, 36, 300. 305, 327 Genmer, D.R.. 4.32.298, 301. 320 Geschwind, N.,194 Geuze. R.H., 284 Ghez, C.. 277 Giannini. J.. 76 Gibbs, J.C., 328

366 Gibson, E.J., 333 Gibson, J.J., 332 Gilhooly, K.J.. 124, 161. 162. 325, Gill, E.B., 140 Gilmaltin, K.. 323 Girouard. Y.,5 Glaser, R., 18. 77, 85. 89, 90, 91. 96, 102. 109. 110, 160. 161. 162. 163, 165. 167, 182, 183. 184. 255, 325, 329, 33 1 Glencross, D.J.. 318. 319, 339 G o b , C., 161, 167 Goldsmith. J.. 216 Gomez-Meza, M. I 6 4 Goldstein, ., 217 Goodale, M.A., 45 Goodman, D.. 44. 330 Goossens, D.P., 231,233 Gopher, D., 283.287 Gordon, M.E., 116 Gordon, N., 284 Gomch, R.L., 245 Gough, H.S., 190 Gould, D.. 76 Goulet, C.,112, 113, 115, 116, 117, 124. 125. 126, 139. 140. 141, 142. 146. 164 Gracco. V.L.. 49,206,207,208, 302 Graham,K.G.,263. 265,266 Graham.S..35.62.112, 137,138,255,258, 319. 321. 322, 323 Green A.J.K.. 124, 161. 162. 171.325 Greene, T.R., 160 Groen, G.L.. 8. 18, 79, 320 Grosgeorge. B.. 111 Gruel, S.M., 231. 233 Guezennec, J.Y., 111, 113, 115 Guthrie. E.R.. 300 Guy. J.R.F., 190

H a m , H.. 113 Hah, S., 304 Hall, W.B., 190 HallC, M. 113. 115 Halstead, W.C.. 190 Hamlet, S., 206 Hannah, A.E.. 248 Hannah, T.E.. 241. 242. 243, 245. 247 Hanson, R.. 216 Harbeson, M., 58 Hardie, M., 49

Author Index Hams, K.S..207. 210 Hams, R.E.. 190 Harter, N., 295, 296, 297, 298, 299, 300, 302, 307. 312, 317 Harter, S., 267 Hartley, J. W., 104 Haskins. M.J.. 127 Hawkins. H.L.. 283 Hawkins. S.W.. 243,248 Hayes, J.R., 76 Haynes. J.L.. 190 Haywood, K.M., 112, 164 Healy, W.A., 104 Hecaen. H., 194 Heemsoth, C.. 113 Heikkinen, E.. 242. 243 Hellebraun, F.A.. 242 Helsen, W.. 112, 116. 117, 122. 124, 260, 26 1 Hempel, W.E. Jr.. 19. 72 Hening, E., 49 Hennig, W.. 277 Henry, F.M., 58,274.275.327 Hesslein. R., 190, 196 Heuer. H.. 301 Hill, K.M.. 242 Hirdt. P., 36 Hirdt, S., 36 Hirdt. T.. 36 Hirst, W.. 338 Hobson, D.A., 40 HOC. J-M.. 326 Hockey. R.J.. 115 Hodge. K.. 76 Hoffman, S.J., 75. 78, 113. 116, 322 Hogan, N., 281,287 Holding, D.H., 113. 152. 165, 320, 323. 340 Hollander, Z., 103 Hollerbach, J.M.. 46. 238 Holyoak, K.,12, 13, 14 Homiedan, A., 205 Horn, T.S.. 267 House. A,. 210 Housner, L.D., 166 Houtmans. M.J.M.. 114 Howarth. C.. 111, 113 Hubbard, A.W., 23, 24, 28, 140 Huddleston. Sh.. 112 Huddy, L., 243 Hull, C.L.. 303 Humphries. C.A.. 110, 159. 165. 183. 184. 258. 262

Motor Experflse Hunt, E., 282,287 HunterLE., 327 Hutchinson. J.E., 87. 160, 182,257.258 Hun. J.W.R.. 20 Hyllegard, R.. 165 Imwold, C.H., 75, 113. 322 Inhoff, A.W., 283 Isaacs, L.D., 113, 141. 142, 144, 145 Ivy, R.I.. 276. 277. 278. 279, 280, 281. 285,286,288. 289 Jackson. H.,194 Jackson, J.A., 244 Jackson, M.. 113 Jacobson J. H., 226 Jagacinski, R.J., 304 Jakobson, L.S., 45 Jalavisto. E.. 245 James, W.,307 James, B.. 36 Jeannemd, M., 41,44,46. 152 Jenkins, S., 24, 25, 26 Jennings. P., 279, 280 Johansson. G., 323 Johnson, D.L.. 260, 261. 262, 266 Johnson, E.J.. 79 Johnson, P.E.. 79 Johnson, R.. 140 Johnson, S.C., 96 Jonas, H.S.. 190 Jones, C.M.. 113. 136. 141. 151. 255, 260. 319 Jones, D.F., 79 Jones, I.C., 39. 49 Jones, M.B.. 58 Jones, S., 279, 280 Jordan, M.I., 17. 217 Kahn. R.L.. 241. 243 Kahneman, D., 283,287, 309, 310 Kareev. Y.,62 Kassirer, J.P., 79 Keck, J.W., 190 Keele, S.W., 22. 72, 276, 277, 278, 279, 280. 283, 285, 286, 288. 289, 308. 309, 331 KeUer, F.S., 295, 299, 302 Kelly, P.A., 31 1

367

Kelso. J.A.S., 44, 206. 207. 305, 326, 330, 331, 332, 333, 337 Kemper, T.L.. 194 Kennedy, R.S.. 58 Kennedy. T.M., 31 1 Kent, R.,203 Keogh. J.F., 288 Kim. N-G.. 332. 333, Kirkpatrick, P.,28 Klahr. D., 160 Klapp, S.T., 311 Klein. G.A., 77. 79 Klint, K.A.. 260 Klopfer, D.,85 Knapp. B.N.,320 Knutzen, K.M., 49 Koeske, R.D.. 10,257 Kolers. P.A., 70 Kollia. E.. 207 Koob, E., 233 Koppett. L.. 29. 30, 32 Kopta, J.A., 190 K o s s l p S.M., 277 Koster, W.G., 238 Kotovsky, K..160 Kozhevnikov, V., 210 Kozma, J.A.. 241, 242, 243, 244. 245. 246, 247,248,250 Krane, V., 76 Kraus. AS., 242 Kreighbaum, ., 79 Kristofferson, A.B., 286, 290 Kugler, P.N., 326, 332,333 Kuipers. B.. 79 Landers, D.M.. 336 Larish, D.D.. 150 Larish, J.F.. 332, 333 LaRitz. T., 141, 327. Larkjn, J.H., 320, 331 Larochelle. S., 320 Lawn. B.. 282 Lassen, N.A.. 282 Laszlo, J.I., 288 Lauby. V.W.. 190 Lawrence, J.A.. 79, 160 Lawrence, P.F., 190 Lazar, H.L.. 190 Le Plat, J.. 326 Leavitt. J.L.. 93 Lee, A.M., 256

368

Lee, c..340 Lee. D.. 21 Lee, S.,226, 233 Lee. T.D., 35, 36, 152. 153. 305. 306 Leinhardt, G.. 79 Lemay. M., 194 Lemire, L., 115. 116 Lemon, LA.. 242 Lenhardt, M.L., 243. 248 Lesbats. M., 120 Lesgold, A.M., 76. 85 Levy, I., 194, 1% Lewis, M.W., 77, 163 L e d , M., 194 Likrman, ., 216 Lindblom, B., 205, 213 Lindley, S.,110, 113, 258. 259. 322. 340 Linker, W., 204 Lintem, G., 153. 154, 332, 333 Lishman, I., 21 Liske, D., 45 Ufqvist, A., 206, 207, 208 Logan, G.D., 135 Lombardo.T.J., 332 Lord,S.R., 242. 243 Lotan, M.. 283 Lubker, I.. 205, 210 Lundy-Ekman, L., 281,286,288 Lushene, R.E., 245 Lyle, J., 112, 143 Lynagh, M.. 331 Macgregor. D., 77, 79 MacKenzie. C.L.. 41. 44,45. 46. 50. 302, 311 Macko, K.A., 282 MacNeilage, P.. 216 Maddieson, I., 202 Magill. R.A., 254 Maglischo, E.W..76, 81, 82 Mallows, C.L.. % Mancini, V.H., 75 Marsden, P.. 79 Marteniuk. R.G.,5 , 22. 40. 41, 43, 44. 45. 46, 50. 58, 274, 302, 3 11 Martin, I., 79 Mathews, M.. 216 Mayer. H.,213 McCabe, J.F., 331 McCarty, T.. 206 Mc Clements. J., 110

Author Index

McCorduck, P.. 11. 12 McCrae. R.R., 242 McCullagh, P, 256 McDermon. J., 320 McDonald, P.V., 333 McFadyen, B.J., 40 McFarland. R.. 248 McGee. M.G., 194 McGrath, J.E.. 326 McGregor. I.C.. 233 McKim, W.A., 247 McLaughlin. C.. 25, 329 McLeod, P.N., 22, 24, 25. 26. 329 McNeil. K.. 243. 245, 248 McNeilage, P., 210 McPherson, S.L., 148, 149, 159, 160. 167, 171, 174. 177, 255, 258. 262. 263. 264.265, 320, 322. 325, 328, 337 Meer, B., 242 Meijer. O.G.. 326. 330 Menant G., 111 Meugens, P.F., 44.311 Michaels, C.F., 323 Michaud. D.. 115. 116 Miles. T.R.. 113, 136. 141. 151, 255. 260, 319 Miller, D.B.. 226 Miller, D.I.. 39, 49 Miller, G.A., 307 Miller, J.. 31 1 Millslagle, D., 112, 137, 138, 139 Milner, A.D.. 45 Milner. B., 10, 194 Mishara. B.L.. 247 Mishkin, M., 282 Mitchell, D.H., 320 Mitchell, H., 6. 20 Mtickel, W., 315 Moll, K.,203, 210 Moller, J.H.. 79 M o m . M.J.. 332 Moms, C.D.,152 Mosston, 79 Mourant, R.R., 320 207. 217 Munhall, K.G., Murden, R.. 190 Murphy, M.D..320, 323 Murray, H.G.. 79 Murray. M., 173, 174.242 Munell. K.F.H., 320 Mussa-lvaldi. F.A., 282 Mycielska. K., 337

..

Motor mertise Myers. A.M., 243 Nakagawa, A.. 112 Namikas, G., 302. 303, 312 Naus, M.J., 356, 358 Navon, D., 31 1 Neal, R.J., 332. 344 Neilson, M.D.. 327 Neilson. P.D.. 327 Neisser, U.,328, 338 Nelson, R.C., 39, 149, 170 Netick, A,. 31 1 Neumaier, A., 112, 113. 143 Nevett, M., 165.263, 265,266 Newberry. J., 243, 248 Newell. A,. 12 Newell. K.M., 266, 303, 319.328. 332, 333. 334. 335. 336 Nimmo-Smith. I.. 25. 329 Nisbett, R.E., 161. 326 Nissen, M.J.. 278 Noble, M.E.. 304 Noice, H.. 171 Nolan, M.D., 154 Norman, D.A.. 161, 165. 257.337 Norman, D.E., 320,343 Noms, A.H.. 242 Nottebohm, F., 194 Nougier. V., 20, 25 Nylen. C.O.. 226

369

Parker. S.G.,96 Patel, V.L.,8, 18, 79, 320 Pauick. J.. 112. 302. 303 Paul. A.. 210 Paulignan, Y.,41 Pauwels, J.M.. 112, 116, 117, 122, 124, 311 Penner. B.C.. 160, 171 Perkell. J., 210,215 Perrin, F.A.C.. 274, 275 Perritt, R.A., 226 Petrakis, E., 113 Pew, R.W., 5, 327, 332 Pfau, H.D., 323 Philip M.. 111 Pickleman, J., 190, 196.238 Pieron, M.. 75 Pilitsis, J., 231 Pinheiro, V.E.D.. 75 Pizzimenti, M.A., 39.49 Pleasants. P.. 164 Pokorny, R., 277,278,280.286 Polayni. M., 327 Polson. P.G., 320 Porn. C.. 396 Power, M.I., 6, 22. 183.283, 303 Post, T.A.. 160, 171, 183 Potvin. A.R., 242 Potvin, J.H., 242 Poulton, E.C., 6, 36 Power, F. G. Jr., 104 Powesland, P.F., 320 Prapavesis, H.,113. 260 Pribram. K.H.. 307 Price, P.B.. 190 Prince, .. 217 Prinz. W., 139 Proteau, L., 5. 44 Pumam, C.A.. 44. 78 Pylyshyn Z.W., 226

O’Connor. J.E., 171 Ochs. A.L., 243. 248 bhman, s.,210 Orlovskii. G.N., 327 Omstein. P.A., 256, 258 Osborne, M.M.. 116 Osgood, C.E., 152 Ostry. D.J., 203 Outerbridge. A.N., 327 Owen, D.H.. 333

Quaintance, M.K., 19 Quanbury. A.Q., 40

Paarsalu, M.E.. 35. 62, 112. 137. 138. 255. 258, 319. 321, 322, 323 Paillard. J.. 111 Papin. J.P.. 111. 113. 115 Parker, A.W.. 332 Parker, D.L.. 153. 154 Parker, H., 338

Rao, V.K., 231.233 337 Reason, J.T., Reed, H.B.C., 194 Rees. C.R., 75.266 Rees, E.T., 109, 110, 161, 162, 167, 183, 251 Reid, J.C.. 190

370 Reirnan, P., 77 Reimer, G.D., 40 Reitan. R.M., 194 Reitman, J.S.,62, 113, 137, 320 Reitman Olson, J., 11, % Reuter, J. 143 Reynolds, J.R.. 113 Reynolds, R.f., 165, 323 Rich, S., 5, 72 Richards, J.M.,Jr.. 190 Richardson, A.B., 76, 81. 82 Rikli. R., 242 Rink, J.E..263, 265, 266 Ripoll. H.. 8.24.25.27, 111. 112, 113, 139, 140, 164 Rizdorf, ., 113 Robertson, S.. 5 Robin. A.F.. 87. 160. 182. 257. 258 Rockwell, T.H.. 320 Rodgers. W. M..99 Roland, P.E., 282 Rosenbaum, D.A., 17, 150. 301 Roth. C., 258 Roth, K.. 326 Rowe. J.W., 241. 243 Roy, E.A.. 45.49 Rubinson, H., 85 R a n g , R.O., 244.248 Rumelhart. D.E., 161. 165. 257, 320 Runeson. S., 327. 329 Russell,D.G., 112, 113, 114, 116. 117, 118. 121. 124. 139. 141, 142. 143, 144, 145. 164, 165,255, 260. 261. 339 Russell. E.M., 244, 248 Ryan, E.D..335 Saariluoma. P., I13 Sage, G.H.. 147 Salmela, J.H., 112, 141, 142, 145 Salmoni. A.W.. 35. 95, 99, 106 Salthouse, T.A.. 4. 5 , 8. 9, 31. 32. 242, 341 Saltzman, E.,215, 217 Salvendy, G., 231 Sanders, A.F. 114 Sauget. J., 194 Schmidt, F.L., 327 Schmidt, R.A., 20, 35. 36, 58. 70, 79, 152, 159, 163, 238, 274, 288. 290, 300. 301, 303, 305. 308. 309. 330, 331. 332, 337 Schneider, T., 5 . 319

Author Index

Schneider. W.. 309. 310. 327 Scholz, J.P., 305 SchUner, G.S.. 305 Schot, P.K.,49 Schueneman, A.L.. 190, 196.238 Schwerdt, ., 120 Seng. C.N., 24, 28, 140 Semen, D.J.. 305, 31 I Shaffer, L.H., 311, 320 Shaiman. S.. 206.207 Shallice. T., 343 Shank, M.D., 112, 164 Shapiro, D.C., 303, 310 Shaw. R.E.. 323,326, 332. 333 Shearer, D.E.. 244. 248 Sheppard, D.J., 153, 154 Shepherd, M.,115 Shephard, R.J.. 247 Shertz, I.. 320 Shiffrin. R.M.. 327 Shigeoka. J.W.. 244. 248 Shik. M.L., 327 Shlosser. H..40 Shortliffe, E.H., 319 Shulman, L.S.. 77. 79 Sidtis. J.J.. 196 Sidwall. H.G., 40 Siedentop, D.. 76 Siegler. R.S.. 161. 258, 335 Simon, D.P.. 320 Simon. H.A.. 7. 11, 12. 17, 18. 62. 78. 79. 111. 113, 137, 161, 255. 275. 319, 320, 322. 323, 335 Simonet. P., 113 Singer, R.N.. 319,330 Siwoff. S., 36 Skrinar, G.S., 75 Slater-Hammel, A.T., 22. 23 Sloboda, J.A., 3, 18. 320 Slotta. J.D.. 91 Smith, D., 104 Smith. 1.. 7. 8. 17. 18 Smith, R.E., 339 Snijder, W.. 111, 113 Snoddy, G.S.. 303 Soloway. E., 18, 160. 320 Sosniak. ., 102 Soulibre, D.. 112 Southard. D.L.. 44 Sparrow, W.A.. 319, 330 Spear,B.. 104 Spelke, E., 338

Motor Expertise

Spencer, F.C.. 190 Spencer, H., 307 Spielberger, C.D., 245 Spilich. G.J.. 112. 147, 166. 177. 255. 257, 259. 325 Spirduso, W.W.. 241. 248 Sprafka. S.A.. 79 Spurgeon. J.H., 112, 263. 265. 266 Stacey. C.. 243,250 Starkes. J.L.. 5, 6. 7, 19, 35, 36, 41, 96, 99, 109. 110. 112. 113. 124. 125, 126. 136. 137, 138, 139. 141. 142. 146. 147, 148, 151. 159. 164. 229, 230. 234. 235, 237, 258. 259, 260, 261, 319. 320, 322. 324, 328. 336. 340 Ste-Marie, D.M., 36 Stein, B.S.. 152 Stein. J-F.. 20. 25 Sleiner. I.D., 116 Steinke. T., 40 Stelmach. G.E., 150, 281. 331 Stephenson, J.M., 140 Stem,J.A., 320 Stems. H.. 242 Stevens. K.. 210,216 Stewart. D.M.. 57 Stiehl, J., 256 Stillings. N.A.. 319 Stillman, R.M., 190 Stone, M., 206 Stones, L.. 244 Stones, M.J., 241, 242. 243. 244. 245. 246. 247. 248. 250 Slumpner, R.L.. 22, 23 S u m z E.. 226 Sugden, D.A., 288, 334 Sully. D.J., 374 Sully. H.G., 374 Summers, J.J.. 31 1 Sundberg. I., 212. 213, 215 Sussman, H., 210, 216 Swanson, D.B., 79 Swanson, H.L.. 171 Swinnen. S.P., 44, 305, 306. 310, 311 Syndulko. K., 242 Szafran.J., 320 Taheri, M.A.. 79 Taylor, C.W., 190 Taylor. M.I., 110 Tenenbaum. B.. 135

371

Tenuolo, C.A., 301 Testerman. E.. 256 Teuber, H.L.. 10 Thiffault, C., 112. 151 Thomas, J.R.. 110, 135. 148, 149, 159, 165, 170. 171, 183. 184, 185, 255, 256, 257, 258. 259, 262. 263, 264. 265. 266, 317, 320, 321, 322. 325. 328. 329, 335. 336, 338 Thomas, K.T., 185,256,317,319,325,329, 336 Thompson, M.A.. 183 Thornson,J., 21 Thorndike, E.L.. 152, 303 Thomdyke. P.W.. 17 Tienson. J.L.. 13 Tompkins, R.K., 190 Tourtellotte. W.W.. 242 Trenholm. B.G., 40 Tukey. I.. 216 Tuller, B., 206, 206, 300, 332 Turvey, M.T.. 300, 326. 328, 329. 332, 333. 336, 343 Tyldesley. D.A., 112 Underwood, B.J., 273 Ungerleider, L.G., 282 van Dellen, T., 284 van Donkelaar. P., 40 van Ingen Schenau, G.J., 49 van Santvoord, A.A.M.. 319 van Wieringen. P.C.W., 21. 24. 25 Van Ernmerik, R.E.A., 329, 333 Van Outryve d’YdewaUe. G.. 112. 116, 124 VanLehn, K., 78 Vandendriessche. C.. 311 Vandervoon. A.A.. 242 Vanfraechern, R., 243 Vanfraechem, A.. 243 Vatikiotis-Bateson. E., 206, 207 Verdy J.P.. 111 Vereijken, B., 329 Verschueren, S.. 305. 306 Vesonder, G.T.. 147. 166,255, 259 Vickers. J.N.. 110, 113, 114. 115, 118. 157, 320 Viviani, P., 301 von Hofsten, C., 328. 329, 343 Voss, J.F., 112, 147, 160, 161, 166, 167,

372 170, 171, 177, 183, 255, 257, 259. 325 Vredenbregt, J., 238

Waber, D.P..194 Wade, M.G., 330 Walker, C.H., 166. 183 Wall. A.E.. 110, 322 Walsch. W.D., 111. 113 Walter, C.B., 35.44. 305. 310. 311 Wang. Y.. 85 Wangensteen. O.H.. 189 Wangensteen. S.D., 189 Wann, J.P., 287 Warren. W.H.. Jr., 323. 333 Webster, I.W., 242,243 Weimer. W.B., 343 Weiser. M., 320 Weiss, M.R., 256,258 Welford. A.T.. 35. 72 Weschler, D., 245 Westbroek, D.L., 226. 233 Westbury, J., 210 White, J.L.. 331 White, P.A., 161, 327. 328 Whiteside. T.C.D.. 153 Whiting, H.T.A., 20, 140, 318. 319. 328, 329, 330, 339 Whitley, J.D., 274 Wickens, C.D., 309.310 Wilberg, R.B.,6. 322 Williams, A.M., 147, 149 Williams, H.. 285

Author Indu

Williams. J.G., 147. 149 Willoughby, T.L., 190 Wilson. T.D., 326 Wilson, T.E.. 161 Wing, A.M., 281,286,290 Winter, D.A., 37. 39, 40. 47.48. 49. 50 Wmther, K.T.,256 Witelson. S.F., 194 Wolff, A.S., 320 Wood. J.S., 244, 248 Woollacott, M.. 285, 286.288 Womngham, C.J.. 281 Woman. P.M.. 79 Wright, D.L., 164 Xhingesse. L.V.. 150

Yates. K.E..154 Yekovich. F.R.. 183 YerlCs, M.. 115, 116 Yoshioka, H.. 208 Young, D.E. 152,31 I Young, R.P.. 40. 49 Zakrajsek. D.B., 1 5 Zanone, P.G., 305 Zelaznik, H.N., 303 Zimmerman, .. 206,208 Zminda, D., 30 Zurif, E.G.,194

Motor Eqertise

abilities, 190 abilities approach, 19. 20 acoustic skill,201 acoustic tube. 210-212 actions, 167, 168, 169, 172, 180, 182, 183, 184

advance cue usage, 116, 125, 136. 114 anastomosis, 228 articular phonology, 2 17 attention switching. 283-284 automaticity, 297. 307-309. 311 badminton, 141. 142, 167 baseball, 29-31, 165. 166, 167, 180. 181, 260

basketball, 96. 135, 137, 138. 150 baaing, 28. 184 bottom-up approach , 4 bridge. 137 categorical knowledge, 161, 162 chess, 137 chronometric approach, 114 chunk(ing), 116. 123 cinematography, 37 clinical diagnosis. 75. 79 closed loop, 5, 308 closed skills, 6. 36 clumsiness, 284, 288 cluster analysis, 96-98 coarticulation, 208-2 10 conditions, 168, 169. 184 connectionist theories. 12-14 consistency of performance, 7. 25 cricket, 143 cross-correlation, 43, 44 data smoothing, 40.4 1 decision-making, 135, 136. 142, 144, 146. 150, 151. 152, 160-165, 261-262

declarative knowledge. 73. 77, 95, 96, 103, 106, 110, 258, 325

declarative memory, 95. 103 depth of field, 229 depth perception. 103, 125 diagnostic knowledge, 76, 77, 78. 79 diving, 96, 99, 100 dynamic acuity, 109 dynamical / ecological theories, 332-334

373

dynamic perspective. 304-305 ecological perspective. 21 ecological validity. 328-329 event prediction. 141 exclusivity, 4 expertise (defhtions), 149 expertise approach, 8. 17. 18.27,28 expen behaviour, 6 expen / novice paradigm. 6 eye fixations, 114. 118 eye movement(s), 114. 115. 116. 118 factor analytic studies / approach, 274-275 feedback-based learning theories, 330-331 field hockey, 137, 143 figure skating, 96,W.100 f~ occlusion. I16 f w occlusion approach, 114 Fitts’ tapping, 237,239 football. 112, 137, 138 force control. 278 formants, 214 Go, 113, 137

goals, 160. 168. 169. 172, 181. 184 grasping, 44.45 gymnastics, 113 hardware, 70, 109. 124, 125, 127, hardware hypotheses, 146-147 heterotaxic variation, 297-298 hierarchical cluster analysis, 96 holistic process diagnosis, 91 homotaxic variation, 297-298 hurdle technique, 139 ice hockey, 142, 145, 146, 151 information processing approach, 21-24 information processing model, 160 interarticulator timing, 208 invariance, 300.301 joint angles, 41, 42, 43, 44 judging, 99-102, 103. 106

374 kinematics, 39,40,49, 50 kinetics. 39,48,49 knowledge base, 78,256 knowledge-based paradigms. 324-328 knowledge representation. 160-165, 167, 180. 185 laboratory engineered expertise, 7 learning plateaus, 3M, 303 lifestyle, 241. 243, 244 long term memory, 255, 257-258 mapping, 215-216 marking, 9 meta-cognitive knowledge, 324 microsurgeom, 225,229,230,236,237,238 microsurgery, 225-228,238 microsurgical training. 229-233 microwriting, 234-236, 237-238 modelling, 159 modular approach. 20. 21, 276, 285 module, 277-290 motor control. 26. 71,72. 203 motor expertise. 3 18-345 motor expens, 3. 10 motor learning. 5. 26 motor output, 5 motor pattern. 6 motor programming theories, 331-332 motor skills, 6. 27. 28. 35 movement control, 59-61. 63, 64,69 neuropsychology. 194 node-link structures. 166 open loop. 5, 308 open skills, 6 optoelectric system(s), 37, 38, 39 parallel pmssing, 309-31 1 Parkinson’s disease, 280.281 pattern recognition paradigm(s). 322-323 perception-action links, 21, 26, 149 perceptual abilities, 192, 193. 195 perceptual models, 163-164 perceptual-motor abilities, 193-195 perceptual processes, 158-159

Subject Index

performance. 190 performance variation, 300 peripheral visual range, 109. 125 pembation(s), 206, 207 phonology, 202.216 plasticity, 301-302 postural contml, 247, 248 power law of practice, 303 prediction errors, 144, 145, 146 procedural knowledge. 73.95. 110, 324 procedural memory, 95 proceduralization, 123, 126 p m s s i n g limitations, 4 production models, 263 production system, 110, 116 progression-regression hypothesis, 304 propositional knowledge, 324 protocol analysis, 10, 11 protocol stmcture model, 160. 166-167, 170, 171, 173, 184 proximity matrix, 96 psychomotor abilities, 57-59, 70, 73. 192, 195 psychomotor skills, 193 reaction time, 280, 281 recall. 258-259 recall paradigm, 11 1 recipient paradigm(s). 322-323 recognition paradigm, 11 1 referees, 103 remedialion. 288-289 representative tasks, 18 response prediction, 143-144 response selection, 259. 260 sampling rate, 37 schema theory. 303-304 schemata, 110 secondary aging, 241 self-reportdata, 327 skills. 190 signal detection. 136. 138. 139, 146. 150 signal detection approach, 114 simulators. 153-154 singers. 215 smoking, 243.244. 247, 248, 250. 251 soccer, 258, 261, 262, 266 software,109. 110, 124. 125 software hypotheses, 147-148

Motor Expertise spatial memory, 193, 195 spatial occlusion. 143. 144 Speech. 201-218 speech coordination. 205-208 speed of processing, 257. 258 spon paradigms, 165-166 squash, 142 static acuity. 109 static balance, 243 stimulus display, 150 strategic knowledge. 324 suengthening, 303 stress tolerance, 193. 195 subject generated knowledge, 167 surgical microscope, 228-229 surgical skill, 191, 193, 194, 195, 196, 197 suturing, 229-231. 237.239 swimming, 77, 87. 88. 89 symbol production, 201-202 symbolic connectionism, 12-14 table tennis, 20. 21. 25 telegraphy, 295-296 temporal, 39.49, 50 temporal occlusion. 141, 142 tennis, 113

375

t e r h y aging, 241

think-aloud p r ~ t o c o l ~161 , three dimensional (analyses system), 8 topdown approach, 4 TOPE model, 250 training, 7, 110. 116, 127 transfer of learning, 152-153 transport, 45,46

tuning, 123, 124 typing, 298 video, 37, 38, 39 video game performance, 57-59. 62, 63. 70, 71.72

visual information prumsing, 110, 111, 112, 113, 114, 126. 127

visual search patterns, 136, 139, 140 Vocal vacl, 203-205. 210-212 volleyball, 137, 143, 260 working memory, 255. 256-257

Youth sport. 255-267

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