Brain Literacy for Educators and Psychologists (Practical Resources for the Mental Health Professional)

Brain Literacy for Educators and Psychologists

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Brain Literacy for Educators and Psychologists

Virginia W. Berninger and Todd L. Richards University of Washington Seattle, HTA

ACADEMIC PRESS An imprint of Elsevier Science Amsterdam

Boston London New York Oxford Paris San Diego San Francisco Singapore Sydney Tokyo

This book is printed on acid-free paper. (~) Copyright 9 2002, Elsevier Science (USA) M1 rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Requests for permission to make copies of any part of the work should be mailed to" Permissions Department, Harcourt, Inc., 6277 Sea Harbor Drive, Orlando, Florida 32887-6777. Cover: Two functional magnetic resonance spectrocopy brain images from a child with dyslexia during a language task before and after an instructional treatment for dyslexia. Courtesy of the University of Washington Learning Disability Center Brain Imaging Team. Academic Press An imprint of Elsevier Science 525 B Street, Suite 1900, San Diego, California 92101-4495, USA http://www.academicpress.com Academic Press An imprint of Elsevier Science Harcourt Place, 32 Jamestown Road, London NW1 7BY, UK Library of Congress Catalog Card Number: 2001098829 International Standard Book Number: 0-12-092871-X PRINTED IN THE UNITED STATES OF AMERICA 02 03 04 05 06 MB 9 8 7 6 5 4 3 2

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To Merl Wittrock, Professor, University of California, Los Angeles His vision of brain-based education preceded the Decade of the Brain and inspired the early researchers in educational neuropsychology.

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Contents

Foreword

xvii

Acknowledgments

xix

PART I WHAT ED UCATORS NEED TO KNO WABOUT THE BRAIN Introduction and Conceptual Foundations Brain Literacy for Educators Linking Brain and Literacy Research Systems Approach to Brain and Literacy Instruction Nature-Nurture Interactions Biological Constraints on Academic Learning Educational Constraints on Literacy Learning Techniques for Studying the Brain and Brain-Behavior Relationships in Learning Levels of Analysis in Studying the Brain Comparison of Technologies Life Long Learning Making Connections

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3 6 7 9 11 12 13 13 15 15 17

General Principles of Microstructure and Microfunction Historical Background Microstructure of Brain Architecture

19 21

.o VII

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Contents

Microfunction of the Brain Mental Computations Underlying Mental Processes Recommendations for Further Reading

3

Introductory Level More Advanced Level

30 31

Making Connections

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General Principles of Macrostructure and Macrofunction Historical Background Macrostructure Global Appearance and Protective Features Structural Organizing Principles Mental Geography Geographical Expedition of Brain

Macrofunction Functional Organizing Principles Virtual Tour of the Brain at Work

Comparison of Technologies for Brain Analysis at the Macrolevel Specialized Vocabulary Computer-Assisted Tomography (CT or CAT Scan) Magnetic Resonance Imaging (MRI for Structural Scans) Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) Cognitive Paradigms in Functional Imaging Regional Cerebral Blood Flow (rCBF) Positron Emission Tomography (PET) Functional Magnetic Resonance Imaging (fMRI) Functional Magnetic Spectroscopic Imaging (fMRS) Electroencephalography (EEG) Event-Related Potentials (ERPs) and Evoked Potentials (EPs) Magnetoelectroencephalography (MEG) Comparison of the Imaging Tools

Recommended Reading For more information on neuroanatomy at the macrolevel For more information on macrofunction For more information on the brain imaging technologies

Making Connections

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26 29 30

33 34 34 35 44 46

51 51 61

63 64 69 70 70 70 70 71 71 72 72 73 73 73

74 74 74 74

74

General Principles of Brain Development Fertilization and Neurulation Fertilization

78 78

Six Neural Processes

78

Cell Proliferation Cell Differentiation

79 8O

Contents Cell Migration Synaptogenesis Cell Pruning Myelination

Neuromaturation Emergent Normal Variation In Macrostructure In Macrofunction

Nature-Nurture Processing Mechanisms Other Developmental Issues Different Developmental Trajectories Axes of Development Critical Developmental Periods The Role of Maturation Learning Mechanisms Importance of Tertiary Association Areas in Literacy Development Role of Socioemotional Intelligence

Development of Functional Systems Concept of a Functional System Participation of the Same Structure in More Than One System Functional Reorganization across Development Language by Ear, Language by Mouth, Language by Eye, Language by Hand

Neurological Constraints Postmortem Cytoarchetechtonic Studies In Vivo MRI Structural Imaging Studies DTI Structural Imaging Summary of MRI and DTI Structural Imaging Spectroscopic Imaging Electrophysiological Studies Drawing Conclusions about Neurological Constraints

Genetic Constraints Heritability and Aggregation Studies Segregation Studies Linkage Studies Biological Risk Rather Than Determinism

Recommended Readings Brain Development during the Preschool Years Genetic Constraints Neurological Constraints Functional Language Systems Socioemotional Intelligence

Making Connections

ix 80 80 82 82

83 84 84 84

84 87 87 87 88 90 90 91 92

93 93 95 95 96

96 96 98 100 100 101 101 102

103 103 103 103 104

104 104 104 105 105 105

105

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Contents

PARTII

5

LINKING BRAIN RESEAR CH TO LITERACY RESEARCH

Building a Reading Brain Neurologically Creating a R e a d i n g System from O t h e r Brain Systems Sensory Systems Motor Systems Aural/Oral Language Systems Cognition and Memory Systems Attention and Executive Functions Coordinating C o m p o n e n t Functions in Functional Systems In Vivo Functional Imaging Studies o f R e a d i n g Normal, Skilled Reading Comparison of Normal and Dyslexic Adult Readers Oral Versus Silent Reading Developing Readers Computational Processes Building a Reading Brain during Literacy Development Functional Components Protoliteracy Beginning Literacy Developing Literacy Mature Literacy Wiring the Reading Brain R e c o m m e n d a t i o n s for Further R e a d i n g Vision Language Cognition Attention/Executive and Memory Functions Reading Making Connections

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111 111 116 117 125 129 133 133 136 140 143 145 152 155 155 161 161 162 163 163 163 163 164 164 164 164 164

Building a Writing Brain Neurologically Dispelling Myths about W r i t i n g Not an Inverse or Mirror Image Not Merely a Motor Act Novice Writers Are Not Younger Expert Writers D e v e l o p m e n t a l Trajectory In Vivo Functional Imaging Studies of W r i t i n g

168 168 174 175 175 178

Contents Planning Sequential Finger Movements Executing Sequential Finger Movements Learning New versus Performing Practiced Finger Movements Linking Handwriting to Spelling Spelling Building a W r i t i n g Brain D e v e l o p m e n t a l R e o r g a n i z a t i o n of the W r i t i n g Brain Primary Grade Beginning Writers Intermediate Grade Developing Writers Junior High Developing Writers R e c o m m e n d a t i o n s for Further R e a d i n g Drawing Memory Processes in Writing Acquired Writing Disorders Handwriting Spelling Written Composition Making Connections

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xi 178 178 179 184 184 185 187 187 188 190 191 191 191 191 191 191 191 192

Building a Computing Brain Neurologically D e v e l o p m e n t of Quantitative T h i n k i n g Protonumeracy Counting From Concrete to Abstract Representation of Objects Part-Whole Relationships Multivariate Relationships Math by Hand Crosstalk between the Quantitative and Visual-Special Systems Computing Brain at Work In Vivo Functional Imaging Studies of M a t h Building a C o m p u t i n g Brain D e v e l o p m e n t a l R e o r g a n i z a t i o n o f C o m p u t i n g Brain Increasing Automatization Decreasing Dissociations Increasingly Abstract Conceptualization Recommended Reading Representing Mathematical Knowledge in Linguistic Codes Representing Mathematical Knowledge in Quantitative Codes Representing Mathematical Knowledge in Visual-Spatial Codes Acquired Arithmetic Disorders Math Development Making Connections

196 196 196 197 198 199 199 200 200 201 204 208 208 208 209 209 209 21o 21o 21o 21o 210

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Contents

PARTIII LINKINGLITERACY RESEARCH TO BRAIN RESEARCH

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Building a Reading Brain Pedagogically

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P e d a g o g y for Creating a N o v i c e R e a d i n g Brain Reading Material Model the Reading Process Develop Linguistic Awareness Create Multiple Connections Make Input and Output Units Visible in Multiple Ways Teach Strategies for Self-Regulation Practice Oral Reading until Automatic and Fluent Guide Comprehension Instill Positive Affect toward Reading Systems Approach to Beginning Reading

219 219 219 220 222 224 227 231 232 233 233

P e d a g o g y for Creating a D e v e l o p i n g R e a d i n g Brain Reading Material Teach Linguistic Awareness and Word Study Transition to Silent Reading Explicit Instruction in Comprehension Strategies for Transition from other- to self-regulation Reading-Writing Connections Affect and Habit I m p l e m e n t i n g R e s e a r c h - S u p p o r t e d Instructional Design Principles F r o m Debates to Wars to Collaborative P r o b l e m Solving Recommended Reading History of Reading Instruction Balanced Approach to Reading Instruction Language Processes in Reading Layers of Word Origin Phonological Awareness Orthographic-Phonological Connections Morphology Reading Comprehension Fluency Making Connections

233 233 236 236 237 239 240 240 240 241 244 244 244 244 244 244 244 244 245 245 245

Building a Writing Brain Pedagogically P e d a g o g y for Creating a N o v i c e W r i t i n g Brain Modeling, Strategies, and Guided Assistance Instructional Design Principles for Transcription Authentic Communication Goals Emotional and Motivational Context

251 251 252 255 255

Contents Pedagogy for Creating a Developing Writing Brain Monitor Automatization of Transcription Encourage Linguistic Awareness and Word Study Stimulate Cognitive Subprocesses Transition from Other- to Self-regulation Writing-Reading and Writing-Learning Connections Emotion and Motivation Integration of Technology with Writing Implementation o f R e s e a r c h - S u p p o r t e d Instructional Design Principles Future Directions R e c o m m e n d a t i o n s for Further R e a d i n g Handwriting Spelling Composition Integrating Writing Instruction and Technology Self-Regulated Writing Process Making Connections

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xiii 256 257 258 259 260 261 262 262 263 268 269 269 269 270 270 270 270

Building a Computing Brain Pedagogically Pedagogy for Creating a Novice C o m p u t i n g Brain Encourage Strategies Initially Foster Transition to Automaticity Model Place Value in Multiple Codes Teach Arithmetic Algorithms Link Computing with Language Systems Facilitate Math Problem Solving Pedagogy for Creating a Developing C o m p u t i n g Brain Explore Part-Whole Relationships Find Patterns Increase Speed of Operations Transition to Abstract Thought Foster Self-Regulation, Motivation, and Positive Affect Consider Curriculum Sequence for Logical Structures in Math Subdomains I m p l e m e n t i n g R e s e a r c h - S u p p o r t e d Instructional Design Principles R e c o m m e n d a t i o n s for Further R e a d i n g Conceptual Foundations Automatization Constructive Processes Integrating Verbal and Quantitative Systems Error Analysisand Assessment Research-based Teaching Tips Making Connections

278 278 280 282 283 283 283 284 284 284 284 286 286 287 288 292 292 292 292 292 292 292 293

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Contents

PART IV

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ED UCA TIONAL APPLICATIONS OF BRAIN-LITERA CY LINKS

Implicationsfor Educational Policy School Entrance Age Grade Repetition Early Identification and Intervention Progress Monitoring Educational Diagnosis Teacher Education Dealing with Biocultural Diversity The Educational Pendulum and Educating the Public Impact of an In Vivo Teacher Researcher: From Her O w n Classroom to all the Classrooms in the State Conceptual Approach to Teaching Reading Researcher and Teacher Trainer Impact on a State Recommendations for Further Reading School Entrance Grade Retention Prevention of Academic Difficulties though Early Identification and Intervention and Progress Monitoring and Diagnosis Teacher Knowledge Making Connections

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299 300 302 302 303 303 305 306 307 307 308 309 310 310 310 310 310 310

Implicationsfor Classroom Practice Teachers Implementing Scientifically Supported Teaching Practices Defining Brain-based Education Brain-based Instructional Design Procedures Brain-based Educational Assessment and Intervention True Accountability Recommendations for Further Reading Hope and Cautious Optimism Learning in a Social Context Motivation Teachers at Work in Their Classrooms Teaching Students with Learning Differences Teaching Phonological Awareness

314 315 319 322 322 325 325 325 325 325 325 326

Contents Teaching Word Skills Teaching Reading Fluency Portfolio Assessment Making Connections

x-v 326 326 326 326

Glossary

329

References

335

Index

369

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Foreword

Imagine all the teachers teaching well, experts in nature-nurture interactions, who fill the mind in individually tailored ways, guided by scientifically supported principles of the brain and instruction and by cultural sensitivity.

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Acknowledgments

This book is the result of the authors' ongoing collaboration in brain imaging studies and their joint workshop presentations to educators about the brain. In preparing the book, we drew on course material we each have used for over a decade in teaching neuropsychology to psychologists (Berninger) and basic neuroscience and brain imaging to medical and health sciences students (Richards). We added to this material a rapidly expanding research literature on brain imaging of living people that is relevant to reading, writing, and math. A brief history of the University of Washington Multidisciplinary Learning Disabilities Center (UWLDC) follows because the U W L D C provided the unique context for development of a textbook for educators about the brain. Early in the Decade of the Brain (1990-1999), which was mandated by the United States Congress to promote knowledge of the central nervous system, Dr. David Gray visited the campus of the University of Washington. Dr. Gray was then a Project Officer in the National Institute of Child Health and Human Development (NICHD). In that capacity he monitored a grant awarded by N I C H D to Dr. Berninger to study component processes in normal writing development and writing disabilities. During that visit he urged Dr. Virginia Berninger to organize scientists in multiple disciplines at her institution to study the biological and educational factors in learning to read and write. Following his visit, a group of scientists in Radiology, Medical Genetics, Psychiatry, and Educational Psychology began a dialogue that resulted in the establishment of the UWLDC. Drs. Marty Kushmerick and James Nelson in Radiology served as catalysts in this dialogue and in the resulting cross-campfis collaboration. The center conducts brain imaging, family genetics, and instructional and teacher training studies. Dr. Reid Lyon, Chief of the Learning and Behavior Branch of the NICHD, monitors this center, which is now in its sixth year of operation. He shares the UWLDC's commitment to scientific research on the educational and biological factors affecting literacy acquisition and to dissemination of the research findings to educators. xix

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Acknowledgments

The UWLDC is housed in a College of Education that collaborates with a School of Medicine at the same institution. Dr. Virginia Berninger has her primary appointment in Educational Psychology on the north campus, and Dr. Todd Richards has his primary appointment in Health Sciences on the south campus. We acknowledge the contribution of colleagues on both campuses to the ongoing efforts at the University of Washington to integrate the tools of multiple disciplines in research directed to improving the school learning of all students. In the College of Education, Dr. Robert Abbott directs the statistical core that assists all projects in the UWLDC with data analysis, Dr. Deborah McCutchen directs the project on the role of teacher knowledge in facilitating academic achievement, Dr. Susan Nolen directs the study on the role of motivation in learning differences, and Dr. Virginia Berninger directs the project on instructional interventions and serves as Director of the UWLDC and the related Writing Project. In the School of Medicine, Dr. Wendy Raskind directs the project on family genetics of reading and writing disabilities and serves as Co-Director of the UWLDC, and Dr. Todd Richards directs the project on brain imaging. The UWLDC brain imaging team also includes Drs. Elizabeth Aylward, David Corina, Stephen Dager, Kenneth Maravilla, and Cecil Hayes, and radiology technologists Denise Echelard and Jerry Ortiz. The UWLDC family genetics team also includes Drs. Ellen Wijsman, Jennifer Thomson and Li Hsu, and Ms. Diana Hoffer and Joan Waiss. Graduate students and faculty from seven departments on campus have participated in the UWLDC (e.g., Dagmar Amtmann, Allison Brookes, Laura Green, Sandra Serafini, Karen Vermeulen). The significant contribution of graduate students in school psychology is acknowledged in publications for disseminating research findings to school practitioners. In addition, the UWLDC has benefited greatly from the participation of a Visiting Scientist from Seattle Pacific University, William Nagy, who is an educational linguist. Drs. Katherine Vaughan, Robert Abbott, and Ms. Christina Johnson are key investigators on the related Writing Project. We emphasize the importance of multidisciplinary collaboration and a team approach for research on the brain and education. In the UWLDC we encourage participants to have mutual respect for different disciplines and not just for their own discipline. No one discipline or individual can have complete knowledge of complex human behaviors such as learning. Knowledge advances when individuals with different disciplinary perspectives work together. Dr. Richards's doctoral-level training in neurophysics (applying biology and physics to study of the brain) and extensive experience in brain imaging contributed to preparation of this book. Dr. Berninger's experience as a general and special educator, doctoral-level training in experimental psychology, and postdoctoral training and experience in clinical psychology and developmental neuropsychology also contributed. The book also reflects ongoing collaboration among Drs. Nancy Robinson, Robert Abbott, Wendy Raskind, Ellen Wijsman, and Virginia Berninger on development of mathematical talent among diverse learners. Originally begun with a Javits Grant from the United States Department of Education

Acknowledgments

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R206A20184, this research and related research on math have continued in other ways.

The idea for this book is the result of our conversations with Dr. George Zimmar of Academic Press, about our multidisciplinary experiences in the UWLDC. We are especially grateful to Dr. Zimmar for both his enthusiasm and substantive contributions to the development of this text about the brain for educators. We also thank anonymous reviewers and Christiana Leonard and Robert Abbott who read parts of the manuscript and made valuable suggestions. We also acknowledge the editorial contributions of Anya Kozorez and Angela Dooley, without whose thoughtful guidance and attention to detail, this book would not have reached its final publication. The material presented in this book also has benefited from discussion with colleagues in other institutions. Dr. Christiana Leonard at the University of Florida, Gainesville and Dr. Bernice Wong at Simon Frazier University, Vancouver, British Columbia were extremely generous with their time and enthusiasm for bridging the gaps between neuroscience and education. We also thank the following, many of whom have been consultants to the UWLDC for helpful discussions: Dr. Kenn Apel at Wichita State University, Dr. James Booth at Northwestern University, Chicago; Dr. Zvia Breznitz at the University of Haifa; Dr. Robert Calfee at the University of California Riverside; Dr. Joanne Carlisle at the University of Michigan; Dr. Anne Cunningham at the University of California at Berkley; Dr. Guinivere Eden at Georgetown University; Dr. Steve Graham at the University of Maryland; Dr. Michael Harm at Carnegie Mellon University; Dr. Louisa Moats at the Washington DC site of the Houston-based Literacy Project; Drs. Victoria Molfese and Dennis Molfese at the University of Louisville; Dr. Charles Perfetti at the University of Pittsburgh; Dr. Kenneth Pugh at the Haskins Laboratory and Yale University; Dr. Mark Seidenberg at the University of Southern California; Drs. Sally and Bennett Shaywitz at Yale University; Dr. H. Lee Swanson at the University of California; Riverside; Dr. Joseph Torgesen at Florida State University; Dr. Richard Venezky at the University of Delaware; Dr. Richard Wagner at Florida State University; Dr. Merl Wittrock at the University of California, Los Angeles; Dr. Maryanne Wolf at Tufts University; and Dr. Tom Zeffiro at Georgetown University. Special thanks goes to Dr. Barbara Foorman at the University of Texas, Houston Medical School for allowing us to interview her and showcase her accomplishments as an in vivo teacher researcher who combines instructional research, instructional leadership, and brain research; and to Dr. Joanna Uhry at Fordham University for sharing her Hello Friend/Ennis William Cosby Foundation continuing education model for teachers. We also thank colleagues at the University of Washington. The Virtual Reality Seminar led by Dr. William Winn and the Reading Seminar led by Drs. Joseph Jenkins and Sheila Valencia stimulated some of the ideas included in this book. Professor Emeritus Jack Beal sparked the interest in part-whole relationships as an explanation for some students' struggles with fractions and telling time.

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Acknowledgments

In addition, we express our deep respect for the role of teachers in implementing scientific research in the classroom. In particular we call attention to Jenifer Katahira, Seattle Public Schools, who has demonstrated over and over that outstanding teaching results in outstanding learning for students from diverse cultural and socioeconomic backgrounds; Jeannie Patten, Seattle Public Schools, who has shown that the Matthew Effect (those who start out as poor readers get poorer) can be reversed through collaboration between general and special educators; Robert Femiano, Seattle Public Schools, who has shown that aiming instruction at all levels of language is effective for students whose first language is not English and who are often inappropriately referred early in schooling to special education; and Dr. Mardean Francis, Everett Public Schools, who taught enthusiastically in the U W L D C Teacher Institute and demonstrated repeatedly that educators are eager to implement research-supported instructional practices. We also value the support we received from Dr. Beverly Wolf, former principal of the Hamlin Robinson School; Bonnie Meyer, current principal of the Hamlin Robinson School; and Anita Nason, past-president of the local branch of the International Dyslexia Association. These women, who are carrying on the work started many years ago in the Seattle area by Beth Slingerland on behalf of children with biologically constrained difficulties in learning to read, sustained us during the launching of the UWLDC. Finally, we acknowledge the efforts of others to create a field that bridges the gap between neuroscience and education. Dr. Maryanne Wolf has been a pioneer in applying brain research to reading. For more than a decade, Drs. Bruce Dunn and Suzanne Hidi in the Brain and Education Special Interest Group of the American Educational Research Association, and Drs. George Hynd, Stephen Hooper, and Margaret Semrud-Clikeman, leaders in clinical neuropsychology and school psychology, shared and nurtured the vision of a field of educational neuropsychology. The premature passing of Dr. Dunn has been a great loss to this emerging field. Todd Pdchards thanks his wife, Alicia, and family for their support during this project. Special thanks goes to Anne Richards who tracked down numerous journal articles in record time, in between counting brain pixels, and to Juliet and Carolyn Pdchards who posed for the brain imaging pictures in Chapter 3. Carolyn found the Calvin and Hobbes cartoon presented in Chapter 12. Virginia Berninger thanks her husband, Ron, for many years of fine-tuning her limbic system during the quest to integrate biology, psychology, and education in promoting school learning in diverse student populations. She also acknowledges the many children, parents, teachers, and other professionals from whom she has learned a great deal about the role of the brain in teaching and learning. We are grateful for two grants from the National Institute of Child Health and Human Development that supported the research discussed in this book and, in part, the preparation of the book. The first is an P,O 1, investigator-initiated award, RO1 HD 255858, in progress since 1989. The second is a Center Grant, P50 33812, in progress since 1995.

PART

W H A T ED UCA T O R S NEED TO K N O W ABOUT THE BRAIN

I

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Introduction and Conceptual Foundations

BRAIN LITERACY FOR EDUCATORS Teachers are entrusted with a noble profession--educating minds. It is ironic, therefore, that teachers are given no professional preparation about the brain. The mind is the brain at work. Other professionals who serve students with learning problems do receive professional preparation about the brain and nervous system: speech, language, and hearing specialists; physical therapists; occupational therapists; nurses; physicians; and audiologists. The main purpose of this book is to change this disparity in training by providing a textbook on the brain that is written specifically for teachers and other professionals in the field of education. With this purpose in mind, the goal is to introduce cognitive neuroscience as a conceptual foundation for educational practice. In Chapter 11 we explain how medicine became more effective when it was grounded in biological science rather than atheoretical medical procedures. We suspect that educational practice might also become more effective if grounded in a scientifically supported conceptual framework integrating neural, cognitive, linguistic, and developmental science rather than atheoretical pedagogical procedures. Thus this book can be used in preservice professional preparation programs and inservice continuing education for teachers. Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

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Brain Literacy for Educators and Psychologists

The book might be used as a supplementary textbook in a course offering in the area of literacy (reading and writing) or numeracy (literacy in the quantitative domain), or as the textbook in a course focused exclusively on the brain and education. However, prospective and practicing teachers are not the only intended audience. Neuropsychologists, school psychologists, and clinical psychologists who assess children with school learning problems may also find the book useful in translating assessment results into instructional interventions for reading, writing, and math. Neuroscientists who do basic research may also find the book helpful in considering the educational applications of their research, which are not always straightforward without a big picture view of what teaching and learning entail. For the educational psychologist who is interested in learning more about the brain's role in learning, the text offers an introduction to neuroscience in the context of more familiar educational research. Part II contains one of the few reviews of the rapidly expanding field of in vivo brain imaging studies related to academic learning. One book cannot cover all existing knowledge of the brain. The Decade of the Brain (1990-1999) generated a wealth of new information about the brain. Technological advances continue to fuel the knowledge explosion. Neuroscience is a cutting-edge field that is generating new knowledge rapidly. So much new knowledge is available that a synthesis of it is needed, especially from the perspective of reducing this knowledge to educational practice. Textbooks that focus on application ofneuroscience to learning or developmental disorders typically are geared to these disorders as they present in clinics in medical settings rather than schools. Often such textbooks include rare medical conditions, do not address brain functioning in normally developing students, and do not discuss effective instructional treatments for students with the more common learning disorders. In contrast, this textbook will focus selectively on those aspects of neuroscience that are most relevant to educators whose professional role is to teach academic subjects and manage behavior. In addition, it will focus mostly on learning and development in the school setting, and on normal learning and behavior, with some attention to the most common learning and behavioral disorders. Throughout the book we adopt a developmental perspective m the process of learning reading, writing, and computing over a relatively long time window m r a t h e r than the traditional perspective of adult clinical neuropsychology on the loss of previously acquired functions. The aim is not to transmit a wealth of facts to be memorized. Rather, the goal is to present sufficient information, along with general principles, so that teachers can understand and apply these concepts as problem-solving tools. For example, teachers with knowledge of both facts and general principles about the brain should be able to (a) troubleshoot why a student may be having difficulty in learning an academic skill or behaving in an age appropriate way and (b) generate alternative instructional approaches for improving learning or behavior. Teachers with a grasp of general principles can also be critical consumers of the evolving field of neuro-

Introduction and Conceptual Foundations

5

science. Although factual information is increasing rapidly and can quickly become outdated, the general principles are more likely to survive the test of time. Caveats about the limitations of current tools and knowledge in neuroscience will also be discussed. At the same time, neuroscience, just like other disciplines, has specialized vocabulary. To understand the concepts, one must learn the technical vocabulary. The text aids the reader in learning this new vocabulary in three ways. First, each technical term will be defined the first time it is used in the text. Second, technical terms also are included in the glossary so if they wish, readers can refer, to definitions when terms appear again in the text. Third, information about word origin is provided to make the words seem more meaningful than they appear at first glance. Learning new technical vocabulary can be a daunting experience, but is also an excellent exercise in perspective-taking. Readers may understand what some students experience when given texts that are full of unfamiliar words. The text also aids the readers in four ways in the task of using this new vocabulary to learn new concepts. First, Part I provides a short overview of knowledge of brain structure, brain function, and brain development that is needed to understand the current brain imaging research in Part II. Second, each chapter has figures and tables to illustrate information both graphically and verbally. Sometimes a great deal of complex information is summarized in a table to prevent the reader from getting bogged down in detail in text and to serve as a reference in the future. We provide some level of detail but emphasize the general principles underlying the detail. Third, readers are encouraged to complete the recommended coloring activities in The Human Brain Coloring Book (Diamond, Scheibel & Elson 1985). This hands-on approach to learning neuroanatomy was developed for medical and other students. Completing designated units in that text aids in integrating names and visual representations of brain structures. Although this approach to learning neuroanatomy is not as direct as actually dissecting the brain of a person who died, it is nonetheless an eflqcient way of learning the structures that have the most educational relevance for school learning. Finally, throughout the text, references are made to relevant material later in the text or to prior text, tables, or figures. These references (with markers of where in the text the future or past material will be) should help the reader make connections between concepts, much like brain function depends on making connections between processes distributed throughout it. Although the density of information may sometimes be overwhelming, many teachers who attended workshops on brain literacy conducted by the authors have thanked them for making this information available to them m and in a way that enables them to join in the conversation with other professionals who have had more professional preparation related to the brain. We recognize that Part I may require a different approach to learning than ordinarily is required in preservice or continuing education for teachers. However, our experience in presenting this material in formal courses is that teachers are willing to take the time and effort to

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Brain Literacy for Educators and Psychologists

learn the educationally relevant aspects of neuroscience and are eager for more information on this topic. In fact, educators are among the star students who fulfill expectations for rigorous academic performance when the level of expectation is set high.

LINKING BRAIN AND LITERACY RESEARCH The title of the book is a double entendre. On the one hand, the aim is to give teachers knowledge about the brain. On the other hand, the aim is to increase their knowledge of recent research on effective instructional practices in literacy. That is, the overall goal is to increase teachers' literacy both about the brain and instructional practices for reading, writing, and math. Although literacy traditionally referred just to reading, contemporary use of the term has extended to writing and mathematics. Sometimes literacy in mathematics is referred to as numeracy. In this text we refer to it as computing, which includes low-level calculation (arithmetical operations) and high-level problem solving. To achieve the overall goal, the links between the brain systems and the components of literacy instruction are emphasized. This synthesis of brain research and of literacy research is done from the perspective of their implications for classroom practice, as discussed in Parts III and IV. However, as already discussed, the material provides a conceptual framework and scientifically supported instructional design principles, which teachers can use to design and modify their own instructional programs to meet individual learning differences among students. Neural science does not yield teaching methods or procedures to impose on students in the classroom without taking into account a good deal of educational and psychological research on learning and teaching over the past century. To demonstrate the relationship between the brain and teaching reading, writing, and math, Parts II and III are closely linked. The first chapter in Part II discusses how to build a reading brain neurologically, and the first chapter in Part III discusses how to build a reading brain pedagogically. Likewise, the second chapter in Part II discusses how to build a writing brain neurologically, and the second chapter in Part III discusses how to build a writing brain pedagogically. Similarly, the third chapter in Part II discusses how to build a computing (math) brain neurologically, and the third chapter in Part III discusses how to build a computing (math) brain pedagogically. The math brain is referred to as the computing brain rather than calculating brain because math involves more than low-level calculation. The emphasized general principle is that each of the effective instructional components has corresponding brain structures and functions to support them. In selecting which research studies on the brain and on education to highlight, we tried to achieve a balance between the original, pioneering work in the field and examples of cutting-edge research in progress. However, it was still necessary, due to space limitations, to be selective and representative rather than comprehensive. From time to time, we also draw on our own research to illustrate the unfolding

Introduction and Conceptual Foundations

7

process and challenges of conducting brain research on educationally relevant issues. We indicate when a consensus seems to be emerging based on replicated research findings across research groups, and also when there is still controversy or the need for further research. This perspective is needed so that research is perceived as a work in progress rather than yet another "authority" to be accepted uncritically. We also discuss the historical development of research on the brain and on education in terms of the major research questions fueling these fields of inquiry at different times in history. Both fields of inquiry have been constrained by the technology and research tools available, as well as social and political issues, at different historical periods. We discuss the strengths and limitations of our current research tools and emphasize the advantage of using more than one kind of tool. We also mention, when relevant, the kind of sample studied m animals, normal humans, humans with learning differences who are otherwise healthy, or humans with medical conditions mbecause sample characteristics limit generalizability of findings. Although the main focus is on brain systems and teaching practices related to reading, writing, and math, other brain functions related to classroom learning, such as working memory and executive functions for self-regulation, are also covered. Working memory is a mechanism for temporary storage of incoming information and/or internally generated ideas while processing tasks are carried out. Self-regulation applies to many functions including attention and level of activity. Children with severe problems in self-regulation of attention may be diagnosed as having attention deficit disorder (ADD), whereas children with severe problems in self-regulation of their behavior and activity level may be diagnosed as having attention deficit/hyperactivity disorder (ADHD). Other children have these problems to varying degrees. Children who have problems in working memory or self-regulation of attention, neither of which can be directly observed, have invisible handicaps that may mask their ability to think. Children who have problems in self-regulation of activity level have visible handicaps that affect their observable behavior in learning environments. Both working memory and self-regulation may affect the ease with which children learn to read, write, and compute. We provide information that should help educators understand children who have been diagnosed with ADD or ADHD, but mainly focus on how attention, memory, and self-regulation play important roles in the learning of all students.

SYSTEMS A P P R O A C H TO B R A I N A N D LITERACY INSTRUCTION The conceptual perspective offered by this book is unique in that it is grounded in a systems approach in which many different components and their interrelationships are considered. In traditional science, investigators focus on one variable at a time,

8

Brain Literacy for Educators and Psychologists

and try to rule between mutually exclusive alternative causal mechanisms in explaining that one variable. In the traditional scientific method, scientists conduct experiments in which one or more variables are varied systematically and other variables are kept constant. This traditional scientific method continues to generate new knowledge, but increasingly, scientists are turning to multidimensional research approaches that investigate the interrelationships of multiple variables that operate in complex systems (Gallagher & Appenzeller 1999). Increasingly, scientists are recognizing that human beings are complex biological systems (with many moving parts) who live in complex social and cultural systems. This recognition is posing new challenges for how we go about doing science. Teachers face a similar c h a l l e n g e - how to integrate many different instructional variables to help students learn within complex classroom systems. Instruction cannot be reduced to a single, unitary method that teachers "do" to students. Unlike a computer programmer, teachers cannot directly program students' minds. Teachers only can provide instructional hints. These hints have multiple components and need to be delivered in ways that "package" the hints to help students create functional systems for learning and performing in the classroom. To complicate the task, learners vary in how effectively they use these cues to learn. Their brains can affect how effectively they use instructional cues. To add a layer of complexity, brain systems--not a single structure but a set of structures--affect how learners use these cues, and learners may vary in how well the components of their brain systems work together. Consequently, both the systems of functional parts inside a student's brain/mind and the classroom system in which instructional hints are delivered affect the learning process. It follows that it is not always a straightforward process to translate scientific research about a single causal mechanism in learning to the complexities of brain systems of learners and instructional systems of teachers as they interact dynamically with students in classrooms. Furthermore, teachers typically are not given sufficient preparation in how they might optimally construct learning environments, from the perspective of working systems in which the minds of teachers and students interact. In Parts I and II we lay the groundwork for the complexities of systems of brains and minds at work and in doing so draw on the work of a Russian neuropsychologist, A.R. Luria (1973), who introduced the notion of functional systems of a brain at work. However, Luria based his conclusions on study of individuals with brain damage, whereas we base ours on study of normally developing individuals with and without learning differences and not on those with brain damage. Although Luria did not study the processes of teaching and learning academic subjects in the same depth or setting as contemporary researchers in many disciplines do, we credit Luria with the fundamental insight that multiple brain structures may be involved in one function and that the same brain structures can participate in more than one functional system. In Parts III and IV we offer research-supported approaches to applying the notion of functional systems to instruction. Our approach is to integrate the

Introduction and Conceptual Foundations TABLE 1.1

Major Components of Functional Systems for Academic Skills Reading

Low-l.mvel High-I_~vel

9

Word recognition Comprehension

Writing Transcription Composition

Math Calculation Problemsolving

teaching of low-level and high-level skills close in time so that they function in concert. Table 1.1 lists one low-level and one high-level component skill in each of the functional systems for reading, writing, and math. This approach goes beyond balance as to which components are included in instruction, to consideration of how they are coordinated in time, much as a conductor has to coordinate multiple musicians in the orchestra to function together. The functional brain system approach is compatible with the social constructivist theoretical orientation of current instructional research, which was inspired by another Russian psychologist, L. Vygotsky (1978, 1982, 1986). This theoretical orientation has three key tenets. First, learning should be studied by tracing development over time. Second, learning occurs during social interaction when the social exchange is internalized or represented in the learner's mind. Third, the mechanism that drives learning is the nudging of learners along their zones of proximal development, marked on one end by level of independent problem solving and on the other end by level of performance with guided assistance (scaffolding) by an adult or more able peer. Vygotsky rejected the single-factor explanations of development. Like Luria, his pupil, he viewed higher mental processes as functional systems. He thought that scientific child psychology should be based on a solid biological foundation, but that mental development could not be reduced to biological fact0rs--both biological and social factors are needed to explain mental development (Wertsch 1985). Vygotsky and Luria (1930) proposed that behavior should be studied within the context of evolutionary, historical (cultural), and ontogenic (individual) development, each of which has its own unique set of explanatory principles. The Luria-Vygotsky research partnership, with each focusing on a different side of the nature-nurture equation, serves a role model for acknowledging the nature-nurture interaction in learning to read, write, and do math. Considerable research is needed, however, before we can specify more precisely and fully the mechanisms underlying nature-nurture interactions in learning academic skills. The discussion that follows pertains to developmental disorders in which a child struggles to learn to read, write, or do math. It does not pertain to acquired disorders in which previously learned skills are lost due to injury, stroke, or disease. NATURE-NURTURE

INTERACTIONS

Some educators and educational researchers resist or reject the role of the brain or genes in learning and behavior. One reason for this position may be that they equate

10

Brain Literacy for Educators and Psychologists

the brain and genes with biological determinism. They believe that neurological explanations are equivalent to claiming that brains and genes determine destiny. In reality, only in special instances are learning and behavior completely determined by biological influences. For example, environmental factors cannot reverse destruction of brain structures and loss of brain functions associated with certain genetic conditions (e.g., muscular dystrophy), medical diseases (e.g., Huntington's chorea), or traumatic injury (e.g., severe head injury). In most cases, however, such extreme biological conditions are not operating, and biological variables may constrain (exert influences of varying degree), but not completely determine, learning and behavior. Instructional variables and other environmental factors also influence learning and behavior. Constraint refers to how many degrees of freedom there are. Consider, for example, the degrees of freedom operating in color choice when buying a new car. Seldom, if ever, is a specific model for a given manufacturer available in any color (full degrees of freedom--full choice). Each year each model is available in a small set of color options (constrained degrees of freedom--limited choice). Some models have more color options than others (variations in degrees of constraint operating). If color is the priority in selecting which model to buy, there will be constrained degrees of freedom (some models are out because they are not available in a specific color); however, model selection is not fully determined because usually there are two or more models available in a particular color (some constraints but also some options). In full biological determinism there are no degrees of freedom (only one color choice and only one model). If all biological influences fall within the normal range and do not significantly constrain learning, students may be able to learn well despite variations in the instructional environment. Nature does not interfere with nurture for these robust learners who may succeed in different kinds of learning environments. If, however, biological constraints are operating, the nature and severity of these constraints can influence whether students learn in specific instructional environments. The degree to which biological constraints are operating will affect the degree to which individually tailored instructional environments are needed for learning to occur. Individually tailored environments are not necessarily one-to-one tutorials, and they can be created in group settings. Some students with biological constraints on their learning can learn but may (a) require more explicit, systematic, intense, and sustained instruction than classmates; and (b) struggle more or have to work harder than classmates to learn an academic skill. In the case of substantial biological constraints, the reasonable level of student learning outcome may have to be adjusted. Nature alone seldom determines learning outcomes m nurture is also determining and becomes even more important when biological constraints are operating.

This general principle of nature-nurture interactions stands in direct contrast to biological determinism; it means that the brain is both an independent (causal) variable and a dependent (outome) variable (Berninger 1994; Richards et al. 2000). That is, brain variables exert influences on learning, but under some circumstances

Introduction and Conceptual Foundations

11

also may be changed, to some degree, in response to instruction. Put another way, the brain is an organ that allows an organism to act on the environment and that can be changed in constrained ways as the organism interacts with the environment. This general principle of nature-nurture interactions will be emphasized for four reasons. First, teachers are an important influence on the nurture side of the naturenurture equation for literacy learning. Second, teachers are held accountable for student learning outcomes, but teachers are not the only variable influencing these outcomes. Inherited learning differences may also exert constraints on learning outcomes. When these biological constraints are operating, they need to be acknowledged, and expectations for student learning outcome and teacher accountability must be modified appropriately. Such modifications do not eliminate high expectations for learning but rather adjust those expectations realistically on an individual basis. Third, research does not support the myth that students who initially struggle in literacy learning will learn to read and write magically when biological maturation renders them ready for literacy learning. The nurture side of the nature-nurture equation is just as, or even more, important in the early stages of literacy learning. It is just as wrong to overemphasize, as it is to ignore or reject, the nature side of the nature-nurture equation. Fourth, some educators believe that an emphasis on the biological factors in learning is incompatible with an emphasis on the multicultural factors in learning. In reality, both these emphases acknowledge the importance of diversity in teaching and learning and are not mutually exclusive. In fact, much of the available research on biodiversity that is reported in Part II is based on culturally diverse samples from around the world, including Asia, Northern and Southern Europe, the Middle East, and the Americas. Both investigators and participants in brain imaging research include people of color. In the last chapter the implications of nature-nurture interactions for educational policy and classroom practice will be explored, with a special focus on improving the education of learners from diverse cultural backgrounds.

BIOLOGICAL CONSTRAINTS ACADEMIC LEARNING

ON

There are at least two sources of biological constraints in learning to read. One source of biological influence is genetic. Often genetic constraints just place the student at risk, meaning that the quality of the instructional environment may be more important than it is for those students who have not inherited risk factors. The number of inherited at-risk factors may influence the degree to which the student struggles with reading or writing or math. For this reason Part I includes a brief review of research on the genetic constraints on literacy. However, even though there are genetic influences operating on learning to read, the contribution of environmental influences may be as great or greater (Olson, Forsberg & Wise

12

BrainLiteracy for Educators and Psychologists

1994). Moreover, genetic influences may constrain in the direction of talent as well as disability (Chorney et al. 1998). Some students learn academic skills with ease and reach levels well beyond most of their peers. Genetic influences are also the source of normal variation in mental processes and abilities within the same student and across students. Learning differences that are intraindividual (within learners) and interindividual (between learners) should not necessarily be equated with learning disabilities. This general principle of normal variation among all students (Berninger 1994) will be emphasized. Its significance for educational policy and classroom practice will be discussed in Part IV. Another source of biological influence is neurological. Shortly after fertilization of the egg, the fetal brain and nervous system begin to develop. The neural cells increase rapidly in number and migrate to form brain architecture. Genes are like road maps in directing this neural migration (Barnes 1986). Like road maps, genes provide general guidelines for the journey, but neural cells, like all travelers, are at the mercy of events along the way that are outside the traveler's control (e.g., traffic accidents, construction delays, etc.). Neuroscientists refer to these events as stochastic processes, which are probabilistic events influenced by many factors, including chance. Typically, the combination of gene instructions and stochastic processes results in normally developing brains. Occasionally, severely damaged brains result from gene mutations (abnormalities), stochastic processes, poisons from environment, or adverse pregnancy or labor events. In other cases, neural migration proceeds normally for the most part, but minor anomalies (differences that are not damage) in how the brain is wired result in learning differences. Brain anomalies make it difficult, not impossible, to learn specific academic skills. Part I contains a chapter on brain development that reviews research on these neurological influences on learning language. Although this book focuses on the biological factors in normal learning rather than rare neurological conditions, some of the research results are based on individuals with learning differences that have a biological basis (e.g., dyslexia). Their learning differences provide clues to what brain structures and functions are necessary for normal learning, which is disrupted in a specific way if those structures and functions differ in some way. Also, in Part IV we discuss the kinds of biologically based learning differences teachers are most likely to encounter in the general education classroom.

EDUCATIONAL CONSTRAINTS LITERACY LEARNING

ON

A major educational constraint is that teachers often do not get sufficient preparation at the preservice level for the enormously challenging task they face in helping all students reach their expected level of reading and writing (Berninger 1994). This task is challenging not only because of cultural diversity but also because of

Introduction and Conceptual Foundations

13

biodiversitynboth cultural diversity and biodiversity contribute to the normal variation among learners. Another educational constraint is that schools are not organized to take into account the developmental window in which beginning literacy skills are most easily acquired n the K-2 period (see Chapter 4). All too often children must fail for a long time before the school realizes the extent of their difficulties ~ often in third grade or later. Many schools are beginning to make progress in providing more early intervention and prevention, but much work remains to institutionalize early identification and intervention for literacy problems in all schools. Yet another educational constraint is the myth that, just as children learn speech rapidly (usually by age 3), literacy skills can be mastered in the first three grades. The reality is that, in an increasingly technological society, expected literacy outcomes require instruction and practice over a much longer developmental window (K-12 as a minimum and often college and graduate training for some literacy goals). The unfortunate consequence of this myth is that many students do not receive explicit literacy instruction throughout schooling geared to the unique requirements of reading and writing in specific academic content areas, each of which has unique vocabulary, background knowledge, genres of discourse, and learning strategies. Literacy instruction is needed throughout schooling and not just in the beginning stages of learning to read and write. Parts III and IV consider, from a developmental perspective, the implications of contemporary research on both the brain and literacy for instructional practices throughout K-12 schooling.

T E C H N I Q U E S FOP,. S T U D Y I N G T H E B R A I N A N D BRAIN-BEHAVIOR RELATIONSHIPS IN LEARNING

Levels o f Analysis in Studying the Brain The brain can be analyzed at many levels. As illustrated in Table 1.2, the brain can be analyzed at the levels ofmicrostructure and macrostructure. Microstructure refers to the very tiny (microscopic) units of analysis. Macrostructure, on the other hand, refers to the larger units of analysis, which are sometimes, but not always, visible to the naked eye. Electrochemical activity of neurons and their connections are of critical importance for learning and behavior at the level of brain microstructure. General principles of neuron structure and connectivity therefore will be reviewed in Chapter 2. However, current technology does not lend itself readily to scientific investigation at this level of analysis in human learning. Thus, the microstructure and microfunction of the brain will be introduced, and recommendations will be made for further reading on research at this level of analysis, much of which is based on animal models and technical knowledge in molecular biology. However, the main focus will be on analysis of the brain at the level of macrostructure and macrofunction for two reasons. First, current research knowledge on language,

14

Brain Literacy for Educators and Psychologists

reading, writing, and math in living people is most appropriately discussed at this level. Second, it is at this level of analysis that the links between the brain and instructional practice are clearest. Still, it is the case that complex and very specific brain structures and activities at the microlevel contribute to the macrofunctions in brain systems (Kandell, Schwartz & Jessel12000). Someday research tools may exist that permit study of h o w the microstructure and microfunction of the brain contribute to the learning of academic skills. The macrostructure of the brain has many landmarks that have been given names. Learning the names of these landmark structures is like learning brain geography. Like the earth's geography, these structures are continuous and do not have clearcut boundaries. Rather, the names are based on typography ( c o m m o n kinds of neurons and their placement in the multiple spatial dimensions of the brain's architecture), their geographic proximity in the brain's architecture, or c o m m o n functions (structures that are activated by similar kinds of sensory input, m o t o r output, or cognitive tasks). However, understanding h o w the brain works requires more than k n o w i n g these verbal labels, which are not explanatory mechanisms. For one thing, these structures may participate in more than one functional system, and their function at any one m o m e n t may depend on which system or systems may be activated at the m o m e n t . For another thing, it is likely that these neural systems may undergo functional reorganization over the course of development (Luria 1973). A general principle for understanding the brain at the macrolevel (see Table 1.2) is that the spatial dimensions of the brain and its functional mechanisms can be analyzed in terms of three axes: front+-~back, top,--~bottom, and left~--~right. This theme will be emphasized in Part I. The research that identified differences b e t w e e n the right and left hemispheres of the cerebral cortex will be discussed

TABLE 1.2

Levels of Analysis in the Brain/Mind a

BRAIN A1KCHITECTURfE Microstructure Chemical molecules

BRAIN FUNCTION Microfunction Chemical activity of single molecules

Neurons (cells)

Chemical/electrical activity of single neurons

Neural connections

Transmission and reception of electrical signal between neurons

Macrostructure Large collections of neurons (organized along the bottom-up axis the right-left axis the back-front axis)

Macrofunction Electrophysiology Metabolic activity Blood flow Chemical activity Functional systemsfor mental activity

aStructure (left side) and function (right side) may be related but not in a one-to-one fashion.

Introduction and Conceptual Foundations

15

briefly. However, we emphasize that research has not supported the notion of learning styles for academic subjects (Stah11999). We explain why, based on research, there are no learning styles for sensory modality (visual versus auditory versus kinesthetic) or for side of the brain (right versus left). An alternative approach for thinking about learning differences is proposed, in which functional systems for reading, writing, and math draw on component processes throughout the brain and not just on the fight or left side. Each child has a profile of abilities for many processes, some of which are sensory-based but most of which are sensory-free and abstract (see Chapters 3, 4, and 5). Children vary within their own profile and from one another in the relative patterning of strengths and weaknesses in these processes. Despite these intra-(within) and inter- (between) differences due to normal variation, all children benefit from instruction aimed at all the necessary component processes for a functional system at a particular stage of development. In Part III we make practical instructional recommendations for translating these general principles based on functional systems into classroom practice.

Comparison of Technologies Early research on brain-behavior relationships relied on correlations between observed behavior of patients before they died and their damaged brain structures observed during autopsies. This approach had the disadvantage of not being able to observe the working brain as it performs specific tasks. Recent technological advances have resulted in a number of tools that can be used to study brain functions while living people perform specific tasks. Part I discusses the technologies used historically as research tools to study the brain's structures and functions at both the microlevel and macrolevel. Any one of these technologies provides important, but incomplete, information about brainbehavior relationships. Together, these technologies may give a fuller understanding of brain structures and their functions for biological substrates at different levels of analysis. However, most of the brain research reported in Part II is based on functional imaging technologies that study the brain at work. Part I contains a tutorial with information about how each of these structural and functional imaging techniques can be used to study literacy and numeracy. This tutorial compares how these technologies differ in the biological substrate they assess, their spatial resolution, their temporal resolution, their invasiveness (e.g., exposure to radioactivity), and their cost.

LIFE L O N G L E A R N I N G Educators' professional development begins with preservice course work and student teaching, but should continue throughout their professional careers.

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BrainLiteracy for Educators and Psychologists

Recommendations for further reading are included at the end of each chapter for those readers who wish to deepen their knowledge of the topics in a particular chapter. In particular, Eliot's (1999) and Diamond and Hopson's (1998) books on brain development during infancy and the preschool years might be read along with this book to gain a developmental perspective on the brain prior to school entry. That perspective will provide a conceptual foundation for the subsequent brain development during middle childhood when a major developmental task is becoming literate. At the same time, we explain in Part I why teachers can still make a difference during the school years, even in the case of children who had less than optimal environmental stimulation during the preschool years (see also Thompson & Nelson 2001). All too often, continuing education for teachers relies exclusively on in-service workshops rather than graduate-level courses for academic credit. This model of inservice education is inadequate for many reasons, but, in the case of brain-based education, it is especially problematic because these workshops often are not taught by individuals who are themselves brain researchers with access to the current knowledge in the field. There is often a long lag until that information trickles down to practitioners, and, in the process may become distorted--sometimes, but not always, because of the spin put on it by the media, who enjoy a good story but are not necessarily bound by the limits of the scientific evidence. It is possible that some of what is being presented as "brain-based" may not be grounded in the most up-to-date scientific knowledge of the brain. As is true for all consumers, the buyer should be cautiously aware that what is packaged as "brain-based research" may not have been adequately evaluated for its educational applications. For this reason, in this book we include only research that has been published in peer-reviewed journals, or other outlets in which researchers with established reputations in peer-reviewed research have been invited to share their work. A unique feature of Part IV is a summary of how the brain-based educators may think differently than those without research-supported knowledge of the brain. We also emphasize that the fields ofneuroscience and educational neuropsychology are not yet to the place where we can go directly from brain scan to lesson plan. Nevertheless, we believe that critical consumption of the rapidly growing neuroscience findings may help teachers become more thoughtful about instructional practices and learning differences. The time has come for a book that synthesizes brain research and literacy research--in which the contribution of each field is dually recognized on equal footing. However, teachers need to be thoughtful, critical consumers of the information generated by this emerging field. Thus, at the end of each chapter we offer a section, "Making Connections", which poses questions. Some questions urge readers to go beyond the material stated and reflect upon it. Some prompt readers to integrate information in the chapter with their background knowledge. Some encourage readers to synthesize the information and create a schema for organizing it in their minds. These questions can be answered individually in personal reflections or shared reflections during group discussions.

Introduction and Conceptual Foundations

17

A theme in this book is that the relationship between the fields of brain science and education must be bidirectional (Berninger & Corina 1998). Not only may neural science inform education, but also education may inform neural science. On the one hand, the information, general principles, and caveats in our book may enable readers to become critical consumers of a rapidly growing neuroscience research literature. On the other hand, education may contribute by helping neural scientists flame the kinds of questions that should be asked to generate a body of research information about the brain that is educationally relevant. Some educators may also participate in research that combines teaching and brain imaging by comparing students with specific problems in literacy acquisition before and after specific kinds of instructional intervention. Teaching is an important part of the experimental design in such research on nature-nurture interactions (Pdchards et al. 2000). Educators who keep abreast of the rapidly expanding research literatures on brain and on literacy will have the necessary knowledge base for becoming leaders in designing instructional practices and influencing educational policy (see Part IV) that are supported by science.

MAKING CONNECTIONS Questions preceded by* may be most appropriate for graduate students. 1. Did any of the ideas in Chapter 1 surprise you? If so, which ones? For each idea that does not fit into your existing conceptual schemes, make a point of looking for research evidence in the chapters that follow to support the authors' claims. When you finish the book, evaluate whether there is evidence to support their claims on each of these points. Also at that time, assess how your thinking may have changed as a result of reading this book. 2. What are some other examples, besides the ones given by the authors, to illustrate the difference in meaning between determinism and constraints in explaining human behavior? 3. How do you think nature and nurture might interact in learning to read, write, and do math? 4. What might be some alternatives to learning styles in explaining the individual differences among students in the ways they learn? 5. Can all individual differences in a classroom be eliminated? Is biodiversity as important as cultural diversity in explaining learning differences? Within cultural groups, could biodiversity also account for important individual differences in how students learn? 6. H o w important is research evidence for identifying effective instructional practices? How might educators decide which instructional methods to use if there is not a body of scientific knowledge about instruction? Is research evidence based on studies inside or outside the classroom sufficient? Do we also need research on

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Brain Literacy for Educators and Psychologists

how to implement research-supported instructional practices in real-world classrooms? What is the difference between science and engineering? Are there parallels between those distinctions and the learning sciences and education? *7. If teachers cannot directly program students' brains through teaching, why do they need to know about the brain? Are there advantages to learning systems in which teachers cannot directly program brains? How might brains self-program their minds to learn? In what ways might instruction be important to this indirect process of self-programming?

General Principles of Microstructure and Microfunction

HISTORICAL

BACKGROUND

Scientific advances are often preceded by long periods of controversy, and scientific research on the brain is no exception. Alcmaeon of Croton (ca. 500 B.C.) proposed that mental processes are located in the brain. Empedocles (ca. 495-435 B.C.) countered that mental processes are located in the heart. For the next 2000 years the brain hypothesis and the heart hypothesis were hotly debated. This controversy took so long to resolve because it was debated rather than tested empirically. Once the scientific age dawned and research tools were developed to test the alternative hypotheses experimentally, considerable evidence pointed to the brain as the seat of mental processes (see Kolb & Whishaw 1996, for further discussion of the history of this controversy). Subsequently, another controversy arose about what the basic unit of brain structure is. This question could not be answered by simply inspecting the brain's physical appearance, which looks like jelly with no obvious structural organization. Again, two alternative hypotheses competed for support. According to the neuron hypothesis, the nervous system is composed of discrete cells that are not physically connected. According to the nerve net hypothesis, the nervous system is Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

19

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Brain Literacy for Educators and Psychologists

a continuous network of interconnected fibers. Three technological developments contributed to resolving this controversy relatively quickly m in approximately 150 years compared to 2000 years for the heart versus brain controversy. First, laboratory procedures were devised for transforming the gelatinous matter of the brain into fixed, hardened tissue that could be sliced thinly for analysis. Unfortunately, this technique required the sacrifice of animals or study of people after death. Second, staining techniques were developed that, when applied to the thin tissue slices, revealed exquisite, intricate details about brain structure. Third, the invention of the microscope allowed scientists to examine the stained thin tissue slices under the microscope and discover yet additional details about brain structure and organization not visible to the naked eye. The wealth of evidence based on these technological advances clearly supported the neuron hypothesis: The brain and nervous system are composed of microscopic units m c a l l e d neurons and glial c e l l s - - t h a t are not physically connected with each other. (See Kolb & Whishaw 1996, for further discussion of how technology resolved this controversy.) As is always the case with science, answering one question raises more questions. If the neurons are not physically connected, how can they interact with each other? In this chapter we first discuss what has been discovered about the physical structure of the neurons m t h e building blocks of the nervous system. Then we discuss what has been found about how physically separated neurons communicate with each other. Tables 2.1 and 2.2 summarize the major research tools that contemporary scientists use to study the brain at the microlevel.

TABLE 2.1

Research Tools for Studying the Microstructure of the Brain

Stains

Highlighted Structures

Nissl

Cell bodies and cell identificationby size and shape of cell body

Myelin

Fiber tracts

Golgi Weigert

Organization of entire cell--its shape, dendrites, cell body, and axon Horizontal layers and vertical columns in cortex

Injected fluorescentdye

Projections of one neuron to other neurons

Silver methods

Degenerated axons or terminals

Histofluorescence

Localized neurotransmittersand their distribution (e.g., dopamine, norepinephrine, epinepherine, and serotonin)

Microscopes

Dimensions in Stained Tissue Slices

Light or electron (magnified about 1500 times)

2-dimensional

Scanning electron

3-dimensional

General Principles of Microstructure and Microfunction TABLE 2.2

21

Research Tools for Studying the Microfunction of the Brain

Recording Electrical Activity with a Very Tiny Electrode a

Intracellular

Electrode placed directly in single cell

Extracellular

Electrode placed adjacent to one or several cells

Electrical Stimulation during Neurosurgery

Neurosurgeon systematicallyapplies electrical stimulation as patient performs specific tasks and notes which cells are disrupted by the electrical stimulation. a< 0.001 mm in diameter; record change in single cell's electrical potential relative to other cells for specific stimuli; amplify signal and analyze visual or auditory representation of signal.

MICROSTKUCTURE

OF BRAIN

ARCHITECTURE

Like people, neurons are both alike and different. Most neurons have three c o m m o n components surrounded by a membrane (thin skin): dendrites, a cell body, and an axon. As shown in Figure 2.1, an elaborate branching system of dendrites (Greek for tree) leads into a cell body, which, in turn, leads to an axon fiber (Greek for axle) that ends with terminals (Latin for little enclfeet). Also see Units 2-1 and 2-2 in The Human Brain Coloring Book (Diamond et al. 1985). (Note: T h r o u g h o u t Part I, a unit refers to the D i a m o n d et al. Coloring Book, whereas a figure refers to this b o o k unless otherwise noted.) Despite these c o m m o n c o m ponents, neurons do not look the same, and their overall structure varies depending on what they are specialized to do. Figure 2.2 illustrates such structural variations found a m o n g highly specialized neurons. Likewise, Unit 2-3 in The Human Brain Coloring Book (Diamond et al. 1985) provides examples of the dendrites, cell body, and axon in specialized neurons that differ in overall structure and physical appearance. For example, the pyramidal cells projecting from the cortex are shaped like a star (Goldman-Rakic 1992). Figure 2.3 shows h o w different kinds of stains highlight different structural features of the neuron (see also Table 2.1). T h e branching structure o f dendrites is ideally suited for collecting information from other neurons. Length of the dendrites varies from a few microns to millimeters. Each dendrite has spines, which are receiving terminals for multiple sources of information. The cell body contains a nucleus and structural elements for preserving cell shape and for transmitting substances. The nucleus manufactures proteins and other substances such as enzymes. These proteins form receptors on the cell's membrane,

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Brain Literacy for Educators and Psychologists

FIGUR_E 2.1 A typical neuron, showing some of its major physical features. From FUNDAMENTALS OF HUMAN NEUROPSYCHOLOGY by Bryan Kold and Ian Q. Whishaw 9 1980, 1985, 1990, 1996 by Worth Publishers. Used with permission.

maintain the dendrites, and serve as transmitter chemicals for the axons. The enzymes stimulate chemical reactions needed for cell maintenance and function. The nucleus also contains chromosomes and a nucleolus, which are genetic material. The chromosomes contain D N A (deoxyribonucleic acid) that regulates the growth and development of the cell and the production of proteins used in the communication process among neurons. The nucleolus produces rlKNA (ribosomal ribonucleic acid). Enzymes act on D N A to produce m R N A (messenger ribonucleic acid), which joins with rlKNA to form polyribosome, the template on which proteins are formed. Each neuron has only one axon, which originates at the axon hillock, where it receives information from the dendrites. Axon length varies from a few microns to

General Principles of Microstructure and Microfunction

23

F I G U R E 2.2 The nervous system is composed of neurons, which are nerve cells, each of which is specialized as to function. The schematic drawings show the relative size, shape, location, and configuration of some neurons. From FUNDAMENTALS OF HUMAN NEUR.OPSYCHOLOGY by Bryan Kold and Ian Q. Whishaw 9 1980, 1985, 1990, 1996 by Worth Publishers. Used with permission.

m o r e t h a n a meter. M o s t axons have branches w i t h little k n o b s called terminals n o t directly. A synapse ( G r e e k for union) is a small space that separates the a x o n terminals f r o m the dendrites (or o t h e r

that c o m m u n i c a t e w i t h o t h e r n e u r o n s N b u t

parts) o f o t h e r neurons. This space contains the c h e m i c a l substances, w h i c h , if

24

Brain Literacy for Educators and Psychologists

FIGURE 2.3 Cross-section of neocortex stained by three different methods; the six cortical layers are indicated. Golgi stain reveals the shape of the aborizations of cortical neurons by completely staining a small percentage of them. The Nissl method stains the cell bodies of all neurons, showing their shapes and packing densities. The Weigert method stains myelin, revealing the horizontally oriented bands of Baillarger as well as vertically oriented collections of cortical afferents and efferents. Reprinted with permission from THE HUMAN BRAIN: AN INTRODUCTION TO ITS FUNCTIONAL ANATOMY 3/e by John Nolte. Copyright 1993 by Mosby Year Book.

released, influence the activity o f other neurons. These substances are neurotransmitters, w h i c h are chemicals that change the voltage o f nerve cells on the o t h e r side o f the synapse, allowing the nerve impulse to travel across the synapse (see Figure 2.4). Thus, physically separated neurons can c o m m u n i c a t e at a functional level, as the electro-chemical signal travels across the synapse, as is discussed further in the section on m i c r o f u n c t i o n o f the brain. Also see Units 2-4 and 2-5 in The

Human Brain Coloring Book ( D i a m o n d et al. 1985).

General Principles of Microstructure and Microfunction

25

FIGURE 2.4 Electrical-chemical communication among neurons takes place across a narrow gap between the sending nerve's bulbous axon tip and the receiving cell's thornlike spike. Reprinted with permission from MAGIC TREES OF THE MIND: HOW TO NURTURE YOUR CHILD'S INTELLIGENCE, CREATIVITY, AND HEALTHY EMOTIONS FROM BIRTH THROUGH ADOLESCENCE by M. Diamond andJ. Hopson. Copyright 1998 by Penguin Books.

Surrounding the entire n e u r o n is a cell membrane, which is a double layer o f lipid (fat) molecules. T h e cell m e m b r a n e has two structures that regulate w h e t h e r the electrical state is neutral (balanced charges a m o n g the chemical ions that have positive or negative charges) or charged (excess o f negatively charged ions). T h e first structure is a channel with gates (spaces) through which specific chemical ions pass. Some gates are the fight size for some chemicals but not others to pass through. T h e second structure is a set o f pumps for m o v i n g ions in and out o f the cell. T h e cell m e m b r a n e also has receptor sites. Some receptor sites are for peptides and hormones that enter the cell and are transported to the cell nucleus. O t h e r receptor sites are for receiving electrical charges from other neurons and for releasing substances that attach to the receptor sites on other neurons. Thus, the n e u r o n is an elegantly complex electro-chemical computer. See Unit 2-7 in The Human Brain Coloring Book (Diamond et al. 1985).

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BrainLiteracy for Educators and Psychologists

Not all brain cells are neurons. Glial cells (Greek for marrow) provide support and nourishment for neurons. Some glial cells wrap around the cell membrane to form a white coveting called a myelin sheath. The exposed gaps in the axon between these glial cells are called the Nodes of Ranier, which are in the insert in Figure 2.1. These support cells are called glial in the Central Nervous System (CNS, which includes the brain and spinal cord) and Schwann Cells in the Peripheral Nervous System (PNS, which includes the nerve-muscle pathways outside the CNS). See Unit 2-6 in The Human Brain Coloring Book (Diamond et al. 1985).

MICR.OFUNCTION

OF THE B R A I N

Neuronal activity requires energy. Brain cells rely on glucose for all their energy, and without glucose, die. They extract it from blood because the brain has no mechanism for storing glucose. Mitochondria (Greek for thread + granule) in the cell body absorb the glucose and metabolize (process) it to make ATP (adenosinetriphosphate), which fuels the processes producing the electrical and chemical activity of neurons. Two kinds of electrical activity regulate neuronal functioning. The first involves graded potentials, and the second involves all or none potentials. The graded potentials are like an analog computer with continuous signal. The all or none potentials are like a digital computer with a binary signal that is either on or off. Thus neuronal activity is like a hybrid computer that is both analog and digital. The source of the graded potentials are the dendrites. Most synapses involve dendrites, with the result that much neural activity involves graded potentials that have important implications for the computational processes discussed in Chapter 5. When dendrites are stimulated by upstream neurons, the voltage changes in proportion to the intensity and distance of stimulation. Depending on the nature of the stimulation, dendrites may decrease (depolarize) membrane potential or increase (hyperpolarize) membrane potential. Graded potentials may summate spatially or temporally. For example, two graded potentials that are close spatially may be summed in additive fashion, but a graded potential that is depolarized may be subtracted from the one that hyperpolarizes. Repeated stimulation close in time will summate in additive fashion, becoming larger than any single stimulation, but the resulting graded potential will depend on the strength of the two stimuli and the interval between them. If one stimulus hyperpolarizes and the other depolarizes close in time, the two stimuli will combine in subtractive fashion. The summated depolarization needed for a graded potential to reach threshold is about 50 mV above resting potential. If the net effect of all the graded potentials in a neuron reaches threshold, then an action potential is set into effect in the axon hillock where the graded potentials are summated. The all or none potential, in contrast to the graded potential, is the result of alternation between resting potentials and action potentials. During the resting

General Principles of Microstructure and Microfunction

27

potential, the positive and negative charges of chemical ions on the inside and outside of the cell membrane give rise to a - 7 0 m V potential. W h e n stimulation from downstream neurons or summated potentials from dendrites disrupts this potential, an excess of negative ions inside the cell membrane causes an action potential, which travels along the axon to its terminals, where chemical substances are released (see Unit 2-7 in Diamond et al. 1985). This action potential is the nerve impulse that sometimes is referred to as the neuronal or neural firing. The action potential lasts only briefly and the negative resting potential between the inside and outside of the cell is restored. The released chemicals are neurotransmitters in that they travel through the synaptic space and bind momentarily to receptor sites on other neurons (postsynaptic sites), allowing the action potential to transmit an electrical signal to those downstream neurons. That signal creates either an excitatory postsynaptic potential (EPSP), increasing the probability that the downstream neuron will fire, or an inhibitory postsynaptic potential (IPSP), decreasing the probability that the downstream neuron will fire. That is, the all or none potential either turns downstream neurons on (excitatory effect) or off (inhibitory effect). About 80 percent of central synapses are inhibitory. The electrochemical transmission process across the synapse takes about 0.5 milliseconds. Then the neurotransmitter is inactivated by a number of different mechanisms. Different synapses are regulated by different neurotransmitters. Calvin and Ojemann (1980) use the analogy of the lock and key. The neurotransmitter is a key that is designed specially to open some locks (recep:or sites) but not others. In contrast to the graded potential, the all or none potential that travels down the axon either fires or does not, depending on whether a threshold is reached. The nerve impulse travels slowly in unmyelinated axons, about 1 to 100 meters per second. The nerve impulse has about 0.1 volt of electricity and lasts only about 0.001 second. However, axons may vary in how often they fire. Firing rate ranges from 100 to 1000 times per second. The speed of the nerve impulse may be increased in axons in which glial cells have formed a myelin sheath. This sheath enables saltatory conduction (Latin for skipping) because the action potential can jump from one N o d e of Ranier to the next N o d e of Ranier, skipping over the insulated portion of the axon. Myelin sheaths can increase speeds of the neural impulse up to 300 miles per hour. Myelin has another special property that increases speed. Its white color is due to its lipid (fatty) composition, which serves as insulation that conserves metabolic energy. Axons consume energy only at the Nodes of Ranier or unmyelinated portions, not along the myelinated portions of the axon. See Figure 2.1 and Unit 2-8 in The Human Brain Coloring, Book (Diamond et al. 1985). One of the major discoveries in neuroscience during the latter part of the twentieth century was the chemical pathways (see Figure 2.5) that alter behavioral tone of the other pathways (Mesulam 1990). Inside the brain, different groups of neurons are connected to one another that use a specific chemical pathway associated with

28

Brain Literacy for Educators and Psychologists

Cerebral Cortex

Basal Forebraine*" ~ ~ ~ C h ~ ' ~ Hypothalamus~

~.-eBrains ~ Locus

TegmentalArea

PedunculopoTitineNucleus LaterodorsalTegmentalNucleus FIGURE 2.5 State-setting,chemicallyaddressed connections of thalamus and cortex. Interrupted lines are minor connections. The question mark means that the pathway is not firmly established. Ach=acetylcholine; DA=dopamine; His=histamine; NE=norepinephrine; Ser=serotonin. From Mesulam, Annals of Neurology, Copyright 9 1990. Reprinted by permission ofJohn Wiley & Sons, Inc.

a specific neuro transmitter. They are widely distributed in the brain and have connections with all cortical areas and thalamic nuclei. According to Mesulam, five classes of chemical pathways exert different kinds of effects on cortical processing states: cholinergic, histaminergic, dopaminergic, serotonergic, and noradrenergic. Specific chemical pathways may influence learning. For example, norepinepherine increases signal to noise ratio (activation of neural firing over and beyond random firing of the brain at rest), precision timing of neural firing, and specificity of neural transmission. Other specific chemical pathways may influence emotion, motivation, and behavior in the classroom. For example, noradrenergic pathways modulate novelty-seeking behavior and resistance to distraction. Dopaminergic pathways influence encoding of reward and effort needed for cognitive activity. Cholinergic and monoaminergic pathways affect motivation. Some of these chemical pathways project from the reticular formation (see Chapter 3) to the attentional system and can influence general arousal and receptivity to learning. Research is needed on the role of these chemical pathways in school learning and performance. In addition to synaptic mechanisms already discussed, second messenger systems also operate. In such systems any of a large number of neurotransmitters may be involved in regulating gate control. A receptor protein containing a chain of amino acids accepts the transmitter substances, which, in turn, initiate a chain of biochemical events. This second messenger then exerts effects that are not related to gate opening but rather to other cell functions; for example, hormone regulation of target cells or synthesis and expression of genes.

General Principles of Microstructure and Microfunction MENTAL COMPUTATIONS MENTAL PROCESSES

29

UNDERLYING

The previous sections of this chapter highlighted the general principles of the structure and function of the brain at the microlevel. As with any complex phenomenon, the details can be overwhelming and exceptions do occur in these general principles. Indeed, cutting-edge research is focused on unraveling the details for the specific neuro-chemical mixes and mechanisms for different groups of highly specialized neurons. Reading this research requires advanced technical knowledge of fields such as molecular biology, neurophysics, and/or neurophysiology that are multidisciplinary and integrate chemistry, physics, and biology. Nevertheless, we begin our tutorial on the brain for educators at the microlevel for three reasons. First, although it is easier to study brain structure and function in human learning at the macrolevel, it is always the case that structure and function at the microlevel are contributing to structure and function at the macrolevel. Most of what we know about the microstructure of the brain is based on simple animals, in which the components of the neuron are easily visualized under the microscope; for example, the squid that has a giant axon, lobsters, or roaches. Most of what we know about the microfunction of the brain is based on (a) inserting tiny electrodes into single brain cells of cats or primates to record their electrical activity in response to specific stimuli or (b) electrically stimulating specific regions of the brain in patients before neurosurgery to study how that disrupts specific functions (see Table 2.2). Although behavior is often attributed to mechanisms at the macrolevel, one should not forget that underlying macrofunctions are microlevel neural events. Second, most students have normally developing brains. Much of what is known about brain-behavior relationships at the macrolevel is based on abnormal brains. A major focus of research on abnormal brain structure and function has been to identify the locus of brain damage or disease. Although locus of damage is often relevant to medical management of patients, pinpointing where something has been damaged or malfunctions in the brain is not the same as offering an explanation of how the brain works under normal conditions. For example, demonstrating that a patient who has lost ability to read has damage to the left temporal lobe does not explain how children without brain damage learn to read. Understanding structures and functions at the microlevel, even though this understanding may be based on animal models, may stimulate educators and educational researchers to envision how skills like reading and writing are acquired in normally developing brains. The skill acquisition process may depend on the computations carried out by collections of individual neurons. We return to this idea when we discuss connectionist models of reading in Part II. Third, understanding the mechanisms of dendrites and axons may help educators conceptualize what learning is. Here are some hypotheses about what the learning process may involve:

30

Brain Literacy for Educators and Psychologists 9 A change in which neurons synapse in performing a specific task (i.e., which neurons are talking to which neurons) 9 A change in the strength of a connection among those synapses, such that it takes less effort (work) to perform the same task and thus less metabolic energy 9 A change in the number of synapses required to perform a task, such that the process becomes more efficient and uses less metabolic energy 9 A change in the spatial and/or temporal summation pattern of graded potentials of dendrites, which trigger the digital action potential of axons 9 A change in the threshold needed for the action potential of axons to fire 9 A change in the pattern of action potentials across axons (how many fire and how often they fire)

Thus, learning probably involves specific kinds of changes in the neuronal connections in the brain of the learner, and these changes involve how neurons functionally communicate with one another. Neither teachers nor researchers can directly observe how these connections change during learning. However, researchers can mathematically model the graded additive and subtractive processes of dendrites and the on-off digital computational processes of axons in learning to read, write, and compute, and they can evaluate whether these models predict children's learning curves for specific academic skills. Contemplating these potential changes in neural connections can be mindboggling. One estimate is that there are 1014 synapses in the human brain (Barnes 1986). All these cannot be changed on the basis of instruction, but also not all these synapses are hard-wired. Some can be changed in response to instruction. Learning is a process of changing connections for which neurons are talking to each other, how many neurons are talking to each other, how loudly they are talking to each other (via their weights or strengths), and their temporal and spatial patterns of talking.

RECOMMENDATIONS

FOR FURTHER

READING

The following four readings and one web site were the source of much information on the microstructure and microfunction of the brain in this chapter. These readings contain additional information for readers who wish to deepen their knowledge in understanding the brain at the microlevel.

Introductory Level Calvin, W. & Ojemann, G. 1980. Inside the brain. Mapping the cortex, exploring the neuron. New York: Meridian. Diamond, M., Scheibel, A. & Elson, L. 1985. The Human Brain Coloring Book. NewYork: Coloring Concepts. Distributedby Harper and Row, NY. Kolb, B. & Whishaw, I. 1996. Fundamentals of Human Neuropsychology, 4 th ed. New York: W. H. Freeman.

General Principles of Microstructure and Microfunction

31

More A d v a n c e d Level

Kandel, E., Schwartz,J. &Jessell, T. 2000. Principles ofNeuroscience, 4 th ed. New York: McGraw-Hill. Atkins, D. August, 2001 Exploration of the neuron, http://gwis2.circ.gwu.edu/~atkins/Neuroweb/ synapse.html/#second

MAKING CONNECTIONS Questions preceded by * may be most appropriate for graduate students. 1. What are some other examples of man-made or nature-created phenomena, beside the brain, that can be described at multiple levels of analysis, proceeding from small units to larger units? 2. Controversies in neural science often have taken years to resolve until suflqcient scientific information is available to resolve the different points of views. What kinds of controversies is education grappling with at the beginning of the twenty-first century? Might scientific research play a role in resolving these controversies? If so, how? If not, why? 3. What is the source ofthe huge number ofindividual differences amongpeople in so many domains? Is it the differences in gene sequencing (words and syntax formed by four chemicals, see Ridley 1999)? Is it due to differences in experience? Is it due to interactions between genes and experience (nature- nurture interactions) ? *4. How might diet and medicine/pharmacology affect neural function for academic learning? *5. How might individual differences in chemical pathways affect behavior in the classroom? *6. Does the location of a process in the brain's neuroanatomy describe the neural computations underlying the process? Why is it important to differentiate between location and computations in neural architecture? *7. In what ways might study of normal children learning academic skills contribute to neural science in a way that study of people with disease, injury, or congenital disorders might not? How could mathematical models of graded dendritic and digital axonic processes increase our understanding of the learning process?

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General Principles of Macrostructure and Macrofunction

HISTORICAL

BACKGROUND

Analysis of the brain at the macrolevel is as dependent on the current state of technology as is analysis of the brain at the microlevel. The research tools available at the end of the nineteenth century and beginning of the twentieth century, when modern research on the brain at the macrolevel began, relied on animals who were sacrificed or humans who died of natural causes. Behavioral changes prior to death were correlated with brain structures found at autopsy to be damaged. In the case of research with animals, selected brain tissue was purposely destroyed and changes in behavior before and after the destruction were noted prior to death; loss in a specific behavior was then correlated with the specific destroyed structure. In the case of research with people, individuals who suffered from nature-inflicted diseases and injury (e.g., strokes or tumors) were assessed prior to death to identify which behaviors became abnormal; and then the loss in specific behaviors was correlated to the neurological structures that appeared damaged at autopsy. Results of animal studies often were generalized to the human brain because animals and human brains have generally the same structures, even though some structures, like the cerebral hemispheres and cerebellum, are larger in humans, and the comparable structures may have different patterns of connectivity in humans and different animals. Brain La'teracyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All [Lights of reproduction in any form reserved.

33

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Brain Literacy for Educators and Psychologists

These remained the major research tools for studying brain-behavior relationships until the latter part of the twentieth century when technology became available for imaging live people. These recent technological advances have revolutionized how brain structure and function are studied at the macrolevel. At the end of this chapter we compare and contrast the research technologies that are currently available to study the human brain at the macrolevel in living people and animals, and also illustrate slices of brain tissue taken with these tools on different planes in multidimensional space. This visual-spatial orientation is useful in interpreting published brain images that differ as to whether they are side views, back-tofront views, or top-down views. These technologies are used to study not only the brain's structural architecture but also the brain at work as it performs a variety of functions. Educators who understand basic principles of brain structure and brain function at the macrolevel can become informed consumers of the research literature these tools are generating. In Part II we provide overviews of research findings in the imaging literature that are specifically relevant to the functional systems for reading, writing, and computing, and of the functional systems on which they draw during development m motor, aural/oral language, cognitive and memory, attention and executive functions. Much of this literature is in its infancy but nevertheless may be useful in helping educators conceptualize the complexity of what the human brain accomplishes in learning to read, write, and compute.

MACROSTRUCTURE Global Appearance and Protective Features Most of the currently used research tools scan the brain or record its activity without observing it directly. In contrast, neurosurgery provides a unique opportunity to view a living human brain directly because preparation for neurosurgery requires removal of a portion of skull (Calvin & Ojemann 1980). Viewing the brain through this opening in the cranium (Greek for mixing bowl), which is a protective bony structure, observers see pinkish brown, soft tissue. The living brain is not gray (or grayish brown) or hard. The brain of a deceased person becomes gray and hard when treated with formalin ( a chemical) or alcohol to preserve it for purposes of analysis. The pinkish color is due to the massive network of blood vessels on the surface. This blood supply is crucial for two reasons. First, blood is the energy source for the glucose that fuels the neural processes of the brain (see Chapter 2). Second, blood also supplies the oxygen, which is needed for burning the glucose to generate metabolic energy; without oxygen brain cells die. The brain receives one-fifth of the blood that the heart pumps. The middle cerebral artery provides the blood for the speech and language areas that are especially important in school learning. However, despite the brain's need for blood, the blood-brain barrier protects the brain from unhealthy substances that may be circulating elsewhere in the body. Glial

General Principles of Macrostructure and Macrofunction

35

cells (see Chapter 2) wrap around the blood vessels to prevent many of the molecules in the blood from entering the brain. Only small molecules like oxygen, carbon dioxide, and glucose can enter. Other protective devices for reducing the likelihood that living brain tissue will be harmed are also evident. Under the skull are three protective membranes: the outer dura mater (Latin for hard mother), the middle arachanoid (Greek for resembling a spider's web), and the inner pia mater (Latin for s@ mother). Together, these three membranes are the meninges. In addition, clear colorless cerebral spinal fluid (CSF) fills four cavities called ventricles (Latin for bell)~) in the brain. CSF circulates through the brain in the arachnoid layer; this fluid carries away the metabolic wastes produced by the brain. Further probing below the outer, wrinkled, pinkish-brown surface of the exposed living brain reveals a lot of white matter. These are the myelin sheaths covering the axons (see Chapter 2) as they leave the rich blood supply of the neurons on the outer surface. Even deeper probing reveals collections of darker gray cells, including the substantia nigra, which is colored black by the pigment of the dopamine transmitter. Additional probing might detect the mottled colored matter of the reticular activating system (Latin for net) in which grayish brown cell bodies are mixed with white myelinated axons, or might reveal the pink hue (related to iron content) of the red nucleus.

Structural Organizing Principles Mere physical inspection of the living brain does not reveal, however, the elegant organizing principles of this complex structure containing an estimated 180 billion cells on average. Over a century of research has discerned at least twelve structural organizing principles (see Table 3.1) for the macrostructure of the brain, which weighs on average only about three pounds. The first structural organizing principle (collections) is the assembly or bundling of single units into larger units. A nucleus (Latin for net) is a large number of cell bodies, with their characteristic gray color. Assemblies of more than one nucleus are nudei. A tract (old French for stem) is a large number of axons, which, if myelinated, have their characteristic white color. Assemblies of tracts are fibers. In general, "gray matter" refers to assemblies of cell bodies of neurons where capillary blood vessels may also occur; and "white matter" refers to axons covered with myelin. The second structural organizing principle (connections) is the neural pathway, whereby one unit (a single neuron or collection of neurons) has sequenced connections with other units (a single neuron or collection of neurons). Horseradish peroxidase is a protein that can be used to stain one cell and all its connections with other cells in the brain. Although the graded and all or none electric potentials within a single neuron are unidirectional (see Chapter 2) and pathways are sequenced connections in one direction, different pathways can exert reciprocal influences

36

Brain Literacy for Educators and Psychologists

TABLE 3.1

Summary of Organizing Principles at the Macrolevel

Structural Organizing Principles

Functional Organizing Principles

1. Collections of single neurons or neuronal parts into larger bundles

1. Division of labor

2. Neural pathways in which individual neurons or bundles of neurons are connected sequentially

2. Variable structure-function relationships

3. Biochemicalpathways (neurotransmitters)

3. Localizationand distribution of functions

4. Cytotechtonic architecture (regional neuronal types)

4. Lateralization

5. Layered architecture (rows and columns)

5. Redundancy

6. Convolutions (folds) on top layers

6. Alternativepathways

7. Bottom-up and top-down axes of communication

7. Normal variation

8. Ipsilateralpathways on the same side

8. Parallel processing

9. Right-left and left-fight axes of communication

9. Functional systems

10. Crossingprinciple (to contralateral or opposite side)

10. Government (executive) functions

11. Back-front and front-back axes of communication

11. Necessaryand sufficient conditions

12. Primary, secondary, and tertiary association areas

12. Plasticity

on a brain s t r u c t u r e - - i n which one pathway communicates sequentially in one direction and a related pathway communicates sequentially in the other direction. In this way nuclei (see first structural organizing principle) may feed information forward, and other nuclei may feed information backward in a neural network. T h e third structural organizing principle is the biochemical distribution of specific neurotransmitters (e.g., dopamine, epinephrine, norepinephrine, serotonin, and acetylcholine). These chemical pathways facilitate neural transmission within specific neural pathways. Although considerable progress has been made in describing these biochemical pathways and the mental or physical disorders that can result if they are not functioning normally, little is k n o w n about h o w these biochemical pathways may facilitate or interfere with learning of academic skills or respond to psychopharmacologic agents given to school-aged children. T h e fourth structural organizing principle is the spatial proximity of structurally similar pathways in neural architecture. Brodmann advanced the field of neuroscience with his discovery that variations in cell structure could be used to describe regional differences in cortex, which are referred to as cytoarchitectonic regions. Instead of naming these cytoarchitectonic regions, he gave them an identifying n u m b e r (see Figures 3.1 and 3.2). T h e fifth structural organizing principle is that of layers within regions. In some regions neural pathways are organized into layers, each of which is structurally different. Most of the outer part of the cerebral hemispheres has six layers (see Units 3-11 and 5-31, D i a m o n d et al. 1985), the outer part of the cerebellum has three

General Principles of Macrostructure and Macrofunction

-

"

/

(ast,,

37

41 40 , ~ 3 9 ~ 1~

./

FIGURE 3.1 Brodmann'sareas of the cortex. Areas 12-16 and 48-51 are missingin original sources. HeaW solid lines are histologically distinctive boundaries. Light solid lines are less distinct boundaries. Dotted lines are boundaries that are not distinct and gradually merge into each other. Reprinted with permission from TEXTBOOK OF NEUR.OANATOMY 2/e by H. Chandler Elliott. Copyright 9 1963, 1969 by Lippincott Williams & Wilkins.

layers (see Unit 5-14, D i a m o n d et al. 1985), and parts of the limbic system have three layers. Figure 2.3 shows the layers of the cerebral cortex revealed with the different kinds of stain. Evidence also exists that an additional organizational device of columns may be superimposed on the layers of cerebral cortex (Mountcastle 1957). T h e sixth structural organizing principle is the convolutions or folds. This folding occurs in cerebral cortex (in forebrain) and cerebellar cortex (in hindbrain); see Figures 3.3 and 3.4, and the structural scans at the end of this chapter. This folding, which gives parts of the brain its wrinkled appearance, allows more area of brain tissue to fit within a small space. W h e r e the folds rise above the surface like the ridge of a mountain, the fold is called a gyrus (pl., gyri). A shallow area or valley between the gyri is a sulcus (pl., sulcO. An even deeper valley is a fissure. T h e folds are gray matter covering the white matter below, and these folds or convolutions occur in both the cerebrum and cerebellum. T h e seventh structural organizing principle is the bottom-up or top-down axes of communication, called projection pathways (see Units 5-17 and 5-18, D i a m o n d

38

Brain Literacy for Educators and Psychologists

F I G U R E 3.2 Brodmann's anatomically defined areas of the human cerebral cortex. Reproduced from Von Economo and Koskinas (1925).

General Principles of Macrostructure and Macrofunction

39

FIGURE 3.3 The central nervous system (CNS) can be divided into six main parts. Reprinted with permission from PRINCIPLES OF NEU1KOSCIENCE by E. Kandell, J. Schwartz, and T. Jessell. Copyright 9 2000 by McGraw-Hill. Reproduced with permission of The McGraw-Hill Companies.

et al. 1985). Nerves along this axis link the subcortical and cortical structures. For the primary projection pathways of the major sensory and motor systems, see the following figures in Eliot (1999): auditory (Eliot Figure 10.2), visual (Eliot Figure 9.2), somatosensory (touch and position in space) (Eliot Figures 5.1 and 5.2), and motor (Eliot Figures 11.1, 11.2, and 11.3). Table 3.2 in this book summarizes much of the information contained in the figures verbally. The eighth structural organizing principle is the communication path within the fight side or within the left side. Fibers that connect pathways on the same side of the cerebral cortex (either right or left) are association fibers (see Unit 5-33, Diamond et al. 1985). For example, two language areas on the left side m B r o c a ' s and Wernicke's (see Figure 3 . 4 ) m are connected by the arcuate fasciculus, an association fiber deep in the cortex (see Figure 3.5).

40

Brain Literacy for Educators and Psychologists

F I G U R E 3.4 Cortical areas involved in vision, language, somatosensory function, motor functions, and thinking. Adaptation by T. L. Richards of figure that first appeared in Von Economo and Koskinas (1925).

The ninth structural organizing principle is the right-left or left-right axis of communication. Pathways that connect the right and left hemispheres are called commissures (see Unit 5-33, Diamond et al. 1985). The corpus callosum is the largest of these interhemispheric white fiber bundles. See BA 33 in Figure 3.2 for corpus callosum in an artist's rendition of the brain, and Figures 3.11, 3.14, 3.15, 3.16, and 3.20 for corpus callosum in brain scans. These interhemispheric pathways may TABLE 3.2 System

Primary Projection Pathways (Sequential Relay Stations for Neural Signal) Projections in Sequential Order

Auditory

Cochlea in inner eara to auditory nerve to cochlear nucleus in lower brain stem b to superior olive in higher brain stem to inferior colliculus in midbrain to medial geniculate nucleus (MGN) or body (MGB) in thalamus to primary auditory cortex c (BA 41 in temporal lobe)

Visual

Retina [receptor cells d to bipolar cells to ganglion cells] to diverging subcortical path (the brain stem's eye movement control system) and cortical . path [optic nerve through optic chiasm e] to lateral geniculate nucleusJ(LGN)in thalamus to primary visual cortex g (BA 17 in occipital lobe)

(continues)

General Principles o f M a c r o s t r u c t u r e and M a c r o f u n c t i o n T A B L E 3.2

41

(continued)

Somatosensory

h

i Skin receptors to spinal cord to brain s t e m (crossover to opposite side) to ventral p o s t e r i o r lateral nuclei (VPLN) in thalamus to p r i m a r y s o m a t o s e n sory cortex (BA 1, 2, and 3 in the parietal lobe)

Vestibular j

Two peripheral organs with mainly brain stem, cerebellar, and spinal cord connections: semicircular canals k and otolith 1

Smell m

Epithelial cells in nose to olfactory bulb to ventral cerebral cortex (e.g., entorhinal and orbitofrontal)

Taste n

R e c e p t o r cells on the tongue ~ to medulla in lower brain stem to pons in upper brain stem to thalamus

Motor p

P r i m a r y m o t o r cortex q + s u p p l e m e n t a l m o t o r area r and p r e m o t o r cortex r to a direct p a t h (corticospinal tract to motor neurons in spinal cord) s and an indirect p a t h to brain stem

aMeaning like a snail shell, this organ converts sound vibration to electrical signals and contains a basilar membrane that vibrates to different frequencies of sound waves. Different sound frequencies are separated in space along the auditory projection pathways from sensory detectors to cortex. b Input from both ears merges here to localize sounds in space. CAuditory information is constructed from pitch, loudness, and location information. dlKods are sensitive to light and are important in peripheral vision, and cones are sensitive to color and are important in foveal vision. eGanglion cells crossover to contralateral side here. fContralateral projection from right or left visual field of stimulus to the opposite side of the thalamus. glpsilateral (same side) projection from thalamus to cortex, but each side of the cortex gets input from both eyes. hseparate paths exist for touch, temperature, pressure, pain, and proprioception--the sense of position and movement of body and limbs. 'The proprioception sense also has receptors in the muscles and joints. JThe only sense to function solely below the cortex, this sense is located in the vestibule (opening in the skull) near the cochlea. It perceives body movement and degrees of balance in reference to gravity and motion (including eye movements), and thus helps to maintain smoothness of action. Not presented in terms of sequential pathways. kThe three semicircular canals are filled with fluid and oriented to three perpendicular planes in space; they detect head turns. /The otolith organs include the saccule that detects linear movements side to side and up and down, and the utricle that activates when head position is changed relative to gravity, for example, when tilting the head or lying down. mSmell (olfaction) is a chemical sense. nTaste is a chemical sense that interacts with olfaction. ~ exist for sweet, salty, bitter, and sour. Pin contrast to sensory circuits that flow in one direction--from bottom to t o p - - t h e motor circuits are more complex and flow in both directions from and to the world with lots of feedback loops in the system. The pathway summarized here is for voluntary movement from top down toward the world. qlnitiates motor acts. (BA 4 in the frontal lobe.) rlnvolved in higher level planning and executing of complex sequences of movement. SConnected to muscles that excite a bicep motor neuon, which contracts, and inhibits a tricep motor neuron, which relaxes; changes are sensed by proprioceptors in the muscles.

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FIGURE 3.5 Arcuatefasciculus deep in the language system of the brain relative to Wernicke's Area and Broca's area on the cortical surface. Permission to reproduce from University of Washington Digital Anatomist Program. Prepared and authorized by John Bolles, Health Sciences Center for Educational Resources, SM20, University of Washington, Seattle, WA 98195. connect homologous (corresponding) structures on the fight and left sides of the brain. The tenth structural organizing principle is the crossingprinciple that links sensory input and m o t o r output to the fight-left axis. In general, each half of the brain responds to sensory stimulation from the opposite side of the external world, and each half of the brain controls m o t o r movements on the opposite side of the body. N o t surprisingly, this crossing principle results in many crossings of sensory and m o t o r fibers in the center. For example, stimuli from the right visual field (right half of each visual stimulus the eye sees) project to the left side of the brain, and stimuli from the left visual field (left half of each visual stimulus the eye sees) project to the right side of the brain (see Figure 3.6). Although stimuli that enter the ears have connections to both sides of the brain, there are more fibers that cross from each ear to the contralateral (opposite) than ipsilateral (same) side. Thus, the fight ear-to-left side connection and the left ear-to-right side connection are the strongest. The right side of the brain controls the m o t o r output on the left side, and the left side of the brain controls the m o t o r output on the fight side.

General Principles of Macrostructure and Macrofunction

43

FIGURE 3.6 When fixating a point, each eye seesboth visualfields, but sends information about the right visual field only to the left hemisphere and information about the left visual field only to the right hemisphere. The left and right hemispheres normally communicate through the corpus callosum. From LEFT BRAIN, RIGHT BRAIN by SallyP. Springer and Georg Deutsch 9 1981 by SallyP. Springer and Georg Deutsch. Used with the permission of Worth Publishers.

This crossing arrangement holds for the face and arms, but not the legs, for which each side o f the brain has m o t o r connections. T h e olfactory and taste systems, which are chemical senses, do not have this crossing arrangement. T h e eleventh structural organizing principle is the back-front andfront-back axis of communication. Stimuli from the external world tend to enter the rear of the cortex and m o v e forward during processing. M o t o r responses for acting on the world tend to be emitted from the front of the cortex. T h e twelfth structural organizing principle is a system ofpathways that vary in h o w directly they are linked to the external environment. Some pathways are dedicated only to receiving or sending to the external world, but others are dedicated only to receiving input from other internal pathways and synthesizing that input with other input. Primary projection areas receive inputs from sensory pathways (e.g., visual) or send outputs to m o t o r pathways. Thus, primary projection areas, also called unimodal

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pathways, have direct connections to the outside world (the external environment). In contrast, surrounding secondary association areas receive inputs from primary projection areas but do not communicate directly with the external environment as the primary projection areas do. The secondary and tertiary pathways integrate signals from other pathways in the internal environment. The secondary association areas are called heteromodal pathways when they receive input from more than one kind of sensory modality. Tertiary association areas, which receive inputs from the secondary association areas, are not sensitive to specific sensory modalities. In this hierarchy, signals are recoded at each level and become progressively more complex and abstract and less directly influenced by sensory or motor information from the environment.

Mental Geography Given these basic principles of structural organization, we can now explore neuroanatomy at the macrolevel. Much like the early explorers of the earth, brain researchers have named brain sites on the maps of the brain they construct. Just as a coastline is not a straight line and mountains do not have level tops, the brain surfaces are not smooth or straight. Also, in analogous fashion, boundaries are not well demarcated, and one structure is continuous with another. Designating boundaries is often analogous to designating political units (e.g., the states in the United States, or countries in North America) in which arbitrary boundaries are imposed on continuous geographical space. Unfortunately, not everyone has used the same system for the arbitrary naming of sites in brain geography. The numerous, sometimes conflicting, systems of naming can be confusing. One of our favorite names is substantia inominata (Latin for nameless substance; Kolb & Whishaw 1990, 17). The numbers for Brodmann's regions (Figures 3.1 and 3.2) are prefixed with BA in honor of his contribution to defining regions based on cell density and appearance. Table 3.3 defines some of the vocabulary that frequently is used when verbal labels, rather than numerals, are used in naming brain sites. Refer to this table, along with Table 3.4, in decoding the names of brain sites both during this chapter and in reading Part II, which reports results of brain imaging studies. For example, left inferior posterior temporal region means that the structure lies on the left, at the bottom, towards the back, of the temporal lobe. Neuroscientists make ample use of compounding familiar words in creating new vocabulary; for example, if a circuit involves the inferior posterior occipital and temporal lobes, they may refer to it as the inferior posterior occipitotemporal region or junction. Dorsal may reduce to dorso or inferior to inferio when compounded with other spatial prefixes. Linguists might find the evolution oflanguage in naming brain structures an interesting topic of study. The language of neuroscience has referents in both the visual-spatial and verbal world; using the language of neuroscience requires the coordination of the visual and linguistic representational systems. The visual system provides coordinates for navigating in three-dimensional space (moving along the front-back, side-to-side,

General Principles o f Macrostructure and Macrofunction TABLE 3.3

Vocabulary Used to N a m e Brain Locations

Label

Definition

Ventral or Inferior

Bottom of Bottom~--~Up axis

Superior

Top of Bottom~-+Up axis

Lateral

Right or left side of Right+-+Left axis

Medial

Middle of Right,-~Left axis

Posterior

Back of Back+-+Front axis

Anterior

Front of Back~-~Front axis

Ipsilateral

Two structures on the same side

Contralateral

Two structures with one on the opposite side

Proximal

Close structures

Distal

Far structures

Afferent

Appproaching the center

Efferent

Leaving the center

Ascending

Projecting upward

Descending

Projecting downward

Dorsal

Posterior

Ventral

Anterior

TABLE 3.4

W e l l - K n o w n Landmarks in Cerebral Cortex a

Lateral Gyri

Medial or Ventral Gyri

Sulci

Angular gyrus

Lingual

Central sulcus

Parietal lobule

Fusiform

Lateral (Sylvian)

Superior and inferior parietal gyrus Supramarginal gyrus

Cingulate Parahippocampal

Superior temporal Middle temporal

Inferior temporal gyrus

Orbitofrontal

Inferior frontal

Middle temporal gyrus

Middle frontal

Superior temporal gyms

Intraparietal

Precentral gyrus

Cingulate

Postcentral gyrus

Paracingulate

Inferior frontal gyrus (orbitalc, triangular, opercular)

8

Middle frontal gyrus Superior frontal gyrus Heschl's gyrus Planum temporale aoften used in reporting results in brain imaging studies on reading, writing, and math. bBroca's area. CNot the same as orbital frontal cortex, (a transitional area to orbital frontal gyri).

45

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Brain Literacy for Educators and Psychologists

or bottom-up planes; see Table 3.3 and Figures 3.14 through 3.20), and the language system provides verbal labels for specific structures (see Table 3.4). These verbal codes have to be mapped onto the visual coordinates. Neuroscientists use the term mapping to describe how values in one representational system are related to values in another representational system. Thus, like literacy learners in the classroom, explorers of the brain's neuroanatomy must coordinate the visual and auditory language systems and cannot rely exclusively on the visual or auditory senses alone.

Geographical Expedition of Brain We begin the geographical expedition of the brain in the reverse direction (bottomup) from that used for viewing it during neurosurgery (top-down). The bottom-up organization, which has three main divisions, reflects the direction in which the animal brain evolved, and does reflect, approximately, the order in which brain structures develop in the embryo. The lower division contains the spinal cord and hindbrain (see Figure 3.3). The spinal cord mediates between the PNS and the brain in the CNS. The spinal cord has a dorsal (near the back) system for receiving somatosensory information from the external environment and a ventral (near the belly) system for movement (control of skeletal muscle) in the external environment. In uptight creatures like man the dorsal system is also called posterior and the ventral system is called anterior. The hindbrain contains the cerebellum, brain stem, fourth ventricle, and cranial nerves. The cerebellum (see Units 5-13 and 5-14, Diamond et al. 1985) sits below the occipital lobe of cerebral cortex. It has convolutions (folds) and gray matter on the outside coveting white matter (myelinated axons) on the inside. The brain stem (see Units 5-1, 5-2, and 5-12, Diamond et al. 1985) contains the midbrain, pons, and medulla oblongata, and has an inner c o r e - - t h e reticular activating system. This netlike structure, also known as the reticular formation, is a diffuse set of nuclei and fiber tracts in the core of the brain stem that has many connections with the rest of the brain stem, the spinal cord, the cerebellum, andforebrain (e.g., ascending tracts to the thalamus and descending tracts from the cortex). The brain stem is a relatively small mass of tissue that is dense with nuclei and tracts. All ascending somatosensory pathways go through the brain stem, and most motor pathways originate in or go through it. The brain stem has many fibers connecting the lower brain to the forebrain. These fibers transmit signals in both directions--bottom to top (ascending) and top to bottom (descending). The pineal body in the brain stem does not have a fight side and a left side, which is why medieval philosophers thought it was the seat of the soul (Kolb & Whishaw 1990). The brain stem is also the site of origin or termination of the cranial nerves. The twelve cranial nerves in the CNS keep the brain in contact with the external world (see Unit 6-1, Diamond et al. 1985):

General Principles of Macrostructure and Macrofunction

47

9 Olfactory (smell) 9 Optic (vision) 9 Oculomotor (eye movements) 9 Trochlear (eye movements) 9 Trigeminal (masticulatory movements) 9 Abducens (eye movements) 9 Facial (facial movements) 9 Auditory vestibular (hearing) 9 Glossopharyngeal (tongue and pharynx) 9 Vagus (heart, blood vessels, viscera, and movement of larynx and pharynx) 9 Spinal accessory (neck muscles and viscera) 9 Hypoglossal (tongue muscles) The middle division contains the third ventricle and two structures in the lower forebrain, both of which have many connecting pathways with the brain stem and the forebrain: the hypothalamus and the thalamus (see Figure 3.3a, and Unit 1-3, Diamond et al. 1985). The hypothalamus (Greek for lower room) is in front of the midbrain in the lowest division. The hypothalamus is a very small structure accounting for only 0.3 percent of the brain's weight, but essential for life functions (see section on macro-function). The thalamus (Greek for inner room) (see Units 5-16 through 5-19, Diamond et al. 1985) receives information from brain stem relays and sends it on to cerebral cortex. For example, the lateral geniculate body (LGB) receives visual projections and sends to BA 17 in the occipital lobe of cerebral cortex. The medial geniculate body (MGB) receives auditory projections and sends to BA 41 in the temporal lobe of cerebral cortex. The ventral posterior lateral nuclei (VPL) receives touch, pressure, pain, and temperature projections and send to BA 1, 2, and 3 in the parietal lobe in cerebral cortex (see Table 3.2). The thalamus receives and sends projections to the secondary and tertiary areas of the cortex. Another part receives from temporal cortex and sends to frontal cortex. The upper division contains the upper parts of the forebrain ~ the limbic system, basal ganglia, cerebral hemispheres and c o r t e x ~ a n d the lateral ventricles (see Figure 3.3). The upper division has many structures that receive from and send to the middle and bottom divisions of the brain. The limbic structures (Latin for border or hem) include the hippocampus (sea horse), septum (partition), cingulate (girdle) gyrus (also known as cingulate cortex) in the frontal lobe, parahippocampal gyrus in the temporal lobe, amygdala (almond), mammillary bodies, olfactory lobe, and fornix (see Figure 3.7). The basalganglia are nuclei lying under the anterior (front) regions of the cortex (see Figure 3.3). As shown in Figures 3.7 and 3.8, this region contains the amygdala, which is categorized as belonging to both the limbic system and basal ganglia, and the putamen (shell), caudate nucleus (tailed nucleus), and globus pallidus (pale globe). The brain region that contains both the putamen and caudate nucleus is also called the striatum. The striatum receives information from

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Brain Literacy for Educators and Psychologists

FIGURE 3.7 Limbicsystem.Reprinted with permissionfrom STUDENT'S ATLASOF NEUROANATOMY by W. Hendelman. Copyright 9 1994 by W. B. Saunders Company.

the thalamus and all over the cortex (see Figure 3.8). Note that the striatum is not the same as the striate in visual cortex (striatal cortex in occipital lobe). The basal ganglia have many ascending fibers to sensory and motor cortex and descending fibers to midbrain. They also have many connections to thalamus and limbic structures. The basal ganglia also have distinct pathways for different neurotransmitters. The cerebrum contains two cerebral hemispheres divided by the longitudinalfissure into a fight side, left side, and outer cortex. The cortex (Greek for outer bark) is a thin coveting of cells surrounding the cerebral hemispheres, and sometimes is called the neocortex because it is found only in mammals. The number of layers of the cortex ranges from four to six, but is six in most places (see Figure 2.3, and Units 5-31 and 5-32, Diamond et al. 1985). Each layer is so thin that it corresponds to about two spaces in a word-processed document. Yet, because of all the folding this thin coveting (1.5 to 3.0mm) has a 2500 cm 2 area and accounts for 80 percent of the human brain. Both the fight and left cerebral hemispheres have four lobes with characteristic upward folds (gyri) punctuated by downward tucks in the folds (sulci): occipital, temporal, parietal, and frontal (see Figure 3.3b, and Unit 1-1, Diamond et al. 1985).

General Principles of Macrostructure and Macrofunction

49

FIGURE 3.8 Two basal ganglia circuits. On the left, substantia nigra is interconnected with striatum. SNc, the pigmented part of substantia nigra, projects to the striatum, which consists of the caudate and putamen. The striatum projects to the reticular part of the substantia nigra, which projects to the thalamus. On the fight, the subthalamic nucleus has reciprocal connections with globus pallidus, which projects to the thalamus on two pathways. On both the left and fight, the thalamus projects to cerebral cortex, which projects to the striatum. Adapted from THE HUMAN BRAIN: AN INTRODUCTION TO ITS FUNCTIONAL ANATOMY 3/e by John Nolte. Copyright 9 1993 by Mosby.

These lobes are named for the skull bones under which they lie. Fissures define boundaries b e t w e e n the lobes (see Unit 1-2, D i a m o n d et al. 1985). The lateral (sylvian) fissure divides the temporal from the parietal and frontal lobes (see white space between BA 47, 44, 43, and between BA 38, 52, 41 in Figure 3.1, and comparable darkly colored space in Figure 3.4). O p e n i n g this fissure exposes Heschl's gyms, a primary projection area for auditory stimuli (see BA 41 in Figure 3.4), and the insula, which is buried deep in the cortex (see Figure 3.9). M a n y of the important structures in the functional systems for literacy, including Heschl's gyrus and insula, are not on the surface of the cortex. The front-most region of the cortex is called the prefrontal association cortex (see BA 46 in Figure 3.4), which is a very large expanded region of cerebral cortex in humans compared to animals. T h e central sulcus divides the parietal and frontal lobes (see the line b e t w e e n BA 1, 2, and 3, and BA 4 in Figure 3.1, and the comparable areas in Figure 3.4 that are filled with solid black dots or are white, respectively). The calcarine fissure is in the occipital lobe (BA 17, best visualized in Fig. 3.2), which is not as clearly demarcated from the other lobes as the other lobes are from each other. Table 3.4 summarizes some of the w e l l - k n o w n cortical landmarks for readers to refer to w h e n reviewing the results of imaging studies. It is coordinated with Figures 5.1 (surface structures) and 5.2 (deeper structures), which show the location of these in the brain. As we conclude this geographical expedition, keep in mind that we have explored only a single brain. Across normal individuals, there are many

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Brain Literacy for Educators and Psychologists

FIGURE 3.9 The frontal, temporal, and parietal opercula were removed to expose insula, which is buried in cortex. Reprinted with permission from THE HUMAN BRAIN: AN INTRODUCTION TO ITS FUNCTIONAL ANATOMY 3/e by John Notte. Copyright 9 1993 by Mosby.

individual differences in the precise location of these landmarks. Mthough the major features of brain typography are similar across individuals, there are variations in size and shape. Neuroscientists call these individual differences in the normal brain normal variation. Brain differences do not always mean brain damage or disability. In addition, although casual physical inspection of the brain suggests that the fight and left sides are symmetrical, more careful analysis under the microscope or in a structural imaging brain scan may reveal fight-left asymmetries in size and shape of specific corresponding structures on the right and left side of the brain (Geschwind & Levitsky 1968). Individuals vary in the degree to which they show fight-left asymmetries in various brain structures. Thus, normal variation in brain structures may be found within the same individual (intraindividual differences) and among individuals (interindividual differences). Also, as we conclude our tour, we note that finding specific structures in real brains (images or autopsy specimens) can be tricky. N o t only is there normal variation in structure (shape and volume), but also the same structure might look very different from a back-to-front, side-to-side, and bottom-up view. Textbook illustrations that point out these structures from different perspectives in multidimensional space illustrate the complexity of the brain. This complexity derives from both the sheer number of parts and their juxtaposition in multidimensional space when viewed from different perspectives (see Figures 3.14 to 3.20). To isolate these parts in real brains (autopsy specimens or scans of live people) requires expert knowledge in neuroanatomy acquired through years of professional training and experience.

General Principles of Macrostructure and Macrofunction

51

MACROFUNCTION

Research Approaches. Five research approaches have been used to generate knowledge of the brain's macrofunction: cytoarchitectonic maps, projection maps, functional maps, clinical cognitive neuropsychological models, and cognitive neuroscience models. Cytoarchitectonic studies analyze the structural properties of specific kinds of neurons and their geographic location in the brain's architecture, with the goal of linking neurons of similar structure in the same brain region with a particular function. (See Figures 3.1 and 3.2 for Brodmann's labeling of cytoarchitecture). Projection maps trace axons from receptors to brain sites for a specific sensory system or from brain sites to skeletal muscle for a specific motor system. Projection maps are easier to identify for the primary projection pathways (see Table 3.2) than for the secondary and tertiary association areas; refer back to the twelfth structural organizing principle. Functional maps are based on (a) electrical recordings of which neural structures are stimulated in an animal by a specific kind of stimulus input (e.g., a horizontal line, a vertical line, a diagonal line, or a curved line presented to the visual system); or (b) electrical stimulation of specific brain regions in human patients before neurosurgery to determine where the stimulation interferes with a specific function (e.g., naming or phonological judgment); see Table 2.2. The latter approach is possible because the brain is insensitive to pain and touch and can be stimulated directly while the patient is awake and able to perform cognitive tasks. Clinical cognitive neuropsycholo2y analyzes the component processes in complex skills, administers tasks designed to assess each component process, and then teases apart which component processes for a skill are and are not functioning normally (McCarthy & Warrington 1990). In Part IV we recommend that classroom teachers task analyze children's responses to different components of curriculum to determine which components of reading, writing, or computing children are learning and which ones require special instructional assistance. Cognitive neuroscience models serve as theoretical frameworks for interpreting results of brain imaging studies (Booth & Burman 2001; Frackowiak 1994). Functional imaging data are not interpretable apart from such models on which the tasks given are based.

Functional Organizing Principles Functional organizing principles exist for function as well as structure at the macrolevel. The twelve functional organizing principles, which are summarized in Table 3.1, are now discussed. The first functional organizing principle is division of labor. Different brain structures are specialized to perform different tasks. In the early history ofneuropsychology

52

Brain Literacy for Educators and Psychologists

- - t h e study of brain-behavior relationships m t h e r e was a controversy about whether all brain tissue could perform all functions (the mass action theory) or whether specific brain tissue could perform only certain functions (localization theory). The evidence for mass action was that, following surgical destruction of specific brain tissue, functions associated with that region were spared. Subsequent evidence showed that functions are not always spared. Some structures are essential and removing them disrupts a system, but others may participate in a function but no disruption follows removal. Localization theory was discredited for a number of years because of its perceived similarity to phrenology, which was popular at the end of the nineteenth century and claimed that different functions were associated with specific bumps on the head (Kolb & Whishaw 1996). Geschwind, a neurologist, made localization theory credible again in the mid-twentieth century. At some levels of the nervous system, specific kinds of neural tissue are specialized for certain jobs. The second functional organizing principle is variable structure-function relationships. Despite specialization of function, the relationship between structure and function is not always isomorphic (directly related in a one-to-one fashion); that is, one function for each structure. Many of the well-known landmarks in brain geography (e.g., Wernicke's area, limbic structures, reticular activating system) do not operate as a single functional unit. For example, language comprehension depends on brain regions that include not only Wemicke's area but also other brain regions. The limbic system does not appear to function as a single "emotional brain" as originally proposed by Papez (1937), even though some of the limbic structures (e.g., septum and amygdala) appear to play a role in emotion. Likewise, the reticular activating system in the brain stem has structures that regulate arousal and consciousness via ascending projections to the thalamus and cortex, but its structures are involved in many other functions as well. The cerebral cortex contributes in complex ways to many functional systems. The sheer number of functions associated with some structures is amazing. For example, the insula, which is an important structure for learning to read (see Chapter 5) and which is buried deep in cerebrum (see Figure 3.9), has been implicated in all the following functions: motor planning for speech, fine motor movements of lips, larynx, and face, visceral sensory functions (taste, olfactory, and gastric sensations), motor functions (respiration and gastrointestinal activity), gross body movements, vestibular connections, secondary somatosensory associations (Dronkers 1996), retrieval of name codes (Semrud-Clikeman et al. 1991), and selective attention (Leung et al. 2000). Clearly, a structure that appears to be homogeneous from a neuroanatomical perspective may participate in the functioning of many different neural circuits, which vary as to which other neuroanatomical structures are included for a particular function. Nevertheless, certain structures have more regional specialization than others. For example, selective damage to the basal ganglia results in specific impairment in movement. In contrast, selective damage to the hippocampus is not as specific in its impairment.

General Principles of Macrostructure and Macrofunction

53

Brodmann's cytoarchitectonic units (Figures 3.1 and 3.2) are of interest because structurally similar, geographically close cells, for example, prefrontal cortex, may participate in the same function, for example, storing new memories. Primary projection areas are more likely to be associated with a specific function (related to sensory modality or motor act) than are the surrounding secondary and tertiary areas that integrate multiple sources of input and may colocalize information from other sensory modalities. Primary projection maps have been identified for the cortical visual system (the posterior occipital lobe, BA 17), the auditory system (the superior temporal gyrus, BA 41), the somatosensory system (the postcentral gyrus of the parietal lobe, BA 1, BA 2, and BA 3), and the motor system (the precentral gyrus or motor strip of the frontal lobe, BA 4); see Table 3.2. Figure 3.10 shows the specificity of primary projections for specific motor (A side) and somatosensory functions (B side) in cortex. These projections are said to have topographic organization (mapping of one structure to one function) because there is a close relationship between the specific location in the brain map and the function of the neural tissue, which is highly specialized for the kind of stimuli in the external world to which it responds or the kind of skeletal movement it can perform on the world. Note that the most motor cortex is devoted to mouth and hand functions m motor functions that play prominent roles in literacy learning. In contrast, the secondary projection systems surround the primary projection areas and receive their input from the visual, auditory, somatic, and motor input fields that are distributed throughout the cortex. These secondary projection pathways, which support cross-modality integration within a lobe, are more

/'//:,"/,C%" !

% l - ' ' t " ' ~"m''""~ j'w

-'~176176176

"f~ll

ill

FIGURE 3.10 Topographic maps of human motor (A) and somatosensory (B) cortex based on electrical stimulation of brain surface of conscious patients undergoing neurosurgery. The size of a part of the homunculus (person) is roughly proportional to the size of the cortical area devoted to that part. Reproduced from Penfield and Rasmussen (1950) in The Human Brain: An Introduction to its Functional Anatomy 3/e by John Nolte. Copyright 1993 by Mosby Year Book.

54

BrainLiteracy for Educators and Psychologists

likely to participate in multiple functions (one-to-many mappings) than a single function (one-to-one mapping). Also, connections are reciprocal between the primary sensory and secondary cortical fields within a lobe. Sensory and motor functions can be integrated but the mapping is not a simple one-to-one correspondence. Moreover, most of the cortex consists of tertiary association areas, which receive input from the secondary projection areas. These pathways that respond to more abstract, complex stimuli tend to play an important, although not fully understood, role in higher-order thinking skills. This point is important because sensory modalities are often given a central role in learning styles for academic subjects. In fact, most school learning may depend more on abstract cortical computational processes in tertiary association cortex than on sensory modalities alone. Such abstract computational processes, which we discuss in Chapter 5, are not readily reduced to simple structure-function correspondences. The third functional organizing principle is combined localization and distribution offunctions. The idea that brain functions are localized in specific regions stems from the nineteenth century, when neurology became a medical specialty. Neurologists noted that certain kinds of aphasia (loss of language due to stroke or tumor) are linked to specific kinds of brain damage. A neurologist named Broca discovered that patients who lost ability to speak fluently, but could still understand speech, had damage to a region in the left frontal lobe. A neurologist named Wernicke discovered that patients who lost ability to understand speech, but could still speak fluently, although not necessarily sensibly, had damage to a region in the left temporal lobe. The first kind of aphasia associated with language production became known as Broca's aphasia. The second kind of aphasia associated with language understanding became known as Wernicke's aphasia. However, at the same time another neurologist recognized that brain function is more complicated than a simple one-to-one correspondence between structure and function, as localization implies. Jackson (1887) proposed that mental processes could be studied from the perspective of their level of construction in the nervous system rather than from the perspective of their location in the brain. Luria (1973), who observed that Jackson had been ahead of his time, emphasized that although some brain functions m for example, simple sensory and motor f u n c t i o n s - can be studied in terms of their localization in the nervous system, the more complex higher-order functions cannot be studied solely on this basis. That is yet another reason why the overly simplistic notion that there are visual learners and auditory learners or learning styles flies in the face of what is known about brain function. Learning and thinking depend greatly on these higher order functions that may transcend a single sensory modality. Two major developments in the mid-twentieth century led to an emerging recognition that brain function can be described at multiple levels. The first development was the emergence of cognitive psychology as a discipline for studying complex, higher order mental processes independent of localized processes in

General Principles of Macrostructure and Macrofunction

55

specific brain structures (Neisser 1967). The second development was the evidence that damage to certain localized processors results in disruption of complex, higher order processes such as language (Geshwind 1972). By the end of the twentieth century, a consensus emerged that some brain functions are localized, but others are distributed throughout the brain in neural networks. A complete understanding of the brain at work is based on both kinds of representation of brain function. Neurologists who study loss of already established functions have increased our knowledge of the localization of functions. Cognitive psychologists and cognitive neuroscientists who develop computational models of mental processes, which are distributed throughout neural networks that are not localized to a single neuron or region, have increased our knowledge of higher-order brain functions. The organization of the outer part of the cerebrum, cellebellum, and some limbic structures into layers may facilitate certain kinds of mental computations. The three outer layers of the cerebral cortex are the least understood but may contribute the most to the brain waves recorded by EEG (see comparison of research methods at the end of this chapter). Sometimes scientific controversies are resolved by acknowledging that competing hypotheses are both correct, but explain different aspects of a phenomenon. A metaphor that aptly describes this coming together of disparate views of the nervous system is orchestration of mind (Posner et al. 1988). Analogous to the orchestra, localization is like the individual musician playing his or her specific instrument in a specific location and time, whereas distributed functions are like the sum of the efforts of each individual musician in creating the total production. The sum is greater than the parts and cannot be understood totally on the basis of each individual musician's performance apart from the whole group's performance. Results of brain imaging research generally are consistent with this hybrid view in which relationships between brain structures and functions are both localized at one level of analysis and distributed at another level of analysis. Mesulam (1990, 610) summarized it well: "Behavior is not contained in the neuron or in the anatomical site but in the grids of connectivity that are both localized and distributed... The flexibility inherent in this system provides the driving force for maximal adaptability to the e n v i r o n m e n t . . . It is adaptability that c o u n t s . . . " In addition, Mesulam made an important distinction between the neuroanatomical, computational, and psychological/behavioral levels of analysis, which we will use in Parts II and III to integrate brain-based and instruction-based research on reading, writing, and math. The fourth functional organizing principle is lateralization. This principle is a special case of division of labor for localized functions: Unique functions are associated with homologous (corresponding) cytoarchitectonic regions on both sides of the brain. Some of the first evidence of this specialization by side of the cerebral hemispheres came from the split-brain experiments performed on patients with intractable epilepsy. This operation severed the corpus callosum that c o m m u nicates between the right side and the left side of the brain. As shown in Figure 3.11,

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FIGURE 3.11 Severingcorpus callosum during split brain operation for patients with intractable epilepsy. Cortical hemispheres are disconnected, but midbrain structures are still connected via the collicular commissures, permitting some communication between right brain and left brain. Adapted from illustrationby Eric O. Mose. Reprinted from Sperry,R.W. 1964,The Great Cerebral Commissure, Scientific American.

this severing of a major pathway on the fight-left axis interfered with the crossing principle. As a result of this disruption, visual stimuli projected to the fight visual field (left hemisphere) could no longer cross over to the fight side, and visual stimuli projected to the left visual field (fight hemisphere) could no longer cross over to the left side. After the operation, patients participated in laboratory tasks in which they were asked to respond, either nonverbally (e.g., by pointing) or verbally (e.g., by speaking), to stimuli presented visually. If stimuli were projected to the fight hemisphere (and stayed on the fight side because the corpus callosum had been cut), patients were able to answer questions correctly, by pointing to drawings, but not by speaking. If stimuli were projected to the left hemisphere (and stayed on the left side because the corpus callosum had been cut), patients were able to answer questions correctly, by speaking, but not by pointing to drawings. These studies suggested that the fight side of the cerebral hemispheres is specialized for nonverbal skills, such as interpreting pictures, whereas the left side of the cerebral hemispheres is specialized for verbal skills, such as speech. In addition, many clinical neuropsychological studies and autopsy studies of adults who lost selected functions before death provided further evidence on lateralization of function: The left hemisphere seems to be specialized for language and functions that require sequential processing, and the fight hemisphere seems to

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be specialized for visual spatial skills and functions that require simultaneous processing. See Springer and Deutsch (1985) for a comprehensive review of this extensive literature on lateralization of function. Unfortunately, these research findings were overgeneralized by the media and some educators, who have promoted the idea that individuals differ in whether they have fight-brain or left-brain learning styles. Although some brain functions are lateralized, it does not follow that there are left-side and fight-side learning styles. There are at least three reasons why the neuropsychological research on lateralization does not support the notion of fight-brain and left-brain learning styles. First, the corpus callosum is only one of the commissures through which the fight brain and left brain communicate. Figure 3.11 shows that subcortical pathways also exist. Indeed, once the split-brain subjects practiced the experimental tasks, they often learned to speak about what was presented to their fight hemisphere, probably because they developed subcortical right-left pathways that communicated with their left hemispheres, even though the cortical corpus callosum had been severed. Second, because individuals with intractable seizures, strokes, and tumors sustain brain damage, their brain functioning may not generalize to that of normally developing, nondamaged brains. Third, the fight-left axis is only one level of functional organization in the brain, which also has bottom-up, top-down, backfront, and front-back communication pathways at the macrolevel and complex neural processes at the microlevel. In the functional brain imaging studies reviewed in Part II, seldom does brain activation occur only on one side of the brain. Even when brain activation is detected, it is impossible to know whether the activated neurons are causing excitation or inhibition of postsynaptic synapses (see Chapter 2). Lateralization, or dominance of one side of the brain for a specific function, may be like taking the lead in a dance in which both sides participate to varying degrees and in different ways. Optimal learning requires that both sides of the brain work together cooperatively. The fifth functional organizing principle is redundancy. Biological organisms often come with spare parts. We have two eyes, two ears, two arms, two legs, two kidneys, two lungs, five toes, and five fingers. This redundancy has survival value w i f one part fails, the other(s) may be able to perform without it. Brain function may also capitalize on the benefits of having more than one pathway for neural communication, in case one pathway fails temporarily or permanently. This organizing principle has practical educational applications. One of the pathways supporting a particular function may not be working wbecause of genetic constraints, chance neurological events (like traffic accidents during neural migration), lack of appropriate environmental experiences or instruction, or lack of practice. However, the function may still seem normal because other existing pathways support it. Thus, even students who appear to be learning normally may have brain differences in structures for which there are spare parts or circuits that take over and cover for the ones that do not function normally. In Part III we discuss possible instructional implications of redundancy.

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The sixth functional organizing principle is alternative pathways. A software programming metaphor applies to this organizing principle. If ten software engineers are asked to program a computer to accomplish a specific task, they may come up with ten different programs, that is, ways to accomplish the same goal! Each program will be constrained by the hardware of the computer and the languages in which the hardware can be programmed. Nevertheless, within these constraints, the exact steps and order of steps within the program are likely to vary across the individual programmers. The human brain may also have this kind of programming flexibility to some degree in tertiary cortex: Within the constraints of the neural architecture specified by the human genome, individual students may create alternative neural pathways for learning and performing the same academic skill. This organizing principle also has practical educational applications. There is probably more than one way to teach students effectively and more than one way to learn effectively. The reason is that the neural connections in the brain, at least in tertiary association areas, can be orchestrated differently to accomplish the same task! This principle of alternative pathways has advantages for biological adaptation to a changing environment. As the environmental challenges change, the range of potential solutions for dealing with change is increased if the brain is not preprogrammed to solve a given problem in a constant way. The possibility of alternative pathways does pose unique challenges for scientists who try to sort out which mechanisms characterize all human learners and which ones have degrees of freedom to vary across learners and teachers. The possibility of alternative pathways should also encourage teachers to seek new approaches to providing instructional clues if their first approach fails. The seventh functional organizing principle is normal variation. Both genes and experience in the environment create the normal variation in the brain structures and functions that underlie learning differences. Students start out with a unique mix of genetic endowment and environmental opportunities to nurture their developing minds. Structural brain imaging has documented this normal variation in neuroanatomy (Leonard et al. 1993; Toga et al. 1993). Thus, it is not surprising that there is also normal variation in specific functions; that is, individual differences along a continuum within the normal range. Variability also is introduced through cultural practices that vary among social groups and through variation in home practices among families within a culture. School environments also introduce normal variation m seldom do all schools or all teachers provide exactly the same instructional environment. Differences are not necessarily abnormal. In fact, the more diversity in a social system (i.e., individual differences across learners), the higher the probability the social system (i.e., the society) will adapt to a changing environment. N o human law can eliminate normal variation. Law can only guarantee fights to develop one's endowment and opportunities, as long as others' fights to do so, too, are not violated. However, normal variation does have implications for educational policy, and is considered in Part IV.

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The eighth functional organizing principle is parallel pwcessing. Although individual neural pathways fire in sequential manner, more than one pathway may be firing in parallel at the same time to accomplish the same job, and the brain is probably doing more than one job at a time. Thus, at one level of analysis the brain is a sequential processor, but at another level of analysis it is a parallel processor. Yet, resources for doing more than one thing at the same time are limited. We will return to this notion of system constraints when we discuss working memory. The ninth functional organizing principle is functional systems. To accomplish complex jobs and to do more than one job at a time, brain processes become organized in functional systems. During the course of learning, component parts learn to work together. Here is one example of how multiple component structures work together as a system. The visual system has separate "what" and "where" pathways (Mishkin, Ungerleider & Macko 1983; Ungerleider & Haxby 1994). The where pathway locates objects in three-dimensional space. The what pathway identifies objects or familiar features, detects color shape, and perceives fine detail. The where and what pathways separate in the retina and in the c o r t e x - - t h e where pathway goes dorsal (upward) from occipital to parietal lobe, and the what pathway goes ventral (downward) from occipital to temporal lobe. However, a normally functioning visual system requires the participation of both the what and the where pathways. For example, when a preschooler is learning oral language, the where pathway helps move the visual field to the targeted object in the environment that a speaker is naming. The what pathway helps the language learner perceive the perceptual detail in the object for purposes of visually recognizing it. Together these pathways help the developing child form connections between the objects, which are located in a visual spatial environment, and the verbal labels. These connections between names of objects and their referents in the physical world are the foundation on which the language system is built. Another example is how subcomponents function together in motor systems. Substantia nigra, red nucleus, and superior colliculus are all involved in the integrative aspects of motor control. The basal ganglia initiate and organize motor movement and influence the cortex, which also exerts influence in planning motor acts. The cerebellum is involved in sensory-motor integration and skilled movement (e.g., of the legs, arms, fingers, and mouth) and may also be involved in any kind of learning that requires fine-tuning of processes. Cerebellum, which is one tenth of the brain and contains half its neurons, is like the air traffic control of the nervous system (Eliot 1999) in that it receives input from the senses and motor cortex and can modify motor commands to match intended movement and monitor whether movement is going according to plan. See Figure 3.12 for the descending motor system, which draws on motor cortex, basal ganglia, cerebellum, brain stem, and spinal cord; there is also an ascending motor system. Injury to basal ganglia (deep in brain under cortex and on top of brain stem and adjacent to thalamus) can interfere with initiating voluntary movement like talking and walking.

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FIGURE 3.12 Descendingmotor control pathways. On the left side, these pathways directly affect motor neurons and motor programs. On the right side, cerebellumand basal ganglia exert indirect effects on movement by influencing output from cerebral cortex to brain stem and spinal cord. Basal ganglia receive input primarily from cortical sources, but cerebellum receivesboth cortical and noncortical input. Adapted with permission to reproduce from THE HUMAN BRAIN: AN INTRODUCTION TO ITS FUNCTIONAL ANATOMY 3/e by John Nolte. Copyright 9 1993 by Mosby.

Some parts of the brain may participate in more than one functional system, which is the set of structures that get activated sequentially, and in parallel, to accomplish a task. For example, the thalamus is a triaging system where sensory tracts relay before going to the cortex where there are different systems for the different modalities. Different functional systems may draw on the same, as well as unique, brain parts. Functional systems may reorganize over the course of develo p m e n t - which parts participate and h o w they are interrelated may change for a particular function. In Part II we emphasize h o w the functional reading, writing, and computing (math) systems may draw on c o m m o n as well as unique c o m p o n e n t processes and may reorganize over the course of skill acquisition from novice to expert. T h e tenth functional organizing principle is government. W i t h multiple processes occurring at the same time and many parts contributing to more than one specific functional system, mechanisms are needed to control and regulate the brain at work, or chaos will occur. It is unlikely that a homunculus (little man), like a single conductor directing the orchestra, is in charge. Rather, m u c h like the American government that has built-in checks and balances with executive, judicial, and legislative branches of government, a variety of self-regulation mechanisms seem

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to operate in the prefrontal cortex, which houses the government for regulating brain functions. Neuropsychologists refer to this set of governing mechanisms as the executive functions. Before these governing mechanisms are fully operational for self-regulation, adults provide other-regulation for the developing learners; see Chapter 4. Put another way, during early development and school-age development, parents and teachers serve as the prefrontal lobes; that is, they provide the other-regulation until the prefrontal cortex can assume all the reponsibilities of selfregulation (Stuss & Benson 1986). In contrast to the American government that has three branches of government specified by the constitution, the total number of mechanisms participating in the brain's government is unknown but researchers are making progress on this front. The eleventh functional organizing principle is the contrast between necessary and suj~icient conditions. There may be necessary conditions for component parts of a system to function; without these the system does not function. However, the necessary conditions alone may not be sufficient. Additional components may also be needed for the system to function, or to function efficiently, or to function optimally. Research studies may identify necessary component processes for learning academic skills, but these may not be sufficient. Classroom teachers need to take into account additional factors in implementing research-supported instructional practices in the classroom to promote student learning; see Parts III and IV. The twelfth functional organization principle is plasticity. Neurology and neuropsychology have devoted a great deal of research to the plasticity of the damaged brain, but in this book we consider plasticity of the nondamaged brain. The brain's structure and function does change, but in constrained ways. If the brain changed too easily and constantly, chaos might result. If the brain never changed, we could not adapt to a changing environment, and there would be no reasons for formal schooling of the young. In Part II we discuss what is known about how the brain may change as literacy skills develop. In Part Ill we discuss how teaching may change how the brain reads, writes, and computes.

Virtual T o u r o f the B r a i n at W o r k

We now revisit the same brain sites covered on the geographical tour, but this time we tour from the perspective of function. This perspective is possible because an imaginary virtual environment transports us, on command, through a mental model of the brain at work. Before putting on your goggles, we caution that this functional tour may make your brain dizzy because more than one thing is happening at a time. In contrast to the geographical tour, where you had to coordinate the visual-spatial and language system, you now need to coordinate many different functional systems at the same time. Fortunately, most of the time the brain spares us this dizziness by allowing only a small portion of these functions into our conscious awareness at any point in time. Literally, most of the time the right side does not know what the left

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side is doing, the back does not know what the front is doing, and the bottom does not know what the top is doing. Yet, they are all working together! From the menu, we select the brain of a typical student on a typical school day. We begin in the lower division, where there is much activity. The brain stem is maintaining the student's life through respiration. The reticular activating system (RAS) in its inner core is regulating the cycle back and forth between sleep and wakefulness and degrees in between these extreme states of arousal or consciousness. These states change throughout the school day, and learning depends on whether the student is in a conscious, aroused state when instruction is delivered. Too little arousal or too much arousal may interfere with learning, and arousal level is not under the student's direct control. This lower division is also a buzzing hub of sensory messages from the classroom to the upper divisions in the brain, from the upper divisions to lower divisions of the brain and on to the classroom, and within subdivisions of the lower division. The cerebellum is busy fine tuning sensorymotor integration, coordinated movement, and thought, as brain function changes in response to changes in the classroom, and the brain influences the classroom environment through its overt acts. The same kind of sensory, motor, and sensorymotor communication between the lower and upper divisions of the brain occurs on the playground. The sheer amount and range of this kind of activity is overwhelming m both in the classroom and on the playground. We move upward to the middle division where the tiny hypothalamus is also engaged in an amazing number of life-sustaining jobs such as regulation of food intake during snack and lunch, body temperature, blood pressure, heart rate, autonomic system response, endocrine function, social-emotional behavior, and movement. The larger thalamus appears to be a grand central station through which all the sensory information from the outside world, except smell, is passing and getting rerouted on its way to the upper division. Depending on the kind of sensory information (e.g., visual, auditory, touch, pressure, pain, or temperature), it is redirected to a route dedicated just to a specific kind of sensory information. This grand central station also seems to be a center for triaging information from the internal mental world. Information from one subdivision of the upper division arrives here and then is sent upward to another subdivision of the upper division. While listening to instruction and engaged in independent learning activities, the internal mental world is humming. As we move to the upper division, we continue to be overwhelmed by the sheer volume of activity. The basal ganglia are receiving inputs from the thalamus that are sent on to the cortex and receiving inputs from the cortex that are sent down to the thalamus. Some of the inputs are informational, whereas others are procedural and aimed at an internal operation or overt motor response downstream for a specific component skill for reading, writing, or computing. Some have few, if any, intervening synapses to traverse, but others have many. The more the circuits are transmitting from the lower to middle to upper, and from the upper to middle to lower divisions, the more the hippocampal circuits are active, as if they support in

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some way all this mental activity. As we approach the topmost division of the cerebrum, we observe the specialized computing facilities of its outer core. In contrast, to the lower and middle divisions that coded and transmitted low-level sensory information, these high-level work stations (see Figures 3.3a and 3.3b) seem dedicated to processing not only sensory but also sensory-free information they receive for the grander, larger, higher-order thinking jobs of the functional reading, writing, and computing systems. However, just as the individual units of the other divisions are constantly communicating with one another, these topmost processors communicate frequently with one another and constantly send messages to the other divisions. These are the Cross-Talking Computers of Mind m the crowns of the gyri that are particularly active when high-level thinking jobs are in progress. This complex communication process appears to be orchestrated not by a single high commander but rather by a panel of control agents dedicated to self-regulation and conflict management in the complex system where many computations are happening at the same time. Next, we select from the menu options in the virtual world specific primary projection pathways and navigate their routes for processing specific sensory information in the classroom for specific kinds of movement. See Table 3.2 for each of these, which we tour separately. We are impressed with how specialized the brain is, with separate pathways for processing different kinds of information and for producing different kinds of behavioial acts. All in all, we end this virtual tour with the perception that this is a great society of mind, with complex, but coordinated, communication among its members and procedures for conducting and regulating its varied activities that at times require conflict management (Minsky 1986). We turn now to a different approach to viewing the brain m from the perspective of in vivo imaging of the brains of living people.

COMPARISON OF TECHNOLOGIES ANALYSIS AT THE MACROLEVEL

FOR BRAIN

Much of the research reviewed in Part II draws upon the following tools to study the macrostructure and macrofunction of the brains of living individuals. The goal of the following tutorial is to provide educators with sufficient knowledge of these tools so that they can become informed consumers of brain imaging research findings that are relevant to learning to read, write, and compute. The tutorial begins with an explanation of the vocabulary used to describe where in the brain a specific structure or function is observed. Then each of the tools is described. Finally, Table 3.5 compares and contrasts each of these technologies on features such as nature of brain information (structural or functional, biological substrate assessed), spatial resolution (if relevant), temporal resolution (if relevant), invasiveness, and cost.

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TABLE 3.5 Tool

Imaging Tools for Studying Macrostructure and Macrofunction Biological Substrate

CT or CAT scana MRI b

Neuroanatomy

EEG c ERPs d

Kind of Image

Spatial Resolution

Temporal Resolution

Cost

Invasiveness

Low

Yesp

Structural

Fair

N/A

Neuroanatomy

structural

Excellent

N/A

High

No

Electrical activity

Brain waves at rest

Poor

Fair

Low

No

Electrical activity l'm

Brain waves during task

Poor

Excellent

Low

No

EPse

Electrical activityl'n

Brain waves during task

Poor

Excellent

Low

No

9

.

rCBFf

Blood flow

Functional

Fair

Poor

Moderate

Yes`/

PET g

Blood flow

Functional

Moderate

Poor

High

Yes`/

flVIRIh

BOLD ~

Functional

Excellent

Moderate

High

No

t[MRSi

Chemical activity

Functional

moderate

Poor

High

No

MEG j

BOLD ~

Functional

Good

Excellent

Very high

No

DT-MRI k

White matter

Structural

Good

N/A

High

No

aComputer-Assisted Tomography bMagnetic Resonance Imaging (structural only) CElectroencephalography dEvent-related potentials eEvoked potentials fRegional cerebral blood flow, prior to PET gPositron Emission Tomography that measures rCBF hFunctional Magnetic Resonance Imaging /Functional Magnetic Spectroscopic Imaging JMagnetoelectroencephalography kDiffusion Tensor Magnetic Imaging /Mostly cortical dendritic activity mTime-locked to stimulus, task required nTime-locked to stimulus, no task required ~ Oxygenation Level Dependent response PX-rays '/Radioactively labeled substances

Specialized Vocabulary As e m p h a s i z e d in this chapter, the brain can be analyzed along t o p - d o w n , right-left, or f r o n t - b a c k axes. F i n d i n g structures or l o c a t i o n o f activity in brain scans d e p e n d s o n u n d e r s t a n d i n g w h i c h o f these axes was used to scan the brain. T h e c o m p u t e r regulating the brain i m a g i n g p r o c e d u r e s generates a very thin slice o f the living brain along o n e o f these axes. D e p e n d i n g o n w h i c h o f these axes was used to generate the image, specialized v o c a b u l a r y is used. As s h o w n in Figure 3.13, if the c o m p u t e r

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scans the brain from the top down, an axial slice results; if the computer scans the brain from the side along the right-left axis, a sagittal slice results; and if the computer scans the brain from the front to back, a coronal slice results. Figure 3.14 shows h o w a sagittal (side) slice can be related to an axial (top-down) slice. Figure 3.15 shows h o w a coronal slice can be related to a sagittal slice, and Figure 3.16 shows h o w a coronal

FIGURE 3.14

Relationshipbetween axial and saggital views.

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FIGURE 3.16

Relationshipbetween coronal and axial views.

slice can be related to an axial slice. Figure 3.17 shows h o w a sagittal slice can be related to an axial or coronal slice. Figure 3.18 shows h o w an axial slice can be related to the whole brain and the front-back and right-left axes w h e n looking from top down. Figure 3.19 shows h o w a coronal slice relates to the whole brain and the t o p - d o w n and right-left axes. Figure 3.20 shows h o w a sagittal slice relates to the whole brain

General Principles of Macrostructure and Macrofunction

F I G U R E 3.18

MRI axial slice and top brain view.

67

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FIGURE 3.20 MP,.Isaggitalslice and lateral brain view.

and the t o p - d o w n and front-back axes. Note that for both the axial and coronal slices the left side is on the right side, and the right side is on the left side. This convention was introduced by radiologists who, when facing a patient, view the patient's left side on their own right side and vice versa. N o t all neuroscientists use the convention; so one should check whether the left side of the brain is on the right- or left side of a brain image. Next we describe the brain imaging techniques that are currently used for clinical and research purposes. All these techniques are referred to as in vivo because they can

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be given to living people. In general, these imaging techniques allow us to look inside brains at the surface cortical areas and/or deep inside the white matter and nuclei such as the thalamus. Each of these imaging techniques, however, assesses a different biological substrate in the brain, and any conclusions about the brain's structure or function needs to be restricted to that substrate; for example, macrolevel or microlevel neuroanatomy, blood deoxygenation, chemical activation, or electrical activity. Some of the techniques generate images that show the exquisite structural detail in a living brain. For example, many of the macrostructures discussed earlier in this chapter are evident in Figures 3.14 to 3.20. Other techniques generate images that show where the brain is activated while it performs a specific task. Other techniques record the electrical activity in brain waves as the brain rests or performs an activity. The techniques vary as to whether they generate spatial information about neuroanatomy or temporal information about information processing or both kinds of information. Those that generate spatial information differ in their spatial r e s o l u t i o n - - h o w precise they are in locating a function in space. The techniques also vary in the resolution or precision of the temporal information they generate. Some are accurate for only an interval of a few seconds and may miss important brain events that take place on a temporal scale measured in milliseconds (thousandths of a second). Others are accurate in a measurement scale calibrated in milliseconds. Some require the person to lie very still in a magnet, which is a confined tubular space that generates a powerful magnetic field, but others require only that electrodes be pasted or capped onto the scalp. Some are invasive in that they expose the person to ionizing radiation or inject the person with radioactively labeled substances, whereas others are noninvasive because the person is not subjected to radiation or radioactivity. All these techniques assess brain at the macrolevel; that is, large collections of neurons contribute to the resulting neuroanatomical, neurophysiological, or electrophysiological information. The first three techniques generate structural information about neuroanatomy, whereas the subsequent ones all generate information about brain activity or function.

Computer-Assisted Tomography (CT or CAT Scan) C T scans use X-rays to create axial slices of the brains (planes that are illustrated in Figures 3.14, 3.16, 3.17, and 3.18 for M R I scans). The X-rays are invasive because the patient is exposed to radiation. C T scans are obtained while the patient lies quietly and is not performing a task. Thus, the scans reveal structural information about fluid and bone structure rather than neuroanatomy or functional information about the brain at work. C T scans often are used clinically to assess whether infants or young children have had intraventricular bleeds due to damage to one or more of the ventricles that contain cerebospinal fluid. Such bleeds are a sign that some brain

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damage has occurred. However, just because damage is not evident on a CT scan does not mean that none has occurred because their spatial resolution is not as good as that of some of the other techniques.

Magnetic Resonance Imaging (MRI for Structural Scans) M R I scans, in contrast to CT scans, are not i n v a s i v e - the patient is not exposed to any ionizing radiation. In addition, unlike CT scans, MILl scans can be programmed to generate data in any plane and thus axial, sagittal, or coronal slices. MILl scans utilize the fact that the body contains hydrogen nuclei (protons) that absorb and give offenergy in the presence of a magnetic field. MILl scanners (see 1Kichards 2001 for a picture) use a very powerful magnet to create a strong, steady magnetic field that realigns the hydrogen protons to create a picture with exquisite detail about neuroanatomy (see Figures 3.14 to 3.20, which were generated with MILl). Increasingly M R I scans are used clinically to make medical diagnoses.

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) D T - M R I is a very recent technique. D T - M R I uses the diffusion of water along restricted pathways to study white matter tracts. For example, Klingberg, Vaidya, Gabrieli, Moseley, and Hedehus (1999) applied this technique to study the myelination and organization of frontal white matter in children.

Cognitive Paradigms in Functional Imaging All the techniques that follow, except EEG at rest, require use of cognitive tasks. Early imaging studies used hierarchical tasks and the subtraction method. At each increasing level in the hierarchy a task was hypothesized to require one additional processing operation than the immediately preceding task lower in the hierarchy. The activity for the task one step lower was then subtracted from the task one step higher. Demonet, Wise, and Frackowiak (1993) criticized this hierarchical approach and noted the limitations of the subtraction method. As a result, many imaging researchers are now designing cognitive tasks in which multiple variations are compared to a common baseline (Frackowiak 1994).

Regional Cerebral Blood Flow (rCBF) This technique predated Positron Emission Tomography, which is discussed next, and used injection of radioactively labeled substances to track the blood flow as

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71

adults performed a variety of cognitive and language tasks. Mthough this technique generated much of the early research information about reading in adults, it is not used very much anymore.

Positron Emission Tomography (PET) This technique, which also measures rCBF, injects radioactive substances such as carbon-l l, fluorine-18, oxygen-15, or nitrogen-13 into the blood stream (Karuski, Horwitz & Rumsey 1996). The PET scanner uses a ring of detectors to measure and localize the radioactive signal from the body and thus measure rCBF. Certain radioactive substances can be used to monitor brain activation because the activated regions of the brain will selectively pull in the radioactive substances associated with increased blood flow and metabolism. Much of what we know about adult reading and reading disability in adults with developmental dyslexia is based on PET research. However, this technique is invasive, generally is not approved for use with children because the risk from radioactivity in the developing brain is unknown, and requires an expensive cyclotron.

Functional Magnetic Resonance Imaging (fMRI) In contrast to structural M R I , fMRI measures different physiological properties of neural activation while a person performs a cognitive task. The resulting scan is therefore functional in that it shows where the brain is active during the cognitive processing the task requires. The reason that this technique can show where brain activation occurs is that synaptic activity results in local increases in blood flow because of energy demands from glucose, oxygen, and nutrients, all of which are supplied by the blood to the brain (see Chapter 2, and Sanders & Orrison 1995). The fMRI measure that localizes cognitive or language functions is known as the Blood Oxygenation Level Dependent response (BOLD). The BOLD response is sensitive to processes outside the neuronal cell (extracellular). The tMRI technique can localize the area of activated brain to within one cm (Hillyard 1998), which is considered excellent spatial resolution. However, the temporal resolution of fMRI is poor. It is a misconception that the brain is simply "lighting up" as it performs its work. The published patterns of localized activation are based on comparing experimental conditions (tasks) of interest and a baseline or control condition or task. Thus, where the activation occurs depends on which tasks are used for the experimental and comparison conditions. A very recent new application (fc MRI) studies patterns of functional connectivity while subjects rest but do not perform a task (Cordes et al., 2000). Acquiring and analyzing the results o f t M R I scans is very labor intensive and time consuming, as well as expensive. A multidisciplinary team including neuroradiol-

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ogists, neuroscientists, computer scientists, physicists, engineers, and psychologists usually is required. As a result, flVItkI is not practical now for clinical purposes, but is very useful for research purposes. With further technological and conceptual advances in designing and interpreting cognitive tasks, f M R I may become clinically useful in diagnosing reading, writing, and math disability and assessing response to treatment for these disorders. Clinical utility of f M R I is likely to be greatest/fused along with measures that have been validated at the behavioral level as well.

Functional Magnetic Spectroscopic Imaging (fMRS) Like flVIRI, this technique is used for research purposes and requires a multidisciplinary team. It uses the same equipment as f M R I for scanning, but different computer software. Like f M R I , fMRS can detect a signal from the brain using a detector inside a large magnet. The main difference between f M R I and f M R S is that the magnetic resonance signal in f M R I gives information about the spatial position of water in brain, but flVIRS gives information about both spatial position and chemical information in brain. The f M R S technique cannot detect the activity of all molecules m only those for which the chemical spectra are known for brain. One of these chemicals is lactate, which is a metabolite in the process of using energy. Lactate activation is of interest because it may reflect metabolism both outside (extracellular) and inside (intracellular) the neuronal cell (Sappey-Marinier et al. 1992), in contrast to the BOLD response on flVIRI that assesses blood flow only outside the cell. However, fMtLS does not have the spatial resolution of fMILI and does not permit conclusions about localization of brain activation as precisely as fMtLI does.

Electroencephalography (EEG) In contrast to these other techniques, EEG records brain waves during mental activity through electrodes placed on different positions of the s c a l p - from 19 to 128. This technique usually does not stimulate the brain electrically but rather provides a graphic display of the electrical activity that the working brain generates; thus, it is not invasive. The brain wave signal is weakened in strength as it passes through the bony skull. Therefore, a gel (paste-like substance) is used to improve the conductance of the electrical signal, and the recorded signal is amplified before it is analyzed. The resulting signal from the brain at rest is more likely to reflect cortical activity, which is nearer the surface, than subcortical activity. This technique provides some spatial information - - for example, where a signal was recorded, although such information has to be treated cautiously because where it is originated and where it was recorded are not necessarily the same given the complexity of neural connectivity (see Chapter 2). Given this complexity, it should not be surprising that interpreting the resulting brain waves simply by visual inspection can be a challenge.

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Event-Related Potentials (ERPs) and Evoked Potentials (EPs) In contrast to free-running EEG, both ERPs and EPs are recorded in reference to a time-locked stimulus. ERPs and EPs can be recorded using either EEG electrodes or MEG detectors, which are described in the next section. Therefore, the resulting brain waves are an excellent source of temporal information for neural events that rapidly change from one brain region to another region during processing. EPs are especially useful with newborns because they do not require participation of the person while the electrical signal is recorded. Audiory EPs can assess hearing in infants and visual EPs can assess visual acuity in infants. ERPs are administered while the person performs a task, and thus are useful in assessing components of brain waves associated with higher order cognitive processes. ERPs and EPs are relatively inexpensive to acquire compared to techniques that require a magnet and multidisciplinary team. However, the resulting data do require sophisticated data analysis. A disadvantage of ERPs is that they record brain electrical activity after it passes through the bony skull, which weakens the signal. Sometimes researchers record directly on the cortex ofpatients before neurosurgery; the results of these studies provide valuable information that is more precisely localized as to the source of the ERP signals.

Magnetoelectroencephalography (MEG) This technique is similar to EEG. Its spatial resolution is not as precise as fMRI, but it has excellent temporal resolution. An array of highly sensitive magnetic field detectors (> 100 positions) is placed around the head so that magnetic fields can be recorded from the brain. For both ERPs and MEG, activation is measured by averaging the electromagnetic signal over many repetitions of the same stimulus. Like ERPs, MEG is time-locked (synchronized) to the onset of a stimulus and is not invasive or hazardous to children. MEG is not widely available yet because it is very expensive.

Comparison of the Imaging Tools Table 5.5 provides a succinct overview of these various research tools for studying living brains at the macrolevel. In addition to the material in this chapter, the following are recommended to obtain more information about each of these tools: structural M R I (Leonard 2001), functional M R I and MRS (Richards 2001), EEG, ERPs, and EPs (Languis & Wittrock 1986; Molfese, Molfese & Kelly 2001), and PET (Lyon & Rumsey 1996). These techniques will continue to e v o l v e - both the hardware and the software are continually upgraded. As Dr. Kenneth Maravilla, Director of Imaging in the University of Washington Radiology Department, has quipped, the technology does keep c h a n g i n g - - b u t so does the language we are

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trying to study. O n l y language changes m o r e slowly than technology, w h i c h gives researchers in this area s o m e t h i n g for w h i c h to be grateful!

RECOMMENDED

READING

In addition to the r e c o m m e n d e d readings at the end o f C h a p t e r 2, the f o l l o w i n g are also r e c o m m e n d e d for further reading.

For more information on neuroanatomy

at the m a c r o l e v e h

Hendelman, W. 1994. Student's Atlas of Neuroanatomy. Philadelphia: W. B. Saunders Co.

For more information on macrofunction: Luria, A.R. 1973. The working brain. An introduction to neuropsychology. New York: Basic Books. Luria, A.R. 1980. Higher corticalfunctions in man, 2nd ed. New York: Basic Books. Springer, S. & Deutsch, G. 1985. Left brain. Right brain, revised Ed. New York: W. H. Freeman & Co. Stuss, D. & Bensen, D. 1986. The frontal lobes. New York: Raven Press.

F o r m o r e i n f o r m a t i o n o n the b r a i n i m a g i n g t e c h n o l o g i e s : Frackowiak, R. 1994. Functional mapping of verbal memory and language, Trends in Neuroscience, 17:109-115. Languis, M. & Wittrock, M. 1986. Integrating neuropsychological and cognitive research: A perspective for bridging brain-behavior relationships. InJ. Obrzut & G. Hynd, eds., Child neuropsychology, Theory and research. 1:209-239. New York: Academic Press. Leonard, C. 2001. Imaging brain structure in children, Learning Disability Quarterly. 24:158-176. Lyon, G. R. & Rumsey, J. M., eds. 1996. Neuroimaging: A window to the neurologicalfoundations of learning and behavior in children. Baltimore, MD: Paul H. Brookes. Molfese, D., Molfese, V. & Kelly, S. 2001. The use of brain electrophysiology techniques to study language: A basic guide for the beginning consumer of electrophysiological information, Learning Disability Quarterly. Richards, T. L. 2001. Functional magnetic resonance imaging and spectroscopic imaging of the brain: Application of fMRI and fMRS to reading disabilities and education, Learning Disability Quarterly. 24:189-204.

MAKING

CONNECTIONS

Q u e s t i o n s p r e c e d e d by * m a y be most appropriate for graduate students. 1. W h a t kinds o f evidence exist that s h o w sensory modality alone cannot explain individual differences in learning in school age children and youth?

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2. Touring the brain's neuroanatomy required coordination of both the visual spatial system and the language naming system of the tourist. How might students have to coordinate more than one brain system in learning specific school subjects? 3. Why should the original research findings on split-brain patients not be overgeneralized to conclude that students have fight-brain and left-brain learning styles? 4. What might be the educational relevance of the structural organizing principles of the brain? What implications might these organizing principles have for a systems approach to instruction? 5. What might be the educational relevance of the functional organizing principles of the brain? What implications might these organizing principles have for a systems approach to instruction? 6. How are primary, secondary, and tertiary areas of cortex different? How might each contribute to school learning? Why are tertiary association areas so important for school learning? *7. If you became a member of a multidisciplinary team doing brain imaging research, what kinds of questions would you suggest for the research to address? What kinds of educational questions might brain imaging research answer? What kinds of educational questions could not be answered with brain imaging research? What are the most important questions we should be asking in educational research at the dawn of the twenty-first century?

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General Principles of Brain Development

In this chapter we cover the most important general principles for understanding brain development. We begin with fertilization, neurulation, and the six neural processes that begin before birth, and proceed to the unique mechanisms by which nature-nurture interactions drive brain development after birth. For this overview, we draw on the outstanding syntheses of the developmental neuroscience literature provided by Diamond and Hopson (1998) and Eliot (1999). Next, we consider a variety of developmental issues that have implications for literacy development, including developmental trajectories, developmental axes, critical periods, maturation, learning mechanisms, the role of tertiary association areas in literacy acquisition, and the role of emotional intelligence. Then we flesh out the concept of functional systems in the brain at work, and differentiate four functional language systems m Language by Ear, Language by Mouth, Language by Eye, and Language by H a n d - - t h a t are on their own developmental trajectories. Finally, we discuss neurological and genetic constraints that may constrain the ease of becoming literate.

Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

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FERTILIZATION AND NEUtLULATION Fertilization

Brain development begins at the moment of conception. A miraculous, but amazingly predictable, set of events begins when a father's sperm penetrates the mother's ovum (egg). The fertilized egg, which contains 23 chromosomes from each parent, begins to divide. Within a few days, the fertilized egg has developed into a blastocyst of 32 cells. Three to five of these cells give rise to all the rest of the cells in the embryo; the rest become the placenta that attaches, by the second week, to the wall of the mother's womb and nourishes the embryo as it evolves into a fetus. Refer to Unit 3-1 in Diamond et al. (1985). Neurulation, the development of the brain and nervous system, begins 19 days after fertilization. By the twenty-fifth day, the neural tube closes, first in the middle, then at the head end, which becomes the brain, and then at the tail end, which becomes the spinal cord (see Figure 4.1, and Unit 3-2, Diamond et al. 1985). The three emerging segments of this neural tube become the midbrain, forebrain, and hindbrain, respectively (see Chapter 3, and Unit 3-7, Diamond et al. 1985). Cerebral convolutions (folds) are visible at 24 weeks, and increase throughout gestation (see Figure 4.1). At birth only one-third of the cortex will be on the surface--the rest will be tucked below the surface. At birth, the brain will weigh about 400 g, but by eleven months it will double in size (850 g) and by age three months it will nearly triple (1100 g) and not shrink until about age 30 (by 100 g from then until age 75). Six neural processes drive early brain development: cell proliferation, cell differentiation, cell migration, synaptogenesis, cell pruning, and myelination. The last three continue after birth. However, experience begins to play a role in brain growth during gestation and plays a major role following birth (Diamond & Hopson 1998; Eliot, 1999). The brain reaches its adult weight around age 14. Most of the growth after birth is attributed to myelination (which is partially regulated by both genetic mechanisms and some environmental control) and dendritic growth (which is under some genetic control but also is responsive to experience). For example, 83 percent of the dendritic sprouting occurs after birth. There is a lot of dendritic growth and branching during the middle childhood years when literacy skills are acquired.

SIX N E U R A L P R O C E S S E S Much of this research relied on structural analysis of embryos (1 to 7 weeks gestation) or fetuses (8 to 38 weeks gestation) that are spontaneously or purposely aborted.

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F I G U R E 4.1 Prenatal development of the human brain showing a series of embryonic and fetal stages. From FUNDAMENTALS OF HUMAN NEUROPSYCHOLOGY by Bryan Kold and Ian Q. Whishaw 9 1980, 1985, 1990, 1996 by Worth Publishers. Used with permission.

Cell Proliferation B e t w e e n the fifth and twentieth w e e k 50,000 to 100,000 n e w cells are generated each second; some are neurons and some are glial cells. Most neurons are formed by four months gestation. During the peak in neuron genesis about a half million neurons are created each minute. By birth children have most of the neurons they will have, but the glial cells continue to be produced, at a low rate, throughout the life span. Some additional n e w neurons may be produced later in gestation or after

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birth, for example, in the hippocampus. Unlike cells in the PNS that can regenerate, the neurons in the CNS generally do not regenerate if they d i e - - r e c e n t research suggests that there may be some exceptions to this general principle.

Cell Differentiation Different types of cells are generated. For example, the cells in the cortex have a characteristic pyramidal or star shape. See Figure 2.2 for examples of different cell structures, which are associated with different functions in the nervous system.

Cell Migration As soon as they are generated, neurons begin migrating, but the migration process is only halfway complete when the neural generation process is complete. Genetic codes probably signal to where in the neural architecture the different kinds of neurons should migrate, but are not the only factor regulating the neural migration process. Glial cells guide the migration process. They make the tracks to which neurons attach; neurons use these tracks as scaffolding for the journey. In the cerebral cortex the neurons migrate into place first in the lower layers, and then climb to the higher layers on the scaffolding provided by the glial cells. Refer to Units 3-11 and 5-31 in Diamond et al. (1985).

Synaptogenesis The forming of potential synaptic connections in the cortex begins at about seven weeks after conception, but continues after birth, especially the first two years of hfe, but probably throughout life. (see Figure 4.2). Neural circuits are wired in the following way. Axons send out shoots in response to chemical cues from nerve growth factor, to nearby and distant neurons (their dendrites or other neuronal parts). Up to 200,000 contacts may be formed in some places, and as many as 15,000 contacts may be formed in many places. Some of these are probably random. Genes direct the growth of axons and dendrites to their approximate locations, but once they are in place in brain architecture, experience will shape their functional connections. Potential synapses compete to survive on the basis of a struggle for nerve growth factor and sufficient incoming stimulation. Neurons that are hooked up but not used may die. Others are eliminated by programmed cell death (pruning) discussed in the next section. Huttenlocher and de Couton (1987) studied the increase in synapses and development of dendrites and branching patterns in synapses in the visual system (striatal cortex) from seven months gestation to two years after birth. Between the

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FIGURE 4.2 Golgi-stainedsections of human cerebral cortex taken from equivalent areas of the anteriorportion of the middlefrontal gymsat differentages.Although densityof neurons doesnot appear to change, complexityof dendritic arborizationsdoes changewith age. Reproduced from Conel (1939, 1941,1951, and 1959) in The Human Brain: An Introduction to its Functional Anatomy 3/e by John Nolte. Copyright 1993 by Mosby Year Book.

second and fourth months there is a tenfold expansion in synapses. The number of synapses peaks at the fourth month. At the tenth month the number of synapses levels offby about 60 percent, to adult levels. However, between age four and ten, the synapses in the visual cortex continue to be sculpted by experience. These investigators proposed that this dynamic sculpting of synapses is the neural basis for the plasticity of the nervous system that allows humans to continue to learn as they interact with their environment. In frontal lobes (middle frontal gyms) Huttenlocher (1979) identified two phases of synaptic growth. During the first phase in the first year of life the dendrites grow, many synapses form, and the cortex becomes thicker. Although synaptic density of newborns and adults is comparable, the patterning of their synapses differs. Presynaptic projections are irregular in width in newborns but separated into welldefined shapes in adults. During the second phase, from the first birthday to adolescence, two processes occur simultaneously--development of new dendrites and synapses along with selective pruning of redundant or unused synapses. The maximum density of synapses is at age 10, with selective pruning occurring between 10 and 16.

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Huttenlocher (1990) compared the structural development in the posterior cortex (occipital lobes) and the anterior cortex (frontal lobes) and found that it is comparable in some ways but different in others. More recently, Huttenlocher and Dabholkar (1997) compared the development of synaptic connections in auditory cortex (Heschl's gyrus) and prefrontal cortex (middle frontal gyrus). In both regions synapses begin to form in the fetus before 27 weeks gestation, but they develop more rapidly in the auditory cortex than prefrontal cortex. Also, synaptic pruning is completed earlier in the auditory cortex (by age 12) than in the prefrontal cortex (by mid-adolescence). These contrasting patterns in neural development may have implications for the sensitive periods in acquiring aural/oral language, reading and writing, and executive functions, which are discussed in Part IV.

Cell Pruning Not all neurons that are born survive. Initially the brain is overconnected with too many synapses. Such overconnection is not energy efficient because synapses require metabolic energy. For example, Chugani's PET study (Chugani, Phelps & Mazziotta 1987) showed that a brain at age three uses twice as much metabolic energy as the adult brain does. In the most extreme form of overconnection, seizures can occur due to too much electrical crosstalk. Pruning begins at seven months and continues to adolescence, when the brain reaches adult levels of efficient metabolic activity. Up to 40 percent of the synapses may be eliminated by 21 months. Between childhood and adolescence about 20 billion synapses are lost each day. Pruning occurs in the visual cortex between ages one and ten, and in the frontal lobes from age seven to adolescence.

Myelination Unmyelinated axons may not be able to fire successive action potentials fast enough to transmit information, or may require excessive energy to do so. Myelinated axons that can fire more rapidly result in more effective and efficient neural circuits. Genes control the timing of the formation of myelin, which is 80 percent lipid and 20 percent protein. At birth the spinal cord and brain stem are almost fully myelinated. That ensures that the parts of the brain needed to sustain life are functional. Just after birth, the midbrain and cerebellum begin to myelinate. By age two the cerebellum, which helps the cortex regulate timing of behavior, is fully myelinated, supporting walking and talking. The subcortical forebrain, which includes the thalamus, basal ganglia, and parts of the limbic system, also myelinates during the first two years of life. These structures play important roles in sensory and motor learning and emotional regulation early in development.

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However, areas needed for awareness, alertness, memory, and thinking (e.g., cortex, hippocampus, and reticular activating system in the brain stem) may take ten or more years to myelinate fully. Higher-order cortical association areas myelinate more slowly than do the primary cortical areas. The myelination process spans the preschool years through adolescence and even young adulthood. Some parts of the limbic system also myelinate slowly. For example, the hippocampus in the limbic system myelinates very slowly, which is why there are no conscious memories of early life events. The anterior cingulate, which plays a role in emotional expression, myelinates more quickly than orbitofrontal cortex and amygdala that play a role in regulating emotion. Thus, children may express emotions early in development, but get control over their emotions only later in development not only because of maturation but also because of learning from experience. Children improve from age three to puberty in ability to discriminate closely spaced sounds--probably due to myelination and synaptic refinement in the auditory cortex. These genetically constrained patterns of myelination explain, in part, why some functions are easier for children earlier in development and other functions are mastered later in development. The myelination process results in increased white matter tracts over the course of development. We are often asked if increasing the fat in the diet would help the axons build the fatty myelin sheath more quickly. Myelination is probably constrained by genetically regulated maturation, and may also be influenced, to some degree, by experience. Whether diet (eg. eating omega 3 fatty acids) also plays a role is a topic of research.

NEUROMATURATION Increasingly in vivo brain imaging is being used to study neuromaturation (e.g., Paus et al., 1999). Recent imaging studies add to the pioneering work of Huttenlocher and colleagues. Structural magnetic resonance imaging (MRI) has been applied to study developmental change in volume of gray matter and white matter. Pffefferbaum, Mathalon, Sullivan, Rawles, Zipursky, and Lim (1994) studied individuals ranging in age from three months to 30 years. They found that brain volume increased 300 mL from three months to ten years in both sexes. Cortical gray matter peaked around age four, and then decreased. In contrast, cortical white matter increased steadily until about age twenty and was more likely to stay constant through the fifth decade of life. Diffusion tensor imaging (DTI) has also been applied to study of progressive myelination of axons during neurodevelopment. Conturo et al. (1999) studied 111 individuals ranging in age from four to seventeen who demonstrated age-related increases in white matter density in fiber tracts for motor and speech functions. The developmental pattern suggested that myelination is a gradual process that continues from childhood through adolescence. Of special interest, the left arcuate fasciculus (see Figure 3.5), which links Wernicke's area and

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Broca's area, showed increasing myelination. Klingberg et al. (1999) showed that the frontal lobes of children, who were on average ten years old, were less myelinated than those of adults, but that, surprisingly, the organization of the axons in the fight temporal lobe of adults was more mature than in the left temporal lobe.

EMERGENT

NORMAL VARIATION

In M a c r o s t r u c t u r e

Primary sulci, which are formed by the seventh month of gestation, do not show much structural variation across individuals, but the secondary and tertiary sulci, which are not fully formed until the first birthday, do vary tremendously in structure across individuals (Eliot 1999). See Chapter 3 for a definition of sulcus and the distinctions among primary, secondary, and tertiary cortical association areas. However, exceptions to this generalization occur in Heschl's gyrus (including the primary auditory area) and the primary visual area. For example, Leonard, Puranik, Kuldau, and Lombardino (1998) studied individuals ranging in age from 5 years to 65 years and noted that normal variation in neuroanatomy persists throughout the life span. They speculated, based on Van Essen (1997), that this structural variation may be the result of differences in neural connectivity.

In M a c r o f u n c t i o n

Electrical stimulation of the brains of patients who perform research tasks prior to neurosurgery has shown that the rear one-third of the frontal lobes, which is used in speech, is relatively uniform across individuals in contrast to the considerable variation across individuals as to exact locations of language processing in the temporal and parietal regions (Calvin & Ojemann 1980; Ojemann 1983). Nevertheless location of the speech center exhibits some normal variation across individuals.

NATURE-NURTURE

PROCESSING MECHANISMS

The six neural processes just discussed are partly regulated by genes, but the 80,000 genes in the human genome probably do not code specific wiring directions. Genes influence the developmental sequence of skills, which is remarkably similar across children and cultures. For example, across cultures motor development proceeds from sitting, to crawling, to walking, to running, and language development proceeds from the one-word stage to the two-word stage, to telegraphic speech (multiword with some syntax missing), to syntactically complete utterances around

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age three. However, experience, as the brain interacts with the environment, influences which synapses are retained (active ones) and which are eliminated (inactive ones) (Diamond & Hopson 1998; Diamond, Krech & Rosenweig, 1964; Huttenlocher 1979; Huttenlocher & de Couton 1987). Initially many brain hook-ups are random, but then are modified by experience. Neurons are built to communicate with the e n v i r o n m e n t - - t o receive information from it, to think about this information, and to act upon it (Diamond & Hopson 1998; Eliot 1999). Environmental experiences do alter the brain in constrained ways. Diamond et al. (1964) provided a groundbreaking demonstration of this naturenurture interaction. Shortly after birth, rats from the same litter were randomly assigned either to a complex and stimulating environment with toys and other rats, or to a nonstimulating environment in a toyless cage with no other rats. Eighty days later the rats were sacrificed and their brains were examined. Rats that lived in stimulating (enriched) environments had thicker visual cortexes by 6.2 percent than those living in nonstimulating (deprived) environments. Other research (Connor & Diamond 1982; Greenough, Volkmar & Juraska 1973; Holloway 1966) showed that dendritic branching accounted for the thicker cortex in the rats in the enriched environments (see Unit 3-12, Diamond et al. 1985). Greenough et al. replicated the original findings for visual cortex but not the frontal cortex. This pattern of results suggests, at least for rats, that environmental stimulation exerts its greatest effect on cortical areas that code incoming information from the environment rather than on cortical areas that are involved in acting on the enviroment or coordinating mental processes. Development of the frontal areas in humans, which is where our highly powered cortical Cross-Talking Computers of Mind are for abstract thinking, may require a different kind of environmental stimulation than do the posterior areas that respond to sensory stimulation. Further research is needed on this issue, which is relevant to use of computer games in developing thinking skills (see Diamond & Hopson, 1998). Subsequently, Scheibel (199!) extended the initial work with rats to humans. Simonds and Scheibel (1989) traced dendritic development in the cortex of young children from 3 to 72 months. At birth most neurons in cortex have dendrites with only first and second order branches. These lower order dendritic branches, which are more likely to be under genetic control, split into four branches, and by six months there are third- and fourth-order split offs (Diamond & Hopson 1998). By the second and third year, fifth- and sixth-order branching occurs. Dendrites that are more stimulated (e.g., left-brain structures used for language) are more likely to have the higher order branches than the dendrites that are less stimulated (e.g., fight-brain structures that are less used for language). The longer a dendrite grows, the more likely it is to divide. In just one dendrite there may be as many as eight branching points. Jacobs, Schall, and Scheibel (1993) discovered that in older adults the dendrites (particularly at the third and fourth levels) increased in relationship to the years of education. Lower-order dendritic branching may be more genetically constrained, but higher-order dendritic branching may be more influenced by experience. Exactly

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how lower-order and higher-order dendritic branches may differ in their effect on the analog computations at the microlevel (see Chapter 2) has not yet been investigated. Experience changes dendrites in specific ways. The spines on dendrites grow, change shape, or shrink in response to interactions with the world. Long lollipop-like spines, with long stalks and small heads, are associated with a little experience, whereas the umbrella-like spines, with large heads and short stalks, are associated with more experience. During learning a nubbin spine may transform into a lollipop structure, or a lollipop structure might transform into an umbrella-like structure. Interactions with the environment may cause sprouting of dendritic branches and spines in other areas of the brain as well m not just cortex. Brain growth after birth is mainly attributed to the burgeoning (branching and spine sprouting) of dendrites, the "magic trees of mind," in response to experience (Diamond & Hopson 1998). The brain could not fine-tune its neural circuits without interactions with the environment. For example, infants (Latin for without speech) are capable of perceiving and producing all the speech sounds of all languages, but the speech they hear shortly after birth wires their circuits for the speech sounds in the language spoken in their environment. By six months, their speech sound perception has been finetuned to the speech sounds to which they are exposed (Kuhl et al. 1992). Experience, however, may not only fine-tune neural circuitry but also cause structural changes in genes (DNA). Brooks, Cory-Slechta, and Federhoff (2000) treated mice with labeled Nerve Growth Factor (NGF) genes and then exposed them to one of three kinds of experience: repeated spatial learning with a learning component, repeated rate performance with a performance component only, or standard housing with no special experience. The investigators measured labeled gene expression of NGF in the cholinergic pathways between forebrain and hippocampus as a function of the opportunity for learning. Gene expression did not change in the rats in normal housing. It did change in the other two groups of "activated" mice m b y 63 percent relative to controls in the rats that received a learning component and by 21 percent relative to controls in rats that received only a practice/performance component. Essential, life-sustaining functions for maintaining life may be hard-wired, but much of brain function is n o t ~ the brain has the capability to reprogram itself as it interacts with the environment (Eliot 1999; Posner 1979). There is a lengthy period of overconnectedness until environmental interactions pare brain circuits down to mostly useful, efficient connections. Specific circuits can become hard-wired when they are used enough. When learning a new skill, the brain may use more circuitry, but after learning the brain may become more efficient and use less circuitry. Alternatively, the brain may use substitute circuits. Genes and neurons may work together to create a crude wiring diagram in the brain, but only experience finetunes the wiring of the circuits (Diamond & Hopson 1998; Eliot 1999). Thus, development depends on genes and neurons in the brain as well as learning experiences as the brain interacts with the physical environment and with other brains in the social/cultural environment.

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O T H E R D E V E L O P M E N T A L ISSUES

Different Developmental Trajectories Different brain functions have different developmental timetables. Behaviors that have a longer time course of development are more modifiable (Diamond & Hopson 1998). The standard developmental sequence for the sensory systems is skin sensation, then balance, then taste, then smell, then heating and then vision (Diamond & Hopson 1998). Hearing begins early and matures gradually, whereas vision emerges late and matures quickly (Eliot 1999). In Part III we discuss the developmental sequence for component skills of functional reading, writing, and math systems, which may be more flexible because they are organized over a larger time course. Next we summarize generalizations about brain development, but huge individual differences may occur in these time tables.

Axes of Development Most brain development proceeds from bottom to top (Eliot 1999). The reticular activating system in the brain stem, which regulates breathing, heart rate, blood pressure, body temperature, calmness, and anxiety, is functioning in newborns. The amygdala and other limbic structures (e.g., hypothalamus, thalamus) mature next and regulate sleep, appetite, alertness, emotional reactivity (aggressiveness and impulsiveness), emotional content, and self-regulation of emotional reactivity, and attachment to others. Although the amygdala and its connections are functional at birth, the cortical part of emotional brain, for more mature feelings, is not yet functional. Motor skills also proceed from lower to higher: spinal cord to brain stem to primary motor cortex to higher order motor areas in the frontal lobe that integrate and guide purposeful action. The cortex takes longer to mature than the other structures, which may be why formal schooling over a relatively long developmental period (K-12) is needed for cognitive development in a postindustrial society. Brain development also proceeds from fight to left. During gestation, convolutions form in the fight cerebral cortex before they form in the left cerebral cortex. Between the third and sixth months, dendrites on the fight are longer and have more branches than those on the left (Simon& & Scheibel 1989). Between eight and eighteen months, the dendrites on the left grow longer and branch more. This dendritic growth during the second year corresponds to the emergence of the expressive language system (Language by Mouth). In the fourth year the interhemispheric commissures myelinate, allowing crosstalk between the cerebral hemispheres. Brain development also proceeds from back to front. Synapses grow relatively rapidly in the visual system in the rear compared to the frontal lobes in the front or

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anterior regions. The language system also develops from back to front. Posterior structures for understanding language tend to develop before the anterior structures for producing language. Synapses peak in the left temporal parietal zones between 8 and 20 months and between 10 and 24 months in the left frontal areas. Wernicke's area myelinates by age two, but Broca's area myelinates between ages four and six. Thus, children's ability to understand (receptive language) may develop more quickly than their ability to express their ideas (expressive language). The arcuate fasciculus that connects Wernicke's and Broca's areas is very slow to myelinate, indicating that ability to coordinate receptive and expressive language may also develop slowly over time. In addition, the language system may reorganize in a frontward direction. Infants process both content words and grammar in the rear of the brain, but 11 year-olds process the content words in the rear but grammar in the front (Neville, cited on p. 173 of Diamond & Hopson 1998). The frontal lobes are the last area to form fissures, and frontal synapses are pruned more slowly. Myelination for frontal lobes may continue into the young adult years (e.g., twenties). Thus the executive functions that are housed in the dorsolateral prefrontal cortex and regulate functions such as inhibiting, managing conflict, goal setting, planning, persisting on task, monitoring, attending and self-regulating, and supervising working memory are among the last functions mastered in development.

Critical Developmental Periods

Although dendrites retain the ability to grow and branch throughout development, certain functions appear to have critical periods, that is, developmental windows during which they are most easily learned and after which they may not be learned or may be learned only with great difficulty. Depriving organisms of developmentally approprite interactions with the environment can have deleterious effects on development of the nervous system. Hubel and Wiesel (1970) surgically tied the eyelids of kittens shut in order to deprive them of visual stimulation at different ages. Not only did deprivation result in structural changes to the visual system but also deprivation exerted its greatest effect during a developmentally sensitive time period. Biologists have studied the critical developmental periods in which functions are most easily acquired for many species, for example, vision in cats (Hubel & Wiesel 1970) or song in the mynah bird (Rausch & Scheich 1982). In humans, critical periods have been studied for vision (Wiesel 1982), language (Diamond & Hopson 1998; Eliot 1999), and cortical activity in the different lobes of the cerebrum (Chugani 1998). Binocular vision, that is, coordinating both eyes, appears to have a critical developmental period. If "lazy eyes" are not patched by age four, children's binocular vision may never be normal (Diamond & Hopson 1998). Vision is highly

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malleable until age two but less so until eight or nine, after which the wiring is pretty much in place (Eliot 1999). This developmental window suggests that the vision of all children should be screened around three, before the school years, to identify those whose binocular vision is not developing normally. There are nine reported cases of feral (wild) or socially isolated or congenitally deaf children who were raised without any human interaction and language stimulation during the critical period for language. Of these, only six, who were rescued before age seven, learned to speak. However, even though six learned some word meaning, their syntax and ability to isolate sounds were significantly more impaired than their vocabulary acquisition. The critical period for learning a first language is up to age six or seven, but first languages are learned most easily up to age three or four, and second languages are harder to learn after puberty. The window of opportunity for learning the phonology and syntax of a second language is greater before puberty (Diamond & Hopson 1998; Elliot 1999). Chugani (1998) has conducted PET studies with children whose parents granted informed consent even though PET is considered an invasive procedure. He compared the developmental periods of maximal functional activity (high glucose metabolism) for different brain systems. He discovered that the most active regions in newborns are the primary sensory and motor cortex, cingulate cortex, thalamus, brain stem, vermis of cerebellum, and hippocampus. By two to three months of age, the parietal, temporal, and primary visual cortex, basal ganglia, and cerebellar hemispheres are also more active. Between six and twelve months, frontal cortical activity increases. At age four, the cerebral cortex uses twice as much glucose as the adult brain. For cortical activity, glucose consumption remains high during early and middle childhood until adolescence, when it approaches adult levels. These developmental findings should be encouraging to educators: They suggest that although the developmental window for sensory and motor systems may peak during the preschool years, the developmental window for cortical activity is wide open during middle childhood and adolescence, the peak period for formal education. Teachers can make a difference during middle childhood and adolescence in sculpting the brain for high-level thinking skills. However, reading (Language by Eye) may have a critical developmental period early in formal schooling. In a large-scale study of 10,000 children with reading disabilities, 82 percent diagnosed in grades 1 or 2, 46 percent diagnosed in grade 3, 42 percent diagnosed in grade 4, and 10-15 percent diagnosed in grades 5 to 7 were brought up to grade level (Keeney & Keeney 1968). Earlier diagnosis and intervention may be more effective because there is less emotional interference due to chronic school failure. At the same time, it may be easier to create connections between the functional systems for aural/oral language and reading/writing around age six when the instructional environment is sculpting the neural machinery of the tertiary cortical zones, as discussed in this chapter. To the extent that reading is critical for development of thinking skills, it is important to make sure that the reading system becomes functional early in schooling.

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The Role of Maturation Early in the twentieth century, Gessell (1925, 1928) popularized the nativist view that behavior is genetically determined. The nativists believe that (a) learning depends on the unfolding of biologically preprogrammed timetables, and (b) instruction and practice cannot alter this timetable. Unfortunately, this nativist view, which has not been supported by neuroscience research in the late twentieth century, is still promulgated through workshops aimed at teachers, especially in early childhood education. Gessell's nativist views were based on motor rather than literacy development. Recent research rejects the hypothesis that practice and experience have little to do with motor development (Eliot 1999). As we will discuss in Part IV, experience, and not just biologically preprogrammed maturation, plays an important role in literacy development. Maturation refers to genetically preprogrammed instructions guiding synaptogenesis, dendritic branching, and myelination. Genes set limits on when a skill is first possible, but once it is possible, the skill will develop only if taught and practiced. The challenge is to provide sufficient instruction and practice when the learner is becoming maturationally ready--just entering the developmental zone when a skill is usually acquired m and not just assume that the skill will develop on the basis of maturation without experience. In Parts III and IV we offer an alternative to the nativist view m t h e nature-nurture interaction perspective for learning academic skills. This nature-nurture interaction perspective on the brain emphasizes the multiple domains of development, which are on different developmental trajectories within the same child and across children.

Learning Mechanisms Not only maturation but also learning mechanisms contribute to brain development. At least six learning mechanisms are involved in stimulating the dendrites that branch and sprout in response to academic instruction (Eliot 1999). These include habituation, novelty seeking, classical conditioning, operant conditioning, imitation, and verbal learning including language mediation. To learn, an organism needs to be responsive to changes in the environment but also selective as to what is responded to among the vast array of potential stimuli. Nature therefore has built in a gating mechanism whereby the organism responds for a period of time to what is novel, but once what was new becomes familiar, the organism ceases to respond further to that input. When the organism no longer responds to the same stimulus (once novel, now familiar), the organism is said to have habituated. Thus, habituation is the progressive decline in responding that serves a selective function in that it allows the organism to screen out constant stimuli that may have been informative once but no longer are. To avoid a steady state of habituation, however, nature has built in a complementary learning mech-

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anism m novelty seeking (curiosity) that causes the organism to be on the lookout for what is new (unfamiliar) in the environment. Organisms need to regulate a balance between habituation and novelty seeking, and children who have difficulty in self-regulating such a balance may have difficulty learning in the classroom. For example, children who habituate too easily may crave novelty and engage in noveltyseeking behaviors more than is normal in the instructional environment. As a result they cannot maintain attentional focus long enough to attend to instruction, to practice skills sufficiently to automatize them, and to create precise representations of specific words in long-term memory. Thus, an attentional problem may underly their problems in learning written language. Classical conditioning creates a paired association (connection) between two stimuli that occur closely in time or space. Once the connection is formed, then detection of one of the stimuli activates the other through spreading neural activation. Such connections might explain some learning on implicit memory networks that do not usually require conscious attention (see Chapter 5). Operant conditioning is learning an association (making a connection) between an individual's own action and another stimulus, often a reward that increases the probability of repeating the act or a punishment that decreases the probability of repeating the act. Imitation of a concurrent (present) or delayed (absent) stimulus, which may be verbal or nonverbal, is another learning mechanism. Initially, because of constraints in the memory system, the young can imitate only what is happening in the environment. Eventually, as memory capability increases, ability to imitate acts or repeat words that are represented in memory, but not in the immediate situation, emerges. Verbal learning may be specific to human children who use language to achieve cognitive goals. For example, talking may be used to (a) self-instruct during the learning process (verbal mediation), (b) relate a sequence of events in life (a narrative) that leads to understanding of cause and effect relationships, or (c) discuss cognitive concepts.

Importance of Tertiary Association Areas in Literacy Development A seventh learning mechanism--cross-talking computational neworks in tertiary or heteromodal cortex (see Chapter 5 ) - emerges around the sixth birthday, which is a major transition period in cortical development. The frontal lobe is becoming increasingly functional due to increasing myelination, synaptogenesis, and synaptic pruning. Thus, it is not surprising that formal schooling or increased responsibilities, like working in the fields or caring for younger children, begins around the sixth birthday across cultures (Eliot 1999; Hooper & Boyd 1986). Tertiary cortical processors are less controlled by external stimuli than are the primary cortical processors for sensory and motor functions, Video games, which stimulate the visual cortex, but not the prefrontal cortex, may promote learning via

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the novelty seeking mechanism, but not the kind of learning involved in planning, organizing, and thinking, which requires engagement of the powerful cortical computers of the prefrontal cortex (Diamond & Hopson 1998). These prefrontal computers are situated in a multilayered arrangement of over a third of a billion neurons that are shaped like a pyramid or star. Each neuron may have up to eight branching parts and many spines on each of the branches. The resulting surface area of the dendrites (branches and spines) is thus even vaster than the surface area of cerebral cortex, which varies from 1/32 to 1/4 inch in thickness. That is why a child with attention deficit disorder (which is thought to involve the frontal lobes) may be able to watch television for hours on end. Television can be processed by the posterior cortex and does not require the kind of sustained intellectual effort that prefrontal cortex does for achieving academic goals. Thompson and Nelson (2001) are developmental scientists who appreciate the importance of critical developmental periods, but they caution that the current emphasis on stimulating children during early development before age three may overlook the fact that significant brain development occurs after age three. In their view, for which they provide scientific evidence, it is important that children receive early stimulation, but that will not necessarily be sufficient to forestall future developmental problems without continuing stimulation. Children cannot be given some kind of intellectual booster shot before age three that will sustain their cognitive development throughout the life span. Developmentally appropriate environmental stimulation is needed throughout childhood and adolescence. The mind is still plastic during the school years m in fact Chugani's (1998) PET studies suggest that middle childhood may be a peak time for cognitive development. Even though some children may not have had optimal stimulation during the preschool years, there is still a lot teachers can do beginning at about age six and continuing through to adolescence to stimulate students' powerful Cross-Talking Computers of Mind in the tertiary association areas of cortex. Despite wide variation in amount and kind of early stimulation, neuroscience has shown that the brain has the capaci W to adapt across the life span (Thompson & Nelson 2001).

Role of Socioemotional Intelligence In addition to the seven learning mechanisms that were just discussed, socioemotional and motivational variables also influence the learning of academic skills. Learning in a social environment requires self-regulation of emotionality, which is reactivity to situations. Emotional intelligence, which is recognizing, interpreting, and responding appropriately to one's own feelings and others' feelings, therefore, plays an important role in academic learning. For example, the ability of preschoolers to manage impulsivity and delay gratification predicts their academic success in high school (Goleman 1994). School learning also depends on the execu-

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tive functions needed for goal-directed (i.e., motivated) behavior. These include setting goals, making plans for reaching goals, implementing plans, monitoring the implementation process, and modifying goals and plans as needed. Parts of the limbic system (see Figure 3.7), which is situated between the cerebral cortex and brain stem, play a role in both socioemotional functions and motivation. The lower core of the limbic system below the cortex is involved in bodily expression of emotion and goal-directed behavior. The upper core of the limbic system in the cortical regions (a ring of gyri in the frontal, parietal, and temporal lobes) is involved in conscious awareness and self-regulation of emotion and goaldirected behavior. Animal studies (Isaacson 1982) have shed insight on the role of the lower core of the limbic system in emotional and motivated behavior. The amygdala, which contains nuclei in the anterior, subcortical portions of the temporal lobe, appears to regulate emotionality. Removing the amygdala produces a tame, sluggish animal that does not seek social contact. Lesioning (damaging) the amygdala results in an animal that is reluctant to initiate behavioral acts. Electrical stimulation of the amygdala can suppress aggressive activities. Amygdala lesions early in life produce hyperactivity in animals. The septal area, which lies under the anterior and middle regions of the corpus callosum, also appears to play a role in socioemotional behavior and motivation. Septal lesions cause animals to increase social contacts, decrease locomotor activity (hypoactivity), and display rage reactions, hyperemotionality, or hyperreactivity. The hippocampus, which occupies the subcortical medial and ventral areas of the temporal lobe, also appears to play a role in socioemotional behavior and motivation. Hippocampal lesions can induce hyperactivity and distractibility that increase reactivity to unexpected stimuli. Hippocampal lesions can also interfere with habituation ~ with the result that the organism takes longer to realize that repetitive stimuli are familiar. These lesions can also create perseveration~ with the result that the organism repeats the same response over and over, even when the response is not instrumental in achieving a goal. Human studies (Eliot 1999) show that the amygdala in the lower core is highly connected to structures in the upper core of the limbic system such as the orbitofrontal gyrus and cingulate gyms. Damage to the orbiofrontal gyms alters social .judgment and results in impulsive or rude behavior. Damage to the anterior cingulate impairs motivation.

DEVELOPMENT

OF FUNCTIONAL

SYSTEMS

Concept of a Functional System Luria (1973) introduced the concept of a functional system in which specific brain structures distributed in different brain locations are activated when the brain is at work performing a specific job. The functional system is the total set of brain

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structures and component processes that are activated in time to perform the task. The brain has many different functional systems, which draw on common and unique structures and functions to accomplish different tasks. We propose that some tasks, like reading, writing, and math, are so complex that they draw on multiple functional systems (see Part II). Based on existing knowledge of brain structure-function relationships, Luria (1973) proposed three separate functional units, which emerge at different stages of development. The first functional unit regulates arousal and responsiveness to the environment. Structures in the reticular activating systems participate in this functional unit, which is operative at birth and thereafter. The second functional unit obtains, stores, and processes externally and internall,] generated information. Structures in the occipital, temporal, and parietal lobes participate in this unit. According to Luria, this functional unit develops in three waves that correspond to the maturation of three cortical zones--the primary projection pathways, the secondary association areas, and the tertiary association areas (see Chapter 3). The primary zone that governs sensory and motor functions develops during the first two years of life. The secondary zone that integrates sensory and motor functions begins to develop during the second year of life but continues to develop throughout the preschool years. The tertiary zone, which integrates the secondary zones and engages in sensory- and motor-free abstract processing begins to develop at about age six when children start formal schooling, and continues to develop throughout schooling. The third functional unit is involved in programming and regulating functions and is located in the frontal lobes. This third unit also begins to develop around the time children begin formal schooling, but takes even longer to mature than the second unit--well into adolescence and even early adulthood. Hooper and Boyd (1986) noted the parallels between the neurodevelopmental phases of Luria's second functional unit and certain stages of Piaget's (1952, 1970) model of cognitive development. The first phase corresponds to the development of sensori-motor intelligence, whereas the second phase corresponds to preoperational thought, and the later stages of the third phase correspond to operational thought. Hooper and Boyd also hypothesized that the third neurodevelopmental phase of the second functional unit, that is, activation of the tertiary cortical zones, is a prerequisite for learning academic skills. That is why around the world formal schooling may not begin until around age six when the tertiary cortical zones become functional. Preschool experiences are valuable in stimulating age-appropriate development in various domains, but still children need the appropriate instructional experiences during the school years to learn academic skills. Sometimes reading (or another skill) emerges precociously at an early age, suggesting that tertiary frontal computers may have matured earlier in some children--at least for specific functional systems. However, these early bloomers (e.g., three- and four-year-olds who can read) generally are not sufficiently precocious across all areas of development that they

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could meet the requirements of formal schooling in an all-day first grade classroom (e.g., fine motor activities, written assignments, sitting still for prolonged periods of time, and listening to instructional language for long time periods). Sensory and motor systems are necessary for learning to read, write, and do math, but they are not sufficient for developing these academic skills B myelinated neural structures for sensory-free and motor-flee abstract reasoning are also necessary. The powerful computers of the third zone of cerebral cortex provide this reasoning capability. Because most cerebral cortex is tertiary association area, children have ready access to the abstract computations needed for learning academic skills beginning at about age six. That is another reason why the concept of learning style based on sensory modalities lacks explanatory power. Learning to read, write, and do math depends on computations above the sensory processes, which are necessary but not sufficient. More research is needed on how all three functional units described by Luria work together to support academic learning.

Participation o f the Same Structure in More Than One System Functional systems are efficient because the same structure can be used for more than one purpose. Here are some examples of dual participation. The thalamus is involved in triaging sensory information during its journey to cortex, but is also involved in selective attention. The hippocampus participates in both the working memory system (Olton, Becker & Handelmann 1980) and the limbic system, involved in emotion and motivation. The upper region of the anterior cingulate in the front half of the cingulate gyrus plays a role in attention and is activated when a person pays careful attention m t h e more difficult the task the more that this region may be activated. However, the lower region of the anterior cingulate plays a role in emotional expression. This property of functional systems that allows them to draw on the same brain structures to achieve different functions is called flexible orchestration (Berninger, Abbott, Thomson & P,.askind 2001).

Functional Reorganization across Development Luria (1973) hypothesized that functional systems may reorganize across development. That is, the structures that participate in a functional system may change as the brain undergoes biologically preprogrammed maturation, which affects synaptogenesis, myelination, and lower-order dendritic branching, and experiences environmental stimulation including instruction, which affects the higher-order dendritic branching and spine growth. What may change is not only which macrostructures are orchestrated to achieve the goal but also their pattern of connectivity, and/or any of the microlevel learning mechanisms listed at the end of Chapter 2. Put

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another way, the brain's potential for flexible orchestration allows it to change over the course of development in how it performs academic tasks.

Language by Ear, Language by Mouth, Language by Eye, Language by Hand Many think of language as a single skill or think of language as different from reading or writing. For years many believed that language developed during the preschool years, reading developed during elementary school, and writing developed during junior high and high school. Research is casting doubt on this belief. The four functional language systems differ in more ways than their sensory mode of input or motor mode of output. These systems are on their own separate, but overlapping, developmental trajectories that interact in predictable ways over the course of development (Berninger 2000a). These language systems are Language by Ear (which begins in utero if the fetus is auditorially stimulated--see Diamond & Hopson 1 9 9 8 - - o r at birth), Language by Mouth (which begins with the first vocalization of the newborn), Language by Eye (which begins with the first book an adult reads to an infant or preschooler), and Language by Hand (which begins with the first mark an infant or preschooler makes with a writing implement). We compare and contrast these language systems, which are sometimes referred to as aural language, oral language, reading, and writing, throughout this book. Although they draw on many of the same mental processes, these language systems may orchestrate their component processes differently to achieve their unique goals. In addition, all these language systems draw on brain structures and functions that are involved in nonlanguage functional systems (e.g., the visual systems for object recognition, memory systems for storage and retrieval of information, attentional systems for directing focus, and executive systems for governing processes). Rudimentary literacy skills (Language by Eye and Language by Hand) begin to emerge early in development before formal instruction in reading and writing. Both Language by Eye and Language by Hand continue to be refined across development, with refinement often extending well beyond the traditional school age years.

NEUROLOGICAL CONSTRAINTS

Postmortem Cytoarchetechtonic Studies Autopsy studies were performed on the brains of eight individuals who had a history of reading problems but did not die of brain-related injuries. All these studies involved labor intensive and expensive analyses at the microlevel of neuroanatomy (see Chapter 2). Drake (1968) studied one child. Galaburda, Sherman, Rosen,

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Aboitz, and Geschwind (1985) examined four adults or adolescents. Humphreys, Kaufman, and Galaburda (1990) analyzed three adults. Collectively, the results showed the following. Gross inspection of the brains revealed that the most noticeable difference associated with reading problems was that the planum temporale (see Figure 4.3), a triangular region spanning the temporal and parietal lobes, were symmetrical in the reading disabled. In contrast, normal individuals tend to show leftward asymmetry in which the left planum is larger than the right planum. However, at the microlevel, a number of structural anomalies (brain differences in wiring or neural circuitry) were evident: dysplasias, ectopias, and polymicrogyria. Dysplasias are disordered layers in cerebral cortex in which an excessive number of large cells distorts the normal organization of cerebral cortex into columns and layers. Ectopias are the neural elements in the first layer of cerebral cortex that are generally not found there. Polymicrogyria (many small gyri) are the result of excessive folding and the absence of columnar organization. These microstructural anomalies may be due to errors in neural migration early in gestation (Galaburda et al. 1985). These anomalies occurred in the temporal, parietal, and frontal regions of cortex but showed variation across individuals as to whether they were only on the left, only on the fight, or bilateral (on both sides). These structural anomalies probably interfere with the normal wiring at the microlevel, thereby altering the connectivity of neurons (Galaburda et al. 1985) and affecting the activity of functional systems in which they may participate. Thus, brain anomalies in neural wiring may exist from the earliest stages of gestation, but their developmental impact may not be observed until later in development when children are expected to learn to read and write.

FIGURE 4.3 Planumtemporale (black) is larger on left side, where it contains Wernicke's area, than on right. This asymmetry appears about 7 months gestational age. Arrows point to parietal bank of planum (see text). Adaptation from Elliott (1999). Reprinted with permission from Geschwind and Lewitsky 1968. Science, 161: 186.

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The existing autopsy studies, like all research, have limitations, which include the following. First, the pool of donated brains for research purposes is small. Second, little information was available, and substantiated with test results, about individuals whose brains were analyzed. Third, those individuals may have had a variety of disabilities rather than a single disability. Fourth, there were no control brains from normal children who had also died. Developmental histories are consistent with mental retardation, primary language disability (Language by Ear and Language by Mouth), or reading disability (Language by Eye). To some extent, the in vivo studies of riving brains may be able to overcome these problems by more carefully characterizing the cognitive, aural language, and written language skills of participants.

In Vivo MRI Structural Imaging Studies

A controversy followed these autopsy studies as to whether the differentiating feature between disabled and abled readers is symmetry (fight = left) or reversed asymmetry (fight larger than left) of the planum (see Figure 4.3). The planum is of interest because it contains the auditory association cortex. Initially, evidence from structural imaging studies supported both positions. Larsen, Hoien, Lundberg, and Odegaard (1990) reported a greater percentage of symmetrical plana in dyslexics, whereas Hynd, Semrud-Clikeman, Lorys, Novey, and Eliopulos (1990) reported a higher percentage of reversed (rightward) asymmetries in dyslexics. Then Leonard et al. (1993) made an important differentiation between the temporal bank and the parietal bank of the planum. Her research team found that dyslexics, in contrast to controls, tended to have enlarged parietal banks of the planum on the fight (see the arrow in Figure 4.3). Thus, differences in findings across research groups may be related to the different ways researchers defined the boundaries of the planum (see Chapter 3 for discussion on the fact that boundary definitions are always arbitrary to some degree). More recent evidence from several research groups indicates that reversed rightward asymmetry of the planum (an enlarged fight planum) is not the defining feature of dyslexia (Best & Demb 1999; Eckert & Leonard 2001; Leonard et al. 2001b; Rumsey, Donohue, Brady, Nace, Giedd & Andreason 1997), which is a disorder specific to reading words (Language by Eye). However, the normal leftward asymmetry of the planum predicted both good reading and good language skills, suggesting that this structure may play an important role in language development in general and not just in reading (Leonard 2001). In vivo structural imaging studies are pointing to other candidate regions for the hallmark neuroanatomical constraints on learning to read. Anomalies have been reported in the surface gyri and sulci of the temporal and parietal lobes, the insula buried deep in the temporal lobe, the volume of gray tissue in temporal lobes, especially on the left, and the magnocelluar pathways in the visual and auditory systems.

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Leonard et al. (1993) observed that dyslexics had extra gyri in the parietal operculum, which O]emann (1988) discovered has sites specific to reading and not other language functions. This structure contains the supramarginal gyrus, the angular gyrus, and the parietal bank of the planum. Leonard et al. also found that dyslexics had duplicated Heschl's gyri bilaterally. Heschl's gyrus is on the anterior boundary (frontal bank) of the planum temporale and contains Brodmann's Area 41 (primary auditory cortex; see Table 3.2) and Brodmann's Area 42 (see Figures 3.1, 3.2, and 3.4). These duplicated Heschl's gyri may affect learning Language by Ear and Mouth, which, in turn, affects learning Language by Eye and Language by Hand. Cutting edge research (Eckert & Leonard 2001; Leonard et al. 2001a; Leonard et al. 2001) documented that specific language disability (Language by Ear/Mouth discrepant from nonverbal reasoning) is associated with a different pattern ofneuroanatomical constraints (Gauger, Lombardino & Leonard 1997) than is specific reading disability or dyslexia (Language by Eye discrepant from general reasoning or verbal reasoning); the neuroanatomical pattern is similar for specific language disability and low listening comprehension (verbal reasoning). Language disability is associated with small, symmetrical plana and other smaller than normal auditory cortical regions. In contrast, dyslexia is associated with extra gyri in auditory cortex and exaggerated cerebral and cerebellar asymmetries. The best predictors of specific language disability are low cerebral volume, the size of Heschl's first gyrus, and reduced leftward asymmetry of the planum. The best predictors of dyslexia are the size of Heschl's second gyrus on the left, the combined asymmetry of the planum temporale and planum parietale, the marked rightward cerebral asymmetry, and an anterior cerebellar leftward asymmetry. The smaller the first Heschl's gyrus is, the poorer listening comprehension is; but the larger the second Heschl's gyrus is, the worse phonological decoding is (pronouncing pseudowords). See Leonard (2001) for an overview of these findings, which if replicated, will represent a major advance in understanding different etiologies for reading disorders. Some reading problems may be related to underdeveloped or atypical Language by Ear and Language by Mouth, whereas other reading problems may be specific to Language by Eye in individuals whose Language by Ear and Language by Mouth might exhibit subtle anomalies despite being in the normal range (Corina et al. 2001). The original autopsy studies that found a lack of leftward asymmetry of the plana may have studied mostly individuals with specific language disability (Language by Ear and Language by Mouth) rather than specific reading disability (Language by Eye). Even so, that groundbreaking research showed how minor anomalies in the wiring diagram of cortical architecture can alter language learning. Other research has documented differences between dyslexics and controls on the cortical surface. Habib and Robichon (1996) and Hiemenz and Hynd (2000) reported extra cortical gyri and sulci in individuals with phonological processing deficit, a defining feature of dyslexia. Yet other research (Hynd et al. 1990; Pennington et al. 1999) found differences between dyslexics and controls in insula

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(see Figure 3.9), which is buried deep in the cerebral hemispheres. This finding is important because it replicated across laboratories and linear and volumetric measures of the insula. Eliez, Rumsey, Giedd, Schmitt, Patwardham, and 1Keiss (2000) showed that carefully defined dyslexics and controls differed in total brain volume; although dyslexics had 8 percent less than controls, they differed only in the gray matter in the temporal lobe, especially on the left, and not in the frontal, occipital, or parietal lobes. Green et al. (1999), who used a novel MRI-based surface reconstruction technique that models curvature of the cerebral cortex in three dimensions, found that dyslexics differed from controls in the anatomical organization in a region containing the superior temporal plane and the inferior bank of the posterior ascending ramus of the Sylvian fissure. Livingstone, Rosen, Drislane, and Galaburda (1991) reported electrophysiological and postmortem evidence for anomalies in the magnocellular, but not the parvocellular, pathways of the visual system in dyslexics. The magnocellular system, with pathways from the thalamus to the primary visual area, is called the fast visual system because it responds quickly and is sensitive to movement and location; it feeds into both the "where" pathway that travels upward to the parietal lobe and the "what" pathway that travels to the temporal lobe (Ungerleider & Haxby 1994). The parvocellular system, with pathways from the thalamus to the primary visual area, is called the slow visual system; it responds slowly, is sensitive to fine detail, and connects with the "what" pathway that travels to the temporal lobe (see Chapter 3). The auditory system also has magnocellular and parvocellular pathways that correspond to fast and slow auditory processing, respectively (Kass & Hackett 1999). Whether the auditory magnocellular system exerts contraints on aural/oral language learning or reading and writing requires further research.

DTI Structural Imaging Klingberg et al. (2000) used diffusion tensor magnetic resonance imaging to compare adults who were good and poor readers. Compared to the good readers, the poor readers showed decreased diffusion anisotrophy (more random diffusion of water) bilaterally in the temporal and parietal lobes, reflecting less myelin on the axons in this region. The investigators noted that less myelin may explain the impaired functional connectivity (i.e., the strength of communication ) among the auditory and visual cortices involved in language. Degree of this anistropy on the left was correlated with reading achievement.

Summary of MRI and DTI Structural Imaging Brain anomalies have been detected in specific areas of the temporal, parietal, and occipital lobes; that is, the posterior regions of the brain.

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Spectroscopic Imaging Rae et al. (1998) compared localized proton magnetic resonance spectra in adult dyslexics and controls. Groups did not differ chemically in the frontal lobes. On both the left and fight, dyslexics had a lower ratio of choline-containing compounds to N-acetylaspartate (NA) in the temporal and parietal lobes, and the dyslexics' ratio was even lower on the left than on the fight. In dyslexics the ratio ofcreatine to NA was decreased only on the fight. Choline is a marker of overall cell density, which is higher in white matter than gray matter and in glial cells than neurons. The lower choline to NA ratio in dyslexics may therefore reflect less myelination. Creatine is a marker of cellular energetics. NA occurs only in neurons and is a marker of neuronal density and mitochondrial activity.

Electrophysiological Studies In a unique longitudinal study, a team of developmental psychologists collected auditory evoked potentials (EPs) in 186 full-term newborns and assessed their language development at age three (Molfese & Molfese 1985) and age eight (Molfese, in press) and their reading at age eight (Molfese 2000). Quantitative parameters in the EPs fight after birth differentiated (a) level of language development (high or low) at age three, (b) verbal IQ (low, average, or high) at age eight, and (c) reading (normal, poor, and dyslexic) at age eight. Good readers were reading at their expected level based on verbal reasoning, and so were poor readers who were, compared to the good readers, lower in verbal reasoning, but within the normal limits. Dyslexics, on the other hand, were reading significantly below their expected level based on verbal reasoning. These differences in the electrical activity of the newborn may have been due to structural anomalies in cerebral cortex that are thought to result from cortical neural migration errors (Galaburda et al. 1985). Electrophysiological activity recorded over both hemispheres, not just the left hemisphere, predicted subsequent language development (Molfese 2001, in press). Language differences at age eight were greater than language differences at age three, suggesting that experience increases individual differences in language. Molfese et al. (2001) have argued that if we understood the immediate consequence of these early wiring anomalies on subsequent language development, we might be able to intervene earlier to prevent language problems. Such an intervention would not involve teaching reading to the newborn but rather the kind of language stimulation that is developmentally appropriate for newborns. Another longitudinal study is in progress in Finland. Children in families with and without a history of dyslexia are being followed from birth. By age six months differences in the ER.Ps have been found for those who are and are not at risk for dyslexia by virtue of their family history (LeppSnen et al. 1999; Pihko et al. 1999).

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Mental processes may occur on different time scales (Minsky 1986; Posner & McCandliss 1999). EPs and ERPs are very sensitive to the temporal parameters of mental processing (see Chapter 3). Thus, Breznitz (in press) used ERP techniques to test a theory of temporal constraints during word recognition. These constraints are related to the differences in time scales for the auditory-phonological system and the visual-orthographic system. Poor readers differed from good readers in temporal parameters (a) within the auditory-phonological system and (b) between the auditory-phonological system and visual-orthographic system. In other words, these two systems, both of which must communicate with each other during word reading, do not connect within the time constraints of word recognition as well in dyslexics as in good readers.

Drawing Conclusions about Neurological Constraints Neuroanatomical constraints that affect the ease of learning aural/oral or reading/ writing may involve more than one brain region and exert their effects throughout a neural circuit. One hypothesis about neural circuitry is that reading disability stems from asynchronies in spatially mismatched auditory, visual, and motor topographic maps (Leonard 2001). Whatever neuroanatomical constraints are operating, nurture interacts with these constraints imposed by nature. Eckert, Lombardino, and Leonard (2001) showed that children with double constraints (low socioeconomic status and rightward plana asymmetry) had very poor reading, children with single constraints (low socioeconomic status but leftward plana asymmetry, or middle class with rightward plana asymmetry) had average reading, and children with no constraints (middle class and leftward planum asymmetry) had superior reading. Thus, neuroanatomical differences constrain, but do not determine, reading achievement by themselves. Environment also plays a role. A model in which number of brain anomalies predicts biological risk is consistent with a view in which biology is not all determining. Indeed, normal controls and members of families with an individual with dyslexia also have neuroanatomical differences m j u s t not as many as the affected individuals do (Leonard et al. 1993). What may differentiate dyslexics from controls and family members is the number of neuroanatomical differences, which are an index of degree of biological risk for reading problems (Leonard et al. 1993). Leonard's lab has identified these structural anomalies as candidates for an index of biological risk: duplicated Heschl's gyrus on either side or a large duplication on the left; a larger parietal than temporal bank of the planum; unusual structural organization in the Sylvian fissure; marked rightward cerebral asymmetry in general; marked leftward asymmetry of the anterior lobe of the cerebellum; and combined leftward asymmetry of the planum with anomalies in the sylvian fissure (Leonard et al. 1993; Leonard et al. 1998).

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GENETIC CONSTRAINTS Early in the twentieth century Thomas (1905) speculated that dyslexia, which was then called congenital word blindness, had a hereditary basis because it ran in families. First-degree relatives (parents and siblings) are more likely to be affected than seconddegree relatives (grandparents, aunts, and uncles), but the problems tend to occur across generations. Finucci, Guthrie, Childs, Abbey, and Childs (1976) reviewed early family studies documenting that reading problems tend to run in families; but that alone does not prove that a phenotype (behavioral expression) is genetic in origin. DeFiles, Fulker, and LaBuda (1987) provided the first compelling evidence, from a behavioral genetics perspective, that reading disabilities are genetically constrained. The proband (target) twin in pairs ofdizygotic twins (who share half the same genes on average) regressed more to the mean in reading than did the proband twin in pairs of monozygotic twins (who have identical genes).

Heritability and Aggregation Studies To estimate whether genetic constraints may be operating on a phenotype, twin studies compute heritability coefficients, and family aggregation studies compute correlations between biological parents, between parents and offspring, and between siblings. Heritability studies have demonstrated that phonological and orthographic coding skills are probably heritable (Olson et al. 1994). Aggregation studies have demonstrated that phonological short-term memory, accuracy and rate of phonological decoding, and written spelling are probably heritable (Raskind et al. 2001).

Segregation Studies Reading disability is a heterogeneous disorder. Many different genes may influence how the disorder expresses itself at the phenotypic (behavioral) level. However, segregation studies that investigate patterns of genetic transmission suggest that there may be major genes that can be identified (Pennington et al. 1991; Wi]sman et al. 2000). Mode of genetic transmission is not fully understood but does not seem to be sex-linked (transmitted on the X or Y chromosome). Several studies show that females are about as likely to be affected as males, although there is some evidence that females may compensate more than males and that males may be more severely affected (Raskind 2001).

Linkage Studies The goal of linkage studies is to identify the chromosome on which a major gene for influencing reading or related language skill may be located. To date, evidence for

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linkage has been found for chromosomes 15 (Smith et al. 1983), 6 (Cardon et al. 1994), 1 (Grigorenko et al. 2001), and 2 (Fagerheim et al. 1999). Each of these, except Chromosome 2, has been replicated across research laboratories, although not always in the same location on the chromosome. See Table 1 of Raskind (2001) for results of linkage studies to date.

Biological Risk Rather Than Determinism just as Leonard et al. (1993) proposed a continuum for biological risk based on the number of constraints at the neuroanatomical level, the number of deficits at the phenotypic level may reflect constraints at the genetic level. The number of phenotypic deficits (e.g., in orthographic, phonological, and rapid naming) predicted severity of reading problems (Berninger et al. 2001). Even if genetic constraints are operating, however, heritability estimates indicate that environmental constraints contribute as much or more to the variation in reading skill (Olson et al. 1989). Quality of teaching is likely to make a particular difference in the academic achievement of students at biological risk for learning literacy skills.

R~COMMENDED

READINGS

Much of the material in this chapter about neural mechanisms, nature-nurture interactions, and developmental issues is based on the first two recommended readings, which are sources of additional information on these topics. The other recommendations for further reading are the source of much of the material on the genetic constraints, the neurological constraints, and functional language systems.

Brain Development during the Preschool Years Diamond, M. & Hopson,J. 1998. Magic trees of the mind. How to nurture your child's intelligence, creativity, and healthy emotions from birth through adolescence. New York: Penguin Books. Eliot, L. 1999. What's going on in there? How the brain and mind develop in thefirst five years oflife. New York: Bantam Books.

Genetic Constraints Raskind, W. 2001. Current understanding of the genetic basis of reading and spelling disability. Learning Disability Quarterly. 24:141-157. ILidley, M. (1999). Genome. The autobiography of a species in 23 chapters. New York: Harper Collins.

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Neurological Constraints Eckert, M., Lombardino, L. & Leonard, C. 2001. Planar asymmetrytips the phonological playground and environment raises the bar. Child Development. 72:988-1001. Huttenlocher, P. 1990. Morphometric study of human cerebral cortex development. Neuropsychologia. 28:517-527. Leonard, C. 2001. Imaging brain structure in children. Learning Disability Quarterly. 24:158-176. Molfese, D., Gill, L., Simos, P. & Tan, A. 1995. Implications resulting from the use of biological techniques to assess development. In L. DiLalla & S. Clancy-Dollinger, eds. Assessment of biological mechanisms across the life span, 173-190. New York: Erlbaum. Molfese, D., Molfese, V. & Kelly, S. 2001. The use of brain electrophysiology techniques to study language: A basic guide for the beginning consumer of electrophysiological information. Learning Disability Quarterly. 24:177-188.

Functional Language Systems Berninger, V. 2000a. Development of language by hand and its connections to language by ear, mouth, and eye. Topics in Language Disorders. 20:65-84.

Socioemotional Intelligence Goleman, D. 1994. Emotional intelligence. Why it can matter more than IQ. New York: Bantam Books.

MAKING

CONNECTIONS

Questions preceded by * may be most appropriate for graduate students. 1. If normal individuals show variation in their neuroanatomy, what implications might that have for individual differences in brain function? Could this kind of biodiversity be a source of individual differences in learning during the school years? W h y might members of the same cultural group exhibit individual differences in their neuroanatomy? 2. W h a t additional evidence, from a developmental perspective, was presented in this chapter that makes it unlikely that sensory modality or right-brain left-brain learning styles can explain learning of academic skills during the school age years? 3. Different regions of rat brains in the Greenough study responded differently to the same kind of environmental stimulation. H o w might sensory and sensorym o t o r stimulation affect h u m a n brains differently than intellectual stimulation? Are there developmental periods w h e n one kind of stimulation is more appropriate developmentally than others? Do computer games provide sensory-motor or intellectual stimulation? W h a t kinds of environmental stimulation do the frontal lobes, where the Cross-Talking Computers of M i n d are, need for optimal development?

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4. What is the biological definition of maturation? What role does differential maturation of brain regions play in the order of developmental milestones from infancy through the preschool years? How do both maturation and experience contribute to child development? How would you explain to educators and parents that Gessell's views on maturation, although widespread and popular, have not been supported by research? 5. What are the most important brain changes around age six in all cultures? What is the educational relevance of these changes to literacy acquisition? 6. What is the evidence, from a biological perspective, that teachers can still be a major influence on brain development during middle childhood? *7. List all the brain mechanisms by which the brain represents experience during learning. Explain how these allow the brain to be both a dependent variable that changes with experience and an independent variable that responds to the environment. Is human behavior stimulus-bound (under the exclusive control of events in the external environment) or responder-controlled (determined exclusively by internally generated control processes)?

P A R T II

LINKING BRAIN R E S E A R C H TO LITERA C Y RESEAR CH

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Building a Reading Brain Neurologically

The neural architect's plan for building a reading brain involves a r e m o d e l - - n o t new construction. The mind/brain creates the functional reading system by building upon previously acquired brain functions. This remodel is possible because the brain has the potential for flexible reorganization in which the same structures can participate in more than one functional system (see Chapters 3 and 4). This chapter begins with an overview of the most important existing brain functions for literacy acquisition m sensory, motor, aural/oral language, cognition/ memory, and attention/executive control. All these participate in many functional systems and may also undergo further development themselves during the formative years of literacy acquisition. For each function, we discuss how it is relevant to literacy and what has been learned about it from in vivo functional imaging studies. In vivo means living people and functional means their brains were scanned while they performed tasks. We discuss each function separately, but learning depends on these various systems working together in a cooperative manner in a functional system. To explain how the Reading Brain is constructed from other functional systems, we also need to understand how these components drawn from other systems work together to perform a new function m reading. Therefore, the chapter continues with a synthesis of what has been learned about the functional reading system from Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

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in vivo brain imaging studies and computational models of brain processing. Most functional imaging studies have been done with adults and are relevant to the developmental outcome of the process of learning to read. A few, however, investigated children and adolescents during the school age years and are relevant to the journey toward mature literacy. Finally, the chapter concludes with a discussion of how a reading brain is probably constructed during the remodel process in which other functional systems are recruited to create a new functional system. This remodel does not happen instantaneously, it develops over time. For this reason, we discuss development of the Reading Brain from the novice stage to the expert stage. Readers may refer to Figures 5.1 and 5.2 for some of the brain structures often mentioned in results of brain imaging studies. Readers may also refer to Table 3.3, which defines the adjectives often used to define where a particular region of activation o c c u r r e d ~ o n a bottom-up, side-to-side, or back-to-front axis, and to Figures 3.14 through 3.20, which illustrate these visual-spatial axes in the brain. Table 3.4 summarizes the structures depicted graphically in Figure 5.1 (structures on the external surface) and Figure 5.2 (structures deeper in brain). Sometimes numbers for Brodmann Areas are also given (see Figures 3.1 and 3.2). By coordinating verbal information in the text and tables with the visual spatial depictions of structures in maps of the brain, readers will gain a fuller appreciation of how the brain may also rely on coordination of multiple coding schemes in memory while learning to read.

FIGUR_E 5.1 Gyri,sulci, and fissures on the surface of the cortex that are often referred to in the in vivo brain imaging studies for reading and related systems.

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FIGUR_E 5.2 Brainstructures deep in the brain that are often referred to in the in vivo brain imaging studies for reading and related systems.

CREATING A Rs BRAIN SYSTEMS

SYSTEM FROM

OTHER

Sensory Systems Three sensory systems contribute to the functional reading system. The auditory sense contributes indirectly through its contribution to speech perception, which in turn, contributes directly to reading, and will be discussed in the context of aural/ oral language in another section in this chapter. The visual sense contributes indirectly through its extraction of visual features (e.g., vertical, horizontal, or slanted lines or curves; Hubel & Wiesel 1979) from the visual stimulus, as well as directly through its links with the orthographic component for the visual word form, created by linking the visual stimulus with aural/oral language, as explained later in this section. The vestibular sense may play a role in regulating eye movements during reading.

Visual System N o t only must children have near-point visual acuity (so that they can see what they are reading close at hand) but also they must have far-point visual acuity (so that they can see the blackboard or other visuals a teacher might use in front of the class). Most schools screen for far-point acuity with eye charts but often do not check children's

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near-point vision. Although visual acuity is necessary, it is not sufficient for the visual system to participate in the creation of a functional reading system. To learn to read, a child must also be able to extract visual features from written words. Initially, beginning readers use an already existing visual feature extraction system for visual stimuli in general, which is not dedicated just to written words. Eventually they create a new system that is dedicated only to written words; that is, visual stimuli that are recoded into language. This orthographic processor for letter strings develops by making connections between units of written and spoken words (Berninger 1994). Thus sound codes in speechplay afundamentalrole in the recoding of visual

stimuli into language; these recodedstimuli are stored as orthographic wordform representations. The initial extraction of visual features occurs on both sides of the occipital lobe in primary visual or striatal area and posterior medial extrastriatal association cortex (see Figure 3.4) (Bookheimer et al. 1995; Salmelin, Helenius & Service 2000). However, the processing specific to the visual word form occurs in the left inferior occipitaltemporal cortex, which is where written symbols are first linked to language symbols. Nobre, Allison, and McCarthy (1994), who recorded directly from the cortex of patients before neurosurgery, identified two components of this visual word form center. The first, which is in the posterior region of the left fusiform gyms (see Figure 5.2), responded equally to real words and pronounceable pseudowords but was not sensitive to semantic content. That is, it was responsive to small elements in a linear array that could be recoded into sound units that are smaller than a whole word. The second, which is in the anterior region of the left fusiform gyms, responded to verbal labels and semantic content and context. Nobre et al. argued that this second center is where letter strings become real words that can be named. However, Price, Indefrey, and Turennout (1999) speculated that processing in left fusiform gyms is still prelexical (smaller unit than the word) and does not become lexical (same size unit as a word) until the written word form is named in the lingual gyms (see Figure 5.2). Further research is needed to determine the precise relationships between prelexical and lexical orthographic processing and the role of the fusiform gyms and lingual gyms in creating orthographic representations of visible word forms. Salmelin et al.'s (2000) MEG study provided information on the time course for the initial visual feature extraction and the subsequent conversion of visual features into orthographic codes (letters in word units). They showed that visual feature extraction for any visual s t i m u l i - - n o t just w o r d s - - o c c u r s about 100 ms after a visual stimulus appears, but that processing of letter strings that is specific to visible word forms begins after 150 ms. This time frame is consistent with that reported by Posner and McCandliss (1999), which was based on combining EIkPs with PET and fMt(I: visual feature extraction between 50 and 100 ms after stimulus input and synthesis of an orthographic word unit at about 150 ms. Thus, normal adult readers activate, in sequence, both the general visual feature detector and the orthographic word form processor for letter strings. In learning to read, children need to process not only single words but also sequences of words in written text. Despite a common sense notion that vision is a

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purely spatial sense, considerable research evidence indicates that this spatial sense also has temporal properties. Physiologists have studied three kinds of visual functions involved in temporal processing of visible language: for sustained versus transient processing, for fast visual motion, and for eye movements (Eden, Stein, Wood, & Wood, 1994). The sustained mechanism focuses on simultaneous visual detail, whereas the transient mechanism is sensitive to global analysis of temporally changing incoming visual information. Lovegrove and colleagues (e.g., Lovegrove, Martin & Slaghuis 1986) found that many children with reading problems have difficulty with transient processing and also exhibit visual persistence in which details outlast the physical stimulus and interfere with processing details during the next fixation pause (see Willams, Lecluyse, and Rock-Faucheux, 1992). Eden, Van Meter, 1Lumsey, Maisog, Woods, and Zeffiro (1996) provided the first compelling in vivo functional brain imaging evidence that poor readers have a deficit in fast visual processing, which is thought to occur in the magnocellular visual system (see Chapter 4). Adults with a childhood history of reading problems differed from normal reading adults on tasks requiring judgments about moving visual stimuli but not stationary visual stimuli. This difference occurred in an occipito temporal area (V5 in occipital/MT in middle temporal) that visual motion tasks activate. Again, the stimuli were not written words but may have activated visual processes that affect the processing of written words. Demb, Boyton, and Heeger (1998) replicated the findings of Eden et al. Cornelissen, Hansen, Hutton, Evangelinou, and Stein (1998) showed that deficits in the visual motion center interfere with accurate perception of letter positions in a word. Precise representation of orthographic word forms requires two kinds of letter information--identity of letters and position of letters in letter strings. A bottleneck in the visual motion processor in the occipital cortex could interfere with transmission of letter position information to the orthograpic word form processor in the temporal cortex (fusiform gyms). Letter position information is relevant to identifying spelling units that are phonologically recoded in applying the alphabetic principle to decode written words (see Chapter 8). Demb, Boynton, and Heeger (1998) demonstrated that reduced activation in the motion-sensitive visual cortex might also impair reading rate. The relationship between the fast visual system and reading rate is of interest because (a) many readers become accurate in word recognition but struggle with rate of reading, (b) rate problems may depend, in part, on speed of coding orthographic units, and (c) rate of reading is related to reading comprehension (Perfetti 1985), the main goal of reading. Alternatively, the fast visual motion deficit may impair reading indirectly via its effect on attention (Steinman, Steinman & Garcia 1998) or via its role in regulating occular (visual)-motor control of eye movements, as we discuss next. While reading text, the eyes are constantly in motion even though we are usually not consciously aware of this motion. These eye movements have two major

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components m saccades and fixations. Saccades are rapid eye movements from one fixation point to another. To give a sense of how rapid these saccades are, about 170 occur per minute. In contrast, a fixation is a pause during which written words are processed in foveal vision (dot between fight and left visual fields in Figure 3.6), which is sensitive to visual detail rather than brightness contrasts. Fixations, which account for 95 percent ofreading time, last longer than saccades. Fixation duration depends on stimulus properties, difficulty of text, and reading ability of the reader. Much like the ebb and flow of ocean waves, saccades tend to alternate between moving forward and then backward (regressions). Between the forward movements and regressions, fixation pauses occur and take in a perceptual window that includes fixated letters and letters to the fight offixation. This perceptual window may vary between seven or eight letters in beginning readers to as many as 20 letters in skilled readers. (For further review of research on eye movements, see Eden et al. 1984; Rayner 1978.) Often faulty eye movements are the consequence rather than the cause of trouble in reading the written text (see Rayner 1978, for a review of the psychological research on this issue). However, physiologists who study the visual system have identified two subtle ways in which poor eye movements may cause poor reading (Eden et al. 1994). First, poor readers may have problems in fixation stability at the end of a fixation pause for small targets. Increasing print size may reduce this problem in fixation stability. One reason that text books for beginning readers usually have larger than normal print is that large fonts help children, who are.just learning to program their eyes for navigation along a string of written words, to stabilize their vision during fixation. Second, poor readers may have lower amplitudes during vergence eye movements that allow readers to maintain binocular vision while pursuing a single target in depth. Eye patching is sometimes effective in dealing with poor vergence eye movements. Little, if any, research exists on whether poor readers with abnormal vergence eye movements are the same ones whose eyes should have been patched before age four to correct abnormalities in their binocular vision (Diamond & Hopson 1998), but were not. The eye movement stability problems of preschool children with uncorrected binocular vision deficits may interfere with their ability to begin to form connections between spoken and printed words as adults read books to them. Future research should examine literacy acquisition in children with corrected and uncorrected binocular stability deficits. Conflicting results between research groups (Rayner 1978; Eden et al. 1994) regarding the relationship between eye movements and reading difficulties may be related to (a) differences between samples in the number of participants who have an uncorrected binocular vision/stability problem, or (b) failure of researchers to assess participants' vergence eye movements and stability at the end offixation pauses. Future research on the role of eye movements in reading should provide such information about the participants. It is clear, however, that eye movements are not controlledj ust by the visual system. Frontal cortex also plays an important role in regulating eye movements. From a brain systems perspective, attentional or executive dysfunctions, poor text comprehension, and visual mechanisms (includ-

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ing cranial nerves and the vestibular system; see Chapter 3 and Table 3.2), may all contribute to problems in regulation of eyes as they travel along fines ofwritten text. Children with reading disabilities in comprehension (not single word reading, the hallmark of dyslexia) may comprehend better when a blue transparent overlay is placed over text; but there is no evidence that Irlen lenses are effective in treating reading disability (Solen, 1998; Williams et al., 1992). Blue filters may aid the transient visual system (Solen, 1998; Williams et al., 1992). To summarize, there is a growing body of research evidence that has been replicated around the world that shows that, compared to good readers, poor readers may have suble sensory deficits in visual processing. Fascinating topics for future research are (a) how these various mechanisms may or may not be related to phonological disorders discussed later in this chapter, and (b) whether these sensory physiological mechanisms may singly, or in combination, contribute to problems in orthographic coding and processing in short-term and long-term memory. However, etiology and treatment are separate issues. A process may cause a bottleneck in creating a functional system, but the fix for the bottleneck usually involves more than direct training of the deficient process in isolation. The basic research on the physiology of the visual system is valuable in providing an understanding of one reason some students struggle in learning to read. However, we emphasize that there is no research evidence that visual training exercises in isolation improve the reading of these students (Keough & Pelland 1985). Only comprehensive, balanced reading instruction improves reading (see Chapter 8). There is, however, research evidence that eye patching during the critical developmental period for vision in the preschool years prevents binocular vision difficulties (Diamond & Hopson 1998). Vestibular Sense

Abnormalities in the vestibular sense may also interfere with the programming of eye movements (see Table 3.2; footnote j). One recommended treatment for dyslexia is the same medicine used for treating motion sickness. At first glance this recommendation may seem odd. However, another abnormality associated with the vestibular sense is motion sickness in cars, boats, or planes. If one can show that a particular child has both motion sickness and eye movement problems, then a blind clinical trial with motion sickness medicine might be warranted. However, it is unlikely that this treatment alone will be effective in improving reading of a child with dyslexia; systematic reading instruction will also be needed. Conclusions

At present, school systems screen for visual acuity problems but not visual problems related to (a) binocular vision during the developmentally sensitive preschool period (see Chapter 4), or (b) the just discussed deficits in the fast visual system or eye movements (stability at the end of fixations and in maintaining binocular vision).

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It would make more sense to patch children with binocular vision difficulties during the preschool years when the problem can still be corrected, than to patch their eyes while they read during the school years. Neither do most reading researchers assess their samples for binocular vision or vestibular deficits. Thus, in some cases, subtle sensory problems relevant to learning to read may not be detected in either school age populations or research samples. Despite these oversights, there is good reason to believe, based on a half century of reading research, that children do not improve in reading, no matter what factors may be contributing to their reading problems, unless they receive appropriate instruction in reading. There is no research evidence that there are any quick fixes that train only sensory or occular-motor processes or medicate for motion sickness and then generalize to improvement in the overall level of functioning of the reading system. Thus, our best advice to researchers is that further investigation of sensory processes beyond simple acuity should continue so that we do not miss subtle sensory deficits that some children may have. O u r best advice to schools and parents is that, no matter what the cause of a reading disability, the best treatment is comprehensive instruction in reading and not sensory or sensorymotor training outside the context of reading instruction.

Motor Systems Gross and fine motor skills

The nervous system has two types of motor systems, which are differentiated by size of muscles involved. The gross motor system involves the large muscles of trunk and limb; it maintains posture and guides locomotor behavior (moving from place to place). The fine motor system involves small muscles of the mouth, arms, and hands; it is used to talk and manipulate objects. The gross motor system plays an important role in children's playground and athletic activities, but the fine motor system is more important for learning to read, write, and compute. An oral-motor system that regulates mouth movements is the most relevant to learning to read and is discussed here. The grapho-motor system that regulates finger and hand movements is the most relevant to writing and computing, and thus will be discussed in Chapters 6 and 7, respectively. The eye movements discussed in the last section are part of the occular-motor system that plays a role in reading but were discussed under sensory functions because the fixation pauses that account for most of the eye movement time involve vision.

Motor Planning, Control, and Execution Motor systems have three components that need to be coordinated in time: one for planning the serial motor movements, one for controlling motor processes during output, and one for executing a motor act. For example, in the case of the oral motor system, the speaker must plan not only what to say (content) and how to say it (word selection) but also how to articulate the sounds. Then the speaker must exert

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control as those mouth movements are executed. Few children have paralyzed mouths; some have control problems during articulation and need speech therapy. Oral motor planning, which affects the temporal coordination of mouth movements, is more likely to go undetected; children may be able to perceive and produce speech sounds, but the timing and prosody of their speech may be disrupted even though articulation is normal (Dronkers 1996). Oral motor planning may influence performance during oral reading, a major instructional component of beginning reading programs. Children, whose mild oral motor planning problems during speech are not observable except to a trained clinician, may stumble when asked to read aloud. Oral reading may tax their already poor oral motor planning with the additional burden of translating written words into speech sounds and prosody. If the fluency of oral reading (associating a written word with a spoken word) is disrupted because of poor oral motor planning, then the connections that normally form, with practice, between letter strings and their names (verbal labels) in the orthographic word form center where letter strings become words (e.g., in anterior left fusiform gyrus; Nobre et al. 1994) may not be acquired. The child will appear to struggle in automatic word recognition. Caution is in order, however, in that an oral motor planning problem is only one of many different kinds of problems that can interfere with children learning to name written words automatically. Children who have oral motor planning problems may exhibit the following problems in their oral reading as well as their speech: hesitations, false starts, repetitions, and use of filled pauses such as "um." The resulting dysfluent oral reading may be a source of embarrassment, with the result that children with oral motor planning problems may begin to avoid reading (especially in front of others). Children who are generally accurate in oral reading of single words, but frequently exhibit oral reading dysfluencies in text, should be referred to speech and language clinicians for two reasons. First, if the underlying problem is oral motor planning (dyspraxia or impaired + planning), that should be treated. Second, the method of teaching connections between written and spoken words might be modified for these children to avoid the embarrassment of their oral dysfluencies (see Chapter 8). Not all children with oral reading difficulties have oral motor dyspraxia. Dysfluent oral reading can also result from problems in (a) accuracy of decoding words, (b) rate of recognizing familiar words, (c) comprehending sentence syntax, or (d) accessing the intonation or melody of spoken language. However, some children's oral reading problems may be due to undiagnosed oral motor difficulties.

Aural/Oral Language

Auditory Sense (Audition) and Language For a long time educators have recognized the link between the auditory sense (audition) and language development (listening and speaking). Schools routinely

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screen students' hearing acuity with audiometry. However, just as the visual sense involves more than far-point acuity, the auditory sense involves more than detection in otherwise quiet conditions of tones of varying frequency and amplitude. Tallal, Stark, and Mellitis (1985) advanced the understanding of selective language impairment (SLI, also known as specific language disabihty or developmental language disorder) in which language is significantly delayed relative to normal cognitive (thinking) development. They showed that one cause of SLI is difficulty in processing rapidly changing acoustic signals in auditory short-term memory. This difficulty interferes with receptive understanding of speech (Language by Ear, or, aural language) and/or expressive language (Language by Mouth, or, oral language). Schools do not routinely screen preschoolers for impaired auditory processing of rapidly changing acoustic signals. Prevalence of these deficits in the general population is unknown. Research is needed on whether early identification and treatment of these problems might reduce the severity of, or ehminate, SLI. Research is also needed on the most effective instructional approaches for teaching reading to children with SLI, who have the additional challenge of difficulty in aural and/or oral language and may need somewhat different approaches to reading instruction than do those with dyslexia, a specific reading disabihty in which word reading is impaired, but aural/oral language is generally within normal bruits for age.

Auditory and Articulatory Skills Auditory processing is the not the same as aural language. Auditory processing of an incoming stimulus that enters through the ear has its own primary projection pathway (see Table 3.2), which is not specific to language. Nonlinguistic sounds are also processed in this pathway. Language, unlike the senses, has no end organs (Liberman 1999) with a primary projection area dedicated solely to it. Language, therefore, teams up with different sensory and motor output systems to create specialized functional language systems that also draw on modality-free association areas. One of these systems is Language by Ear, which teams up with the auditory sense for the purpose of understanding incoming speech signals produced by other speakers. Waber et al. (2001) reported a significant, but modest, relationship between children's auditory processing skills and academic achievement. However, Liberman (1999) showed that the articulatory gestures involved in speech production are also involved in speech perception. According to Liberman's Motor Theory of Speech Perception, speech perception draws on articulation (a motoric end organ through the mouth). Because listeners are also speakers, they have stored representations of the articulatory features used to produce words. Thus the incoming speech signal (speech percept) is not constructed solely from the auditory acoustic signal that enters through the ear; it is also constructed from stored representations for those articulatory gestures for words. Consistent with the Motor Theory, Corina et al. (2001) found that both normal and dyslexic children activated the primary motor area

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(precentral gyms), which may be involved in storing articulatory gestures, during an auditorially presented rhyming task that required speech perception and a phonological judgment but did not require an oral-motor response. However, it is not clear whether this "ear-mouth teamwork" is necessary instructionally to develop phonological awareness, which is necessary to learn phonological decoding (Bradley & Bryant 1983; Bruce 1964; Gleitman & Rozin 1977; Liberman, Shankweiler, Fischer, & Carter, 1974; Wagner & Torgesen 1987). Wise et al. (1999) found that older struggling readers benefited as much or more from phoneme manipulation activities in working memory rather than articulatory feedback in learning phonological decoding. Torgesen et al. (1999) found that beginning struggling readers, and Torgesen, Mexander, Wagner, Rashotte, Voeller, and Conway (2001) found that older struggling readers, benefited from articulatory training in a reading program that was balanced and had other necessary instructional components. The reported standardized scores for expressive language in the Torgesen et al. (2001) study suggest that many in that sample had primary language disability. One possible explanation for the differences in findings for this sample and the Wise et al. sample is that articulatory feedback is more beneficial for developing phonological awareness when students have primary language disabilities than when they have specific reading impairment but normal aural/oral language development (except for subtle phonological awareness problems). Research is needed to identify those children who are impaired in speech perception (and not just in phoneme awareness) due to auditory processing problems (Tallal et al. 1985) or subtle difficulties in storage or retrieval of articulatory representations of speech m and to identify the most effective instructional interventions for teaching them to read. We simply do not have sufficient research evidence on the relative effectiveness of (a) direct training in auditory processing of rapidly changing acoustic signals, (b) articulatory feedback paired with phoneme production, and (c) phoneme manipulation-only training. Such comparisons need to be made under conditions in which comprehensive instruction in reading is and is not provided in addition to the specific kind of auditory/articulatory/phonological training. This research is needed for well-defined clinical populations including those with primary language disability and those with dyslexia (specific reading impairment in reading single words out of context). For those with dyslexia, this research is needed for children with a single deficit in phonological awareness, a single deficit in rapid automatic naming, and a double deficit in both phonological awareness and rapid automatic naming. Wolf and Bowers (1999) showed that children who have a double deficit in phoneme awareness and rapid access to names codes have the most severe reading disabilities. They probably lack both the sublexical and lexical procedures (Castles & Coltheart 1993) for creating connections between written and spoken words. It may be that articulatory training is beneficial in developing the sublexical route for alphabetic principle and not for the lexical route for automatic word recognition.

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Unmet Needs in Research and Practice Neither schools nor researchers are adequately screening infants for speech processing problems (Molfese 2000, in press), or preschoolers for possible problems, in auditory processing of rapidly changing acoustic stimuli (Tallal et al. 1985) to identify those children who have specific language impairment during the preschool years that may interfere with learning to read during the school years. Neither schools nor researchers are differentiating those children who have difficulty with reading because of primary language impairment versus those who have trouble with reading because of specific reading impairment (e.g., dyslexia) (Leonard et al. 2001a, 2001b). More research is needed on whether reading problems in school-age children are due to specific aural/oral language disability (Language by Ear and Language by Mouth) versus specific reading disability (Language by Eye), and on which instructional approaches are most effective for each kind of disability.

Language Representations Language itself is a complex representational scheme with multiple components that are anatomically separable. How these components function together close in time depends to a large degree on their history in working together and on what the task is. Language scientists have learned three important lessons about language. First, the representational scheme involves different levels that may draw on some common components, but each level is not housed in a single place in the brain, even though the levels are neuroanatomically separable. Second, language components can be orchestrated for reception (understanding a received input) or expression (producing an output), but separate, modular (insulated) brain centers do not exist for receptive and expressive language. Third, language activates both sides of the brain, even though one side may devote more volume to language than the other. These properties of the neuroarchitecture for language allow the brain to create different functional systems for language, each linked to a different end organ (ears, mouth, eyes, and hand). Table 5.1 lists the different levels of representation of language in the brain. These are not totally independent of one another--just separable in terms of neural systems involved in their representation. To accomplish jobs, they function together. So at one level of analysis these levels are structurally separable, but at another level of analysis they are functionally integrated. An analogous architecture with separable, but functionally integrated, structural units is the single dwelling unit in American culture. For purposes of analogy, assume a house constructed of bricks, which are like words, the basic building blocks. Linguists call these basic word units lexical items. All the words stored in the mental dictionary comprise the lexicon, which is analogous to the total number of bricks in the wall of the house. Bricks also have internal structure that can be described at levels of analysis below the brick unit--ranging from the mix of

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TABLE 5.1 Levels of Language Representation in Language by Ear and Language by Mouth Aural/Re ceptive Language by Ear

O ral/Exp ressive Language by Mouth

Acoustic and articulatory feaures Phonetic categories Phonemes Morphemes

Articulatory gestures Phones

Phonological word forms Semantics Morphologya Syntax/Grammar Conversation-listener Instruction-pupil

Name codes Semantics Morphologya Syntax/Grammar Conversation-speaker Instruction-teacher

Subword

Word

Clause Discourse/Text

aStructure of meaning including lemmas, which signal grammatical information; for example, inflectional suffixes that mark tense or number or derivational suffixes that mark part of speech (Hagoort, Brown & Osterhut 1999).

ingredients from which a particular batch of bricks was baked (clay and other substances) to the individual molecules and their internal parts and forces. These smaller units within a brick are analogous to the sublexical units that are smaller than a w o r d (e.g., phonemes) but play important roles in w o r d learning. The house also has structure at a level m u c h larger than the individual bricks. Single layers of bricks can be arranged in variable ways as long as some constraints are met (e.g., all layers in a wall are the same length). Likewise, in language, utterances or sentences can be arranged in many different ways as long as another kind of constraint m for w o r d order (syntax) m is met. At an even higher level, patterns that give a wall a distinctive design can be constructed from the way individual layers contribute to an overall pattern. Sometimes even larger structures ~ stories vertically arranged on one a n o t h e r ~ are part of the architecture, just as discourse in language is sometimes chunked into larger units like chapters in a b o o k or scenes in a play. Even though structure exists at different levels, all the levels function together to provide shelter. This analogy between levels of structure in conventional architecture and neural architecture is not perfect. In conventional architecture all the structures are in the same house, but in language the structures have physical locations that are distributed throughout the brain. An analogy might be a Picasso painting in which the components are not necessarily in physical proximity at one level of perception but are nevertheless psychologically integrated at another level of perception. That is a major lesson learned about language from electrostimulation studies of patients before neurosurgery (Ojemann 1983, 1991) and the functional brain imaging studies with h u m a n beings (Binder et al. 1997; Damasio et al. 1996). We n o w

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discuss the original neurolinguistic model of language, based on postmortem studies, and then the new model for which there is rapidly accumulating evidence from brain imaging studies. Previously, neurolinguists thought that the center for understanding aural language was in Wernicke's area in the left temporal lobe, that the center for producing oral language was in Broca's area in the left frontal lobe, and that these centers were activated sequentially in time proceeding from Wernicke's to Broca's (see Figure 3.4). In the revised model (Mesulam 1990), Wernicke's area lies at the semanticlexical pole, and Broca's area lies at the syntactic-articulatory pole; both can be activated simultaneously (Fried, Ojemann & Fetz 1981). Wernicke's area (core in the posterior superior temporal gyrus surrounded by heteromodal association areas BA 37, BA 39, and BA 40) is a multidimensional information matrix about soundmeaning relationships of words. Broca's area (core in BA 44, surrounded by premotor association area BA 6 and heteromodal association areas of prefrontal cortex BA 45, BA 47, and BA 12) is also a multidimensional information matrix, which is specialized for processing and producing morphology and syntax and possibly for sequencing responses on the basis of concepts, complex information, and goals (see Figures 3.1 and 3.2). The multidimensional grids in this distributed neural network interact with each other. They have a special advantage for computing complex operations rapidly because they are based on one-to-many mappings (same behavior in different sites) and many-to-one mappings (different behaviors in the same site). We do have separable functional systems for understanding oral language by ear (receptive) and producing oral Language by Mouth (expressive); but each system is not housed in its own physical or structui'al location in the brain that other systems cannot use. The separate language systems may orchestrate their common components differently depending on the task at hand. The classic neurolinguistic approach to lateralization of language function is also changing. Beeman and Chiarello (1998) gathered evidence for a compelling argument that the cerebral hemispheres play complementary roles in language rather than only one hemisphere playing a solitary, dominant role in language. These roles are distinct, parallel, and mutually supportive. Table 5.2 summarizes the complementary roles, based on currently available research evidence, organized by level of language. These research findings dispell the popular myth that there are left-brain and fight-brain learning styles. Language learning, which is so critical to learning academic skills, requires the complementary cooperation of both sides of the brain. In the review of imaging studies that follows, bilateral activation means that the homologous or corresponding structures on both sides of the brain were working together to perform a task.

In Vivo Imaging of Aural~Oral Language Most of this imaging has employed tasks that do not require an oral response; mouth movements introduce motor artifact that make it difficult to interpret the results

Building a Reading Brain Neurologically TABLE 5.2

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Complementary Roles of Left and Right Sides of Brain in Language a

Level of Language Subword phonetic categorizationb Word speech signal meaning decoding Sentence Understanding

Left Cortex

Right Cortex

Based on place of articulation

Based on voice onset time

Linguistic features Selects a single interpretation from many

Acoustic features, prosody Multiple meanings, distant semantic relationships, metaphors Visual details in words

Abstract processing Sensitive to syntactic constraints; uses syntax to interpret

Maintains activation of individual words independent of context

Predicting, making inferences

Diffuse activation of multiple related concepts

Discourse Understanding

aBased on Beeman and Chiarello (1998); no simple dichotomy of right hemisphere and left hemisphere processing style. bBased on Molfese and Molfese (1986). unless special techniques are used to bypass or minimize motor artifact (Barch, Braver, Sabb, & Noll, 2000; Eden & Zeffiro 1999). Most of this imaging has also used adult subjects. Individuals vary as to whether they are weak activators or strong activators who yield useful data for drawing conclusions about language processing in the brain (Fitzgerald et al. 1997). Language tasks activate systems for sensory, motor, short-term memory, and attention functions as well as for language (Binder et al. 1997). For example, left frontal regions in and near Broca's region participate in receptive language, not just language production, and may play an executive role in coordinating m o m e n t to-moment language processing (Binder et al. 1997). Auditory s t i m u l i - - e i t h e r nonlinguistic tones or linguistic w o r d s - - a c t i v a t e the planum temporale (Binder et al. 1996; Binder et al. 2000). Bilaterally, Heschl's gyrus is more activated by tones than by noise, suggesting that it is more sensitive to auditory stimuli with temporal structure than noise (Binder et al. 2000). Acoustic and phonetic features of spoken language activate superior temporal gyrus, whereas semantic processing of spoken language activates a large, distributed neural network (Binder et al. 1994). Only auditorially presented words activate a distributed network involving the superior temporal sulcus, the middle temporal gyrus, angular gyrus, and lateral frontal lobe (see Figure 5.1) (Binder et al. 1996). Taken together, this pattern of results suggests that aural language has a neural architecture in which words are first processed like any auditory signal, then their acoustic properties are processed, and then their language-specific properties are processed. The bilateral temporal sulcus may be where auditory stimuli are first processed as language.

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This architecture for aural language has parallels in the visual system in which written words are first processed as visual stimuli and are not processed as language until further downstream (see the section, "Visual System," earlier). Different neural tissue appears to be specialized for coding words phonologically versus semantically. Bilaterally, superior temporal sulcus is more activated by speech stimuli than by auditory tones (Binder et al. 2000). However, superior temporal sulcus is equally sensitive bilaterally to real words and pseudowords, suggesting that it is dedicated to the phonological rather than the semantic features of words and may be a phonological word form center (Binder et al. 2000). Superior temporal gyms and superior temporal sulcus have the most activation overall for speech sounds, suggesting that both may be part of a receptive phonological word form center. Three regions have stronger activation to real words than pseudowords and do not overlap with areas specific to the phonological word form in relevant contrasts: posterior inferior temporal gyms (BA 20/37), angular gyms (BA 39), and the border between posterior middle and inferior temporal gyri (BA 21/37) (see Figures 3.1, 3.2, and 5.1) (Binder et al. 2000). These regions may function as a center for the semantic coding of visual or phonological word forms. Phonological, semantic, and syntactic processes are represented separately in the brain even though language understanding requires that listeners integrate these levels (see Frederici 1998, for a review of evidence from electrophysiological studies). In general, task demands are more predictive than stimulus properties of words as to which parts of neural network for language are likely to be activated (Frederici, Opitz & yon Crammon 2000). Single words presented auditorially activate auditory cortex and upper left temporal lobe bilaterally; but word meaning is more distributed in pars triangularis bilaterally and in the left temporal gyms and sulcus. Highlighting syntactic information increases activation in planum bilaterally and in left frontal operculum (Frederici, Meyer & yon Crammon 2000). Activation in left pars opercularis increases when sentences are syntactically more complex, but perisylvian regions (in and on either side of the sylvian fissure) are not affected by syntactic complexity (Caplan, Alpert & Waters 1998). Note that the left frontal operculum and pars opercularis are part of Broca's area (see Table 3.4). Morphosyntactic processes may be carried out in the anterior part of the superior temporal gyms and left temporal and left frontal regions (Hagoort, Brown & Osterhout, 1999). Lexical representations are the most diffuse in their representation, whereas morpho-syntactic judgment processes are less diffuse and mostly in the temporal and frontal regions; however, different sites are activated for word ordering during sentence construction (Language by Mouth) than syntactic judgments (Language by Ear) (Bhatnagar, Mandybur, Buckingham, & Andy, 2000). Listening to auditorially presented text activates the superior temporal gyms and surrounding region and the frontal lobes (Fitzgerald et al. 1997). Syntactic processing draws on many language processes distributed throughout the brain and possibly on nonlanguage memory processes (Hagoort, Indefrey, Brown, Herzog, Steinmetz & Seitz 1999).

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Taken together, brain imaging studies based on a variety of modalities (PET, fMRI, and electrophysiology) support the levels of language framework in Table 5.1. They are also consistent with a growing body of behavioral data that indicate that not only phonological but also other levels of aural/oral language function are involved in reading systems (Catts, Fey, Zhang, & Tomblin, 1997; Lombardino, Riccio, Hynd, & Pinheiro, 1997; Scarborough, 2001). Which circuits are activated during aural/oral processing may depend on how practiced and thus automatic a task is. Raichle et al. (1994) compared verbal tasks (naming or generating verbs for nouns) before and after practice with specific words. They identified a nonautomatic circuit that included the left frontal and anterior cingulate cortices and fight cerebellum (see Figures 3.3 and 5.2); this circuit activated when learning novel items but became less active after practice. They also identified an automatic circuit in bilateral sylvian insular cortex (see Figure 3.9) that increased activity after practice, which also changed speed of response, but was less active for novel stimuli. They concluded that the cerebellum activates during learning (nonautomatic processing) but deactivates after practice, which presumably automatizes a process. This conclusion is consistent with Nicholson, Fawcett, Berry, Jenkins, Dean, and Brook's (1999) view that the cerebellum has two major functions in learning new skills and executing learned functions automatically. Primary motor area is not affected by practice, but cerebellum is.

Cognition and Memory Thinking is harder to localize in the brain than sensory and motor processes are. The reason is that higher-order thought processes occur in the tertiary association areas that do not respond directly to sensory stimuli or motoric requirements for tasks, which are easier for experimenters to manipulate (Frackowiak 1994). A brain system in the lateral frontal network in one or both sides of the brain may be dedicated to general reasoning (Duncan et al. 2000). Such a general problem solver might be recruited by a variety of other systems distributed throughout the brain for tasks that otherwise vary in processing requirements. Other systems may affect how the recruited system for general reasoning is used; numerous factor analytic studies have shown that verbal reasoning is different from nonverbal (visual-spatial) reasoning (Sattler 2001). IQ (Intelligence Quotient) tests are somewhat misnamed because they are no longer based on a quotient (mental age divided by chronological age). Rather, they are based on standard deviation units that can be used to compare individuals of different ages across development. In theory, intelligence is the ability of the brain to perform at expert levels in a variety of domains (Gardner 1983), solve practical problems in the real world (Sternberg 1985), and possess common sense (Minsky 1986). Intelligence, as assessed by traditional IQ tests, is abstract reasoning, which predicts scholastic aptitude. Early in the twentieth century psychologists discovered

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that measures of abstract reasoning were better predictors of scholastic aptitude than were measures ofsensori-motor skills, providing yet one more kind of evidence that individual differences in modality-specific learning styles are not the best way to think about learning differences. Individual differences in abstract thinking are also relevant. By the end of the twentieth century, psychologists understood that there were other cognitive processes besides abstract reasoning that are also needed to learn reading, writing, and computing. Thus, abstract reasoning, which IQ tests measure, is relevant to academic learning, but other cognitive processes we discuss in Chapters 5, 6, and 7 also need to be considered. (See Sattler 2001 and Fagan & Wise 2001 for further information on the history of assessing reasoning abilities.) Regardless of how abstract reasoning is supported by neural architecture or psychologists assess it, thinking requires more than reasoning. It also requires content to think about. This content is derived from experience or generated internally but needs to be stored in the brain with ways to access it as needed. Over a century of research on memory supports three general conclusions about the nature of the storage system and four conclusions about the processes that operate the memory system (see Kandell, Kupfermann & Iversen 2000). First, there are many different kinds of storage systems or memory mechanisms. Second, these are distributed throughout the brain. Third, the same items can be stored in multiple ways with multiple retrieval cues. The schema in Table 5.3 summarizes the mechanisms and possible brain locations. Ultimately memory is a series of molecular events in a neural micro-architecture (Mishkin & Appenzeller 1987; also see Chapter 2). However, four processes TABLE 5.3

M e m o r y Mechanisms in the Brain Memory Mechanism

Possible Brain Structure(s)

Short-Term (STM) Phonological store Visual-spatial store Long-Term Memory (LTM)

Explicit (conscious) Semantic (facts) Episodic (events) Spatial Implicit (unconscious) Priming (facilitation due to prior exposure to a stimulus) Procedural (skills, habits)

Left inferior parietal cortexa; b left supramarginal gyrus Right inferior parietal + dorso-lateral occipital cortices a Transfer from STM to LTM (bilateral medial temporal lobes, hippocampus, amygdala)c Hippocampus, thalamus, cingulate, basal frontal cortex a Left hippocampus c'e Prefrontal association areas of frontal cortex c Right hippocampus c Neocortex c Striatum cJ

(continues)

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TABLE 5.3 (continued) Associative (connection between 2 stimuli, activate each other) Emotional Skeletal Operant conditioning (stimulusresponse association) Classical conditioning (stimulus-stimulus association) Working Memory (WM) Central executive (retrieves LTM stores and regulates WM processes) Articulatory loop (verbal rehearsal)

Visual spatial sketchpad (nonverbal rehearsal) Recognition Memory (stimulus reactivates stored representation of that stimulus) for visual object

Amygdalac Cerebellumc Striatum and cerebellumc Sensory and motor systemsc Dopamine pathwaysf prefrontal cortexc'J Left inferior posterior frontal region (Broca's area)a'b Supplementary motor area, premotor cortex, Broca's area, parts of insula, right cerebellumg h Right premotor cortex Cholinergice pathway (Acetylcholine)z Occipital, temporal, amygdala, hipkpocampus 1~ clrcmt J; ventral what pathway 9

Recall Memory (stimulus retrieved from another pathway) for spatial relations

.

Striate cortex to.posterior parietal cortex + hlppocampus ' ; dorsal where pathway k 9

t d

aFrackowiak (1994) bPaulesu, Frith & Frackowiak (1993) CKandell, Kupferman & Iversen (2000) dcircuits involved in acquisition probably also are involved in storage (heteromodal associationareas in prefrontal, parietal, occipital, or temporal lobes); but note that hippocampus is more likely to play a role in making stimuli memorable than in serving as a storage site. eVerbal and visual (and other) retrieval cues in multimodal association areas fGoldman-Rakic (1992) gAwh, Jonides, Smith, Schumacher & Koeppe (1996) hsmith and Jonides (1999) iMishkin (1982) JMishkin and Appenzeller (1987) kungerleider and Haxby (1994)

integrate the various m e m o r y mechanisms at the macro level: coding in s h o r t - t e r m m e m o r y (STM), consolidation, storage in l o n g - t e r m m e m o r y (LTM), and retrieval. S T M codes i n c o m i n g information and, like the 6 o'clock news, provides updates on the external e n v i r o n m e n t ( G o l d m a n - R a k i c 1992). S T M has limitations in b o t h space or storage capacity (7 + o r - 2 n e w bits o f information for humans) and time or duration (a few seconds or less). Information in S T M will fade away unless rehearsed and eventually consolidated, w h i c h means c o n v e r t e d for l o n g - t e r m storage. This conversion involves a structural change in proteins in cells. H i p p o campus (see Figure 3.7) may play a role in b o t h the e n c o d i n g and consolidating

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processes (Squire et al. 1992). In contrast to STM, LTM is thought to have unlimited storage capacity and to be permanent. Some LTM stores are explicit (conscious) and others are implicit (not conscious). However, the contents of LTM may not always be accessible, that is, they may be forgotten temporarily until the key for retrieving specific content is relocated. Sometimes forgetting involves retrieval failure rather than loss of representations. Habituation is a condition in which responding ceases for a stimulus that previously elicited responding. However, research over the past two decades suggests that a fifth memory process m working memory (Baddeley 1986) m may be involved during highlevel cognitive .jobs like thinking. The hallmark of working memory is that it involves both storage and processing of stored information (Goldman-Rakic 1992). Working memory brings the present (contents of STM), past (retrieved or activated LTM content), and future (goals and plans) together in a moment-tomoment "blackboard of the mind" (Goldman-Rakic 1992). W M is also like a desktop of mind in that it is temporary but not necessarily of short duration m it remains active until the task is completed or interrupted. W M draws on STM stores, a rehearsal buffer for actively maintaining these short-term stores, and a central executive (see Table 5.3). This central executive is not a homunculus or little man in the mind directing the higher level thinking processes. It is probably more like a gender-free board of directors with multiple executive roles in task management. For example, at least five executive processes are involved in task management (Smith & Jonides 1999): (a) selectively attending to what is relevant, (b) selecting content from storage, (c) setting goals and creating plans to accomplish a goal, (d) switching among subgoals until the overall goal is reached, and (e) updating and monitoring storage and processing. Both human and animal studies have added to our understanding of memory. Although the temporal lobes are activated during explicit LTM (semantic memory) (Becker et al. 1994), they do not appear to be involved in working memory (Frackowiak, Friston, Frith, Dolan, & Mazziotta, 1997). Frontal lobes are activated when a central executive is needed to manage working memory (Becker et al. 1994; Paulesu et al. 1993). This regulation function is possible because the prefrontal cortex (seat of executive functions; see Table 5.3) has connections with many brain regions that modulate the activities of those regions through excitatory and inhibitory commands without directly participating in those activities. When adults generated all the words they could think of that began with certain letters, activity increased in the left dorsal prefrontal cortical region (LDPFC) (see Table 3.3 and Figure 3 . 4 ) ~ probably because the central executive of working memory regulated the process m and decreased in auditory and superior temporal c o r t e x ~ probably because this task involved more than retrieval from semantic memory (Frith, Kapur, Friston, Liddle,& Frackowiak, 1995). According to Goldman-Rakic, W M is not active at birth but becomes active when the child can represent (store) information about the environment in mind and perform rudimentary executive functions to regulate the processing of those repre-

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sentations. Working memory is limited by the workload it can handle (Jonides et al. 1997). Over the course of development, children can handle increasing workloads, but throughout development, the workload of the task at hand may exceed working memory resources. One way the workload can be reduced is to automatize the lowlevel, rote jobs to free up more capacity for the high-level, thinkingj obs. If this analysis is correct, then achieving the fight balance between automaticity and higher-order thinking may be important for learning and teaching all academic skills (see Part III). Mishkin and colleagues (Mishkin & Appenzeller 1987) made a major discovery that unites the cognitive and behavioral research traditions and that may explain how lower-order automaticity and higher-order reflection need to learn to work together in functional language systems. On the one hand, there is a cognitive pathway. This pathway supports representations of the relationships among items in a cognitive schema but also has important connections with the amygdala (see Figure 3.7), which is rich in opiate neurotransmitters and serves as a gatekeeper that allows information about bodily state emotions transmitted from the hypothalamus to influence what is perceived and learned. This pathway is ideally suited for processing emotionally charged events that are salient in learning and for processing sets of items in which the interrelationships among the items are important. On the other hand, there is a behavioral pathway that supports representations for habits or over-learned responses, with relatively direct stimulus-response links. The striatum (see Figure 5.3, and caudate 4- putamen = striatum in Figures 3.8 and 3.11) is an ideal candidate for this pathway because it receives projections from many areas of cortex and sends fibers to globus pallidus and substantia nigra and thus is a funnel to motor and premotor cortex for controlling movement needed to act on the environment. Recent brain imaging studies with humans suggest that cerebellum (see Figures 3.3 and 3.11) also plays a role in automatization (Nicholson et al. 1999; Raichle et al. 1994). Mishkin had the insight that most kinds of learning draw on both the cognitive and behavioral pathways. Learning is based on cognitive mechanisms that guide knowledge and expectation and draw on information with emotional significance but also on noncognitive, automatic stimulus-response associations. In Part III we draw on Mishkin's insight in discussing effective pedagogy for teaching literacy.

A t t e n t i o n and E x e c u t i v e F u n c t i o n s

Three components, each with its own local network and coordinate system for mapping the internal and external environment, participate in the directed attentional system (see Mesulam 1990, for further discussion). These are the posterior parietal component, the frontal component, and cingulate. Each component draws on common and unique brain structures. Common structures include the brain stem and thalamic components of the reticular activating system (inner core of brain stem in Figure 3.3) and the striatum (see Figures 3.8 [caudate + putamen], 3.11, and 5.3). The reticular activating system

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F I G U R E 5.3 Working memory system during problem solving. A neuron in the fifth layer of prefrontal cortex transmits signals along a chain of neurons in striatum, the substantia nigra, and the superior coUiculus, where they trigger motor responses in the eyes. Impulses from the substantia nigra travel to the mediodorsal thalamus and back to the cortex, indicating that completion of the motor response, and signaling the prefrontal neuron to return to a baseline level of activity. The graphs show the electrical activity of the neurons; inverted triangles indicate the nearly instantaneous travel of the signals. Reprinted by permission from Goldman-Rakic 1992. Elaborate Flow of Neural Signals. Scientific American. IUustration by Patricia J. Wynne.

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modulates arousal in each component. The striatum receives information from each component and synchronizes this information for transmission to many cortical regions. This synchronization is possible because each component of the attentional system has codes for its special kind of information, and for the kind of information in which the other components specialize. So the resulting informational grids relate the different kinds of codes to each other. For example, the posterior component specializes in sensory information but also codes motor information, and the frontal component specializes in motor information but also codes sensory information. The posterior component (dorsal lateral posterior parietal cortex) (see Table 3.3 and Figure 3.3) receives extensively preprocessed information from many sensoryspecific and heteromodal association areas. It is involved in covert shifting of attention. Unlike the primary areas of visual cortex that tend to be wired earlier, this posterior component is wired later in development and sculpts the attentional landscape. Consequently it plays an important role in shifting attention to new targets and thus in learning. The frontal component (see Figure 3.3), in contrast, plans and executes strategies for navigating the attentional landscape. This component is coded in more complex coordinates than is the primary motor cortex (see Figure 3.4), which is based on topographic representation in which there is fairly direct one-to-one correspondence between cortical representation and motor act. This frontal component works with the premotor cortex (see Figure 6.6) to coordinate exploratory limb movements and to regulate whether a target (event or location in space) will become the target of exploration, grasp, or manipulation. It also coordinates the eye movements discussed earlier in this chapter but is not the only brain region involved in controlling eye movements. Units of this frontal component of the attentional system fire just prior to the saccades, whereas the superior colliculus (see Figure 3.11) in the visual-motor system regulates the visual processes during fixation pauses directed to a target of interest. The cingulate component (see Figure 5.2) is the arbiter when conflict arises in the attentional system. To resolve conflict, it may assess the relevance of the sensory information or planned motor act for the bahavioral task at hand. The cingulate is ideally situated for this role as judge in resolving disputes. It is in the center part of the cerebrum just above the corpus callosum, the major fiber tract connecting the cerebral hemispheres on both sides of the brain. Posner and McCandliss (1999) explained why the attentional system is such an important system in regulating human behavior. Because this system has both bottom-up, sensory-driven operations (posterior component) and top-down, executive-driven operations (frontal component), it can be used for reactivating any anatomical area and reprogramming cognitive operations. The bottom-up operations are not just driven from sensory input from the external world. Internal imagery, which uses the same sensory coding as the external world, can also drive the attentional system. Engaging the attentional system may increase neuronal activity, as a function of cognitive load, or may decrease neuronal activity, if the

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system can recruit the area that initially performed the computation to perform it again. For example, activations may increase in a circuit involving the left lateral frontal, posterior, and anterior cingulate areas when processing novel verbal stimuli, and decrease activation in insula; but that circuit may decrease in activation and insula may increase in activation when processing highly familiar, practiced verbal stimuli (Raiche et al. 1994). The level of language emphasized in instruction (see Table 5.1) can also affect pattern of activation. Attention to words increased activation in left frontal regions, whereas attention to sentences increased activation in left posterior regions (Abdullaev & Posner 1998). Attention can affect how the visual word form is coded in fusiform gyrus (Posner & McCandliss 1999). As Posner and McCandliss caution (p. 317), "It is often tempting to think of brain circuitry as a set of fixed anatomical connections among brain areas or neurons within an area. However, any brain area can be anatomically connected to any other area by either direct or indirect routes, providing multiple possibilities for recombining component operations in novel ways. The act of attending to a particular type of information can be thought of as setting up a temporary circuit from higher level to lower level areas." Recent in vivo imaging studies investigated components of the attentional system In one study (Leung et al. 2000), participants were asked to name the color of ink in which a word was written. Sometimes the words were written in the same color, and sometimes they were not. Thus, this task measures brain activity when attending to a relevant dimension and suppressing an irrelevant dimens i o n m w h e n the color of the ink and the name of the word were not the same (e.g., the word red written in blue ink). This task activated anterior cingulate (see Figure 5.2), insula (see Figure 3.9), and premotor and inferior frontal regions (see Figure 6.6). In another study (Casey et al. 1997), children and adults were asked to push a button if any letter but X appeared (Go condition) and not to push the button if X appeared (No Go condition). Children and adults differed in the volume but not location of activation; children activated more brain, probably because they were less efficient and had to work harder at regulating their attention. Age correlated with middle frontal gyrus (see Figure 5.4). Activity in the orbital area of frontal cortex correlated with better inhibition (not responding to X trials), but activity in the anterior cingulate correlated with failure to inhibit (responding to X trials). In both studies the anterior cingulate was activated, but it was not the only brain structure activated during tasks requiring regulation of attention processes. Anterior cingulate has activated on a wide variety of attentional tasks (Carter et al. 1998; Smith & Jonides 1999), suggesting that it may participate in all circuitry for managing attention. Anterior cingulate appears to be especially important in managing conflict between competing responses, deciding how to respond when uncertain, and monitoring responses (Barch et al. 2000).

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FUNCTIONS IN

The various systems that are accessed in the process of creating a reading brain were discussed separately but must be integrated in real time. For example, in oral language production, the following separate component functions are probably activated and coordinated to perform a specific task in the Language by Mouth system: three kinds of word representations m for semantics (meaning), word form (phonological, morphological), and lemmas (morpho-syntactic signals) - - and four kinds of functions~attention, verbal working memory, episodic memory (for encoding unfamiliar words not yet automatized), and executive control (strategies) (c. Price et al. 1999). Thus, Language by Mouth involves far more component processes than simply speech (articulation). Language by Eye probably activates all these representations and functions plus orthographic word forms and specialized executive functions for managing the cross-talk between Language by Mouth and Language by Eye. Figure 5.3 provides another example, based on Goldman-ILakic's (1992) research on working memory, of brain systems working together. The sensory and motor systems are coordinated via cortical connections that send inhibitory and excitatory commands as the monkey makes memory-guided eye movements during problem solving. The working memory, attentional, and executive systems work closely together because they draw on common parts. For storage, W M uses the STM store, which in the case of the monkey is the visual-spatial sketchpad; but for language learners is phonological store in left parietal areas (inferior parietal cortex and supramarginal gyms; see Table 5.3 and Figure 5.1), which lie in the posterior component of the attentional system. For the central executive, W M uses prefrontal cortex (see Figure 3.4), which, like the anterior component of the attentional system, is in the frontal lobes. The central executive may also draw on other executive functions in the government system; these are distributed throughout frontal regions (Stuss & Benson 1986). We turn now to the imaging studies on skilled reading and dyslexia for hints as to which brain regions and functions may be necessary for a functional reading system to develop optimally. However, before reporting the results of these studies we provide an overview of the current state of the art of functional brain imaging in studying reading.

IN VIVO F U N C T I O N A L I M A G I N G STUDIES OF READING Most functional imaging studies of reading scanned adults. Of these, some included only normal readers, but others compared normal readers to developmental dyslexics m adults who have a history of difficulty in learning to read. Sometimes

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the adult developmental dyslexics are compensated (i.e., they finally became average readers even though they may have residual problems detectable only with special tests) and sometimes they are still poor readers. We review only imaging studies for developmental dyslexia m not for acquired dyslexia in which people learned to read easily but lost reading function because of disease, stroke, or injury. Recently studies appeared in the literature for developmental dyslexia in children and adolescents; these are discussed separately because reading is a developmental process that is not yet complete in school age children and youth. Of the various research tools that can be used for functional imaging to study the brain at work (see Chapter 3), PET has been used most often. One reason that adults have been studied more than children is that PET is an invasive procedure in which radioactively labeled substances are injected. Human Subjects Review Boards typically do not approve use of PET for studies with children. Increasingly, researchers are using fMRI and flVIRS, both of which are noninvasive and can be used with both children and adults, and the fMRI and flVIRS research literature is growing. Because functional imaging is very expensive to conduct and analyze, sample sizes tend to be small. Some researchers carefully describe characteristics of their sample and match contrasting groups (e.g., normal readers and dyslexics) on relevant variables like IQ scores, age, and education, but other researchers do not. With rare exceptions, only right-handers are studied. Although 95 percent of right-handers are likely to be left-dominant for speech and language, left-handers may be either left-or right-dominant for speech and language; only invasive, risky procedures that inject substances into arteries can determine dominant side for language with certainty. Restricting samples to right-handers has made interpretation of results easier, but left-handers may be included in future research as imaging techniques are being developed to assess side of language dominance without invasive, risky procedures. Dominance indicates the side of the brain that is most involved in expressive language (total volume) and that may take the lead in the dance (executive control); dominance is not inconsistent with the notion of complementary fight-left roles (Table 5.2) discussed earlier in this chapter in which the nondominant side contributes in specific ways to the language system. Indeed, brain scans during language tasks generally show some activation on both the left and fight, but the sides may vary in the relative patterning. Functional imaging results are not interpretable apart from the cognitive paradigm used to generate the data. Results are very dependent on which tasks were contrasted with which tasks and what was used as the baseline condition to which a given task is compared. When a certain brain region is said to activate, it means that the region was more activated than a control, comparison condition; it does not mean that other areas did not activate m other areas may have activated in all tasks or not at all. A consensus has not yet emerged on exactly which tasks to use and how to analyze data. Slight variations in stimuli or task requirements can affect results

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(where brain activation occurs), even for tasks that are purportedly measuring the same construct like phonological processing (Zemro & Eden 2000). Some of these differences in results may be related to the flexible coding strategies readers can adopt to perform the same experimenter-designed task (Pugh, Rexner & Katz 1994). Some differences may also be related to the different ways that phonological processing is operationalized across studies, ranging from phonemic awareness in aural language to manipulation of phonemes in spoken words, to phonological decoding of orthographic word forms, to phonological coding of word forms in short-term memory (Wagner & Torgesen 1987). Brain activation is measured in pixels (2-dimensional areas) or voxels (3dimensional volumes) that are activated when blood flows to an area to supply the glucose and oxygen needed to refuel the neurons after they have performed a task. Results of brain imaging are also difficult to interpret because we never know if activation means that excitatory or inhibitory neurons are involved. Moreover, it is difficult to interpret amount of activation (number of pixels showing activation compared to adjacent regions). Larger regions of activation may indicate that more of that region participates in a function, and smaller regions of activation may reflect difficulty in engaging that region in the function. Alternatively, a larger amount of activation may reflect inefficiency~more neurons are needed to perform a task and a smaller amount of activation may reflect efficiency~less neural tissue is needed to perform a task. Also, size of brain regions at a structural level is correlated with age, further complicating developmental comparisons (Schultz et al. 1994). For the most part, the research literature contains only results for groups or group differences. With rare exceptions, results are not reported for individuals and have not been shown to be reliable across individuals. Another limitation in interpreting results is that rCBF responses in PET and BOLD responses in fMRI are hemodynamic (blood flow) responses that unfold over many seconds; thus, these neural responses may miss important events happening at smaller time intervals. Brain circuitry can change in a short time scale during an experiment or a longer time scale in development (Posner & McCandliss 1999). Combining ERPs with fMRI is one way to integrate more precise temporal information with spatially precise information afforded by fMRI. Likewise, the chemical activity that unfolds during fMRS occurs over many seconds, but may reflect intracellular processes, in contrast to the extracellular processes of BOLD and rCBF. A general principle to consider is that it is best to base conclusions on multiple imaging tools, all of which have advantages for certain biological substrates and none of which alone explains all brain functioning. More research exists on the low-level processes in reading (e.g. single-word reading) than comprehension of text because it is easier to design and interpret experimental tasks for low-level processes that are simpler and do not activate as many brain regions all at once. For example, most studies of single-word reading use only monosyllabic words; polysyllabic words increase the complexity of the patterns of brain activation (Chee, O'Craven, Bergida, Rosen, & Savoy, 1999).

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Most fMRI studies have used receptive tasks with minimal motoric requirements (e.g., a button press to indicate yes or no) because movement during scanning introduces motor artifact that interferes with interpretation of results. As discussed earlier, procedures are being developed to avoid motor artifact and may lead to more research in which language production (e.g., oral reading) is studied. With a few exceptions, the existing work has focused on inferring neural circuits of mind based on very limited nature-nurture interactions (how the brain responds to an experimenter-designed task). This narrow approach may be changing as researchers are beginning to study the effects of practice and instruction on the brain as a dependent variable that changes in response to environmental input. Without a doubt, brain imaging n both the technology used to scan and the cognitive paradigms used to interpret brain activation m i s in its infancy. It is premature to associate specific functions only with specific anatomical regions until this field of research matures (Price, Wise, Watson, Pattersen, Howard, & Frackowiak, 1994). Nevertheless, despite these limitations, in vivo functional imaging has expanded knowledge of brain-behavior relationships, especially in reading and language, beyond what was available based solely on postmortem studies.

Normal, Skilled Reading Seminal PET studies that launched the investigation of the reading brain focused on processes involved in reading single words (Peterson, Fox, Posner, Mintun, & Raichle, 1988; Peterson, Posner, Mintun, & Raichle, 1989; Posner et al. 1988). When the task involved only auditory coding of heard words or only visual coding of viewed words, different, spatially separated brain areas activated; but when the task required associating auditory or visual codes with semantic codes or with motor output codes, visual and auditory codes activated regions close to each other (Posner et al. 1988). These results are consistent with the distinction between primary sensory areas that are sensory-specific and surrounding association areas that integrate sensory and motor codes. Subsequent research showed that (a) sensory coded stimuli may be recoded linguistically in the superior temporal regions, which activate during tasks requiting decisions about whether letter strings are real words with meaning; and (b) the left prefrontal cortex may be involved in the executive control of reading processes (Frith, Friston, Liddle, & Frackowiak., 1991). These linguistically recoded sensory codes may be stored in two separate lexicons (mental dictionaries) organized by code (word form): a phonological lexicon for the sound form of a word and an orthographic lexicon for the visual form of a word. The lexicon for auditory word form may be in the left superior and middle temporal gyri (Howard et al. 1992), in the left superior temporal gyms (Demonet et al. 1992), or in the inferior temporal/occipital junction (Demonet et al. 1992). The strongest contenders for the orthographic lexicon are the left fusiform gyrus (e.g. Nobre et al.

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1994) and left lingual gyms, where activity in both is significantly correlated with reading exception words that require some lexical-level, word-specific processing (Horwitz, Rumsey & Donohue 1998). Other candidates are the posterior left middle temporal gyms (Howard et al. 1992) or inferior temporal/occipital junction (Frith et al. 1995). (See Figures 5.1 and 5.2 for structures to which this section refers.) The exact location of these lexicons is not precisely consistent across studies and remains a topic of research. Differences across studies may be related to whether the tasks are specific to prelinguistic codes (sensory codes not yet recoded as language codes) or prelexical codes (subword language codes not yet recoded at the word level). For example, the tasks may activate (a) auditory word forms (prelinguistic sound codes), (b) phonological word forms (linguistic sound codes that draw on both auditory and articulatory forms (see the section, "Aural/Oral Language," earlier) or (c) visual word forms (prelinguistic visual codes), or, (d) orthographic word forms (linguistically recoded visual codes). MEG studies discussed earlier in the section "Visual System" suggest that separate systems may exist for prelinguistic visual word forms, prelexical linguistic processing that links orthographic word forms to sublexical phonological segments, and lexical linguistic processing that links orthographic forms to whole word phonological units (names). For aural language input, separate systems may also exist for prelinguistic auditory processing, prelexical linguistic processing (sound segments smaller than the word), and lexical linguistic processing (receptive phonological word forms and name codes for whole words). Whatever the final resolution of exactly where each of these kinds of word codes are stored, the close spatial proximity of lexicons for visual/orthographic and auditory (articulatory)/ phonological word forms in the left posterior regions makes functional communication between them potentially possible. Yet other studies contrast the receptive auditory word form and the expressive articulatory word form. Receptive auditory forms may be stored in left superior temporal gyms (Demonet et al. 1992). Expressive sound forms (name codes) activate inferior temporal cortex, left frontal operculum (Broca's area), left and midline cerebellum, and left thalamus (Brunswick, McCrory, Price, Frith, & Frith, 1999); or left frontal operculum and midline cerebellum (Price & Friston 1997); and may be stored in a naming lexicon that includes both Wernicke's Wortschatz area in the left temporal lobe (BA 37) and Broca's area in the left frontal lobe (operculum) (Brunswick et al. 1999). However, some evidence points to phonological word form storage in the superior temporal region, with the executive functions for segmenting that form in frontal areas (Burton, Small & Blumstein 2000). (See Figures 3.4, 3.11, and 5.1.) Comparison of reading real words and pseudowords may also contribute to clarifying how word forms are represented in memory. Real words have orthographic, phonological, and semantic codes. (See Ehri 1980, for a discussion, from the perspective of psychological theory, of how the mental lexicon represents words in the form of orthographic images, phonological codes, and semantic codes.)

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Pseudowords have orthographic and phonological codes (but not semantic code because they are nonsensical). In some studies no differences in brain activation for real words and pseudowords are observed (Bookheimer et al. 1995), indicating that the tasks activate brain regions for orthographic and phonological but not semantic coding. In other studies, real words activate fusiform gyrus (Herbster et al. 1997), but pseudowords activate left inferior frontal regions (Herbster, Mintun, Nebes, & Becket, 1997; Price et al. 1999) or extrastriate cortex bilaterally (visual feature extraction center), left inferior temporal gyrus (visual word form center), and left frontal regions (Broca's area, an articulatory word form center) (Frith et al. 1995). Presenting the same pseudoword repeatedly so that it is no longer an unfamiliar word reduces activity in fight lingual gyms, suggesting that that structure plays a role in learning to recognize familiar words (Frith et al. 1995). (See Figures 3.4, 5.1, and 5.2.) Real words and pseudowords contrast in another way. Readers can decode both real words and pseudowords by segmenting words into phonological units and then assembling those phonological units; even the so-called irregular real words tend to have mostly phonologically recodable spelling units (see Chapter 8). In contrast, familiar real words, but not pseudowords, can be read by accessing stored representations for whole word phonological units; that is, by directly accessing stored name codes or receptive phonological word forms. So comparison of real-word and pseudoword tasks may also reflect differences in size of phonological unit as well as activation of a semantic (meaning) code. Segmenting and assembling phonological codes may occur in supramarginal gyms, superior temporal gyms, and premotor cortex in Broca's area, whereas addressed phonology may occur in left posterior inferior temporal cortex (BA 37) and left frontal operculum (Brunswick et al. 1999). (See Figures 3.4, 5.1, and 6.6.) Other research has focused on how different kinds of word forms work together to perform specific functions. Such coordinated activity requires participation of executive control processes. A rhyming task (processing segmented phonological units) activates left language areas more than a verbal generation task, which activates prefrontal cortex (retrieval of the name code) and anterior cingulate bilaterally (executive control of the search and retrieval process) (Lurito, Kareken, Lowe, Chen, & Matthews, 2000). Generating examples of words that begin with a specified letter increases activation in striate/extrastriate occipital cortex (suggesting that letters are imaged mentally), left temporal cortex (BA 21 and BA 37, suggesting that the lexicon is accessed), and left frontal cortex (BA 44 and BA 45, Broca's area, suggesting that language production centers participate in preparing the response) (Friedman et al. 1998). Prefrontal cortex appears to play two roles in semantic processing during sentence reading (Fiez 1997). The first role is efforfful retrieval of semantic information from posterior brain regions (and of phonological information that also is activated when reading for meaning); the second role is controlled processing of that semantic information (Fiez 1977). (See Figures 3.1, 3.2, 3.4, and 5.2 for structures named in this section.)

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The semantic network is uniquely designed to interact with either auditory/ phonological or visual/orthographic word forms. For example, the left middle temporal gyms, left fusiform gyms, and right cerebellum activate only for auditory/phonological-semantic but not for visual/orthographic-semantic code connections (Chee et al. 1999). This pattern of results suggests that there are semantic-phonological connections that may be separate from semanticorthographic connections. Comparison of results from many studies suggests that two kinds of lexicons exist m t h o s e dedicated to specific word forms m and an overarching lexicon (in BA 37, Brunswick et al. 1999) that orchestrates the coordination of the other lexicons. The other lexicons code sensory information (probably in primary cortex) or linguistically recoded information (probably in association cortex). BA 37 located near Wernicke's area (below and ventral, see Figure 3.4) is activated by all linguistically recoded sensory codes: orthographic codes for letters and whole written words and phonological codes for phonemes and whole spoken words (names) (Friedman et al. 1998). No wonder Brunswick et al. consider BA 37 a treasure chest for words! A general principle in Chapter 8 is that learning to read requires the coordination of all these linguistically recoded word forms morthographic, phonological, and semantic. Another general principle is that reading for meaning also depends on activating these word forms. Reading sentences for meaning activated middle temporal regions known to be involved in word form processing (Simos, Basile & Papanicolaou 1997). Phonological word forms are activated even during silent reading for meaning (Niznikiew & Squires 1996). Even though a reader is not consciously aware of phonological processes during silent reading, visually presented, familiar words automatically activate both orthographic and phonological codes (Booth, Perfetti & MacWhinney 1999; Frith et al. 1995; Price, Wise & Frackowiak 1996) in implicit memory networks, and phonological codes play a role in creating orthographic representations in memory (Booth et al. 1999). Currently, research attention is moving away from the issue of where in the brain a task causes activation to the functional connectivity (neural circuitry) among different brain regions (Bfichel, Coull & Friston 1999; Cordes et al. 2000). In normal reading of single words, the left angular gyms is functionally connected with the left extrastriate cortex, the left superior temporal gyms (BA 22) in Wernicke's area, and left inferior frontal gyms (BA 45) (Pugh, Mencl, Shaywitz et al. 2000). Both subcortical and cortical regions may be in the neural circuitry: thalamus, basal ganglia, cerebellar hemispheres, fusiform gyms, left and middle temporal gyri, and perisylvian cortex (Broca's and Wemickes' areas including the planum) (Paulesu et al. 2001). (See Figures 3.1, 3.2, 3.4, 3.11, 5.1, and 5.2 for structures named in this section.) The first three regions may activate because many of the processes in the adult subjects are on automatic pilot, while the remaining regions probably activate during access to and integration of the specific word form codes. E1KP studies are sensitive to the temporal course of activation and suggest that initially the

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pattern of connectivity flows from stimulus upward in the system, but later in processing the connectivity involves highly interactive top-down and bottom-up flow of information that is very sensitive to task demands (Niznikiewicz & Squires 1996). To study learning of new skills in already skilled adult readers, Poldrack and Gabrieli (2001) conducted MtLI studies of the process of learning to read mirrorreversed text. They used a special kind of practice involving priming with previously presented stimuli to compare the role of short-term compared to long-term practice in the learning process. During learning a new kind of reading activation increased in left inferior temporal, striatal, left inferior prefrontal, and fight cerebellar regions, and activation decreased in left hippocampus and left cerebellum. Many regions that increased in activation during learning decreased in activation during short-term practice, but long-term practice eliminated that activation altogether. This research suggests that learning can result in a shift from more cognitive, controlled processing to more automatized processing because the cognitive and behavioral pathways share common neural circuitry. Both initial learning and subsequent automatization share common neural networks in a circuit spanning the striatum and frontal lobes. Not only does the brain change during learning, but also practice plays a role in bringing those changes about.

Comparison of Normal and Dyslexic Adult Readers Over a decade of brain imaging studies has shown that dyslexics may either underactivate or overactivate specific brain regions compared to normal readers. Seminal PET (and rCBF) studies stimulated research on the brain differences between adults who learned to read easily and those who struggled with reading (Flowers, Wood & Naylor 1991; Gross-Glenn et al. 1991; Lubs, Smith, KImberling, Pennington, Gross-Glenn, & Duara., 1988; tLumsey et al. 1992; Wood, Flowers, Buschbaum, & Tallal, 1991). Such brain differences may provide clues to what kinds of instruction might prevent struggles in reading. These pioneering studies all pointed to a bottleneck in the left posterior regions in occipital, parietal, and temporal lobes (see Figure 3), consistent with the results of the more recent imaging studies. The initial studies showed the following. Dyslexics activate left peri-insular cortex (see Figure 3.9) less than normal readers (Lubs et al. 1988), consistent with Paulesu et al.'s (1996) findings for adult dyslexics and Corina et al.'s (2001) findings for child dyslexics. On a phonemic task, compensated dyslexics showed more rCBF activation near left Heschl's gyrus, suggesting that, due to metabolic inefficiency, their neural circuitry worked harder during phonological processing (Wood et al. 1991), consistent with a similar conclusion based on elevated lactate activation in child dyslexics in left anterior regions on a phonological task (Richards et al. 1999). On a lettermonitoring task for auditorially presented words, dyslexics had reduced activation in Wernicke's area, but increased activation in the temporal and parietal regions just

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posterior to Wernicke's area; overall normal readers had more activation in left temporal regions than dyslexics (Flowers et al. 1991). On an oral reading task, dyslexics activated more than controls bilaterally in lingual gyms (see Figure 5.2), which may be a pathway between the visual cortex in occipital lobes and language cortex in temporal lobes, suggesting inefficiency in visual word processing or in making connections between the visual stimulus and linguistic codes (Gross-Glenn et al. 1991). In contrast, Breznitz (in press) found that during an auditory rhyme task, dyslexics did not activate cortex near left angular gyms and Wernicke's area, but normals did (Rumsey et al. 1992). Thus, across studies there is a pattern of results showing that dyslexics underactivate in some brain regions, showing unresponsiveness to stimulation but overacitivate in others, showing metabolic inefficiency. (See Serafini et al. 2001, for review of evidence for metabolic inefficiency explanation.) Subsequent research documented that normal readers and dyslexics did not differ in auditory semantic judgments (1Kumsey, Zametkin et al. 1994) but that they differed in a variety of other auditory, aural language, or written language functions. During an auditory tone judgment task, dyslexics activated only in left temporal regions but normal readers activated bilaterally in temporal regions (Rumsey & Andreason 1994). On a continuous performance task with aural syllables (press button when /da/ is presented but not when other vowel/stop consonants are presented; e.g., /ha/ o r / g a / ) , dyslexics differed from normal readers in medial temporal lobe (Hagman, Wood, Buchsbaum, Tallal, Flowers, & Katz, 1992). On a visually presented rhyme task, both dyslexics and normal readers activated in Wernicke's and Broca's areas, but only normal readers activated in insula, suggesting a functional disconnection in the language system of dyslexics (Paulesu et al. 1996). In a study that carefully sampled progressive task requirements from visual only (e.g., line judgments), to orthographic (e.g., letter judgments), to phonological recoding of orthographic representations (e.g., judgments about letter sounds), to semantic coding of phonologically recoded written language (e.g., word meaning), dyslexics underactivated in posterior regions (Wernicke's area, angular gyms, and striatal cortex) and overactivated in frontal regions (inferior frontal gyrus) compared to normal readers (Shaywitz et al. 1998) (see Figure 3.4). Subsequent research confirmed that dyslexics have less functional connectivity on tasks requiring explicit phonological assembly (decoding) (Pugh, Mencl, Shaywitz et al. 2000). Adult dyslexics also are impaired in orally repeating aurally presented words and pseudowords (McCrory, Frith, Brunswick, & Price, 2000). Dyslexics activated less than controls in fight superior temporal and right postcentral gyri and left cerebellum on this task, which did not require reading. Additional research has confirmed this dyslexic pattern ofunderactivation in the left posterior regions and overactivation in left frontal regions. Dyslexics underactivated in BA 37, which has strong connections to left inferior temporal gyms, and medial extrastriate cortex, and overactivated in the premotor regions of Broca's area, BA 6/44 (see Figures 3.1, 3.2, and 3.4) (Brunswick et al. 1999). These researchers, who used naming tasks, attributed the deficit in dyslexia to a problem in lexical

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retrieval and overcompensation for this problem with more effortful sublexical phonological assembly strategies in left frontal regions. This research team also observed less activation in the left operculum in Broca's area (frontal lobe), left cerebellum, and left thalamus (see Figures 3.4 and 3.11), suggesting that there may be both (a) overactivation and underactivation in left frontal regions, and (lo) abnormal activation in subcortical regions. Overactivation in frontal regions may reflect effortful sublexical phonological assembly (Brunswick et al. 1999). The abnormal activation in cerebellum is consistent with other research showing that dyslexics, who exhibit abnormal cerebellar activity, may have difficulty in the acquisition phase (learning to learn) and/or in the subsequent stage when practiced skills should become automatized (Nicholson et al. 1999). Recent research has focused on which specific neural circuits may be disconnected in dyslexics. Evidence to date points to functional disconnections in circuits in the left posterior region. Left angular gyrus may be disconnected with left extrastriatal areas, left superior temporal gyrus, and left inferior frontal gyrus in dyslexia (see Figures 3.4 and 5.1) (Horwitz et al. 1998). Results of another study constrast with the Brunswick et al. (1999) conclusion that dyslexia involves a deficit in word retrieval: Dyslexics showed disruption in functional connectivity in left hemisphere only for tasks involving phonological assembly (Pugh, Mencl, Shaywitz et al. 2000). Consistent with the second study, dyslexics show normal activation in left frontal cortex when making silent lexical decisions, but show less activation in temporal cortex bilaterally and in left inferior parietal cortex when reading words orally (Rumsey, Nace et al. 1997). Most likely, dyslexics may be impaired on only phonological assembly or only lexical retrieval of names, or both, consistent with Double Deficit Theory (Wolf & Bowers 1999). Differences across studies may reflect differences in sample characteristics--whether most participants have a single or double deficit--but researchers are not consistently reporting whether participants are impaired in phoneme manipulation and/or rapid naming. Recent research has also compared adult dyslexics across languages (English, French, and Italian) and found a brain signature that cuts across differences in specific languages (e.g., the regularity in spelling-sound correspondences). Compared to normals, dyslexics showed different patterns of brain activation in middle occipital gyrus, middle temporal gyrus, and inferior and superior temporal gyri (see Figure 5.1) (Paulesu et al. 2001). That is, they showed differences in parts of the brain known to be involved in orthographic processing of written words and of linguistic processing of heard words and written words. According to the investigators, this brain signature may be related to greater individual variability in dyslexic brains or to the weaker connections between the components of the language system. However, all dyslexics in this study were university students with significantly lower freedom from distractibility factor scores compared to controls; this factor reflects ability to selfregulate; that is, to exert executive control in working memory. An alternative explanation for this intriguing, cross-linguistic brain signature is difficulty in the executive coordination of linguistic codes in working memory (Corina et al. 2001).

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MEG studies are informative because they yield information both about spatial location of processing and temporal course of processing. These studies have found differences between dyslexics and controls in temporal processing for the auditory system, the visual system, and higher-level comprehension system. Dyslexics differ from controls in organization of auditory processing on the left side as early as 100 ms after stimulus onset (Heim et al. 1999). Good and poor readers differ in processing brief, rapid successive auditory stimuli at 100 to 200 ms but not at 500 ms (Nagarajan, Mahncke, Salz, Tallal, Roberts, & Merzenich, 1999). For visually presented words, dyslexics and controls do not differ in early visual processing--the first differences emerge at 150 ms when the visual stimulus is first processed as a string of letters (prelexical orthographic processing) (Cornelissen et al. 1998; Helenius, Tarkiainen, Cornelissen, Hansen, & Salmelin, 1999; Tarkiainen, Helenius, Hansen, Cornelissen, & Salmelin, 1999). Dyslexics and controls differ in reading words out of sentence context in the left occipital-temporal region, the left temporal, and left inferior frontal areas (Salmelin et al. 1996). The first difference emerges between 100 and 200 msec after word presentation in the left inferior occipital-temporal regions. Between 200 and 600msec normal readers activate in the left temporal region but dyslexics do not. During this same time period dyslexics show unexpectedly early activation in Broca's area, suggesting that they are using a top-down strategy to bypass defective orthographic word form and phonological word form processing in the temporal lobe. For reading comprehension, dyslexics and controls do not differ in the spatial distribution of semantic processing but rather in the timing and strength of activation--dyslexics show sensitivity to word meaning about 100msec later than normal readers do (Helenius, Salmelin, Servie & Connolly 1999).

Oral Versus Silent Reading In normal educational practice children make a transition from oral reading to silent reading by the end of the primary grades. Silent reading may be more efficient because once we have created a mental lexicon for written words, these words can be recognized more quickly than they can be spoken. For example, two to three words can be pronounced per second (Price et al. 1999) but many more can be read silently per second. In the first report of potential differences between these forms of reading (Lassen, Ingvar & Skinh6j 1978), both modes of reading showed a change from baseline in activation of frontal eye fields, lower frontal regions (including Broca's), promotor frontal cortex, and visual and paravisual areas; in addition, oral reading showed significant activation in rolandic mouth areas (probably related to oral output), and auditory and paraauditory areas (probably due to monitoring of oral output). These findings suggest that silent reading may be more efficient because an oral motor output stage and an auditory feedback loop for monitoring oral reading are eliminated.

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The differences between oral and silent reading may be more complex, however. Results have not always been consistent across studies comparing oral and silent reading of single words. Exposure time of the material to be read may affect observed differences between oral and silent reading across studies (Price et al. 1994). In one study both passive viewing and oral reading of single words activated rolandic cortex bilaterally, mouth region of left rolandic cortex, left buried Sylvian cortex, fight lateral sylvian cortex, left premotor cortex, and supplementary motor area (Petersen et al. 1988). The first two locations are expected for oral reading but not silent viewing. However, visual presentation of words automatically activates phonological representations (Booth et al. 1999), which may in turn activate brain regions involved in articulation, consistent with the Motor Theory of Speech Perception (Liberman 1999). Whether the stimulus input is auditory or visual, adults activate the same brain regions for articulatory output: primary sensori-motor cortex, supplementary motor cortex, and Broca's area and surrounding regions (Peterson & Fiez 1993). Whether vcords are read orally or silently, extrastriate cortex activates (Hagoort, Indefry et al. 1999) (see Figures 3.4 and 6.6). Comparison of oral and silent naming of objects and words shows, however, that circuitry (the set of activated regions) may differ depending on the task at hand (Bookheimer et al. 1995). Silent and oral naming of objects differed primarily in the motor areas. Regions activated only by silent naming included anterior and basal insula and middle frontal gyms. Silent and oral naming of words differed primarily in that silent naming activated inferior temporal structures, which may be sensitive to abstract sound codes less tied to the incoming speech signal, and oral naming activated superior temporal structures, which may be more sensitive to the incoming speech signal during monitoring of oral reading. Oral reading of single words also activated extrastriate cortex in the occipital lobes and fusiform gyrus in the temporal lobes. Thus, silent reading may use an inferior temporal pathway with direct access to the lexicon but oral reading may use a superior temporal-inferior parietal route for phonological decoding (see Figures 3.4, 3.11, 5.1, and 6.6). In another comparison of oral and silent reading, evidence suggests that different neural circuits are activated even though there are some common regions of activation; in this case, premotor areas (Hagoort, Indefrey et al. 1999). Oral reading activated cerebellum, extrastriate cortex bilaterally, superior temporal gyms bilaterally, middle temporal gyms, premotor and sensory cortex bilaterally in frontal lobes. Silent reading activated extrastriate cortex bilaterally, left precentral gyms, fight insula, angular gyms bilaterally, cingulate, superior and inferior frontal regions, supramarginal gyms blaterally, and fight fusiform gyms (see Figures 3.4, 5.1, 5.2, and 6.6). Also, comparisons between the two pronounciation and two silent lexical decision tasks (1Kumsey, Horwitz et al. 1997) show that patterns of connectivity for oral and silent reading are different, suggesting that different neural circuits were activated. Recent research suggests that oral and silent reading may also differ in how processes are orchestrated over time (Price et al. 1999). For example, early visual

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activation in bilateral occipital gyri (see Figure 3.4) was absent during silent reading, but during oral reading, bilateral activation in lingual gyri (see Figure 5.2) was present. Activation in medial lingual gyri, where name codes may be attached first to visual word forms, may be enhanced by explicitly naming rather than silently viewing word forms. If so, there may be an instructional advantage for having students name written words until their orthographic lexicon for word-specific representations is well established. Access to name codes for whole words may facilitate the process of assembling sublexical phonological units. Additional research is needed on the differences between oral and silent reading. These processes appear to vary across studies that differ in stimuli and tasks. None of the studies to date has examined the differences for longer stretches of text than the single word or for children at different stages of reading development. It is encouraging, however, that brain activation during oral versus silent reading is not affected by stimulus properties such as regularity of spelling-phoneme correspondence in normal English readers (R.umsey, Horwitz et al. 1997).

Developing Readers Two studies compared normal children and adults on reading-related tasks. Children and adults activated similar regions in left frontal cortex when silently generating animals and foods that begin with specified letters, but children activated more than adults and showed more right activation (Gaillard, Hertz-Pannier, Mott, Barnett, LeBihan, & Theodore, 2000). On an auditorially presented comprehension task, children activated many of the same temporal and frontal regions as adults, but children activated more in the inferior occipital and anterior superior temporal areas and adults activated more in the anterior central sulcus and anterior middle temporal area (Booth, MacWhinney, Thulborn, Sacco, Voyvodic, & Feldman, 2000). These kinds of studies are important reminders that the patterns of brain activation for adults do not necessarily generalize to children. A number of imaging technologies have been used to compare children who are normal readers with those who are dyslexics (significant dissociation or uneven development of word reading compared to verbal intelligence or comprehension) or poor readers (low achieving but not necessarily underachieving for verbal ability). In the first fMP,.I study, children and youth (mean age 14) performed four hierarchically organized tasks: silent viewing of letter strings, silent reading of nonwords, silent reading of highly frequent real words, and phonological transformations in which the first letter is moved to the end of a word and suffixes are added (Georgiawa et al. 1999). When each of the last three tasks was compared to a common baseline (the letter string task), dyslexics and normals differed only in nonword reading and phonological transformations, indicating that the dyslexics had trouble with phonological processing of visually presented words. For nonword reading, dyslexics and good readers differed in left temporal regions and left inferior

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frontal regions. For phonological transformations, dyslexics and good readers differed in left inferior frontal gyms and left thalamus. In the second fMRI study, dyslexics and controls (age range 9-13 years, matched on age and IQ) performed two auditorially presented language tasks m phonological judgment (Do these words rhyme?) and lexical judgment (Are these both real words?) (Corina et al. 2001). For both tasks, the same pairs ofwords and/or pseudowords were used. The phonological task required selective attention to phonology and disregard of meaning, whereas the lexical task required selective attention to meaning and disregard of phonology. These language tasks alternated with a tone judgment task that required auditory, nonlinguistic processing (Are these tones the same?). Dyslexics and good readers did not differ on the tone task, which served as a common control, just as they had not on the same task during flVIRS (Richards et al. 1999). Dyslexics and good readers did differ on both auditory language tasks, but the pattern of differences depended on brain region. Figure 5.4 shows fMRI activation data from the four brain regions where group by task interactions occurred: inferior temporal gyms (Figure 5.5), precentral gyms (Figure 5.6), middle frontal gyrus (Figure 5.7), and orbital frontal cortex (Figure 5.8). Except for

FIGURE 5.4 Brainlocation of group (dyslexicor control) and task (phonologicalor lexicaljudgment) interactions reported in Corina et al. (2001).

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middle frontal gyms, the interaction also depended on the side of the brain (cerebral hemisphere). As shown in Figure 5.5, good readers showed more left than right activation in inferior temporal gyms on both the phonological and lexical tasks. In fact, dyslexics did not activate at all on the phonological task in left inferior temporal gyms and were less activated overall in this region on both tasks, consistent with one reported locus of the universal signature for dyslexia reported by Paulesu et al. (2001). Inferior temporal gyms may be a region for phonological-semantic mapping (Binder et al. 2000). The tendency of good readers (see Figure 5.9) to activate and ofdyslexics (see Figure 5.10) not to activate in left inferior temporal gyms during the phonological judgment task was consistent across individuals within groups. As shown in Figure 5.6, in the precentral gyms in the primary motor area, the groups differed in auditory language tasks that did not require oral motor production but may have activated articulatory gestures, consistent with the Motor Theory of Speech Perception (Liberman 1999) discussed earlier in the chapter. Good readers showed more left activation on the lexical than phonological task, suggesting that this region is sensitive to articulatory gestures in lexical units. Dyslexics activated more than the good readers bilaterally on the phonological task,

FIGURE 5.9 AllbutoneofsevencontrolbrainsinCorinaetal.(2001)on the top right has activation in circledleft inferior temporalgyrusduring phonologicaljudgment task in fM1kI study.

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FIGU1LE 5.10 All six dyslexicsin Corina et al. (2001)lackactivationin circledleft inferior temporal gyrus during phonologicaljudgment task in fMtkI study. suggesting that this region is sensitive to articulatory gestures in subword units in their brains. As shown in Figure 5.7, good readers had strong bilateral activation on the lexical task in middle frontal gyrus, where dyslexics had weaker bilateral activation. On the phonological task, good readers had more left activation than dyslexics in this region. As shown in Figure 5.8, in orbital frontal cortex, good readers had more left activation than dyslexics on the phonological task and more right activation than dyslexics on the lexical task. Because orbital frontal cortex is involved in inhibition or suppression of irrelevant information (Casey et al. 1997), this pattern of results suggests that dyslexics differ from good readers in suppressing both phonological and semantic information when it is irrelevant. Taken together, these results suggest that, even though dyslexics have good conversational skills and verbal reasoning skills, they also have problems in metalinguistic phonological awareness and in executive coordination of aural language codes for phonology and s e m a n t i c s - both of which must be linked to orthographic codes in learning to read words. These child dyslexics and good readers also differed functionally in insula (see Figure 3.9),.just as adult dyslexics and good readers did in other studies (Lubs et al. 1988; Paulesu et al. 1996). Insula may be involved in automatic retrieval of

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receptive and expressive name codes (see Chapter 3). Child dyslexics and good readers also differ structurally in insula, which is smaller in dyslexics (Hynd et al. 1990; Pennington et al. 1999). In the Corina et al. study individual children within the dyslexic group did not activate and within the control group activated in insula; see Corina et al. (2001) for brain images for individuals. Magnetic Source Imaging (MSI), which like MEG provides both temporal and spatial location information during task performance, demonstrated differences between good and at risk poor readers on a letter-pronunciation task, as early as the end of kindergarten (Papanicolaou et al., in press). Also, poor readers differed from good readers in the second stage but not the first stage of processing written words (Simos, Breier, Fletcher, Bergman & Papanicolaou 2000; Simos, Breier, Fletcher, Forman et al. 2000). In the first study both good and poor readers (age range 8 to 17 years) activated initially in left basal regions in response to visually presented words but differed subsequently: Good readers activated left temporal and parietal regions, wherever poor readers activated fight temporal and parietal regions including angular gyms. In the second study (age range 8 to 17 years) dyslexics showed reduced activity in response to a visually presented pseudoword rhyming tasks in the left temporal and parietal regions (superior, angular, and supramarginal gyri; see Figure 5.1) compared to good readers. Degree of activation in basal temporal cortices did not differentiate good readers and poor readers but degree of timing in angular gyms did. It is possible that even though the amount of activation was similar in the basal temporal cortices, the output that was sent onto the angular gyms was deficient, causing a temporal bottleneck. In fact, an ERP study points to timing differences in both visual/orthographic and auditory/phonological processes as the culprit in dyslexia (Breznitz, 2002). Child dyslexics were slower than good readers (all in the 9 years-5 months to 10 years-9 months age range) on both visual/orthographic and auditory/phonological processes, but significantly more so on the phonological than orthographic tasks. Dyslexics also had significantly larger time gap scores between the time scores for orthographic and time scores for the phonological tasks than did the good readers. The dyslexics' gap scores were highly correlated with their decoding accuracy. This finding is very interesting because it ties together the replicated findings across many studies that dyslexics have deficits in either fast visual processing or in phonological processing. Breznitz's results are also consistent with other electrophysiological evidence that dyslexics cannot be subtyped on the basis of a visual versus auditory deficit (Flynn, Deering, Goldstein, & Rahbar, 1992). That is, reading disorders are not related to learning styles defined on the basis of sensory modality, but rather to the timing of auditory/phonological processing or the temporal relationship between the visual/orthographic and the auditory/phonological processes. All readers, regardless of their level of skill development, must integrate orthographic and phonological codes, which may be on different time scales, in real time.

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An ERP study demontrated two subtypes of reading problems in adolescents (McPherson, Ackerman, Holcomb, & Dykman, 1998). The first subtype has difficulty with phonological decoding, that is,translating the orthographic word form into a phonological word form. The second subtype has trouble with rate of reading and response preparation, which may be related to phonological short-term memory storage. Two flVIP,S studies raise the possibility that lactate production related to metabolic inefficiency may be the cause or result of this timing deficit (Richards et al. 1999; P, ichards et al. 2000). Lactate is involved in brain energy metabolism both as an end product and a substrate (sometimes preferred over glucose) (Serafini et al., 2001) and may be a biological substrate (P,ichards et al., 1999) for verbal efficiency (Perfetti, 1985). Before treatment, dyslexics had significantly higher lactate activation than poor readers in left frontal regions on the phonological judgment task used in Corina et al. (2001). This finding may indicate that dyslexics are inefficient in phonological processing and have to exert more mental effort to make phonological judgments than do good readers. Following phonologically driven treatment, the dyslexics did not differ significantly from the good readers in lactate activation on this task. This finding, which has been replicated (Richards et al., 2001), indicates that with appropriate instructional intervention dyslexics may get more efficient at phonological processing and that the brain changes in response to reading instruction. Likewise, a MSI study showed that prior differences between dyslexics and good readers disappear after phonological treatment (Simos et al., in press). These examples of linking brain imaging and treatment studies yield evidence of nature-nurture interactions.

Computational Processes As discussed in Chapters 3 and 4, many areas of cerebral cortex are modality-free association areas that serve as computers for high level, abstract processing jobs. These are the Cross-Talking Computers of Mind that probably underlie many aspects of reading, writing, and math. However, it is harder to study these high-level processes directly because tertiary association areas do not respond well to simple sensory stimuli and motor tasks, which are easier for scientists to manipulate for research purposes (Frackowiak 1994). Fortunately, the brain can be analyzed on three planes: anatomical, computational, and psychological function (Mesulam 1990). Scientists also have exploited the second approach - - creating computational models of mental processes- to study the computational processes of mind. These models are evaluated empirically in two ways m through computer simulations and by comparing the output of the computer simulations with observed behavioral data produced by humans carrying out the functions specified in the model. The most influential computational models for reading are connectionist models. These models assume that the brain consists of large numbers ofintercon-

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nected units, distributed throughout the brain that send excitatory and inhibitory messages to each other in either a feedforward or feedbackward manner (Rumelhart & McClelland 1986). These models specify input units (like the sensory areas of primary projection areas that receive information from the environment), output units (like the primary projection areas of the motor system that act on the environment), and hidden units (like association areas in between input and output units that build internal representations of the world and have certain computational advantages). Connection weights (strengths) between units are stored that allow mental patterns to be created and recreated, that is, remembered. Learning is a process of readjusting the strength of the connections (statistical co-occurrence of features in input and output) based on experience. The human processing system does not operate on the same time scale, however, as do most contemporary computers. Human information processing occurs on the order of milliseconds, whereas computer information processing occurs on the order of nanoseconds, about 106 times faster. Thus, the human computational processes are thought to occur on neural networks that are not only distributed throughout the brain but are also parallel in t i m e - - t h a t is, many different processing operations are happening simultaneously within as well as across computational networks. Seidenberg and McClelland (1989) developed a connectionist model for single word reading that showed that words could be learned through repeated exposures to a set of words. Unique features of this early computational model were that it relied on a single computational network, used three-letter units called Wickelgrams, and did not rely on rules in the conventional sense, to train a simulated neural network to learn to read a small corpus of words. Instead, connections were created between the input orthographic layer and the output phonological layer through the computations of the hidden layer. A controversy arose over whether a single computational route was sufficient or whether two computational routes were necessary (Coltheart, Curtis, Atkins, & Heller, 1993). The initial connectionist models were better at simulating real word reading, whereas the dual route models were better at simulating pseudoword reading, an indication that what the network learned transferred to novel, untrained words. Results of some imaging studies are thought to be consistent with a single computational route (Rumsey, Horwitz et al. 1997; Herbster et al. 1997), but others are consistent with dual r o u t e s - - o n e for real word reading and one for pseudoword reading (Pugh, Mencl, Jenner et al. 2000). Many believe that models using a single computational route cannot account for all aspects of reading and spelling (Bullinaria 1997) and that both lexical and sublexical procedures for whole word and subword units are involved in the computations (e.g., Castles & Coltheart 1993). Nevertheless, these early connectionist models stimulated further research that led to improvements in modeling mental computational processes. According to Brown (1997), these further improvements include modeling inputs as letters and outputs as phonemes (Plaut, McClelland, Seidenberg, & Patterson, 1996), inclusion of phonological attractors or phonological processing units in the hidden

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units (Harm & Seidenberg 1999), and multiple levels of representation (Norris 1994). Connectionist models also have been used to model reading comprehension and other language processes. In one connectionist model for reading comprehension (Burgess 1998), the HAL system, global co-occurrence of words in different contexts are coded in the context of a word's learning history. Computations are based on the locus of co-occurrences in a context at a higher level of representation than the single word. This computation model accounts for both the semantic and grammatical information extracted from text. Computational models may never reduce to a neuroanatomical pathway that can be detected with the spatial and temporal resolution of current brain imaging technologies. Nevertheless, they offer a potentially productive way to think about the complexity of the human information processing system. They may have implications for pedagogy as discussed in Part III. There may be an instructional advantage for limiting initial training to a small corpus of highly familiar words until children learn the most frequent spelling-phoneme correspondences--either through abstraction from exemplars and self teaching (Share 1995) or through explicit modeling by an instructor. One of the limitations of these models is the number of passes through a corpus of words that is needed before learning can occur (Burgess 1998). There may be a pedagogical advantage to explicit modeling of the connections instead of waiting for the system to abstract the connections on its own. One of the challenges teachers face is that they have direct access only to the visible units a child encounters at input and output and not to the hidden units in the assocation areas of a child's brain where the connections are really computed (Hinton & Sejnowski 1986). The pedagogical challenge is how best to call children's attention to those units that can be made "visible" or explicit to facilitate instructionally the computational processes in association cortex. Despite the limitations of current computational models, this kind of research is an important supplement to functional brain imaging, which may show where neural events occur for a particular task but do not explain what or why. See Chapter 2 for the potential ways the brain's hybrid computers (analog dendrites and digital axons) might summate or subtract electrochemical activity spatially and/or temporally in microlevel computing to generate multidimensional informational grids. Such computational models may potentially explain what is happening during mental activity. Neuroanatomy alone cannot explain the computational processes of the computers of mind. As Mesulam (1990) has so elegantly explained, complex behavior is mapped at a level ofmultifocal neural systems rather than specific brain sites. Brain-behavior relationships are not only both localized and distributed but also depend on multidimensional information grids that are uniquely designed to interact with other multidimensional informational grids, which simultaneously interact with one another until a satisfactory fit is achieved for multiple possibilities and constraints. The computational processes of our powerful Cross-Talking Computers of Mind allow us to solve high-level problems and adapt to ever-changing environments.

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Building a Reading Brain during Literacy Development H o w then does the neural architect build a reading brain from existing systems? To answer that question, we first discuss what components are needed for the wetware, which is the brain's equivalent to computer hardware. These are summarized in Table 5.4, organized by functional component. Most of these comPonents already exist and are recruited by the functional reading system to use for its purposes. In the process four new components are created and these are bolded in Table 5.4: the orthographic word form, the reading lexicon, the cross-talk communication loop for coordinating the reading lexicon with other brain systems and the oral reading and silent reading systems. The probable brain structures listed in Table 5.4 for each of these functions are based on the research evidence reviewed in this chapter and in Chapters 3 and 4. Future research may lead to modifications of the current evidence for structural locations of these functional components. To answer the question of how the brain is built, we now discuss which functional components are necessary to build a reading brain, and which neural structures might support those functions (see Table 5.4). Then, we consider how the functional reading system may reorganize over the course of reading development. Finally, we remind the reader that the Reading Brain cannot be built solely at the level of wetware. The neural circuitry needs to be hooked up through explicit reading instruction and practice, as explained in Chapter 8.

Functional Components To begin with, the Reading Brain needs an arousal unit. Brains that are asleep do not respond to the environmental stimuli that feed into the reading process (e.g., written words in a book or on a piece of paper, the teacher's voice, etc.). The reading brain also needs an attentional system. Too much is happening in the external envioronment for learners to pay attention to everything--that would overload their circuits and cause them to malfunction. The posterior attentional system allows the Reading Brain to select the most relevant information in the external environment for the target of its attention (e.g., the letters in the written word rather than the thickness of the letters; the content rather than vocal qualities of the teacher's talk; the words in the book rather than the dance of sunlight on the window shades or the chirping of the birds outside). Likewise, too much is happening in the internal environment of the mind. The anterior attentional system works with the executive system to help the Reading Brain coordinate attention to the various internal subprocesses needed to accomplish a reading goal. The anterior cingulate monitors and resolves conflicts that may arise between components in the process of reading. All learners will vary from moment to moment and from day to day as to how well their attentional system is functioning. However, students who consistently show signs of distractibility, difficulty staying on task until completion,

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TABLE 5.4

Constructing the Wetware for a Reading Brain Function a

Arousal unit Attentional system Posterior component (sensory information) Anterior component (motor coding and work with executive system) Conflict management component

Aural language systemc Discourse knowledge Syntactic knowledge

Possible Brain Structure(s) b Reticular activating system (RAS) and its connections to cortex Common structures R.AS and striatum Dorsal lateral posterior parietal cortex Frontal cortex Anterior cingulate Orbital frontal cortex (inhibition--suppression of irrelevant stimuli) Distributed networks in cortex, especially frontal Distributed networks in left cortex; left frontal operculum; bilateral planum

Word knowledge codes Phonological

Bilateral temporal sulcus; left superior temporal gyms; middle temporal gyms; angular gyms; lateral frontal region; inferior temporal/occipital junction

Semantic

Posterior inferior temporal gyms; angular gyms; border between posterior middle and inferior temporal gyrus

Morpho-syntactic

Anterior superior temporal gyms; left temporal areas; left frontal areas

Oral Language System Oral motor planning Name codes c

Frontal cortex

Prosody

Right cortex

Inferior temporal cortex; BA 37; superior temporal gyms; left frontal operculum; left and midline cerebellum; left thalamus

Visual System Sensory input Extraction of visual features (nonlinguistic)

See Table 3.2 for primary projection pathway V1 striatal cortex

Letter strings (prelinguistic processing of smaller elements in linear arrays)

Extrastriatal cortex

Ventral what pathway (identity of small elements in linear array)

Occipital to temporal cortex

Dorsal where pathway (spatial relationships of small elements in linear array)

Occipital to parietal cortex

Executive/Government System

Prefrontal cortex (especially left dorsal prefrontal cortex, LDPFC)

Cross-talk between existing systems in constructing new systems

(continues)

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157

(continued)

Regulation of attentional components Selective focus Task Maintenance Transition among subgoals/goals Creation of goals and plans Coordination of multiple jobs as plans are executed Updating and monitoring Conscious reflection: metacognition and metalinguistic awareness Control processes for working memory

Orthographic Word Forms (Codes) d (elements in linear visual array that can be recoded into sound units) Sublexical code connectionse:

Left fusiform gyms, left lingual gyms; left middle temporal gyms; inferior temporal/occipital junction Posterior left fusiform gyms

Letter(s) ---, Phoneme Letters --~ Rime Letters --~ Syllable Lexical code connections f Letters ~ Expressive Phonological (Name) Code Letters --~ Receptive Phonological Word Form d

Reading Lexicon

Anterior left fusiform gyms and/or lingual gyrus

BA 37

Multiway interconnections among Orthographic, Phonological, and Semantic/ Morphological codes Two-way connections: Phonological-Semantic

Left middle temporal gyms; left fusiform gyms; right cerebellum

Orthographic-Phonological (assembled phonology)

Extrastriatal cortex; left inferior temporal gyms; supramarginal gyms; superior temporal gyms; left inferior frontal areas (premotor part of Broca's Area)

(addressed phonology)

BA 37; left posterior inferior temporal area; left frontal operculum; fusiform gyms

Cross-Talk between Reading Lexicon and the Visual Systemd: Eye m o v e m e n t s - fixation pauses

Fovea of retina in eye

Simultaneous (sustained processing; identifying elements) Shifting (transient processing; tracking element position)

Parvocellular system

Eye Movement m Saccades

Three cranial nerves; vestibular nuclei; frontal cortex, cerebellum

Magnocellular system, including V5 visual motion center in occipital cortex

(continues)

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TABLE 5.4

(continued) d

and the Aural/Oral Language Systems d and the Cognitive System Oral Reading and Silent Reading Systems

Shared c o m p o n e n t s - extrastriate cortex and premotor areas

Unique to oral reading

Superior temporal-inferior parietal route with articulatory regions and auditory regions for monitoring oral output

Unique to silent reading

Inferior temporal pathway with direct access to lexicon for abstract phonological word form not tied to the incoming speech signal

Memory Episodic memory for novel words

Prefrontal association areas

Making novel words memorable

Hippocampus g

Long-term storage Implicit (unconscious) network-automatically activated (primed) orthographic and phonological word forms (codes)

Cortical (especially middle temporal areas)

Explicit (conscious) semantic retrieval

Temporal cortex; left hippocampus

Working Memory Phonological STM storage

Left inferior parietal cortex; left supramarginal gyrus

Articulatory loop

Broca's area; supplementary motor area; premotor area; parts of insula; right cerebellum

Central executive

prefrontal cortex

Cognition-- reasoning

Lateral frontal network

Emotions and motivation

Limbic structures m amygdala, septum; hypothalamus- and their cortical connections

Learning Circuits Controlled processing (during learning)

Left lateral posterior frontal; anterior cingulate; right cerebellum activates but insula deactivates; limbic structures like amygdala

Automatic processing (after practice)

Bilateral sylvian insula activates; cerebellum deactivates; striatum

aSee section at the end of Chapter 5 for description of function. bBased on research reviewed in Chapters 3, 4, and 5" however, research findings are not always consistent and future research may modify and extend current understanding of structure-function relationships. CBased on research evidence for processing aural language; refers to knowledge that is stored in longterm memory at different levels of language. dunique to the newly constructed reading system. eNot all letters in a word m only those that correspond to a sound unit smaller than a whole word. fAll letters in a word that correspond to a phonological unit for a whole word. gHippocampus likely plays a role in making stimuli memorable, but is unlikely to play a role as a storage site.

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and problems in switching from one task to another may have attention deficit dimmer and should be referred to a psychologist or physician for evaluation. The reading brain also needs to communicate with the established Language by Ear and Language by Mouth systems. Learning to read is like learning a second language that draws in some ways on knowledge about language gained in learning the first language. In the process of learning aural language children learn about (a) discourse structures (e.g., narrative schema for stories), (b) syntax for ordering classes of words that are used in written sentences as well as spoken utterances, (c) semantic knowledge fm meaning of individual words (vocabulary), and (d) the prosody or musical melody of the syntax of the spoken language. All these levels of language in heard language are also used in reading written language. Early in learning to read, children borrow the naming function of the oral language system to name letters and name words. Later on, when reading text orally, children draw on knowledge of prosody from the spoken language; that is, the music or melody of the sound envelopes in which spoken words are packaged. During oral reading, like in oral language, children must plan their oral-motor productions as well as execute them. The reading brain also needs a sensory system for extracting features of the incoming stimuli from the environment, in this case, visual features in the written words, and secondary and tertiary association systems for translating those features into language and conceptual representations. Early in processing, a general feature extractor, which the visual system uses for many purposes, can extract these features. Then a specialized processor that is dedicated exclusively to visual stimuli composed of small elements in a linear array provides additional coding. It extracts identity and position information about the elements. Next, a processor dedicated only to written words translates the information about identity and position of elements into language representations by connecting the visual elements with units of phonology (sound codes of the language). The reading brain also needs a system for linking the orthographic codes to all the other codes for language. One link occurs in the orthographic word form lexicon where letter strings get linked to phonology at different unit sizes. A second link occurs in the reading lexicon that makes connections among all the multiple codes for representing word size units in language. As discussed earlier in the chapter, a unique property of the human memory system is that it stores the same item in multiple ways m in the case of language this multiple storage system involves codes for the orthographic word form, phonology (articulatory gestures, auditory features, name codes, abstract phonological word forms), semantics (vocabulary meaning), and morphology (structure of word meaning, including affixes that modify word meaning). Once the reading lexicon is accessed from the orthographic word form, the functional reading system gains entry to the aural/oral language systems at the level of syntax and discourse. This third link between the reading lexicon and the aural/oral reading system enables reading comprehension. Constructing the Reading Brain requires considerable assistance from an executive system for governing the multiple components that sometimes work in harmony

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but sometimes come into conflict with each other. A single chief executive officer probably does not head this government. Rather, a group of executives appears to work together to manage the moment-to-moment activities of the Reading Brain. All reading brains fluctuate from time to time as to how well their activities are managed and any conflicts are resolved. However, sometimes all or some of the executive processes of the Reading Brain habitually do not work well together. In these cases, a student may have an executive function disorder that may present as an attentional deficit disorder--inattentive subtype. The executive system plays a special role in constructing the reading brain from other brain systems. The executive system facilitates construction of the orthographic word forms and the reading lexicon by overseeing the process of code integration. During text reading, the executive system manages the online links between the reading lexicon and (a) the incoming stimuli and existing representations in the visual system, (b) existing representations in the aural/oral language systems, and (c) the cognitive system for reasoning. Reading brains that have underdeveloped executive functions may experience difficulty in creating the orthographic word forms, the reading lexicon, or the communication link between the reading lexicon and the other brain systems. Reading brains need a memory system with multiple forms of storage. Reading brains need an episodic memory system for storage of unfamiliar, incoming stimuli from the environment. Initially these are kept in a short-term storage unit. Such a system already exists for the aural and oral language systems--it makes use of phonological word forms for storage. Thus, instead of creating a new form of short-term storage, the Reading Brain also adopts this short-term storage system that relies on phonological word form codes (see Table 5.3). To retain a novel word in this short term store requires that the Reading Brain recode the novel written word into a phonological form for short-term storage m e i t h e r by naming the whole word and/or by phonologically decoding sublexical units into corresponding spoken units. With sufficient practice in consciously naming the word or recoding it phonologically, the word will be consolidated for long-term storage in explicit memory. When the reading lexicon is functionally reorganized to the behavioral pathway (see the section "Developing Literacy" later in this chapter), the implicit memory system will automatically activate, in response to a written word, the various codes in which words are represented in the reading lexicon. The reading brain also needs a memory system for its work activities. This working memory system draws on the short-term and long-term stores (explicit and implicit) and the executive functions to manage its component processes. One of these processes is reasoning or thinking. Other processes are emotion (assigning values or reacting to events with states of mind) and motivation (goal-oriented behavior). Some of the jobs in working memory involve controlled, effortful processing. Others require automatic, effortless processing. The more jobs that are on automatic pilot, the more capacity in working memory for the high-level jobs that require controlled processing.

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Protoliteracy Barron (1991) studied the period of emergent literacy during which many skills are acquired that are necessary for learning to read but the Reading Brain is not yet fully operational. During this stage of reading development, the emerging system is drawing heavily on the naming function of the aural language system and the emerging system for orthographic representations. For example, children learn to name letters. They may also learn to name a small set of highly familiar words they have encountered frequently. Children are also learning proto knowledge about alphabetic principle. For example, they learn that letters can represent the sounds of the aural and oral language (Treiman 1993). As Treiman has shown in a longitudinal study of beginning spelling, the names of the letters play a role in this process. For example, the first sound in the name of b is the phoneme that corresponds with the letter b in alphabetic principle. In children's emergent spelling they first represent the phonetic features involved in articulating the sounds; later on, they represent the abstract phonemes of the language in their spelling (Ehri 1992a; Treiman 1993). A longitudinal study showed that beginning readers also have some knowledge of the legal letter sequences in written English words before they are completely able to pronounce those words correctly (i.e., apply phonological decoding to letter strings) (Berninger

~988). Barron made the important point that as children are discovering connections between letters and sounds, which at one level of analysis are separable, they create functional links between them so that letters automatically activate the corresponding sound codes, and sounds of the language automatically activate the corresponding orthographic codes for letters. Thus, during the protoliteracy period, the functional reading system is beginning to acquire orthographic representations and their connections to phonological codes; but the reading lexicon is not sufficiently operational yet to reliably translate written words into either speech or meaning at the word level (see Table 5.4).

Beginning Literacy Chall (1979) called this beginning-to-read stage the Decoding Stage. The lexicon for orthographic word forms develops as children learn to make connections between written words and the corresponding spoken words at different unit sizes. The reading lexicon develops as children learn to link these newly acquired orthographic word forms to existing phonological word form, name, and semantic codes. Cross-talk between the reading lexicon and the visual system and between the reading lexicon and the aural/oral language systems enables children to read books written for beginners. As the three unique components of the reading system become functional (see Table 5.4), children begin to think of themselves as readers.

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During this stage children rely on both episodic memory for the many words they encounter for the first time in their written forms, short-term memory as they phonologically decode these novel, unfamiliar words, and explicit long-term memory as they retrieve more and more words consciously from their reading lexicon.

Developing Literacy Chall (1979) named the next stage Fluency, and Ungluing from Print. As children practice reading over and over, the functional reading system reorganizes. Words that originally were processed on the Cognitive Pathway (Mishkin & Appenzellar 1987) using controlled, effortful processing (Shiffrin & Schneider 1977) are now processed on the Behavioral Pathway (Mishkin & Appenzellar 1987) in which familar, highly practiced words are activated, upon exposure, through priming in implicit (unconscious) memory (Booth et al. 1999). This reorganization of the reading lexicon from the Cognitive to the Behavioral Pathway has a special advantage for the functional reading system. The goal of this system is increasingly changing from translating the written words into speech to comprehending longer and longer stretches of written text. Understanding text requires working memory, which has limited resources. By functionally reorganizing from conscious, effortful decoding to unconscious, automatic activation of words in the reading lexicon, working memory has more resources to devote to comprehension (Perfetti 1985). The functional reading system also undergoes another kind of functional reorganization during this stage of reading development. This reorganization involves output requirements and the underlying representational system for phonological word forms. Once words are on automatic pilot, it is no longer necessary to read them aloud. Therefore, teachers encourage children to make a transition to silent reading by using their "inside voice." As discussed earlier in this chapter, silent reading has advantages over oral reading in that it takes less time than saying each word aloud and may be more efficient at a brain level because the output stage is eliminated. Although the output requirements, which relied heavily on naming and articulation, are eliminated, it is not the case that phonological processing is eliminated in silent reading for meaning; rather a more abstract, phonemically coded phonological word form is created and accessed (Booth et al. 1999; Fiez 1997). Thus, there may be two kinds of fluency that developing readers must achieve. The first kind is oral reading fluency in which children not only become faster in recognizing words, but their oral reading begins to reflect the melody or intonation of the spoken language. The second kind is silent reading fluency in which children quickly and automatically access orthographic word forms and abstract phonological word forms in their reading lexicons, thereby freeing up limited working memory resources for reading comprehension.

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Chall (1979) observed that achieving such fluent reading enables children, who first had to learn to read, to use reading as a tool to continue learning; that is, to read to learn. Throughout the rest of their schooling they must then learn to coordinate their functional reading system with all the other brain systems involved in learning, including the Cognitive Pathway in the Learning Circuitry, the Cognitive Reasoning System, Long Term Memory for expanding knowledge of the world, the Aural Language System for listening to lectures, and the Language by Hand System for writing about what they have read. The executive government system plays multiple roles in coordinating these various systems for different learning goals.

Mature Literacy Not aH students reach the final stage of literacy discussed by Chall (1979), which she characterized as one in which readers adopt a world view. At this stage readers can use the functional reading system for multiple purposes, including the creation of new knowledge, and can read from multiple perspectives, not only their own. This kind of reading development probably depends more on changes in computational processes for representing the world, for generating new knowledge, and for reflecting on knowledge (the metacomponent of the executive system for metacognition and metalinguistic awareness) than on changes in how a functional system recruits neuroanatomy.

Wiring the Reading Brain At each of these stages of reading development, wiring the functional reading system depends partly on maturational processes (e.g., myelination, synaptogenesis, and synaptic pruning of critical structures that may be under genetic control). Wiring the functional reading system also depends greatly on instructional experiences and practice, which affect dendritic branching (see Chapter 4) and computational processes of the Reading Brain at work.

RECOMMENDATIONS

FOR FURTHER READING

Vision Eden, G., Stein, F., Wood, H. & Wood, F. 1994. Differences in eye movements and reading problems in dyslexic and normal children. Vision Research. 34:1345-1358. Willows, D., Kruk, P,.. & Corcos, E. 1993. Visual processes in reading and reading, disabilities, 265-285. Hillsdale, NJ: Erlbaum.

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Language Moats, L. 2000. Speech to print. Language essentialsfor teachers. Baltimore: Paul H. Brookes. Leonard, C. 1998. Neural mechanismsof language. In H. Cohen, ed. Neurosciencefor rehabilitation, 2nded, 349-368. New York: Raven Lippincott.

Cognition Gazzaniga, M., Ivry, R. & Magnum, G., eds. 1998. Cognitive neuroscience: The biology of mind. New York W. W. Norton.

Attention/Executive and Memory Functions Lyon, G. R. & Krasnegor, N. 1999. Attention, memory, and executive function. Baltimore, MD: Paul H. Brookes.

Reading McCarthy, R. & Warrington, E. 1990. Cognitive neuropsychology. A clinical introduction, 214-240. New York: Academic Press.

MAKING

CONNECTIONS

Questions preceded by * may be most appropriate for graduate students. 1. What further research evidence did this chapter provide that learning styles based on sensory modality and right-left brain differences are not sufficient for explaining literacy acquisition? What further evidence does this chapter provide to support that learning depends on nature-nurture interactions? 2. What are the cognitive and behavioral pathways? H o w do they have to work together to support thinking processes? 3. H o w is the functional reading system created from already existing functional systems? 4. H o w does the functional reading system reorganize over the course of development? What instructional implications might this developmental reorganization have? *5. What does it mean that language has no end organ? What are the implications of this neurological organization for learning and storing different kinds of word forms in memory?

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*6. What is the difference between neuroanatomical, computational, and psychological/behavioral planes of analysis? Why do we need to consider all these planes and not draw conclusions just based on one? *7. What is a one-to-many mapping and a many-to-one mapping? What are examples of each of these kinds of mappings in the real world? What kinds of advantages might these complex mappings have? (Hint: social roles in which one person performs many different functions or multiple people are assigned the same function, or ways of creating access labels for filing systems.)

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Building a Writing Brain Neu ro logically

Like the Reading Brain, the Writing Brain can be constructed from other brain systems. However, despite the widespread belief that the Writing Brain evolves from the Reading Brain, the writing brain evolves from all the other language systems and draws uniquely on nonlanguage systems in ways that the other language systems do not. Also, the Writing Brain is organized differently than the Reading Brain. Thus, we start the story of how the Writing Brain is constructed from other brain systems by dispelling myths about writing: Writing is not the mirror image of reading m it is not simply the output stage compared to the input stage of written language. Writing is not merely a motor act. Writing cannot be explained solely on the basis of skilled writing. Writing is best understood from a developmental perspective. The journey to skilled writing begins in the preschool years. Compared to Language by Ear, by Mouth, and by Eye, the trajectory for Language by Hand is longer. We describe what is known about the normal developmental trajectory for Language by Hand in the preschool and early school years. Compared to reading, few in vivo brain imaging studies have been done for writing. Relevant ones that do exist have focused on the grapho-motor system that the functional writing system uses. The existing functional brain imaging results are mostly based on normal adults who are skilled writers. Further research is needed to Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

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evaluate whether these findings generalize to novice and developing writers and to investigate the nonmotor components of writing. In general, as was the case for reading in Ch apter 5, we do not discuss in vivo brain imaging studies of patients with acquired disorders. The reason is that the way a process breaks down due to stroke, disease, o r injury is not necessarily the way it is constructed during develop ment. However, we make one exception because of the lack of studies on developmental spelling disorder, and discuss a study of acquired spelling disorder in which patients were imaged. Currently, we are studying developmental spelling disorder and hope that others are, too, so that more information will be available about spelling from a developmental neuropsychological perspective. Finally, we draw upon the available brain imaging and developmental research to propose how a writing brain might be constructed from other brain systems. As in the case of reading, the Writing Brain is constructed through the interaction of the developing wetware and experiences in the instructional environment. In this chapter we consider what the components of the wetware might be, whereas in Chapter 9 we consider what the instructional components might be. We also discuss how the functional writing system may reorganize across writing development.

DISPELLING MYTHS ABOUT WRITING N o t a n I n v e r s e or M i r r o r I m a g e

Read (1981) argued compellingly that writing is not the inverse of reading for young children in the sense that division is the inverse of multiplication. Prior to this influential chapter, many educators believed, and some continue to believe, that if children learn to read they can then apply those reading skills in the reverse direction to learn to write. Because handwriting, spelling, and composition are separable components of the developing writing system (Abbott & Berninger 1993), we discuss the implications of Read's insight for each component separately.

Handwriting As discussed in Chapter 5, the reading system extracts general features (e.g., straight, diagonal, curved lines) from written words and then further processes the written word orthographically as a string of letters, which are converted into other linguistic codes. The orthographic processor probably categorizes letters on the basis of their distinctive features that differentiate them from all other letters (Berninger, Yates & Lester 1991). For example, h, m, and n each have a straight line feature on the left and at least one hump to the fight of that straight line; but h is differentiated from the others by a straight line that ascends higher than the hump, m is differentiated by two humps, and n is differentiated by a straight line that does not ascend higher than the hump. Such categorical perception on the basis of abstract distinctive

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features allows the orthographic processor to identify letters that may vary in size or style. In contrast, writing letters requires more than knowledge of general visual features or distinctive orthographic features. Letter production requires a precise, complete visual-motor program (kinetic melody; Luria 1980) for planning and producing each sequentially ordered component stroke; this program must be created, stored, and retrieved on an as-needed basis. For example, h requires two ordered component strokes, m requires three ordered component strokes, and n requires two ordered component strokes. Preschool children across the world start letters at the bottom and proceed in a right to left direction; but once they receive conventional handwriting instruction adopt the directionality for their own written language, which in the case of English is top to bottom and left to right letter production (Goodnow 1977). For example, children typically are taught to start at the top of the straight line and to make the straight line first and then proceed to the hump(s) of h, m, and n. Children also need to attend to the relative size and positioning of letter components on lined paper. For example, the humps in h, m, and n are the same height but the straight line is higher on h than the others. Also, the letters should rest on the line and not float above it. All in all, the visual-spatial analysis requirements for letter production are more complex than the visualspatial requirements for letter recognition in reading words or for letter selection on a computer keyboard. Thus, letter-production in writing and letter-perception in reading are not inverses of each other.

Spelling How functional spelling units (typically one or two letters) correspond to phonemes (see Chapter 5) depends on the direction of the connection. In reading, the connection goes from letter(s) to phoneme, but in spelling, the connection goes from phoneme to letter(s). In written American English, there are alternations in both directions (plausible phoneme options for a spelling unit and plausible spelling options for a phoneme). The asymmetry between reading and spelling single words (Venezky 1970, 1999) stems from the fact that there are more options for connecting a phoneme to a spelling unit than for connecting a spelling unit to a phoneme. For the most frequent spelling-phoneme and phoneme-spelling correspondences in primary grade reading material, which has mostly Anglo-Saxon words (Balmuth 1992), the number of options ranges from one to four (mode, which is the most frequent category, = one) for spelling to phoneme and ranges from one to seven (mode = one) for phoneme to spelling (Berninger 1998b). O f the 71 alternations for spelling to phoneme, 23 (32%) have more than one alternation (mode = 2). Of the 45 alternations for phoneme to spelling, 23 (51%) have more than one alternation (mode = 2). For example, the letter a can correspond to the long-a phoneme (in an open syllable), the short-a phoneme (in a closed syllable), or a schwa (reduced vowel) in reading; but in spelling, the long-a phoneme can be spelled with a single letter a, an a with a

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silent e, ai, ay, ey, or eigh. Although this many alternations is the exception-- not the r u l e - - i t illustrates the general principle that there are more ways to go from phoneme to spelling than spelling to phoneme; in this case six and three, respectively. The upshot of this asymmetry is that spelling is not the simple inverse of reading.

Composing Extracting meaning from text while reading differs from constructing text while writing in the complexity of the task. For the beginning reader, the reading task is to decode the written word into speech and then draw on aural language to comprehend ideas in text. For the beginning writer, however, the first writing task is to generate the ideas and the second task is to communicate them by drawing on both aural/oral language and written language. The composing process involves far more than decoding written language in reverse. Both reading comprehension and composing draw upon motor systems early in development--oral-motor for oral reading and grapho-motor for handwriting during composing. Although oral reading plays an important role in beginning reading, once the transition to silent reading is made, it is no longer a necessary part of the reading system, even though phonology continues to play a role (see Chapter 5). In contrast, grapho-motor transcription (sequential graphic production with a writing implement or sequential presses on a computer keyboard) always remains a necessary part of the writing system, providing yet another example that reading comprehension and written composition are not mirror images of each other. Learning to compose is also not the mirror image of learning to talk. Aural/oral language is acquired in conversation that is characterized by (a) frequent turntaking; (b) cooperative conversational partners that supply possible or partial responses and query to confirm, clarify, and repair messages; and (c) contextualized language embedded in social interaction (Garvey 1977; Garvey & Berninger 1981; Snow 1972; Snow & Ferguson 1977). In contrast, writing requires that children (a) generate discourse on their own without a conversational partner, (b) communicate with an audience that is often more concerned with evaluating the written product than with cooperatively assisting in improving it, and (c) gain control over language divorced from the social interaction in which aural/oral language is used. Even though narrative writing sometimes uses dialogue, for the most part, writing, especially expository writing, is not talk written d o w n - - i t requires self-generated language without social supports during the initial planning and text generation processes. Thus, writing is not the inverse of reading or aural/oral language.

Asymmetries in organization of the functional writing system and functional reading system Even though Language by Hand and Language by Eye draw upon many of the same language components (see Figures 6.1 and 6.2) and nonlanguage components

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IdeaGeneration Transcription* " - . . /

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oooop, moo,o.y-,

I.

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senW]rtencde

Oisciu I

Word

I I Discourse Sentence

FIGURE 6.1 Architecture of language components of the domain-specific functional writing system. From "Writing and reading: connections between language by hand and language by eye" by V.W. Berninger, R.D. Abbott, S.P. Abbott, S. Graham, and T. Richards, 2002, Journal of Learning Disabilities, 35: 39-56. Copyright2002 by PRO-ED, Inc. Reprinted with permission. (see Figure 6.3), these components are organized somewhat differently in the two systems. Figure 6.1 illustrates how word codes and levels of language may be represented in the domain-specific functional writing system. Self-generation of ideas drives the system. These ideas are translated into different levels of language in memory. The codes that are most important for transcribing these language representations into written symbols are orthographic letter-forms and a triangle of phonological, orthographic, and semantic/morphological word codes. The latter codes can be analyzed at the whole word or subword/sublexical levels (see Chapter 8), but note that the phonological code is at the top of this word form

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Word Recognition orthography

phonology

morphology

IT

Syntactic/GrammaticalProcessing

Text-Based Discourse Comprehension ~

]

Situation-Based DiscourseComprehension

FIGURE 6.2 Architectureoflanguagecomponentsof the domain-specificfunctionalreadingsystem. From "Writing and reading: connections between language by hand and language by eye" by V.W. Berninger, R.D. Abbott, S.P. Abbott, S. Graham, and T. Richards, 2002, Journal of Learning Disabilities, 35: 39-56. Copyright2002 by PRO-ED, Inc. Reprinted with permission. triangle because spelling is driven by phonological representations until the orthographic word form can be retrieved automatically from the spelling lexicon. Figure 6.2 illustrates how the same codes and levels of language are represented in the domain-specific reading system. Note that orthographic codes are now at the top of the triangle of word codes driving the word recognition process in the reading system. These codes have to be converted into phonological and semantic/morphological word forms to support processing first at the word level and then at other levels of language w s e n t e n c e and discourse levels. The same levels of language m word, sentence, discourse m are involved in both reading and writing text. H o w ever, composing is driven initially by translation of ideas into larger units of language m at the sentence or discourse level m that are then transcribed word by word to create written text. In contrast, reading is driven initially by word decoding or recognition from which the large units of text at the sentence and discourse levels are constructed. That is, word-level representations are the target of processing in different phases of writing and reading. Both writing and reading draw on the same nonlanguage systems depicted in Figure 6.3 w memory, executive processes, and thinking processes ~ but in different ways. Because the reading system can refer to written text at any time, the memory burden is greatly reduced. In contrast, the writing system is constantly creating, from long-term memory in working memory, text that is most reliably

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Memory Processes

y

Working

Automatic Hlot [

Self-RegulationExecutive

|

Thinking Processes

Managing Attention (focus,maintenance,transitions)

GeneratingOpinion

Goal Setting/Planning

Elaborating

Generating and Applying Strategies

Perspective-taking

Monitoring

Synthesizing

Revising

ConstructingNew Ideas

Accessing and Applying Metaknowledge

F I G U R E 6.3 Nonlanguage components upon which both the writing and reading systems draw. From "Writing and reading: connections between language by hand and language by eye" by V.W. Berninger, R.D. Abbott, S.P. Abbott, S. Graham, and T. Richards, 2002, Journal of Learning Disabilities, 35: 39-56. Copyright 2002 by PRO-ED, Inc. Reprinted with permission.

referred to once it appears on the written page (external or extramemory). Although coding of written words in short-term memory is fundamental to the reading process, short-term memory coding is important only to the reviewing/revising process of writing (Swanson & Berninger 1996). Writing may place a greater burden on working memory than does reading comprehension. Writing is an immense juggling act, with more jobs to do than reading. The writing.jobs include planning (generating ideas and setting goals), translating those ideas into text, transcribing that text, and reviewing and revising it. Given the number of different .jobs in writing (see Figure 6.3), it is unlikely that there is a single central executive control process in working memory during writing, as models of working memory usually assume (Baddeley 1986). Reading may also require multiple executive functions rather than a single

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central executive. However, writing may require more work on the part of those executive processes than reading does. Control processes for extracting the meaning from a finished text (reading) are not as taxing as the executive processes that go into generating and repairing a text until it is deemed a final product (writing). That may be why more individuals read for pleasure than write for pleasure. Components of attention are probably organized differently in the functional reading system (see Chapter 5 and Table 5.4) than in the functional writing system. Although the posterior attentional component is important early in the reading process (see Chapter 5 and Table 5.4), the anterior attentional component is important early in the writing process (see Table 6.2, later). Writing is very dependent on the attentional, executive, and motor systems working together to produce output in the external environment, whereas reading is very dependent on how attention works with the posterior information processing system that receives information from the external environment. Writing is relatively more dependent on Luria's (1973) third functional unit, whereas reading is relatively more dependent on Luria's second functional unit. Both depend on the first functional unit that maintains arousal and consciousness (see Chapter 4). With practice, low-level skills may become automatized in both the functional reading and writing system; the high-level skills of both systems draw on the thinking brain system(s) in a non-automatic, reflective manner.

N o t Merely a M o t o r Act

Writing involves far more than grapho-motor transcription. Just as the oral-motor system is but one of many different brain systems contributing to the Reading Brain (see Table 5.4), the grapho-motor system is but one of many different brain systems contributing to the Writing Brain. In fact, although the grapho-motor system is necessary for transcription, it is not sufficient for transcription to develop. Orthographic coding of letter forms and language codes for phonological, orthographic, and semantic/morphological word forms are also needed (see Figure 6.1). This conclusion is based, in part, on cross-sectional research with 600 students in grades one through six (50 girls and 50 boys at each grade level). In this study (Abbott & Berninger 1993), the orthographic factor had a direct path to handwriting but the fine motor factor did not. The fine motor factor had only an indirect path to handwriting through the orthographic factor. These findings can be interpreted in reference to the distinction between the primary and secondary association areas (see Chapters 3 and 4). Fine motor skills depend, in large part, on primary projection areas. Orthographic coding, in contrast, depends greatly on association areas that link letter strings to phonological codes to create visible language. Handwriting is visible language that is produced via the grapho-motor channel, but it is not merely a motor act involving primary motor projection areas. Handwriting depends greatly on secondary (and possibly tertiary) association areas for creating precise representations of letter-forms in memory, that is, on ortho-

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graphic coding (see Chapter 5). Handwriting also depends on accessing the orthographic representations for specific writing purposes by activating the explicit memory system for conscious retrieval. This finding that handwriting is more directly linked to orthographic coding than fine motor functions illustrates how research may increase understanding of brain systems for literacy in ways that subjective metacognitions do not. The grapho-motor system plays a fundamental role in development of Language by Hand during the preschool and early school years. That is probably why, long after Language by Hand is drawing on many different nonmotor brain systems, metacognitions about writing emphasize its motoric underpinnings. However, orthographic, language, cognition, memory, and executive functions are fundamental components of the functional writing system throughout writing development.

Novice Writers Are Not Younger Expert Writers Hayes and Flower (1980) proposed the most influential model of skilled, expert writing. They based their model on the think-aloud protocols generated during written composition by students in a top-tier university. Briefly, their original model contained three large boxes that were connected recursively rather than sequentially--one each for planning, translating, and reviewing (for purposes of revising) - - and drew on long-term memory and operated in a cognitive task environment. Both planning and reviewing had subcomponents, but the translating box was empty in their model of skilled writing, presumably because this skill had been mastered earlier in writing development. In contrast, in our research program on beginning and developing writing, we discovered at least two subcomponents in translating: transcription and text generation (at the word, sentence, and text levels; see Figure 6.1). We also discovered that working memory, short-term memory, and long-term memory played important roles in developing writing (McCutchen 1986, 1996, 1997; Swanson & Berninger 1996) and that affective, motivational, and social factors as well as cognitive factors were needed to explain developing writing (Berninger, Fuller & Whitaker 1996). These and other findings reviewed in Berninger (1994) and Berninger and Swanson (1994) led us to realize that beginning writing is not a scaled down version of expert writing, just as children are not miniature adults.

DEVELOPMENTAL TRAJECTORY Language by Hand is launched with the "fundamental graphic act" when an infant or toddler discovers that a crayon or pencil leaves a graphic trace on paper, a wall, or other writing surface (Gibson & Levin 1975). For many normally developing children, whose parents do not hide writing implements, this act may occur near the end of the first year of life. Thereafter, children follow a predictable sequence in

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their spontaneous written productions: random scribbling, zig-zag lines, variation in elements without true unitization, linear arrangement of elements in word-like units, true letters, real words, sequences of related words, and finally sentences (Gibson & Levin 1975). This developmental progression from nonlinguistic to linguistic productions takes approximately six years in normally developing writers. The hand is not devoted exclusively to the developing writing system. The hand is also partnered with the developing cognitive system to draw its nonlinguistic perceptions of the world (Goodnow 1977). Nonlinguistic productions also follow a predictable pattern as captured in early childhood assessment instruments: single lines (vertical, horizontal, diagonal), to crossed lines, to circles, to squares, to diamonds. Children can generally imitate examiners producing these forms before they produce them spontaneously in their own drawings. Children also show developmental progression in their pictorial representations of the world (Goodnow 1977). Given that the hand is teamed with both the developing language and cognitive systems, childreh spontaneously illustrate their early written language productions with pictures (Traweek & Berninger 1997). Storybooks for beginning readers that illustrate written text also help beginning writers to link Cognition by Hand and Language by Hand. Thus, it is not surprising that beginning writers tend to "draw" their letters before they automatize retrieval and production of letter forms from memory. However, Language by Hand and Cognition by Hand begin to follow separate developmental paths around age three when children can differentiate pictures and written language productions on sorting tasks (Lavine 1972). By age five, even nonreaders can differentiate between orthographic symbols in the language to which they are exposed and orthographic symbols in other written language to which they have not been exposed (Lavine 1972). Some knowledge of sound-spelling correspondences develops during the protoliteracy stage before children can translate spoken words into written words with conventional orthography (Barron 1991). Likewise, knowledge of permissible letter sequences in the orthographic word form also develops during the protoliteracy period. Beginning readers in first grade can correctly distinguish between letter sequences that are and are not permissible in written language (Berninger 1988; Pick 1978). Developmental dissociations (unevenness) may occur in the development of Cognition by Hand and Language by Hand during the school years. We observe many children who are good artists during the elementary school grades but who have problems in handwriting, and conversely children who have good handwriting but are poor artists. Despite their common developmental origins, Language by Hand and Cognition by Hand become separate functional systems later in development. Orthographic knowledge for spelling-sound correspondences and for orthographic word forms develops somewhat independently. Ability to segment the orthographic word form in short-term memory follows a predictable sequence from kindergarten to first grade: from whole word units to single letters to letter clusters of two or more letters (Berninger et al. 1991). Each of these orthographic

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units can be linked to different unit sizes of aural/oral language (e.g., name codes, phonemes, or rimes/syllables, respectively; see Chapter 5). Attentional strategies also influence development of orthographic knowledge (Posner & McCandliss 1999). For example, phonics draws children's attention to letter units more than whole word instruction does (Barr 1972). Development of word-specific representations for whole word units in long-term memory is influenced by both genetic and environmental factors (Olson et al. 1994). Table 6.1 summarizes the different kinds of orthographic knowledge children acquire early in literacy. This knowledge is both procedural (operations) and informational (content). The idea that orthographic word forms develop somewhat independently of knowledge of orthographic-phonological correspondences is surprising to some, but consistent with evidence from acquired writing disorders that the two kinds of knowledge are stored separately in the brain (McCarthy & Warrington 1990; Roeltgen & Heilman 1984). Development of spelling also follows a predictable developmental sequence in how children represent features of the spoken language with letters (Ehri 1992a; Treiman 1993). Initially, spelling is unreadable because it has no relationship to sound. Then children begin to spell words the way they hear them and represent salient phonetic features with letters. For example, they often use the sounds in letter names as their guide, for example, using h to represent the long-a o r / c h / sound (Treiman 1993). During this stage of spelling development, children's "invented spellings" can be deciphered by adults who think about the speech stream from the perspective of the beginning speller (Chomsky 1979). Subsequently beginning spellers learn to represent sound via the alphabetic principle, TABLE 6.1 Orthographic Knowledge in Developing Writing Procedural Knowledge Naming alphabet letters Writing alphabet letters Naming written words Spelling written words Coding written word forms in short-term memory Attention/segmentation strategies for written words in short-term memory Strategies for spelling unknown words Automatic retrieval of orthographic word forms Informational Knowledge Permissible letter sequences Correspondences between a phoneme and letter(s) Correspondences between letter-names and letter-forms Correspondences between names and orthographic word forms Correspondences between phonological, semantic, and orthographic word forms

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with the result that their spelling begins to represent the more abstract phonemes of the language. Finally, they learn to represent morphemes and their links to phonology (e.g., transforming the noun nation into the adjective national via a suffix while using the same spelling unit a to represent the phonological shift in the first vowel). Note that throughout beginning spelling development, more is involved than visual processing in primary projection pathways or direct retrieval of visual word forms. Children learn to link linguistic codes for spoken words to orthographic symbols (letters) and word forms (letter strings)ma process that involves secondary and possibly tertiary association areas (see Chapters 3, 4, and 5). Composition also follows a predictable developmental sequence as kindergartenets produce word-like productions, then true words, then word combinations, and finally sentences; most first graders can write a complete sentence and illustrate it (Traweek & Berninger 1997). Thereafter, compositions reflect a developmental progression from wheel-like structures (comments or spokes surrounding a common topic or hub), to wheel-like structures with branches off the comments, to list structures for both narrative and expository text, to true differentiation of narrative and expository text structures with genre-specific discourse features, and eventually to text with true hierarchical organization (Berninger et al. 1996).

IN VIVO FUNCTIONAL WRITING

IMAGING STUDIES OF

Planning Sequential Finger Movements Finger movements have a planning component just as oral motor mouth movements do (see Chapter 5). Price et al. (1999) reported that BA 44 (see Figures 3.1 and 3.2) is involved in preparing finger movements and not just mouth movements. According to van Mier, Tempel, Perlmutter, Raichle & Petersen (1998), preparation involves selecting a motor response from among options; this selection process activates anterior cingulate (see Figure 5.2). Preparation also includes creation of a precise timing plan for complex motor sequences and rehearsing the plan in memory; supplementary motor area may play a role in this timing plan. Left premotor cortex may also play a role in the planning, learning, and executing of the temporal plan for sequential movements. (See van Mier et al. for further discussion of these planning issues and Figure 6.4 for the location of the supplementary motor area and premotor cortex.)

Executing Sequential Finger Movements Execution requires motor control. When right-handed adults performed finger movements, the greatest change in tNIP, I activation occurred in the contralateral (left) primary motor cortex (Eden & Zeffiro, 1999). When fight-handed adults

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FIGURE 6.4 Primarymotor cortex, supplementary motor cortex, premotor cortex, and primary somatic sensory cortex from lateral and medialviews. Reprinted with permissionfrom FUNCTIONAL BRAIN IMAGING by William Orrison Jr., Jeffrey D. Lewine, John A. Sanders, and Michael F. Hartshorne. Copyright 9 1995 by Mosby. performed finger opposition movements, the greatest increase in lactate activation during flVItLS imaging occurred in the contralateral (left) putamen (part ofstriatum) and globus pallidus (the only parts of the brain imaged) (see Figure 3.8) (Kuwabara, Watanabe, Tsuji & Yuasa 1995). PET studies of whole brain (Jenkins, Brooks, Nixon, Frackowiak & Passingham 1994; van Mier et al. 1998) showed that execution of finger movements activated primary and secondary motor areas (premotor and supplementary motor areas), parietal areas, cerebellum areas, and other subcortical areas. See Figure 6.4 for location of primary motor areas, premotor areas, and supplementary motor areas. The latter two are association areas that integrate motor codes and other kinds of codes (see Chapters 3 and 4).

Learning New versus Performing Practiced Finger Movements As with verbal learning (see Table 5.4), researchers have identified different neural mechanisms underlying learning new motor sequences and underlying performance of practiced ones. These contrasting neural mechanisms are relevant to Mishkin and Appenzellar's (1987) distinction between the cognitive and behavioral pathways, respectively. However, these pathways may vary with function and brain system. Comparison of Tables 5.4 and 6.2 shows that different neural circuitry may be involved in the cognitive and behavioral pathways for verbal learning and motor learning. The circuits listed in Table 6.2 are based on three PET studies discussed later in this section. Before discussing these studies, we emphasize that the cerebellum

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TABLE 6.2 Constructing the Wetware of the Writing Brain

Function a Arousal Unit Idea Generator d Text Generator d Draws on different levels of aural/oral language to represent ideas in working memory d Transcription Sensory (kinesthetic d, proprioceptive d, vestibular, visual senses) Grapho-motor component d planning finger movements precise timing in executing finger movements temporal aspects of planning, executing, and learning movements motor control selecting motor response hand used for execution

execution

Orthographic Component STM Coding LTM Coding-lexical Phonological-Orthographic Component d

Spelling lexicon d

phonological, semantic/morphological, orthographic codes and their interconnections Attentional System Anterior component (motor coding, work with executive system) Conflict manager and response inhibitor Posterior component (incoming information)

Possible Brain Structures b tLAS-Cortical Circuit; see Table 5.4 Unknown; but see text in Chapter 6 See Table 5.4

See Table 3.5 for projection pathways.

BA 44 Supplementary motor area Left premotor cortex Cerebellum Anterior cingulate Contralateral pirmary motor cortex (see Table 3.4 for pathway) and anterior cerebellum Primary and secondary motor areas (left premotor and supplementary motor areas); somatosensory; parietal, left superior parietal lobule; right parietal lobule; inferior frontal cortex; insular cortex; thalamus; cerebellum; basal ganglia; putamen Left fusiform and left lingual gyri Posterior angular gyrus Supramarginal gyrus; insula; Broca's area See Table 5.4 for reading lexicon, which may be organized differently than spelling lexicon

Common structures tLAS and striatum Frontal cortex Anterior cingulate; orbital frontal cortex Dorsal lateral posterior parietal cortex

(continues)

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181

(continued)

Writing-Specific d Executive Functions/Government

Creating goals and plans Updating and monitoring Reviewing and revising Coordinating multiple jobs Coordinating cross-talk with other systems Supervising working memory Guiding reflections (metacognition) Memory Working Memory Central executive(s) Problem solving space Phonological STM storage Articulatory loop

Long-term memory--explicit (conscious) semantic retrieval Cognition Reasoning Emotions and Motivation

Learning Circuits d Controlled processing (during learning)

Automatic processing (after practice)

Prefrontal cortex (especially left dorsal prefrontal cortex, LDPFC)

Prefrontal cortex (LDPFC) Unknown Left inferior parietal cortex; left supramarginal gyrus Broca's area; supplementary motor area; premotor area; parts ofinsula; right cerebellum Temporal cortex; left hippocampus

Lateral frontal network Limbic structures--amygdala, septum, hypothalamus - - and their cortical connections Prefrontal; supplementary motor area; premotor cortex; cerebellum (especially left); putamen; basal ganglia Left middle temporal cortex; basal ganglia; putamen; left cingulate; orbital frontal cortex; thalamus; striatum; supplementary motor area; right cortical-left cerebellum circuit

aSee Chapter 6 for further description of function. bBased on research reviewed in Chapters 3, 4, 5, and 6; research findings are not always consistent and future research may modify or extend conclusions about structure-function relationships. CBased on research on aural/oral language that is stored at different levels of language. dunique to the newly constructed writing system. m a y play an i m p o r t a n t role in b o t h the cognitive and behavioral pathways. M o r e than t w o decades o f research p r o v i d e c o n v e r g i n g e v i d e n c e that the c e r e b e l l u m is i n v o l v e d in n o t only m o t o r c o n t r o l b u t also in learning o f n e w tasks and p e r f o r m ance o f practiced, a u t o m a t i z e d functions (Jenkins et al. 1994; N i c h o l s o n et al. 1999; van M i e r et al. 1998).

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In the first PET study (Jenkins et al. 1994), normal adults completed three tasks: rest, performing overlearned motor sequences practiced before scanning, and learning new sequences at the same rate of performance. The premotor cortex activated more during learning of new motor sequences, whereas the supplementary motor area was more activated during the prelearned sequence. Not only cerebellum but also putamen and basal ganglia (see Chapter 3 and Figures 3.8 and 3.12) activated during learning and automatization. According to Kuwabara et al. (1995), the basal ganglia may be involved in monitoring motor movements both before and after practice. In the second PET study (van Mier et al. 1998), half the normal adults moved a pen with their fight hand and half moved a pen with their left hand through a maze and a square pattern with their eyes closed (see Figure 6.5). Each adult completed six tasks: (a) resting in which the pen was held without moving it, (b) tracing the maze without practicing it, (c) tracing the maze after ten minutes of practice, (d) tracing a novel maze, (e) tracing an easily learned sequence at high speed, and (f) tracing an easily learned sequence at slow speed. Only the left middle temporal cortex showed a practice-related effect, suggesting that it is part of the behavioral pathway for learning. However, practice-related activations occurred in the same hemisphere and in the same areas whether adults were in the fight-hand or left-hand groups (see Figure 6.6). These practice-related findings led the investigators to conclude that motor learning is coded at an abstract level independent of execution of the motor

F I G U R E 6.5 Experimental setup showing subject lying in the scanner while tracing motor sequences. Upper arm was strapped while the wrist and hand were positioned freely above the writing tablet. Republished with permission of American Physiological Society, from van Mier, Tempel, Perlmutter, Raichle, and Petersen 1998. Journal of Neurophysiology, vol 80:2177-2199; permission conveyed through Copyright Clearence Center, Inc.

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F I G U R E 6.6 Cortical (A) and cerebellar (B) blood-flow changes common to both hands during maze and square tracking compared to rest control. Left on images = left. Top on images = frontal. A = primary motor cortex, B = right dorsal premotor cortex; BB = left dorsal premotor cortex; supplementary motor area; D = inferior parietal cortex; E = right superior parietal cortex; EE = left superior parietal cortex;J = lateral cerebellum; K = posterior cerebellum. Republished with permission of American Physiological Society, from van Mier, Tempel, Perlmutter, R.aichle, and Petersen 1998. Journal of Neurophysiology, vo180 : 2177-2199; permission conveyed through Copyright Clearence Center, Inc.

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response. This conclusion is consistent with the fact that association areas code information more abstractly than do the neural circuits of the primary motor projection pathways that are more directly involved in motor production. In the third PET study (Nicholson et al. 1999), not only normal adults but also adults with developmental dyslexia, who had spelling as well as reading problems, performed a prelearned sequence and a novel sequence of finger movements and rested. Dyslexics activated fight cerebellum less than good readers/spellers on both the new and practiced sequences. Good readers/spellers increased activation more than dyslexics in thalamus and striatum on both the prelearned and novel sequences. On the prelearned sequence, the good readers/spellers and dyslexics also differed in left cingulate gyrus and orbital frontal cortex. Clearly, much more research is needed on the neural circuits involved in new learning and in practiced skills, which may or may not have been automatized. These circuits may differ depending on the kind of knowledge involved. Nevertheless, Mishkin and colleagues' insight-- that different pathways may serve learning of new skills and performance of practiced or automatized old l e a r n i n g ~ is a major contribution to understanding the brain mechanisms in learning.

Linking Handwriting to Spelling Twelve normal adults completed four tasks equated in physical handwriting movements: (a) copying Kanji characters with right index finger, (b) rearranging Kanji characters in a puzzle, (c) writing corresponding Kanji characters for phonographic Hiragana script (phonological-orthographic correspondences), and (d) a control task of tracing a circle (Matsuo et al. 2000). Kanji characters, which have corresponding linguisic codes but do not have corresponding phonological codes like Hiragana script does, uniquely activated fight superior parietal lobule (see Figure 5.1). However, converting sound-based script to another orthographic format uniquely activated Broca's area (see Figure 3.4). The left superior parietal lobule activated in all handwriting tasks, which involved orthographic symbols, but not the control task, which did not involve orthographic symbols. This finding serves as yet another reminder that visual codes do not have links to language codes, but orthographic codes d o - - t h e y are visual links to linguistic codes, which the association areas are uniquely equipped to process (see Chapter 5).

Spelling In a structural CT study (Roeltgen & Heilman 1984), two kinds of acquired spelling disorders or agraphia (without hand function for written language) were identified: lexical agraphia and phonological agraphia. (This disorder is also sometimes referred to as dysgraphia, which also means a dysfunction in using the hand for written language. Developmental dysgraphia can be related to handwriting and/or spelling

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problems--see Chapter 9). For lexical agraphia, lesions occurred in the posterior angular gyrus but not the supramarginal gyrus. In contrast, for phonological agraphia, the lesions occurred in supramarginal gyrus or insula but not angular gyrus. These two subtypes of spelling disorders were also associated with different kinds of spelling problems. Lexical agraphia was associated with relative difficulty in spelling irregular words or ambiguous words in which a sound can be spelled by more than one letter--so the specific word context has to be memorized for the sound-spelling correspondence. In contrast, phonological agraphia was associated with relative difficulty in spelling pronounceable nonwords compared to irregular or ambiguous words. Thus, these two spelling disorders showed a double dissociation in which contrasting neuroanatomical and behavioral patterns were found for each disorder.

BUILDING A WRITING BRAIN Based on existing research, the functions listed in Table 6.2 may be located in the neuroanatomical structures listed under possible brain locations. In contrast to reading, for which the visual, auditory, and vestibular senses play a role, the functional writing system draws on two additional senses--kinesthetic (touch) and proprioceptive (position in space) (see Table 3 . 5 ) - in constructing the transcription component for letter and word production. However, just as the sensory and motor processes play an important and early, but relatively minor, role in constructing the reading system, the same is true of building the functional writing system. Cognitive, language, and executive functions play the major roles in building the functional writing system. Idea generation is a necessary component of the writing system ~ the pump that has to be primed if the text generation process is to flow. In one sense, all composing is creative in that it is generative and creates what previously did not exist. We know more about where babies come from than we do about where ideas come from in the brain. When Dr. Seuss was asked where his ideas came from, he purportedly speculated that they may arise from the desert. That is the phenomenological experience that many writers have--fertile ideas springing from barren nowhere. In fact, ideas are probably simmering in the implicit long-term memory system outside our conscious aware n e s s - until we experience them consciously as they enter our explicit long-term memory system (see Chapters 3 and 5), often in the middle of the night or early in the morning upon waking. Whether ideas are just novel recombinations of existing items in long-term memory stores or whether there is a separate idea generator in the brain requires research. Although some individuals appear to be more creative than others, we simply do not know where and how in the brain ideas are fertilized, hatched, and groomed. The Writing Brain is, however, fundamentally a language system. Without language, the ideas cannot be expressed or communicated with others. As such, the Writing Brain probably draws on all language sources available to it through

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listening, talking, reading, and writing. Thus, the functional writing system constructs itself by building many communication links with already well-established Language by Ear and by Mouth, and with the developing Language by Eye system as well as other components in its own system. Building the Writing Brain also requires construction of a complex government system that draws on central executives (note the plural form) in the working memory system as well as writing-specific executive processes that evolve to serve the unique goals of the Writing Brain m planning writing goals (including what to say, how to say it, etc.) and reviewing and revising text to perfect it. Working memory has space or capacity limitations, in part due to the well-documented space limitations in the phonological short-term memory storage system, but also because of the workspace needed to achieve the other-defined or self-defined writing goals (McCutchen 1996, 1997). As discussed in Chapter 5, this space is sometimes likened to a chalkboard or desk top. Working memory also has temporal coordination limitations--with so many jobs to juggle in the writing process these jobs are vulnerable to interruption in their timing and coordination (Berninger 1999). Timing is as important to language processes and production as it is to the motor processes and production discussed earlier in this chapter. During composing, working memory also works closely with the anterior attentional component (see Chapter 5 and Table 6.2). We will return to the importance of timing in working memory when we discuss general principles for teaching reading, writing, and math in Part Ill. Not only does the Writing Brain rely on stored information retrieved from short-term and long-term memory, but it also requires space in conscious or explicit memory for high-level problem solving that draws on many cognitive processes to achieve its multiple subgoals close in time (Kintsch 1998; McCutchen 1996, 1997; Swanson & Berninger 1996). Hayes and Flower's (1980) cognitive task environment captures this notion of the workspace in memory for achieving multiple subgoals toward an overarching goal. Baddeley's (1986) model of working memory might be modified to add the problem solving workspace. To illustrate the contrast between working memory engaged for high-level goals as well as other kinds of less demanding goals, consider this example. Imagine standing, with a companion, on the deck of a boat on a sunny, warm day with clear skies and a gentle breeze. The short-term memory system will code these incoming sensory experiences. The processing component of working memory may contribute messages from the limbic and cortical system to interpret this experience of relaxation as good and pleasurable. The articulatory loop and/or visual-spatial sketchpad may help prolong through rehearsal the sensation of pleasure. In this example, circuitry for working memory is engaged during relaxation, but the problem solving space is not activated. A comparable case might be made for play-- the major cognitive activity during the preschool years that makes limited demands on the problem solving space of working memory but contributes greatly to cognitive development. Working memory engaged during composing differs in that the problem solving space is very active or needs to be if the writing goals are to be achieved. Writing

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is a problem solving activity (Scardamalia & Bereiter, 1986). Writing is hard work carried out in memory that is literally working, not relaxing or playing, and often working harder than when listening, talking, or reading. Play may be involved in idea generation but not in the hard work of translating and reviewing/revising. Computing can also be hard work carried out in memory when the problem solving space is dedicated to a math goal (see Chapter 7). The problem solving space is also important to reading comprehension, but is particularly vulnerable to overload during writing and math problem solving. We will return in Part III to the problem-solving space in working memory during high-level problem solving; that is, the brain at hard work for thinking goals in the academic curriculum.

DEVELOPMENTAL REORGANIZATION WRITING BRAIN

OF THE

Berninger and Swanson (1994) proposed a model of when the components of the writing system emerge at different phases of writing development. This model is based on research of the University of Washington Writing Project as well as the research of others on developing writing. The model was proposed as a framework for future research to refine and modify, if necessary m not as the final word. Writing research, like good writing, involves continual revision. That said, the following was their best guestimate based on available evidence at the time.

Primary Grade Beginning Writers Of the cognitive components in the Hayes and Flower (1980) model, the translating component emerges first (see Figure 6.7). Of the subcomponents in the translating component, transcription emerges earlier than text generation, and translation emerges before planning or reviewing/revising. This pattern of development makes sense in terms of what is known about brain development that was reviewed in Chapter 4. Transcription draws on grapho-motor and language skills that are served by brain regions that myelinate during early childhood, whereas planning and reviewing/revising skills are executive functions that are served by brain regions (primarily frontal lobes) that begin to myelinate early in development but do not fully myelinate until adolescence (and even into young adulthood). During the primary grades, transcription largely involves controlled processing (cognitive pathway) of the learning circuitry. Beginning writers use strategies for both letter production and spelling; see Varnhagen (1994) for overview of spelling strategies. For most children, the letter production subcomponent of the transcription becomes automatic and switches to the behavioral pathway later in the primary grades, but the spelling subcomponent of transcription component continues to rely on controlled processing to some degree.

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Translating I

I

\ X

/sentenceX~ X (clause)/ X I On-line Planning I On-line algorithms and schematafor planningthe next sentence (local planning)

Some on-line revision of transcribedwords Occasionalon line revisionof sentence

~paragraph~ (multiple / ~ \ clauses) /

FIGURE 6.7 Emergentcomponents of writing systemin primarygrade students. Reprinted from V. Berningerand HL Swansonin CHILDREN'SWRITING: TOWARD A PROCESS THEORY OF THE DEVELOPMENTOF SKILLEDWRITING by E. Butterfield.Copyright1994.With permission from ElsevierScience.

Intermediate Grade Developing Writers Three kinds of functional reorganization occur during the intermediate grades; the first three are illustrated in Figure 6.8. In the first reorganization, the transcription component (for both handwriting and spelling) is relegated to the automatic, behavioral pathway. Increasingly, not only letters but also orthographic word forms (see Steffler et al. 1998) are directly retrieved from memory. Consequently, this automatization of letter and word production frees up limited capacity in working memory (McCutchen 1996, 1997) for the high-level jobs of writing and for the executive processes for self-regulation of the writing process (Harris & Graham 1996). Good writers differ from poor writers at this developmental level in executive functions for initiation and set switching (Hooper, Swartz, Wakely, de Kruif & Montgomery, in press). The second reorganization is the increasing involvement of working memory in the composing process. This reorganization is probably related to the increasing myelination of frontal areas that support the executive processes of working memory. As a result, these executive processes of working memory begin to make connections with the executive processes specific to the writing system. However, these connections begin to emerge, in a limited way, in the post-translation revision process before

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they emerge in the advance planning process. Reviewing/revising is not operating at all levels of language. Although skilled writers that Hayes and Flower (1980) studied may juggle recursively between planning, translating, and reviewing/revising within the same writing session, developing writers, whose working memory capacity is more limited, struggle with linking planning and reviewing/revising to translating and may be able to do so better across writing sessions than within the same writing session. Individual differences exist in the degree to which executive functions are developed to support knowledge transforming strategies (to adapt knowledge to the audience) versus knowledge telling strategies (not adapted to audience) in presentation (Scardamalia & Bereiter 1986,1987; Scardamalia, Bereiter & Goleman 1982). Individual differences also exist in using writing to transform existing knowledge to generate new knowledge (Klein, 1999). The third reorganization (not pictured in Figure 6.8) is the increased number of communication pathways between the writing brain and reading brain. Increasingly, children are asked to write about what they have read, or to read their own writing for purposes of revision. Existing evidence for normal and disabled intermediate grade writers indicates that the functional reading and writing systems communicate best within the same level of language, that is, across the word level or across the text level but not across the word and text levels (Berninger, Abbott et al. 2001, Berninger, Abbott, Abbott, Graham & Richards, 2002).

Working Memory)

Posttranslation Reviewing/Revising

Preplanning

~discourseq ]structures,I ]literacy | I~ enre .J

",~ ,, ~ara!lraph~

FIGURE 6.8 Developingcomponents of writing systemin intermediate grade students. Reprinted from V. Beminger and H.L. Swanson in CHILDREN'S WRITING: TOWARD A PROCESS THEORY OF THE DEVELOPMENT OF SKILLED WRITING by E. Butterfield. Copyright 1994. With permissionfrom ElsevierScience.

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Junior High Developing Writers By the junior high years, the major functional reorganizations involve (a) the greater connectivity between working memory and all the cognitive components of the domain-specific writing system, and (b) emergence of reviewing/revising at all levels of language (see Figure 6.9). The links between working memory and planning and reviewing/revising may involve executive functions, supported by the increased myelination of frontal areas that are approaching adult levels (see Chapter 4). That is why writing instruction may have been postponed until the junior high years in the outdated view of language development (see Chapters 4 and 5). Writing products in junior high begin to resemble those of adult, skilled writers. However, postponement of writing instruction until junior high may miss a critical developmental period in writing for the translation component, particularly the transcription subcomponent. Without the transcription subcomponents (letter and word production) developed to grade-appropriate levels, future development of writing skill may be arrested (Berninger & Graham 1998; Graham et al. 1997). Thus, from a developmental neuropsychological perspective on writing, all components of the writing system should be taught and practiced throughout writing development, well before developing writers are expected to approach adult levels of writing competency (Berninger 1994). Expectations for coordinating the functional writing and functional reading systems to achieve a wide array of literacy goals increase during the junior high years. The consequences of these expectations for junior high readers and writers are not well studied from a developmental neuropsychological perspective. Far more

WorkingMemo

Preplanning

word sentence text

k {~P~176 l wordsentencetext

FIGURE 6.9 Developingcomponents of writing systemin junior high students. Reprinted from V. Berninger and H.L. Swansonin CHILDREN'SWRITING: TOWARD A PROCESS THEORY OF THE DEVELOPMENTOF SKILLEDWRITING byE. Butterfield. Copyright1994.With permission from ElsevierScience.

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instructional than developmental or brain research on writing exists for junior high and high school students (see Chapter 9). Instructional research has implications for hooking up the wetware components of the writing system. In contrast, developmental neuropsychological research (Berninger 1994) has implications for understanding why the functional writing system does not always respond to instructional interventions, and how to construct writing assessment that is developmentally appropriate and instructionally relevant. We return to this topic in Part IV.

RECOMMENDATIONS

FOR FURTHER READING

Drawing Goodnow, J. 1977. Children drawing. Cambridge, MA: Harvard University Press.

Memory Processes in Writing McCutchen, D. 1996. A capacity theory of writing: Working memory in composition. Educational Psychology Review. 8:299-325.

Acquired Writing Disorders McCarthy, R. & Warrington, E. 1990. Cognitive neuropsychology. A clinical introduction, (241-261). New York: Academic Press.

Handwriting Berninger, V. & Graham, S. 1998. Language by hand: A synthesis of a decade of research on handwriting. Handwriting Review. 12:11-25. Graham, S. & Weintraub, N. 1996. A review of handwriting research: Progress and prospects from 1980 to 1994. Educational Psychology Review. 8:7-87.

Spelling Treiman, R. 1993. Beginning to spell. Cambridge, UK: Cambridge University Press. Venezky, R. 1999. The American way of spelling. New York: Guilford.

Written Composition Butterfield, E., ed. 1994. Children's writing: Toward a process theory of development of skilled writing. Greenwich, CT: JAI Press.

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Kellogg, R. 1994. Thepsychology of writing. New York: OxfordUniversityPress. Scardamalia, M. & Bereiter, C. 1987. Thepsychology of written compositions. Hillsdale,NJ: Erlbaum.

MAKING CONNECTIONS Questions preceding by * may be most appropriate for graduate students. 1. Why is writing harder than reading for many students? 2. Why is it important to conceptualize writing as a system with many interacting components? How and why are secondary association areas for integrating sensory and motor codes, and tertiary association areas for abstract integration important in learning to compose? 3. Why is it important to conceptualize writing as a developmental process that starts early and develops over a long period of time? How does brain development contribute to the long developmental trajectory from novice to expert writer? 4. Why is it important to integrate reading and writing across the curriculum in different ways at different stages of writing development? *5. If you were part of a brain imaging team, what kinds of handwriting tasks would you recommend that the team study in developing children? Why? How? *6. If you were part of a brain imaging team, what kinds of spelling tasks would you recommend that the team study in developing children? Why? How? *7. If you were part of a brain imaging team, what kinds of composing tasks would you recommend that the team study in developing children? Why? How?

Building a Computing Brain Neurologically

The Computing Brain (see Figure 7.1) is constructed by borrowing from many other brain systems, just as the Reading Brain and the Writing Brain are. The Computing Brain also borrows from the Reading Brain, for example, in solving word problems in math, and from the Writing Brain, for example, in using its transcription module (grapho-motor component) to transcribe visual notation for numbers during paper and pencil computation. The Computing Brain also borrows from many nonlanguage brain systems (see Figure 7.2). In the case of the Computing Brain, however, the construction process carries over to a completely new domain of knowledge m t h e quantitative domain in which number or quantity is coded. In contrast to the verbal domain that begins by attaching names to objects, thd quantitative domain begins by assigning magnitude or amount to objects. From this simple beginning the Computing Brain develops into an elegant representational system that can be used to describe, explain, and operate upon the structure of the physical world (Case & Sandieson 1992; Hoffman 1998) and solve problems in daily living (Griffin, Case & Sandieson 1992). The Computing Brain illustrates well the general principle of redundancy discussed in Chapter 3. The Computing Brain draws on many different kinds of codes, including (a) quantitative codes along a continuous analog distribution, Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

193

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Conceptual Knowledge Geometry Problem Solving Space

Part-Whole 9 decimals 9

measurement

9 money 9 tellingtime

Computational Algorithms

\1 ~

~ ,f

Place Value

r

FIGURE 7.1 Architecture of domain-specific functional math system. Reprinted from Busse, Berninger, Smith, and Hildebrand2001 in The Handbook of Psychoeducational Assessment: Ability, Achieuement, and Behaviour in Children, eds Andrews, Saklofske,andJanzen. Copyright 9 AcademicPress. (b) numeric codes for visual notation of the symbols for the underlying analog distribution, (c) motor codes for writing numbers in visual notation, (d) verbal codes for names of numbers, and (e) mental imagery that codes quantitative relationships among visual-spatial codes. Thus, the Computing Brain is multilingual in its representational format m d r a w i n g on quantitative, visual, motor, verbal, and imagery codes. Likewise, it draws on many streams of thinking, including quantitative, verbal, and visual-spatial (Robinson, Abbott, Berninger & Busse 1996). O f all the brain systems related to academic learning, the Computing Brain may be the most complex in its construction and needs to borrow from other systems.

Building a Computing Brain Neurologically Dimension 1

vs

Strategies

195

Automaticity

(metacognitive and executive functions) Dimension 2

Short-Term Memory STM

Dimension 3

vs

Working Memory WM

Declarative

vs

Temporal Processing

LTM Procedural Knowledge

Knowledge Dimension 4

vs

Long-Term Memory

vs

Visual/Spatial Processing

Dimension 5

Listening-Speaking

Input-Output

Reading-Speaking

Combinations

Liste nin g-W riti ng Reading-Writing

Dimension 6

Finger Skills (fine motor planning and sensori-symbol integration)

Dimension 7

Concrete Operational

vs

Formal Operational Representations

Representations

FIGURE 7.2 Architecture of domain-general functional systems that work with the domainspecific functional math system. Reprinted from Busse, Berninger, Smith, and Hildebrand 2001 in The Handbook of Psychoeducational Assessment: Ability, Achievement, and Behaviour in Children, eds Andrews, Saklot~ke, and Janzen. Copyright 9 Academic Press. In this chapter we provide a brief overview of the development of quantitative thinking m the most unique feature of the Computing Brain. We then review in vivo brain imaging studies of the Computing Brain. As in the case of writing, these imaging studies for math are focused on normal adults and few have been done. Based on both the developmental framework and brain imaging research, we propose components of the wetware for the Computing Brain. In Chapter 10 we will discuss the instructional experiences needed to hook up these wetware

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components. At the end of this chapter we consider how the Computing Brain may reorganize across math development.

DEVELOPMENT OF QUANTITATIVE THINKING

Protonumeracy Humans are not alone in developing quantitative knowledge. Animals, ranging from rats to birds to primates, have an elementary concept of number (protoquantitative knowledge) (Koehler 1951; Mechner & Gurevrekian 1962; Matsuzawa 1985). Human infant brains also seem to have a rudimentary concept of number (Antell & Keating 1983; Simon, Hespos & Rochat 1995; Starkey & Cooper 1980; Starkey, Spelke & Gelman 1983; Wynn 1992).

Counting Crosstalk between the aural/oral language system, the visual system, and the Computing Brain begins early in development. Initially, young children learn verbal labels for numbers in a standard sequence (i.e., rote counting) without engaging the visual system for objects. True counting begins when those verbal labels are associated with the objects in the physical world with one-to-one correspondence (one number word to one object). True counting requires crosstalk among the quantitative, visual, and oral language systems (for number of objects, recognition of objects, and labeling of objects with number words). True counting leads to creation of an internal mental number line, along which quantity is represented in analog or continuous fashion. True counting thus serves as the conceptual foundation from which both beginning (Fuson 1988) and advanced mathematical thinking (Hoffman 1998) is created. For example, early in math acquisition, counting along an external or internal number line serves as a tool in learning beginning math facts or mental algorithms for computing; see the Conceptual Knowledge and Arithmetic Module components in Figure 7.1. Counting also serves as a conceptual framework for later learning about place value, the concept of infinity, negative numbers, prime numbers, and imaginary numbers. For example, place value is the conceptual framework for dealing with the large quantities that accumulate when counting, which in beginning math is expressed in base ten. Counting experience leads to the concepts that (a) counting can go on forever and thus the quantitative dimension is infinite, (b) counting can go in both directions and create both negative and positive numbers, and (c) some numbers along the number line have special properties (e.g., are primes or imaginary) that can be applied to solving certain kinds of mathematical problems.

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Eventually, students learn to manipulate more than one internal number hne and to think about multiple quantitative dimensions simultaneously (Case & Sandieson 1992). This multidimensional thinking underlies the advanced abihty to explore multivariate quantitative relationships among two or more objects redefined as variables, as discussed later in this chapter.

From Concrete to Abstract Representation of Objects Piaget (1952, 1970) demonstrated the progression of cognitive development from sensori-motor, to preoperational, to concrete operational, to formal operational thought. Each of these stages of cognitive development has implications for evolving mathematical knowledge. Object permanence emerges during the sensorimotor period. It is the ability to represent an object in the mind so that it remains accessible to thinking even when the object disappears from the external environment. A child with object permanence may code whether one or two cookies disappeared when her brother swiped them. During the preoperational period in the preschool years, children learn to think about these internal representations of objects in ways that are less tied to sensori-motor coding (attributes of the external environment). The preoperational child may be aware that mother gave more cookies to one child than a n o t h e r m m o r e is an abstract relative comparison. During the concrete operational period early in formal schooling, children learn to think about these internal representations in terms of concrete manipulations they perform on them. By helping mother bake, the child learns that the same amount of cookie dough can be used to make two-dozen large cookies or fourdozen small cookies. During the formal operational period later in schoohng (usually early or late adolescence), the student learns that the quantities of ingredients in a recipe can be represented symbolically and these quantities can be mathematically manipulated to double or triple a recipe for cookie dough. These operations can be performed mentally on abstract representations of quantitative information by multiplying each quantity by a constant. The concrete experience of working with the cookie dough is no longer needed to perform complex quantitative operations. Thus, across math development, thinking progresses from (a) the number of objects represented internally to (b) their more abstract quantitative properties to (c) the operations performed on these internal representations while simultaneously manipulating concrete objects in the environment to (d) abstract operations or mental manipulations performed on these internal representations. Put another way, in the first two stages, the object is internally represented and coded for quantitative information. In the second two stages, mental routines are created for operating on the quantitative representations m first in terms of concrete manipulations performed on actual objects and second in terms of abstract manipulations performed on symbolic representations of objects. In the process of development

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the objects are transformed into variables, whose identity remains constant but whose quantitative properties varies. Mental operations can be applied to variables, which are mental symbols that can be expressed in visual notation written by hand and read by eye. However, representing quantitative information about an object or variable is not the same as performing operations on those representations. Representations of quantitative information and application of operations are separable processes that may partially, but not completely, share neural circuits. An example of a representation of quantitative information is a math fact (e.g., 3 + 4 = 7). An example of an operation is an arithmetic algorithm for adding columns of numbers. Algorithms (e.g., calculation procedures) are applied to quantitative information (e.g., math facts). Geary's (1993) review of neuropsychological subtypes of arithmetic disabilities distinguished between math fact retrieval and arithmetic algorithms, which are separate components, each distinct from each other, and also from a third compon e n t m visual-spatial understanding of the number system.

Part-Whole Relationships The concept of number facilitates the learning of basic number facts and rote arithmetic algorithms. However, the concept of part-whole relationships is needed to grasp fractions, measurement, algebra, and conceptual understanding of the algorithms for operating on number representations. Lewis Carroll's Alice in Wonderland captures, in verbal fantasy, how this mathematical world of objects and operations on object representations behaves m f r o m the perspective of the developing mathematical thinker. Objects remain the same, that is, retain their identity, despite their transformation in size, that is, their quantitative properties. Objects, like Alice and the other creatures in Wonderland, can be reduced or expanded in size without losing their identity. Objects also remain the same on both sides of a mirror or looking glass (equal sign) m what is on one side can be reversed to create what is on the other side. For example, 3 + 4 = 7, and 7 = 3 + 4; and 3 x 4 = 12, and 12 = 3 x 4. This equivalence of part-whole relationships underlies the reversability of certain operations. For example, 3 + 4 = 7 and 7 - 4 = 3; 3 x 4 = 12 and 12 + 4 = 3. We can undo subtraction by adding and undo division by multiplying because the whole (all the parts) stays constant even if they are rearranged. These conceptual insights that a whole object exists independently of the quantitative proterties of its parts is critical to moving beyond counting along a number line to understanding part-whole relationships. Quantity can expand upward or downward in a different way in reference to an object than in reference to a number line that exists independently of objects. A whole can be divided into a variable number of parts but still retain its identity as a whole. Moreover, for the same whole, a larger number of parts (e.g., 20) may result in smaller parts and a

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smaller number of parts (e.g., 2) may result in larger parts. So more may be less and less may be more when quantity is related to an object that remains constant but can vary in its number of parts and their quantitative relationships to the whole. The concept of part-whole relationships is needed to understand fractions, decimals, money, telling time, and measurement in general. Many students in the upper grades who have learned the basic arithmetic facts and rote operations (arithmetic module, see Figure 7.1) continue to have diflqculty with fractions, decimals, money, and telling time because they have not grasped this part-whole relationship. Algebra also requires an understanding that the number of parts and how they are weighted may differ across the sides of an equation m as long as both sides equal the same whole. Both sides of the equation are identical at the level of the whole but are divided differently into weighted parts. Transformations can be applied to both sides as long as the equality (the whole) is preserved on both sides.

Multivariate Relationships Two other developments propel Computing Brains forward in their mathematical thinking. The first change is an increase in the number of quantitative dimensions of an object that can be attended simultaneously (Case & Sandieson 1992; Piaget 1952). Increasingly, multiple quantitative dimensions (e.g., height, width, and volume) are considered rather than a single dimension (e.g., height only) (Piaget 1952, 1970). The second change is an increase in the number of objects or variables (object symbols) that can be considered simultaneously. Increasingly, the Computing Brain begins to think about functional relationships between two or more variables (symbols for objects) simultaneously and expresses these relationships quantitatively. To the extent that the brain can deal with relationships between two or more variables, it becomes a multivariate thinker rather than a univariate thinker. Multivariate thinkers may combine dimensions by adding or multiplying them (Siegler, 1981). Multivariate thinkers can deal with the complexities of problem solving in both the quantitative and scientific domains.

Math by Hand Math computations are carried out in the problem-solving workspace of working memory in the Computing Brain (see Figure 7.1), but these computations may overload the capacity limitations of working memory. To overcome these capacity limitations, a form of extramemory develops that relies on an external, peripheral system of visual notation. The concept of number is represented in digits (numerals) that are symbols that represent quantity visually. The hand produces those digits (numerals). The Computing Brain links up with the same Grapho-Motor Component as the Writing Brain does to produce Language by Hand. The visual

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notation system for numbers is a kind of visible language with place value as its syntax. A longitudinal study showed that neuropsychological measures of hand function predict children's arithmetic skills early in formal schooling (Fayol, Barrouillet & Marinthe 1998). This relationship makes sense given that the hand plays an important role in this external representation system for producing visual notation of number concepts. Accordingly, the hand plays a major role in learning basic arithmetic facts and operations, which are often expressed in writing (see arithmetic module in Figure 7.1).

Crosstalk between the Quantitative and Visual-Spatial Systems The Computing Brain develops crosstalk with the visual system m with both (a) the sensory features of the incoming visual stimuli and (b) the abstract visual-spatial representations of the physical world that the developing child is acquiring. From this wedding of two representational systems m for quantitative information and for abstract visual-spatial understanding m develops children's geometric knowledge of the world; see Figure 7.1. The quantitative representations inform the visual-spatial understandings and the visual-spatial understandings inform the quantitative representations. This crosstalk creates the conceptual foundations for some kinds of scientific knowledge such as physics (Greene 1999). This communication link also provides a potential instructional tool that capitalizes on the multiple representations of the Computing Brain in teaching math, as is discussed in Chapter 10.

Computing Brain at Work The Computing Brain is fundamentally a problem-solving brain, much as the Writing Brain is. However, it has different kinds of problems to solve. Working memory is critical to this problem-solving effort. From the beginning of true counting, the problem solving space in the conscious part of working memory is put to work. When the problems are fairly simple, they can be solved using internal mental representations. When the problems exceed the capability of working memory, concrete aids in the external environment may be needed, for example, a number line that the brain can see and the hand can touch while counting. Other peripheral aids may also help, such as paper and pencil written computations that use a visual notation system to represent quantity, quantitative relationships, and arithmetic operations. Once basic arithmetic is mastered and the mathematical problems become even more complex, a hand-held calculator may be used to perform the arithmetic operations even more quickly, thus overcoming some of the temporal constraints in the capacity-limited workspace for problem solving. However, a calculator cannot compensate for lack of mental representations of number lines

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and the part-whole concept that are necessary for high-level problem solving. Software that provides computer-generated visual representation of numerical concepts may facilitate high-level problem solving, but research is needed on this topic.

IN VIVO FUNCTIONAL MATH

IMAGING STUDIES OF

To date, functional imaging has been used to study mostly quantitative estimation and computational functions of the brain. For example, in one of the early studies in this field, Dehaene and Cohen (1995) reported that procedural knowledge for arithmetic facts may involve the lenticular nucleus. One goal of many of these studies has been to tease apart how numbers are represented differently in the brain depending on whether visual codes (digits), verbal codes (names), or quantitative codes (distributed along an internal number line in analog fashion) are involved. Another goal has been to tease apart stored representations (e.g., math facts) from operations (e.g., applying computational algorithms). The following studies are representative of the recent studies that have employed ERP, flVIRI, and PET technologies. In one ERP study, Dehaene (1996) asked participants to classify numbers as to whether they were larger or smaller than a target number. He varied whether the target number was an arabic numeral (5), which is a visual notation for the number, or an English word (five), which is a phonological code for the number. These codes also differ as to whether they have underlying continuous representations (arabic numerals) or underlying categorical representation (names). The N1 ERP component (a negative amplitude that peaks about 100 milliseconds after stimulus onset) was symmetrical for the digits but was left lateralized for number names. Thus, number meaning may be accessed differently in the brain depending on the code used to represent the concept of number or the nature of the underlying representation--analog or digital. In a second ERP study, Iguchi and Hashimoto (2000) administered three tasks: adding presented digits, counting presented digits, and counting the number of meaningless patterns. Within the first 300 milliseconds, physical and numerical attributes of the stimulus were identified, but calculation (a procedural computational operation) occurred over a larger time course and was associated with a positive, slow potential, which was probably related to accessing stored knowledge in memory. PET technology has been used to study both addition and multiplication. In one study (Pesenti, Thioux, Seron & DeVolder 2000) four tasks were completed: rest with eyes closed; physical judgment of whether digits or nonnumerical characters were arabic numerals; addition of the same digits as used in the physical judgment task; and comparison of which number in the same pairs was larger. The comparison task activated the fight superior parietal lobe, whereas the addition task activated fight orbital frontal and fight anterior insula. Orbital frontal and insula regions are

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involved in automatic retrieval in other domains (see Chapter 3); thus, it is plausible that they may also be activated in automatic retrieval of math facts and application of algorithms to addition, both of which are well practiced in normal adults. Both tasks activated a left frontal parietal network including intraparietal sulcus, superior parietal lobule, and precentral gyrus. These results differed from those reported by Dehaene and Cohen (1995) in support of a Triple Code Model, in which arabic numerals are processed bilaterally in occipital-temporal regions, magnitude is represented in parietal lobes, and retrieval of math facts occurs in left language areas and left subcortical areas (see Figures 3.4, 5.1, and 5.4). Thus, as was the case with language (see Chapter 5), neuroanatomical location may vary with slight differences in tasks that affect attention and working memory load. Nevertheless, the accumulating evidence from these and other studies indicates that there is not a single computational center in the brain. In another PET study, Dehaene et al. (1996) compared a multiplication task (quantitative operation) to a number comparison task (quantitative representation). For the multiplication task, instructions were to carry out a mental multiplication of two digits and name the answer silently inside the head. For the comparison task, instructions were to decide which of two digits (same pairs as used for multiplication) was larger. Both tasks had common activations, compared to a rest (with eyes closed) condition, in lateral occipital cortex bilaterally, left precentral gyrus, and supplementary motor area (see Figures 3.4, 5.4, and 6.4). This finding suggests that, even with eyes closed, visual codes are imaged, and that even when written computation is not involved, the brain is in a state of readiness for writing. The number comparison task uniquely activated the fight superior temporal gyrus, left and fight middle temporal gyri, fight superior frontal gyrus, and fight inferior frontal gyrus. The multiplication task uniquely activated inferior parietal gyri bilaterally, left fusiform and lingual gyri, fight cuneus, left lenticular nucleus, and BA 8; see Figure 7.3. The authors concluded that magnitude estimation along an analog number line and arithmetic algorithms have partially distinct neural networks. flVIP,.I studies have also added to our understanding of the computing brain. In an event-related flVlt
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FIGURE 7.3 Regions significantly activated or deactivated during multiplication relative to rest. (Lateral and mesial views of hemispheres). LOC = lateral occipital lobe. SMA=supplementary motor areas. PoG = postcentral gyms. SmG = Supramarginal gyms. IFG = inferior frontal gyms. Tha-thalamus. STG = superior temporal gyrus. Cu -- cuneus. FLG = fusiform and lingual gyri. IPG -- inferior parietal gyms. PrC = precuneus. TP = temporal pole. SFG -- superior frontal gyms. MPfc = medial prefrontal cortex. PoC = posterior cingulate gyrus. Reprinted from Neuropsychologia, vol 34, Dehaene, Tzourio, Frak, Raynaud, Cohen, Mehler, and Mazoyer, page 1101, Copyright 1996, with permission from Elsevier Science.

arithmetic p r o b l e m required selection o f a correct answer (4 + 5 = 9? or 77), but an estimated arithmetic p r o b l e m required an approximate answer to the same p r o b l e m (4 + 5 = 8? or 3?). T h e a p p r o x i m a t e task activated parietal lobes bilaterally m o r e than the exact test did, suggesting that the parietal lobes are part o f a quantitative circuit. T h e investigators c o n c l u d e d that there are t w o main c o m p o n e n t s o f calculation circuits m l e f t inferior frontal for exact arithmetic and bilateral intraparietal for a p p r o x i m a t e arithmetic (estimation). B u r b a u d , Camus, Guehl, Bioulac, Caillfi, and Allard (1999) used flVlRI to investigate mental subtraction. Medical students p e r f o r m e d t w o tasks: covert n u m b e r p r o d u c t i o n w i t h calculation and covert n u m b e r p r o d u c t i o n w i t h o u t calculation. For the first task, instructions w e r e to think o f a three-digit n u m b e r greater than 500, subtract a prime n u m b e r (13 or 17) from it, and repeat the process. For the second task, the instructions w e r e to think o f a three-digit n u m b e r greater than 500. T h e first task activated left dorsolateral prefrontal and p r e m o t o r cortices, Broca's area, and inferior parietal cortex bilaterally. T h e second task activated Broca's area

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and left premotor and prefrontal cortex, and hardly any left inferior parietal cortex when calculation was not involved (see Figures 3.4, 5.1, and 6.6). The investigators concluded that mental subtraction may involve a distributed system including left dorsolateral prefrontal cortex (DLPFC) and inferior parietal cortex bilaterally. Rickard, Romero, Basso, Wharton, Flitman, and Grafman (2000) used fl~RI to compare three tasks: simple arithmetic, numerical magnitude estimation, and perceptual-motor control. In the first task, college students verified a multiplication fact (e.g., 4 x 7 = 35, true? or false?). In the second task, they performed a numerical magnitude.judgment (which is larger 24? or 25?). In the third task, they decided ifa 1 was present in a 4-digit string. Compared to the other tasks, the multiplication task uniquely activated BA 44 and parietal cortex bilaterally but greater on the left, but the magnitude estimation task uniquely activated inferior parietal cortex bilaterally (see Figures 3.1 and 3.2). Fullbright, Molfese, Stevens, Skudlarski, Lacadie, and Gore (2000) compared three tasks: matching, multiplication, and a control. For each task, three or four single digit or low value double-digit numbers were presented serially; the target stimulus appeared after a 12-second delay. For matching, instructions were to decide if the target stimulus matched the previously presented stimulus. For multiplication, the task was to decide if the target stimulus was the product of previous numbers. For the control task, the numbers were always zeroes. Left-middle frontal gyrus (see Figure 5.1) and left frontal lobes activated more on multiplication than matching. These results differ from Burbaud et al. (1999) but are consistent with the investigators' conclusion that quantitative processing is dependent on both language and nonlanguage representations. Taken together, these results show that many of the same brain areas are activated by the functional math system as for the functional reading and writing systems. At the same time, some brain areas are uniquely activated for the functional math system. The results as to which brain regions are activated for a particular imaging task were not always consistent across studies, possibly because specific tasks activate only part of a circuit. It is important to remember that a single brain locus most likely does not support a single component of any brain system m rather a neural circuit distributed across brain sites does. Nevertheless, the parietal areas appear to be a component of many of the neural circuits of the Computing Brain.

BUILDING A COMPUTING

BRAIN

Table 7.1 summarizes the wetware components of a functional math brain and their possible locations, given results of current brain imaging studies. The newly constructed components of this brain system are the quantitative component, the arithmetic module with stored math facts and computational algorithms, the visual notation system for number, the visual-spatial system that represents geometric information about the physical world, a grapho-motor component for writing the

Building a C o m p u t i n g Brain Neurologically TABLE 7.1

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Constructing the Wetware for a C o m p u t i n g Brain Function a

Possible brain Structure(s)b

Arousal unit Quantitative Knowledge

d

Reticular activating system (RAS) and its cortical connections

Number concept (quantity) Counting (1-1 correspondence) Number line/analog representation of number

Right superior parietal lobe; inferior parietal cortex bilaterally; parietal lobes bilaterally; right superior temporal gyms; middle temporal gyrus bilaterally; right superior frontal gyms; right inferior frontal gyms; left frontal parietal network; occipital cortex bilaterally; supplementary motor area; left precentral gyms; Broca's area; left premotor; left prefrontal cortex

Place value

Unknown

Part-whole relationships

Unknown

Multivariate relationships

Unknown

Arithmetic Module d

Math facts

Lenticular nucleus; left language and subcortical areas

Computational algorithms

Left inferior frontal and parietal areas

addition

Right orbital frontal; right insula; left frontal parietal network; left inferior frontal area

subtraction

Left dorsolateral prefrontal; inferior parietal bilaterally; left premotor; Broca's area

multiplication

Inferior parietal gyri bilaterally; parietal bilaterally; left fusiform and lingual gyri; right cuneus; left lenticular nucleus; BA 8; BA 44; left middle and frontal gyri; left frontal parietal network; occipital cortex bilaterally; supplementary motor area; left precentral gyrus

Visual-Spatial System

d

Sensory input

See Table 3.2 for primary projection pathway

Extraction of visual features (nonlinguistic)

V1 striatal cortex/striatal cortex

Visual Notation System d for representing number in numerals

Right fusiform gyrus; bilateral occipital-temporal areas; left fusiform and precentral gyri; right precentral and inferior parietal regions

Ventral what pathway for objects (identity of small elements in linear array)

Occipital to temporal cortex

Dorsal where pathway for objects (spatial relationships of small elements in linear array)

Occipital to parietal cortex

Geometrical a (visual-spatial)

Unknown (continues)

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TABLE 7.1 (continued)

Grapho-Motor Component

d

Linguistic Representations of Mathematical Problems--draws on Aural Language Systemc d Math Lexicon Quantitative vocabulary

See Table 6.2 See Table 5.4

Left inferior parietal lobule; right precentral/inferior parietal regions

Visual-spatial vocabulary

Unknown

Arithmetic operations vocabulary

Unknown

Other vocabulary knowledge

Unknown

Executive/Government System

Prefrontal cortex (especially left dorsal; prefrontal cortex, LDPFC)

Cross-talk between existing systems in constructing new system Cross-talk with reading brain in solving word problems

See Table 5.4

Working with attentional system (focus, maintenance, transitions) Creating goals and plans Coordinating multiple operations Control processes for working memory Metacognition (reflection) about math Memory Working memory Phonological STM Visual-Spatial STM

See Tables 5.3, 5.4 See Table 5.3

Central Executive(s)

Prefrontal cortex

Long-Term Storage

See Table 5.3

Implicit (Unconscious) Network-- automatically activated (primed)math facts

Unknown

Explicit (Conscious) Semantic Retrieval

Temporal cortex; left hippocampus

Attentional System

See Table 5.4

Cognition General Reasoning

Lateral frontal network

Quantitative

Unknown

Visual-Spatial

Unknown

Verbal

Unknown

(continues)

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TABLE 7.1 (continued) Emotions and Motivation Learning Circuits Controlled processing-- Learning facts and algorithms Automatic processing-- Automatic retrieval of facts and algorithm application

Limbic structures- amygdala, septum; hypothalamus- and their cortical connections See Table 5.4 Unknown Right cerebellum; right orbital; insula

aSee section at end of Chapter 7 for description of function. bBasedon existingresearchreviewedin Chapters 3, 4, and 5. however, research evidence is not always consistent and future research may modify or extend current understanding of structure-function relationships. CBasedon research evidence for processing aural language; refers to knowledge that is stored in long term memory at different levels of language. dUnique to the newly constructed math computing system. visual notation system, and a specialized math lexicon. This lexicon of single words and phrases is specialized for quantitative concepts (e.g., greater than or less than), visual-spatial concepts (e.g., above, between, diagonal, circumference), and arithmetic operations (e.g., H o w much altogether? H o w much more? H o w many will each have?). As with reading and writing, the Computing Brain must be awake and suflqciently aroused to process incoming information from the environment and conduct ongoing processing within the internal mental environment. Separate representations for arithmetic facts (e.g., sums of 1-digit numbers, removal of 1digit numbers, repeated sums of 1-digit numbers, and repeated removal of 1-digit numbers) and algorithms for operating on these facts are stored in separate, but possibly partially overlapping circuits, in long-term memory. Quantitative knowledge is represented in continuous, analog fashion along a number line. Visual symbols for representing this quantitative knowledge and grapho-motor procedures for writing these visual symbols are also stored in long-term memory. Procedures for representing and manipulating part-whole relationships are established. More than one internal number line may be created for purposes of accessing multiple dimensions in problem solving. Computing Brains show individual differences in how quickly and easily they establish these internal representations of number lines and part-whole relationships. The Computing Brain keeps the executive/government system very busy. To begin with, the Computing Brain recruits the Reading Brain during written math word problem solving and the Writing Brain during written computation. During problem solving, the Computing Brain recruits the executive system to create goals and plans, coordinate multiple operations, monitor ongoing processes, and exert executive control over the working memory system. The executive system also

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reflects upon the math problem-solving process and develops metacognitive awareness of the math domain mthese metacognitions become yet another knowledge source to draw upon in math problem solving. Both the short-term and long-term memory components of working memory are activated. Conscious explicit memory is activated in working memory during math problem solving, but implicit memory may also be activated by math facts that are automatized for retrieval. Both the Cognitive Pathway and the Behavioral Pathway are activated as the Computing Brain learns. The Cognitive Pathway directs the controlled, attention demanding processing, with many links to emotions in the limbic system; the Behavioral Pathway governs the automatically activated, well practiced information (see Chapter 5). The executive governing, attentional, and memory systems work together. The more the low-level skills in the arithmetic module (see Figure 7.1) are automatized, the more the capacity- and temporally-limited resources of working memory are available for attending to high-level, resource-draining thinking jobs in math problem solving.

DEVELOPMENTAL REORGANIZATION COMPUTING BRAIN

OF

Increasing Automatization Strategies play a prominent role in early construction of the Computing Brain. Individual differences are evident in the strategies children apply to learning math facts, retrieving math facts from memory, and applying arithmetic algorithms for addition, subtraction, and multiplication (Geary, Brown & Samaranayake 1991; Siegler 1988a, 1988b). Strategies rely on the Cognitive Pathway for Controlled Processing (see Chapter 5), which drains the limited resources of working memory. With practice of math fact retrieval and application of arithmetic algorithms, arithmetic skills are shifted from the Cognitive Pathway to the Behavioral Pathway. As a result, they are increasingly on automatic pilot. This pathway does not make as many demands on the limited resources of working memory. In fact, automatizing the low level skills may actually increase working memory capacity. As a result, more resources are available for the high-level thinking jobs. Also, because the automatic pathway has less connections to the limbic networks, the more automatic those lowlevel skills are, the less emotional students are likely to be about math; for example, becoming anxious about it, avoiding it, resisting it, and having negative affect about it.

Decreasing Dissociations The components of the Computing Brain do not all develop at the same pace. When these components show marked unevenness in development, these components are

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dissociated. Biodiversity and differences in environmental experiences may account for early uneven development across components of the Computing Brain. A common dissociation in the developing Computing Brain is between a student's skill in visual notation and quantitative understanding. Attentional, visual, and grapho-motor problems may interfere with ability to express in writing what one knows about the underlying number system and one's quantitative reasoning skills. Children may know much more about the world of numbers than they can express in visual symbols in standard notation for the number system. Alternatively, children may learn to produce the visual representations fairly automatically without developing comparable conceptual understanding of the number system. They may lack a precise internal representation of the number line, a working representation of the part-whole concept, and/or ability to think about more than one quantitative dimension simultaneously (i.e., manipulate more than one internal number line at a time). If components of the developing computing brain show marked unevenness at this intermediate level of development, then the Computing Brain may have wiring anomalies and the student should be evaluated for specific math disability.

Increasingly Abstract Conceptualization As already discussed, early in schooling, the Computing Brain thinks concretely about numbers. Manipulating objects such as number lines or rods that are colorcoded for place value units helps the brain perform numerical operations. With development, the Computing Brain reorganizes itself to operate on internal representations of symbols (variables) rather than objects. A burst in computing power accompanies this reorganization, allowing the brain to engage in formal operational thought in which variables rather than objects are mentally manipulated.

RECOMMENDED

READING

The brain has multiple representational systems for storing the same kind of knowledge. Multiple representational systems are especially important in mathematical thinking, which draws on the language, quantitative, and visual-spatial domains. The first three recommended readings illustrate how the mathematical structure in the physical world can be understood in contrasting representational systems.

Representing Mathematical Knowledge in Linguistic Codes Gardner, M. 1960. The annotated Alice. Lewis Carroll's Alice's adventures in wonderland through the looking glass. Cleveland and New York: Forum Books. The World Publishing Company.

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Representing Mathematical Knowledge in Quantitative Codes Hoffman, P. 1998. The man who loved only numbers. The story of Paul Erd& and the searchfor mathematical truth. New York: Hyperion.

Representing Mathematical Knowledge in Visual-Spatial Codes Greene, B. 1999. The elegant universe. Superstings, hidden dimensions, and the questfor the ultimate theory. New York: Vantage, A Division of Bantam Books.

Acquired Arithmetic Disorders McCarthy, 1<. & Warrington, E. 1990. Cognitive neuropsychology. A clinical introduction, (262-273). New York: AcademicPress.

Math Development Geary, D. 1994. Children's mathematical development. AmericanWashington, D.C.: PsychologicalAssociation.

MAKING CONNECTIONS Questions preceded by * may be most appropriate for graduate students. 1. W h y might students have more anxiety over math than other academic subjects such as reading? 2. Compare and contrast the Writing Brain and the Computing Brain. H o w are they alike and how are they different in (a) how they are constructed, (b) how they perform their tasks, and (c) how they develop? 3. H o w might a specific reading disability or a specific writing disability interfere with the construction of a Computing Brain? What might the instructional implications be for math if a student has a specific learning disability affecting handwriting? 4. W h y might so many students have more difficulty with learning fractions, telling time, money skills, and algebra than with learning basic arithmetic operations? 5. N o w that hand-held calculators are available, is it a good idea to rely on those for learning the skills in the arithmetic module and focus just on conceptual

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knowledge, the problem solving space, and different kinds of reasoning (see Figure 7.1)? How might counting have implications for learning both arithmetic and conceptual knowledge of math? Do calculators eliminate the need for having a solid foundation in counting and arithmetic facts and operations? *6. Why might simple math functions like representation of number, retrieval of math facts, or arithmetic operations have so many different reported brain locations (see Table 7.1)? Does this mean the different studies are unreliable? Does this mean that different tasks activate different parts of the neural circuitry and many studies are needed before the complete circuitry can be determined? Does this mean that experimenter-designed tasks are carried out in different ways by participants who use different strategies for the same tasks? Are tasks used in brain imaging studies very sensitive to attentional strategies as Posner and McCandliss (1999) argued? How might individual differences in automaticity of arithmetic facts and operations affect results of brain imaging studies for math? How can teachers and clinicians sort out whether lack of automaticity, ineffective strategies, poor attention, or inefficient working memory contributes to poor performance on math tasks? *7. Compare the kinds of math tasks already studied in brain imaging with all the components needed for the Computing Brain (Table 7.1). What kinds of math tasks would you recommend using in imaging studies to gain a fuller knowledge of the Computing Brain?

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PART

III

LINKING LITERA C Y R E S E A R C H TO BRAIN RESEARCH

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Building a Reading Brain Pedagogically

Neural circuitry (see Table 5.4) is necessary, but not su~cient, for creating a reading brain. Specific kinds of experience in the environment are also necessary to hook up the circuits so that they function (see Chapter 4). Research began on what those experiences might be at about the time research on the brain began m toward the end of the nineteenth century (Aaron & Joshi 1992). Fortunately, considerable research exists on effective instructional components for reading. We do not need to wait until the brain is fully understood to implement research-supported instructional practices in reading. Moreover, as we argued in Chapter 1, research on the brain might benefit from collaboration with researchers who are knowledgeable about effective instructional practices. In this way, a more complete understanding may evolve of how the brain both influences the process of learning to read and is, in turn, changed by the process of learning to read. In this chapter we offer a succinct overview of what is known about effective instructional practices in reading for the novice and the developing reading brain. Two kinds of experience contribute to constructing a functional reading system. The first kind is represented in implicit memory outside conscious awareness. The second kind is represented in explicit, conscious memory. (See Chapter 5 and Table 5.3 for the distinction between implicit and explicit memory.) An example of implicit experience is print exposure (Pacton, Perruchet, Fayol, & Cleeremans, in Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

215

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press; Stanovich, West & Cunningham 1991), for example, when a brain sees written words in books, magazines, newspapers, labels, menus, and so on. Even if the brain cannot decode those words, information about their written word form may be represented in implicit memory (see Chapter 5). An example of explicit memory is listening attentively as an adult reads from a storybook and looking attentively at each word the adult pronounces. From repeated experiences in heating and seeing words, the brain may become consciously aware that the same word can have a spoken form and a written form. Eventually, the correspondence between the two word forms may be represented in explicit, semantic memory. Information in explicit memory can be consciously retrieved, allowing children to begin to name printed words. (See Chapter 5 for evidence from in vivo brain imaging that the brain stores the spoken word form and written word form separately, but has a mechanism for integrating these word forms.) The brain learns in two ways from experience m through induction and deduction. Induction is the abstraction from experience of general principles, rules, patterns, or generalizations. Deduction is the experience of applying general principles, rules, patterns, or generalizations. Brains vary in how well they learn through each of these approaches to experience. Some brains seem to be wired for reading. From the experience of a skilled reader reading to them, they induce from that experience the correspondences between written words and spoken words needed for the brain to begin to read on its own. (See connectionist computational models in Chapter 5). Other brains need explicit cues in the instructional environment to construct connections deductively. Yet other brains have anomalies in the neural circuits and need both very explicit instructional cues and considerably more supervised practice than other brains. Most brains lie in between these extremes and need a mix of these two approaches-- opportunities to induce spelling-sound correspondences from experience in reading and explicit instruction in spelling-sound correspondences for deductive application to reading. To illustrate these contrasting approaches to learning, you are encouraged to read the passage in Table 8.1 for the purpose of inducing the principle about economics and language that is inherent in it, and then to consult Table 8.2 for the purpose of deducing this same principle on the basis of instructional cues that make it very explicit. TABLE 8.1

'Jack and the Twoderful Beans'a

Twice upon a time there lived a boy namedJack in the twoderful land of Califivenia. Twoday Jack, a double-mindedlad, decidedthree go fifth three seek his fivetune. Aftermakingsure that Jack nine a sandwich and drank 8-up, his mother elevenderlysaid, "Threedoloo, threedoloo. Try three be back next Threesday."Jack went fifth and soon met a man wearinga four-piecesuit and a threepee. FifthrightlyJackaskedthe man, "I'm a Califivian. Are you two three?.... Cerelevenly" replied the man, ot:fivingthe high six. "Any two five elevenis?" aCreated by Victor Borge (seeRichard Lederer 1996, and Graves2000).

Building a Reading Brain Pedagogically TABLE 8.2

217

'Jack and the Twoderful Beans 'a'b .

.

.

.

.

Twice upon a time there lived a boy named Jack in the twodefful land of Califivenia. Twoday Jack, a double-minded lad, decided three go fifth three seek his fivetune. After making sure that Jack nine a sandwich and drank 8-up, his mother elevenderly said, "Threedoloo, threedoloo. Try three be back next Threesday." Jack went fifth and soon met a man wearing a four-piece suit and a threepee. FifthrighdyJack asked the man, "I'm a Califivian. Are you two three? .... Cerelevenly" replied the man, Offiving the high six. "Any two five elevenis?" aAccording to Victor Borge, the economy keeps inflating and so should language (by one) (see Graves 2000). bGeneral priniciple of inflationary language, which can be applied deductively: The referent for each number word in bold is a syllablethat is a homophone for the number word that is one less.

Teaching a brain to read requires the application of instructional design principles. In this chapter, we review instructional design principles that we have induced from instructional experiments with students at-risk for learning to read or with diagnosed reading disabilities. Unlike many instructional experiments, the treatments were derived from neurodevelopmental theory of functional brain systems for reading (Berninger 1994): Within a lesson, instruction was aimed at all levels of language, close in time, to create a functional system with coordinated components. Some of these experiments were controlled experiments in which children are randomly assigned to contrasting treatments and control groups. Some were design experiments (Brown 1992; Calfee, N o r m a n & Wilson, in press) in which all the necessary instructional components were provided in order to bring children up to grade level in reading. Controlled experiments allow researchers to make inferences about c a u s a l i t y - - w h i c h instructional components lead to desired learning outcomes, whereas design experiments allow researchers to cause e f f e c t s - - b r i n g about desired learning outcomes. Teaching, which to be accountable must bring about desired learning outcomes, is informed by both kinds o f research approaches. Controlled experiments yield a pool of conceptually validated, empirically supported instructional practices from which to select. Design experiments allow teachers and researchers to evaluate w h e t h e r specific implementations of these for specific students are effective. Table 8.3 summarizes the instructional design principles for transforming a n o n reading brain into a reading brain at the novice stage and at the developing stage. These principles, which are consistent with current understanding o f brain systems at w o r k during reading, can be applied using a variety of commercially available instructional materials. We first discuss each of the instructional design principles and then describe models, based on instructional research in the U W L D C (see Acknowledgements), for implementing these in teaching.

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Brain Literacy for Educators and Psychologists TABLE 8.3

Instructional Design Principles for Creating a R e a d i n g Brain

Novice Stage--monosyllabic and bisyllabic words from the English-German (Anglo-Saxon) layer of the language 1. Model reading by reading to children. 2. Develop linguistic awareness of word forms and their parts and multiple connections between them. 3. Teach alphabetic principle exphcitly (make input units and output units "visible") 4. Encourage strategic approach to decoding new words. 5. Practice oral reading until automatic (words) and fluent (text). 6. Provide guided assistance in comprehension and create a set for reading for meaning and pleasure.

Developing Stage-- polysyllabic words from Romance (Latin and French) and Greek as well as Anglo Saxon layers of the language 7. Continue to develop linguistic awareness of word forms, especially morphological and morphophonological awareness, and engage in word study (including vocabulary meaning). 8. Transition to silent reading--practice until silent reading rate is grade appropriate. 9. Provide explicit instruction in comprehension of text for different genre. 10. Teach strategies for transition from other-regulation to self-regulation of the reading process. 11. Create reading-writing connections for specific goals. 12. Foster positive affect toward reading and create a reading habit.

Implementation of Instructional Design Principles 1. Teach to all levels of language (subword, word, sentence, and text) close in time so that connections can form in temporally constrained working memory. 2. Teach low-level skills for brief time (to avoid habituation) for goal of automaticity (to free resource-hmited working memory for high-level processes). 3. Teach for transfer a. from low-level skills (tools) to high-level skills (constructing meaning). b. from other-regulation to self-regulation. 4. Teach for success a. by modeling. b. by scaffolding (guided assistance). 5. Teach all the necessary components at a student's developmental level in an integrated manner so that the reading system becomes functional. 6. Provide ample opportunity for (a) inductive and deductive learning experiences. (b) practice. (c) reading for pleasure and meaning. 7. Assess frequently using developmentally appropriate criteria and multiple modes (see chapter 12).

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P E D A G O G Y FOlK C R E A T I N G A N O V I C E R E A D I N G BRAIN

Reading Material Primary grade reading material contains mostly words of Anglo (English) Saxon (German) origin; these words tend to be one or two syllables in length, have accents on the first syllable in two syllable words, and occur with high frequency in the spoken language (Beeler 1988). High frequency in both the spoken language and written language encountered by beginning readers increases the probability that the brain may have sufficient exposure to the spoken and written forms of the same word to abstract inductively some of the spelling-phoneme correspondences of the language in the word. Although some Anglo-Saxon words contain exceptions to alphabetic principle (spelling-phoneme correspondences), most of the spelling units in these words can be decoded (Berninger 1998b; Ehri 1992b).

Model the Reading Process Adults and older children model Language by Mouth as they talk to infants and toddlers; from this input, the young child's brain learns oral language. In much the same way, young children benefit from adults and older children reading to them and modeling the reading process. Many preschoolers are read to at home on a regular basis and bring home-literacy experiences to school learning (Adams 1990). However, for two reasons even children who have not been read to at home can learn to read at school. First, at the time of school entry, the Cross-Talking Cortical Computers of Mind in tertiary association areas are beginning to turn on for all children, whether or not their parents read to them at home (see Chapter 4). Second, although it is desirable that children be read to at home, teachers can still provide the missing home literacy experiences by reading to children in kindergarten and first grade. Clay (1985), a teacher/researcher, introduced BIG BOOK/little books in New Zealand and the idea has spread throughout the United States as a way of transporting one-parent-to-one-child lap reading into one-teacher-to-many-children reading in the classroom. The teacher holds the BIG BOOK, which has large print and attractive illustrations, at the front of the room for all to see; as she reads aloud, she points to the words. The children, who each have a smaller version of the teacher's book, also point to the words as they follow along. Many publishing companies offer collections of BIG BOOKS and little books and these collections are now found in many schools in North America. This kind of finger-point reading may stimulate implicit knowledge of visual word forms and explicit knowledge of correspondences between written and spoken words. A small set of these may be learned as wordspecific associations (Gough,Juel & Griffith 1992), which are sometimes erroneously referred to as sight words. Word-specific reading is not really just visual m both an

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abstract orthographic word form and an abstract phonological word form are interconnected in their internal representation in memory (see Chapter 5 and Berninger 1994). From repeated exposure to both the written and spoken forms of a small set of words, the brain may begin to abstract subword spelling-sound correspondences (see connectionist computation models in Chapter 5 and Fletcher-Flinn & Thompson 2000). However, the process of abstracting subword spelling-sound correspondences may go faster if these correspondences are also taught explicitly. Nevertheless, exposure to the written and spoken counterparts of real words is a critical ingredient in wiring the brain for reading. Opportunities to name these real words may call children's attention to the correspondences between the written and spoken word forms (Posner & McCandliss 1999).

Develop Linguistic Awareness The brain would be in chaotic overload if all knowledge of language were in consciousness all the time. From years of research it is well established that most knowledge of language is implicit and only becomes available in consciousness when it becomes the object of attention and reflection, leading to awareness (Mattingly 1972). Such awareness plays an important role in creating the connections that allow written word forms to be created, stored, and retrieved in memory for purposes of reading. As explained in Chapter 5, connections between written word forms and phonological word forms occur at both the whole-word level (e.g., anterior left fusiform gyms, Nobre et al. 1994) and the subword level (e.g., posterior left fusiform gyms, Nobre et al. 1994). The subword connections are especially important in learning to decode (pronounce) unknown words not already represented in memory. See Table 8.4 for examples ofhow attention and reflection can be directed to different parts of spoken word forms (phonological awareness), written word forms (orthographic awareness), and meaning units in either word form (morphological awareness). Psychologists use pseudowords (sometimes called nonwords or nonsense words) to study the process of applying subword connections to decoding because they are not known words already in memory. Our favorite pseudoword is Jabberwocky, which may be familiar to the reader who has read Lewis Carroll's Alice in Wonderland (Gardner 1960). Jabberwocky is really a pseudomorph because its stem is composed of a real word (jabber) + a pseudoword (wocky). However, the derivational suffix -y signaling a noun may be transformed to -s or-ed signaling a verb or-ier or-ial signaling an adjective or-ingly signaling an advert. Accordingly, this word seems like it could be a real word, even though it is not. Figure 8.1 shows the kinds of linguistic awareness the brain has to acquire in order to hook itself up for reading single words. The shaded half of the circles (layers) represents whole word units, and the half with vertical lines represents subword units, which may vary in size. Typically connections are formed between units of corresponding size across layers (Berninger 1994; Berninger, Abbott,

Building a Reading Brain Pedagogically TABLE 8.4

221

Linguistic Awareness of Parts of Word Forms

P h o n o l o g i c a l A w a r e n e s s - Analysis of sound patterns in spoken words

SYLLABLES

/jab (b)er wock y/

ONSET-RIME

/j-ab, (b)-er, w-ock, y/

PHONEMES

/j-a-b-er-w-o-k-y/

Orthographic Awareness m Analysis of letter patterns in written words

WHOLE W O R D - all constituent letters

jabberwocky

SPELLING U N I T - 1- and 2-letter units that correspond to a phoneme j-a-b(b)-er-w-o-ck-y WORD FAMILIES

j-ab, (b)-er, w-ock, y

Morphological Awareness m Analysis of meaning units in spoken or written words

INFLECTIONAL SUFFIX (number, tense, comparatives) jabberwocks jabberwocked jabberwockier PREFIX rp_rejabberwocky post]abberwocky DERIVATIONAL SUFFIX phonologically neutral/transparent

jabberwockial (short o in closed syllable)

phonological shift/complex

jabberwokingly (long o in silent e syllable)

Billingsley & Nagy 2001). Sometimes whole words are referred to as a lexical path and subwords as a sublexical path (Castles & Coltheart 1993; Jackson & Coltheart, 2001). O stands for orthographic awareness. At the whole word level, orthographic awareness involves all the constituent letters in a word, but at the subword level, orthographic awareness involves a subword unit, for example, a one-or two-letter spelling unit that corresponds to a p h o n e m e or a multiletter unit that stands for a spoken rime unit (explained next). P stands for phonological awareness. At the whole w o r d level, phonological awareness may involve receptive phonology for heard words or expressive phonology for named words, but at the subword level, it involves phonemes, syllables, and subsyllabic units ( o n s e t - - f i r s t p h o n e m e or blend in a syllable; and the rime m r e m a i n i n g part of the syllable). M stands for morphological awareness. At the w o r d level, morphological awareness involves the emergent meaning of the word based on the stem plus its affixes, but at the subword level, it involves the stem or root by itself, a prefix, or a suffix. W h e n a brain is beginning to read, orthographic and phonological awareness are important at the

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F I G U t L E 8.1 Word Learning: Circle of Connections. O = orthographic layer; P = phonological layer; M -- morphological layer; S -- syntactic layer (context processor). Dark indicates lexical unit. White with black lines indicates sublexical units. Reprinted by permission from Beminger, Abbott, BiUingsley, and Nagy in DYSLEXIA, FLUENCY, AND THE BRAIN, ed. Wolf. Copyright 9 2001 by York Press.

word and subword levels but so is morphological awareness m of inflectional suffixes that mark number (e.g., a final s), tense (e.g., a final ed), or comparison (e.g., a final -er or-est) (Nagy, Osborn, Winsor & O'Flahavan 1994). Of course, the Reading Brain also activates a large interconnected semantic network in implicit memory of word meanings. Morphology refers to the structural units that convey meaning within the word form; that is, the form of meaning, but not to all the general semantic features that may be represented for the meanings of a word.

Create Multiple Connections To read words, the brain draws on the interconnections among these word forms and their parts. As discussed in Chapter 5, the different word forms (orthographic,

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phonological, and semantic/morphological) are in separate, but partially overlapping, neural networks, mostly distributed throughout the left side of the cortex, but there also appear to be circuits for forging connections among them. Orthographic-phonological connections are forged very early in the normal reading process (Landerl, Frith & Wimmer 1996). The grand lexicon for orchestrating these connections may be BA 37, Wernicke's Wordschatz (treasure chest for words) (see Figure 5.1 and Brunswick et al. 1999). Research suggests that (a) awareness of these word forms and their parts increases the probability connections will form between them, (b) connections are likely to form between parts of corresponding unit size, and (c) multiple connections may form and provide redundant representations (Berninger 1994). As discussed in Chapter 3, redundancy (multiple ways of representing information and procedures) is a general principle in brain organization. Beginning readers who have redundant connections tend to be better readers (Berninger 1994; Berninger, Abbott, Vermeulen et al., in press). Connections may be forged through teaching strategies that encourage reflection; however, with practice, connections tend to become automatic. Another kind of evidence that brains have capability for forming multiple connections between the word forms in Figure 8.1 comes from the history of written languages (Taylor 1981). Written languages have been invented to represent whole word correspondences; for example, a logography in which pictures or abstract line drawings (characters) represent the meaning of a specific word. For example, the logograph for the word brain in hieroglyphics (in the front matter for Kandell Schwartz & Jessell 2000) is composed of four pictures (see Figure 8.2). Written languages have been invented to represent subword correspondences, too. Some of these are syllabaries that link written and spoken syllables; for example, modern Japanese. Other written languages were invented to represent how visual symbols represent the phonemes of the language; these are called alphabetic languages. English is an alphabetic language in which one-or two-letter spelling units correspond to a phoneme (Venezky 1970, 1999); orthographic-phonological code connections are very important in learning to read English, but are not the only code connections involved (see Chapter 5). Building connections between semantic codes via pictorial representations and orthographic codes is effective in teaching

FIGURE 8.2 Egyptian hieroglyphics for the word "brain." Reprinted with permission from Breasted, ed., Hieroglyphics from the Egyptian glossary, THE EDWIN SMITH SURGICAL PAPYRUS, p. 509. Copyright 9 1930The Universityof Chicago Press.

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beginning reading in a language other than one's first language (Biemiller & Siegel 1997).

Make Input and Output Units Visible in Multiple Ways As discussed in Chapter 5, teachers have access only to the input unit (written language stimuli) and output unit (oral language productions) of students' brains and not to the hidden units (internal association areas that form the connections between the input and output units) (Hinton & Sejnowski 1986). Teachers cannot directly manipulate the brain structures of nonreading (or reading) children. Teachers can, however, provide explicit instructional hints by making children more consciously aware of parts of written words (input unit) and parts of spoken words (output units); then the hidden units can abstract from repeated exposure to the same words, the connections (relationships) between them within the brain. That is, wiring the brain to read depends on both deductive approaches (teaching) and inductive approaches (learning) and a partnership between teachers and the learners' brain. Teachers facilitate the learning process, but learners self-program their brains in the association areas. The first instructional cue for making connections between orthographic representations and phonological representations explicit is naming letters. Adults name letters and children repeat what they say while they look at the letter form. In the process most children form connections between the letter code and the name code through paired associate learning. Rapid naming of letters may indicate that the connection between the orthographic code (letter) and the phonological code (name) has become automatic; see Wolf, Bowers, and Biddle (2000) for converging evidence across many studies that rate of letter naming predicts response to reading instruction. Moreover, letter naming provides a bridge to learning correspondences between letters and phonemes (Treiman 1993; Wagner et al. 1997). Letter names contain phonemes (see Table 8.5). As shown in Table 8.5, only five letters have a single phoneme in their name; the rest have two or more. The acts of naming (or writing; see Chapter 9) the letter and thinking about its name may help the brain attend to and become consciously aware of the component phonemes. O f all the letters, w, which has six phonemes if pronounced as three syllables or five phonemes if pronounced as two syllables, has the most potential for developing phonological awareness of phonemes. Other explicit instructional hints include the following: (a) alphabetic principle (naming letters in one-or two-letter spelling units and producing the corresponding phoneme); (b) onset-times (naming letter[s] in onset unit and producing its phoneme[s] and then naming letters in rime unit and pronouncing rime unit), and (c) name letters/say word (name all letters in whole word and pronounce word). Of these, none was more effective than alphabetic principle in learning taught words and transfer to untaught words (Berninger, Abbott, Brooksher et al. 2000) in first

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TABLE 8.5 Creating Orthographic-Phonological Connections through Naming Alphabet Letters Letter

Number of Phonemes

Letter

Number of Phonemes

a

1

n

2

b c d e f g h

2 2 2 1 2 2 2

o p q r s t u

1 2 3 1 2 2 2

i

1

v

2

j k

2 2

w x

6a, 5b 3

1

2

y

2

m

2

z

2

a3 syllables b2 syllables

graders at risk for reading problems. These instructional hints direct attention to visible units by naming them (Posner & McCandliss 1999; and Chapter 5). In providing instructional cues for alphabetic principle we focus on the most frequent spelling-phoneme correspondences of the language (see Fry, Polk & Foutoukidis 1984), and avoid habituation (see Chapter 4) by restricting this cueing to brief instructional periods (5 to 10 minutes daily). Other ways of directing attention to units of written language and making them visible (i.e., the object of attention) include the following, which we have also used in instructional research: (a) color coding the relevant unit, (b) underlining the relevant unit for the student, (c) having the student underline the relevant unit, and (d) asking the student to look carefully at a briefly displayed word and then, after it is covered up, report the letter(s) in designated position(s); for example, first, next to last, third and fourth (Berninger 1998a). The last technique requires that children hold the visual word form in phonological short-term memory while they make orthographic judgments that call attention to word parts. Ability to code both letter identity and letter position in the orthographic word form is necessary to apply alphabetic principle; that is, the phoneme that corresponds to each one- or two-letter spelling unit in an orthographic word form (Berninger 1998b; Berninger et al. 1991). For children who cannot selectively attend to spelling units in written words we have also given them two colored markers to write the spelling units in alternating colors and then produce the phoneme for each spelling unit and blend those phonemes.

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Another demonstrated effective way of directing attention to spelling-phoneme units across letter positions within a word is the word building curriculum developed by Beck and Hamilton (1996, 2000). Minimal pairing of words that differ by just one grapheme is used to direct children's attention to each letter position in the word as the child decodes the word (McCandliss, Beck, & Perfetti, in press). Directing attention to subword orthographic units corresponding to onsets, times, or phonemes has also been shown to be effective in learning to read words (Levy, 2001). Yet another effective approach for developing orthographic awareness of internal word parts in the process of learning to read words is to combine teacher feedback (naming the word) with blocking words to be learned by rime unit (word families) (Levy, 2001). Ways of directing attention to units of spoken language include (a) phonological awareness games in which children repeat a monosyllabic or polysyllabic word and then repeat it again without a designated phoneme or syllable and (b) explicit instruction in alphabetic principle (Adams, Foorman, Lundberg & Beeler 1988; Beminger 1998a; Blachman et al. 2000; Jenkins et al. 2000; O'Connor, NotariSyverson & Vadasy 1998; Perfetti 1985; Vandervelden & Siegel 1997; Williams, 1980). Adding articulatory feedback to phoneme awareness may also be helpful for beginning readers (Calfee et al. in press; Torgesen et al. 1999). Table 8.6 illustrates some of the ways we have made morphology units visible in instruction through the Jabberwocky poem in Lewis Carroll's Alice in Wonderland (Gardner 1960) and games using plausible but improbable words and pseudomorphs (Beminger, Abbott, Billingsley & Nagy 2001). Although morphological awareness develops TABLE 8.6

Creating a Jabberwocky Environment a

I am going to read you part of a famous poem from Alice in Wonderland: "Twasbrillig and the slithytoves did gyre and gimbelin the wabe: all mimsywere the borogroves, and the mome raths outgrabe." Even though you do not know what some of the words mean, it sounds like the poem could mean something. Why? Structure words only have meaning in context (e.g., auxiliaryverbs, conjunctions, articles, prepositions, pronouns): and, the, did, in, all, were Derivational suffixes convey grammaticalinformation (parts of speech): -ig,-y Inflectional suffixes convey grammaticalinformation without changingpart of speech (plurality, tense, comparatives):-s Morphological Play with Words plausible, improbable words:

I like the of the painting. (a) oranger (b) orangeness (c) orangeful (d) orangiest pseudomorphs:

She every afternoon: (a)jittle (b) jittlement (c) jittling (d) jittles

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more slowly and over a longer window of time, morphological awareness begins to emerge early in reading acquisition (Carlisle & Nomanbhoy 1993; Carlisle 1995). The first step in creating interconnections among word forms (and their parts) (Figure 8.1) is to draw attention to them in input and output units. However, consciously reflecting on these forms and parts (e.g., phonemes in the phonological form) may lead to strategies for developing metacognitive or executive control over applying these word forms and their parts to the decoding process (Cunningham 1990).

Teach Strategies for Self-Regulation As emphasized in Chapter 4, the frontal lobes that house the executive functions (see Chapter 5) are still myelinating at the stage of development when brains are beginning to read. Therefore, wiring the brain to read may require considerable external executive coordination; that is, other-regulation in the form of guided assistance (scaffolding) from adults who provide explicit instructional cues. Brains just beginning to read often cannot self-regulate the process on their own. However, one strategy that they can learn to help them begin to self-regulate is application of alphabetic principle, which helps them to sound out unknown words. Even words that cannot be decoded completely have some spelling-phoneme correspondences that are predictable based on alphabetic principle (Berninger 1998b; Ehri 1992b). Teachers should, therefore, not tell beginning readers that English is irregul a r - that sends a message of hopelessness (Why bother trying to learn to read words?). In fact, the brain is skilled at extracting, through induction, regularities in many domains, most ofwhich vary along a continuum as to how predictable a specific phenomenon is. For example, the brain handles one-to-many mappings very well in the phonological-semantic domain. For example, in English there are at least six meanings for "say," four meanings for "sad," seven meanings for "saw," nine meanings for "live" with a short-i, and fifteen for "live" with a long-i. No one claims that English is a deeply irregular language at the meaning level. If the brain can learn multiple mappings between phonology and semantics, it should also be able to abstract inductively one-to-many mappings between orthography and phonology or learn them deductively and apply these strategically. We believe that children can learn these orthographic-phonological mappings relatively painlessly ~f alphabetic principle, a term we prefer to phonics, is taught appropriately. We avoid the word phonics because that kind of instruction often encourages children to read by sounding out words letter by letter, when in fact, in English much of the predictability between letters and phonemes occurs in twoletter spelling units and not one-letter spelling units (Berninger 1998b). We also believe that children should be explicitly taught the alternations (set of optional

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phonemes) for each spelling unit, and that they should be taught all the most frequent spelling-phoneme correspondences in primary grade reading material (Fry et al. 1984) systematically and over a relatively short time interval (several months rather than several years) so that they can apply them strategically. Children should be told that English is predictable but that words vary in how predictable they are Oust like some people). Children should also be given guided assistance in applying alphabetic principle and its alternations to words that vary in spelling-phoneme predictability. See Berninger (1998a, 1998b) for further discussion of these pedagogical concepts. We also avoid the term phonics because it is associated with skill and drill out of context with no teaching for transfer to words and meaningful text. Tables 8.7 to 8.9 summarize how we have implemented these concepts instructionally in a way that avoids needless prolonged drill, but which provides explicit instructional cues and strategies for transfer of alphabetic principle to word and text context for the purpose of reading for meaning. This approach to teaching alphabetic principle and its applications draws on the attentional system, behavioral and cognitive pathways of the memory system, and the executive system (see Chapter 5). TABLE 8.7 Teaching to All Levels o f Language in Supplementary R e a d i n g Instruction (Pull-Out from R e g u l a r Program for Children Most At Risk) First Grade Model a (20 minutes twice a week, one tutor per pair of children for four months) SUBWORD

Teach alphabetic principle b

WORD

Apply alphabetic principle to mono~llabic words that vary in spelling-phoneme predictabilityc' high predictabilityc

TEXT

can /~

moderate predlctablhty '

wh i t e

low predictabilityc

wr o ng

9

.

.

c e

Joint reading of engaging beginning textsf with progessively less modeling and more guided assistance from the teacher: parallel reading--tutor and child read in unison predicted reading m tutor and child read together but every five words tutor stops and child predicts next word assisted reading-- child reads alone but when child cannot decode a word, tutor models application of alphabetic principle to decoding Second Grade Modelg (20 minutes twice a week, one tutor per child for four months)

SUBWORD

Teach alphabetic principle b'h

WORD

14 polysyllabic words selected from text to be read later in session with two exemplar words for each of six syllable types/ and taught this way: Tutor says word, child says word syllable by syllable and then counts phonemes in each syllable using colored tokens, child categorizes each syllable by type, child reads written word.

(continues)

Building a R e a d i n g Brain Pedagogically TABLE 8.7

229

(continued)

TEXT

Read six engaging texts/ (four sessions with each) with tutor providing guided assistance in applying alphabetic principle ~nd syllable patterns and in summarizing or answering questions about text . Third Grade Model / (20 minutes twice a week, one tutor per child pair for four months) b

SUBWORD

Teach alphabetic principle deductively

WORD

Apply alphabetic principle to content/structure words n

TEXT

Read six engaging texts (4 sessions each)~ with teacher cueing all levels of language (word, sentence, text) and CORE p - connect, organize, reflect, extend

Teach alphabetic principle inductively through word sorts m

abased on Berninger, Abbott, Brooksher et al. (2000) bUsed Talking Letters for teaching alphabetic principle and its alternations (Berninger 1998b). Tutor named letter(s) in spelling unit, named pictured word with target phoneme, and produced target phoneme for spelling unit, while pointing to the spelling unit. Child imitated sequence. CSee Berninger (1998b) for the distinction among high, moderate, and low spelling-phoneme correspondence. Each underlined spelling unit is color coded in a different color. dEflective in learning taught and transfer words. eSpelling units containing a long vowel and a silent-e are in the same color. fSee Berninger, Abbott, Brooksher et al. (2000) for titles of books. gBased on Berninger, Abbott, Vermeulen et al. (in press), continuing tutoring for children not brought up to grade level in first grade. hlLeviewed spelling-phoneme correspondences from first grade and learned 13 additional ones. ZSyllable types included closed, open, silent-e, vowel team, r-controlled, and -le, and were illustrated with visual symbols for vowels and consonants on Speaking Syllable cards (Berninger 1998b); spelling units corresponding to phonemes were color coded in written words. JSee Berninger, Abbott, Vermeulen et al. (in press) for titles of books. kPalincsar and Brown (1984). /Based on proposed design experiment in Berninger, Vermeulen et al. (2001) to combine all experimental treatments. mBear, Ivernizzi, Templeton, and Johnston (2000). nContent words are nouns and verbs that have meaning independent of sentence context, but structure words only have meaning in sentence context (prepositions, pronouns, conjunctions, and articles); see Table 8.6. ~ Berninger, Vermeulen et al. (2001) for exact titles. PCalfee and Patrick (1995).

TABLE 8.8 M o d e l for Individual Tutorial to Bring Children U p to Grade Level in R e a d i n g and to Begin to T h i n k o f Themselves as Readers a'b'c (Pullout from R e g u l a r Program for O n e - H o u r Twice W e e k l y for Disabled Readers SUBWORD

Orthographic awareness of whole written word, single letters, and d letter clusters Phonological awareness of syllables and phonemes in spoken words `/

(continues)

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Brain Literacy for Educators and Psychologists TABLE 8.8

(continued)

WORD

Multiple orthographic-phonological connections: Alphabetic principle e Word familiesf Selective reminding in word-specific learningf

TEXT

Decodable textg Oral reading and rereading for fluency; graphing timesf'g Comprehension monitoring Question asking and answering Summarization and story retells

aBased on Abbott, Reed, Abbott, and Berninger (1997). bReading Lessons (Berninger 1998a). CUsed for first and second graders and older, nonreaders in upper elementary grades. dOrthographic Awareness Lessons and Phonological Awareness Lessons (Berninger 1998a). eTalking Letters that teaches alphabetic principle and its alternations (Berninger 1998b). fReading Lessons in Berninger (1998a). gJackson (1989).

TABLE 8.9 First 5 minutes:

CLUB SCHEDULE FOR AT-RISK SECOND GRADERS a Children give password to enter room and share fiddles and jokes b for the "YOU GOT TO LAUGH" contest; folders passed out.

Next 15 minutes: Readers' Warm-Up ORTHOGRAPHIC AWARENESS (letters on automatic pilot) PHONOLOGICAL AWMkENESS (sound games) ORTHOGRAPHIC-PHONOLOGICAL CONNECTIONS (alphabetic principle and its alternations taught with accuracy and rate instructions and applied to Jabberwocky words b) Next 25 minutes: Readers' Work Early Success Readers c for creating routines for purposeful rereading d STORY 1 (NEW Story) DAY 1 Teacher modeling (Teacher reads to children who finger point to words as they follow along.) DAY 2 Choral reading (Teacher and children read in unison.) DAY 3 Home-School Connection (Children take home selection from Story 1 to read to parent over week end) STORY 2 (Story FROM PRIORWEEK) DAY 4 Buddy reading (Children take turns reading to a partner)

(continues)

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231

TABLE 8.9 (continued) DAY 5 Reading to teacher (Individuals read to teacher who takes a running record of oral reading accuracy) Next 10 minutes: Readers' Word Playb'e Last 5 minutes: Clean-up aExtended day model of before or after school clubs that met twice a week for one hour at a time; for students identified by school district as at risk for passing assessmentfor state standards. bTo develop affective disposition for word play. CCooper et al. (1997). dvcithin a two-week block children reread each story five times in different ways. In each session childen read one old story (Day 4 or 5) and one new story (Day 1 or 2). eMOMMY LONGWORDS CONTEST on a variable reinforcement schedule with DR. MRS. SEUSS-GOOSE, wearing a Dr. Seuss top hat and bow tie and a Mother Goose feather mask and wings, showing up with treats for children who brought in long words that met criterion of week (e.g., four-syllable word with accent on second syllable) and board games such as BINGO with structure words.

Practice Oral Reading until Automatic and Fluent As discussed in Chapter 5, the novice reading brain initially uses controlled strategies in the Cognitive Pathway (circuits probably involving left frontal areas) for w o r d recognition, but, as word recognition becomes automatic, transfers this process to the Behavioral Pathway (circuits probably involving striatum and cerebellum). Repeated reading (i.e., practice in oral reading) can facilitate automatization of w o r d recognition (Meyer & Felton 1999), which may lead to fluent reading of text. Many different instructional approaches may achieve this goal of automatizing w o r d reading. O n e way is to use a basal text with a set o f words that occurs over and over in different stories so that children are repeatedly exposed to specific words. Another way is to reread for many purposes a literature-based reading text, which does not control the n u m b e r of times a word appears but tends to have more cognitively engaging reading material. T h e same story can be reread several times for varied purposes with a teacher (Cooper, Pikulski & Au 1997; also see Readers' W o r k in Table 8.11) or trained c o m m u n i t y volunteer (Blachowicz, Fisher & Moskal 2000). Yet another way is to practice naming single words repeatedly with flash cards (Tan & Nicholson 1997) or selective reminding in which only missed words are repeatedly named (Berninger 1998a). Still another way is to prepractice single words before encountering them in text (Levy, Abello & Lysynchuk 1997). Finally, rate of text presentation can be increased, which leads to faster w o r d processing and helps to overcome temporal constraints in working m e m o r y (Breznitz 1987). By the end of first grade, normal beginning readers are showing evidence ofautomaticity in w o r d recognition (Stanovich, C u n n i n g h a m & West 1981). Children whose rate of w o r d reading or decoding lags behind after a year of formal reading instruction are at risk for reading disability ( C o m p t o n & Carlisle 1994).

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However, more than automaticity of single word reading is needed to transfer control of word identification to the Behavioral Pathway. Not only must identification of single words become automatic but also the syntax of text must be connected with the prosody (musical melody of spoken language) (Strecker, P, oser & Martinez 1998; and Table 5.2). Thus, oral reading practice should target not only single words but also connected text (Faulkner & Levy 1999). The fluency that emerges from practicing text at both the word and text level improves reading comprehension by bypassing the temporal constraints in working memory (Breznitz 1987), thereby freeing up more resources for reading comprehension. At the same time, in reciprocal fashion, the better the brain comprehends text, the more fluent the oral reading is. Thus, oral reading fluency enables reading comprehension, but reading comprehension enables reading fluency (Strecker et al. 1998), demonstrating that the components of the functional reading system learn to function together.

Guide Comprehension If the brain cannot identify written words (transform the orthographic form into phonological and semantic representations) efficiently, reading comprehension will be compromised (Perfetti 1985). However, although word identification is necessary, it is not sufficient (Oakhill Cain & Yuill 1998). Comprehension also requires that the brain make inferences beyond what is stated in text and create mental models to integrate the information represented in text (Oakhill et al. 1998). Instruction can aid both of these requirements for reading comprehension. Explicit strategies for reading comprehension should be taught (Bos, Anders, Filip & Jaffe 1989). Dialogue is important in strategy instruction (Palincsar 1986). Calfee and Patrick's (1995) Connect, Organize, Reflect, and Extend (CORE) model for explicit comprehension instruction directs readers' attention to their background knowledge in long-term memory and encourages use of that in going beyond what is stated to construct a meaningful representation of the text. Even if children do not have the necessary background of experience in long-term memory, teachers can provide that background knowledge just before reading the selection (Oakhill et al. 1998). To assist in the integration process, teachers can cue comprehension at each level of language by asking questions that target children's attention to words, sentences, and text (Berninger, Vermeulen et al. 2001). This kind of levels-of-language cueing may help the novice reader go beyond reading word by word to using syntactic and discourse structure in text to guide the comprehension process (Cromer 1970). Directing attention to sentences may aid interpretation of syntax, which is necessary for good reading comprehension (Nation & Snowling 2000; Tunmer, Nesdale & Wright 1987). Asking children to summarize or retell stories may result in greater integration at the discourse level, if children are cued to mark the causal links between events (Oakhill & Yuill 1996; Oakhill et al. 1998).

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Instill Positive Affect toward Reading The limbic system is highly interconnected with the Cognitive Pathway (see Chapter 3). Brains that derive pleasure from reading are more likely to practice reading and transfer the word recognition component to the automatic Behavioral Pathway, thus freeing up limited working memory resources for comprehension. Brains that experience anxiety or frustration with reading are more likely to avoid or resist it. Instructional activities should be designed to maximize success from the very beginning of learning to read so that reading is engaged in willingly and not avoided.

Systems Approach to Beginning Reading A cardinal instructional design principle in hooking up a reading brain is to direct instruction to all necessary components for a functional reading system: linguistic awareness (Mattingly 1972) (Figure 8.1), word-specific reading and alphabetic principle for phonological decoding (Gough et al. 1992), oral reading of text for accuracy and fluency, and comprehension (of words, sentence, and discourse). It follows that instruction should aim at all levels of language and teach low-level skills for word identification and high-level skills for constructing meaning close in time within the same instructional session. This approach to packaging instructional components overcomes temporal constraints in working memory and maximizes transfer of what is learned about word recognition--the tool of reading, to extracting and constructing meaning m t h e overarching goal of reading. Word identification skills should be taught until they are automatic to free up limited resources in working memory for higher-level comprehension goals (LaBerge & Samuels 1974; Perfetti 1985).

P E D A G O G Y FOlK C R E A T I N G A D E V E L O P I N G READ ING BRAIN

Reading Material

Word origin. Beginning in the intermediate grades, reading material changes from predominately Anglo-Saxon words to a mix of words of varied origin. The English language, like the population that speaks the language, is rich because of the large number of i m m i g r a n t s - both words and people (Venezky 1999). Intermediate and high school texts contain a large number of word immigrants from the Romance layer (from Latin and French) and the Greek layer of the language (Balmuth 1992; Henry 1990). The brain needs to be prepared for this transition from Anglo-Saxon to Latinate words for three reasons. The first reason is that the relationship between spoken and written words changes. Beeler (1988) observed

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that we speak English (Anglo-Saxon layer) but read and write Latin. As children increasingly confront Latinate words in their reading material, they are less able to rely on phonological and morphological representations of spoken words already existing in memory. They need to use the meaning and phonology that can be extracted from the written versions of these words to form the interconnections among the orthographic, phonological, and morphological layers of the language (see Figure 8.1). For example, which of the following pseudowords exhibit Germanic (Anglo-Saxon) and which exhibit Latinate linguistic features? moce, tweb, anotic, blop, quatid, exotic, lugate, squide, rebicy, adibity, sprood, jittle, absonine, efflition, queddle, thackle, odational, disqueptage, prosuptive, transuosis See Table 8.10 for the correct categorization of the pseudowords according to word-origin linguistic features that differentiate words from the Anglo-Saxon and Latinate layers. The second reason for preparing the brain for the transition to a different kind of reading material is that some of the relationships between orthography and phonology in the alphabetic principle change as a function of word origin. For example, the p h o n e m e / f / i s spelled f for words of Anglo-Saxon origin but ph for words of Greek origin; the phoneme/sh/is spelled sh for words of Anglo-Saxon origin but ti, si, or ci for words of Romance origin. Words from the Romance and Greek layers tend to have many more syllables than those of Anglo-Saxon origin, and some of the Romance syllables have reduced vowels (schwas) whose spelling must be memorized for specific word contexts (e.g., ambassador, telescope). See Table 8.10. The third reason for preparing for the transition is that morphemes have different patterns of relationships to each other for words of different word origin (Balmuth 1992; Henry 1990). For example, morphemes attach to roots in Latinate words (e.g., presentation) but may combine to form Greek words (psych + ology). Without this preparation for a change in word structure related to word origin, the brain may be perplexed. The principles that worked for the Anglo-Saxon words do not necessarily transfer to immigrant words that constitute a large number of the words in reading material at and above the fourth grade level. See Table 8.11 for instructional approach that incorporated exphcit instruction in word origin. Genre-specific text-structure. Students also need explicit instruction in strategies for reading different kinds of genre as they transition from primary to intermediate grade reading. Primary grade reading material tends to include more narrative than expository text, whereas intermediate grade material tends to reverse the relative mix, with expository texts predominating. Content material. Students also need explicit instruction in vocubulary, text structure, and genre-specific reading strategies for textbooks in each content area of the curriculum (e.g., Math, Science, and Social Studies).

Building a Reading Brain Pedagogically TABLE 8.10

235

Pseudowords from Different Layers of the Language a

Germanic Pseudowords

Latinate Pseudowords

moce

anotic

tweb

quatid

blop

exoric

squide

lugate

sprood

rebicy

jittle

adibity

queddle

absonine

thaclde

effliction odational disqueptage prosuptive transquosis

aThe authors thank William Nagy for constructing these. Note that the Germanic pseudowords are one or two syllables, whereas the Latinate pseudowords are three or more syllables and contain schwa syllables (reduced vowel) for which contextspecific spelling must be memorized. Whereas the accent is on first syllable in the Anglo-Saxon layer, it is often on the second syllable in the Latinate layer. TABLE 8.11 Teaching to All Levels of Language and Genetically Constrained Reading Components in Reading/Science Workshops for Older Students with Dyslexia a SUBWORD Phonological short-term memory

Always teach spoken form of word first and then written form of word Syllable awareness: counting syllables Phoneme awareness: counting phonemes

Efficiency of alphabetic principle

Chanting Talking Lettersc

Multiple strategies for decoding

Phonemes for 1-or 2-letters Onset-rime (word families and analogies) Morphological units (Anglo-Saxon, Romance, Greek layers of word origind)

WORD

Reading bees

TEXT

Science content

SCIENCE EXPERIMENTS

Cognitive engagement

abased on Berninger (2000b); grades 4--6 bWords selected from science texts to be read later in session to represent morpho-phonological patterns in Anglo-Saxon, Romance (Latin), and Greek layers of word origin based on Henry (1990). Alphabetic principle and its alternations (Berninger 1998b). '/Henry (1990). C

.

.

.

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Brain Literacy for Educators and Psychologists

Teach Linguistic Awareness and Word Study The developing brain continues to need instruction aimed at linguistic awareness and word study, even when readers are well beyond phoneme awareness and phonics (Invernizzi, Abouzeid & Bloodgood 1997). Of the kinds of linguistic awareness in Figure 8.1 and Table 8.4, morphological awareness and morphological-phonological awareness increase during the intermediate grades (Mahoney, Singson, & Mann 2000; Singson, Mahoney & Mann 2000). Furthermore, readers learn to deal with morphological transformations that involve not only simple phonological shifts in which phonology remains constant (e.g., happy to happiness) but also that involve complex phonological shifts (e.g., nation to national) (Carlisle 1995). Tyler and Nagy (1989) recommended three kinds of instructional questions to develop linguistic awareness of transformations of roots. First, does meaning and part of speech stay the same? (e.g., like and dislike; act and actor). Second, does the sound stay the same? (e.g., love and lovely; magic and magician). Third, does the spelling stay the same? (e.g., sign and signal; ready and readiness). In addition to linguistic awareness instruction, conscious word study is essential. For students who have not yet mastered alphabetic principle, this word study needs to include a novel approach to applying decoding to wide reading of connected text (Stahl 1998). Phonological training transfers to reading unknown words, whereas word-specific training transfers to reading familiar words (Lovett et al. 1994). Gaskins (2000) has found that encouraging students to fully analyze written words helps them apply their knowledge of spelling-sound relationships to words in text. Invernizzi et al. (1997) showed the utility of word sorts for inductive abstraction of similarities and differences among words on a variety of dimensions ~ spelling, meaning, word derivation, morphological patterns, spelling-meaning connections, and spelling-grammar connections. Students should be guided in applying such inductive learning about word dimensions to fully analyzing written words they encounter. Vocabulary for specific content areas of the curriculum should be taught at all stages of reading development (Anders & Bos 1984). Humor and play with language are important pedagogical tools for developing morphological awareness (Mahoney & Mann 1992).

Transition to Silent Reading Developing brains continue to need assisted practice to improve reading rate (Shany & Biemiller 1995). However, as word recognition becomes increasingly automatic (fast, direct retrieval from implicit memory storage), rate of oral reading may slow down the reading comprehension process. Oral reading takes longer than silent reading because of the additional oral-motor production requirement (see Chapter 5). Temporal constraints in working memory may interfere with reading comprehension if the oral-motor output takes too long, and space constraints in working

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memory may interfere with working memory if the oral motor output takes too much workspace. Thus, for generations, teachers have been telling beginning readers--once they become fluent in oral reading--to start reading with their "inside voices." Skilled readers who wish to test the wisdom of this transition from oral reading to silent reading might record and compare how many words they can read orally in a minute and read silently in a minute. For the most reliable evidence, choose ten passages with an equal number of words, read half of them orally first and half of them silently first, and then average the number of words read per minute across five different passages for each mode of reading. Once the transition to silent reading is made, reading rate should be monitored in reference to the purpose of silent reading--comprehension (Gibson & Levin 1975), which should not be sacrificed for the purpose of speed. For example, on a daily basis children might graph the time taken to read passages of an equivalent number of words and percent of comprehension questions accurately answered. To make clear that fast reading at the expense of understanding what is read is not a good idea, graphs should be compared to make sure that, as speed increases, comprehension remains at acceptable levels, and that speed is adjusted appropriately if comprehension suffers.

Explicit Instruction in Comprehension Word and Sentence Levels

Morphological awareness contributes greatly to reading comprehension in the developing reading brain (Carlisle 2000). Nagy et al. (1994) emphasized that instructional cues at this developmental level should explicitly direct attention to prefixes and derivational suffixes that convey grammatical information about parts of speech. Thus, morphological awareness also feeds into syntactic awareness (the S in Figure 8.1), which is important for reading comprehension (Nation & Snowling 2000; Tunmer et al. 1987). In addition to the morphological awareness activities, systematic vocabulary instruction is needed (Graves 2000). Graves recommended a four-part program: wide reading to increase exposure to a variety of word meanings; explicit instruction in specific words relevant to new concepts (as we have tried to do for neuroanatomical terms in this book); explicit instruction in word-learning strategies (e.g., using context to infer meaning); and fostering word consciousness through a favorable cognitive and affective disposition to words. Discourse Level

Developing reading brains benefit from explicit instruction in strategies for extracting content and structure at the discourse level (Lovett et al. 1996). They must

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also learn to integrate information in increasingly larger stretches of discourse and create mental models of the text in m e m o r y (Oakhill et al. 1998). Both written summarizations and oral collaborative discussions following silent reading contribute to achieving this goal. Written summarizations generate the schema for integrating information in the mental model (Wittrock 1974, 1990). Oral discussions in which dilemmas are posed and resolved collaboratively, by presenting evidence to support positions, encourage integration of background knowledge with information in text (Anderson, Chinn, Chang, Waggoner & Yi 1997; Anderson, Chinn, Waggoner & N g u y e n 1998). In the process of written and oral discourse analysis, the Reading Brain integrates the two fundamental cognitive components in reading comprehension identified by Kintsch ( 1 9 9 8 ) m text-based knowledge and situation-based knowledge (background knowledge + inferential thinking). Table 8.12 summarizes a teaching plan that integrates teaching content reading with other components of reading instruction for the developing reading brain. TABLE 8.12

Directed Reading Activity for Reading in the Content Areas a

I. Teach specialized vocabulary words for topic A. Spelling B. Pronunciation C. Morphological structure (roots and affixes and word origins) D. Meaningincluding precise uses of term, synonyms, and contrasts with similar words II. Pre-tkeading Preparation A. Discuss background knowledge relevant to text to be read B. Explain genre-specific structures of text (e.g., signals for chronological order in history texts, narrative structure in novels, syntax of word problems in math that provide clues to solving them) C. Provideadvanced organizers and strategies for self-regulation of the readingprocess D. Establishpurpose for reading III. Silent Reading of Text IV. Oral Discussion b of Text A. Justify answers to questions or position on basis of sentences that are read orally or reread silently B. Use teacher-guided and reciprocal teaching strategiesc to develop comprehension skills V. Reading-Writing Connections: Written activities that draw upon the text(s) read to

achieve a specific goal VI. Explicit Instruction in Executive Coordination of Listening, Reading, and Writing

A. Strategiesfor note-taking

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TABLE 8.12 (continued) B. Studyskillsfor test-taking C. Strategiesfor report writing D. Strategiesfor oral presentations aSee Table 7.2 in Beminger (1994)for a Directed ReadingActivitythat can be usedwith basal readers in elementaryschool readingprograms. bAnderson et al. (1997, 1998). Cpalincsarand Brown (1984).

Strategies for Transition from Other- to Self-regulation Another important reorganization of the developing reading brain is the increasing progression from other-regulation (teacher-guided) to self-regulation of the reading process. As explained in Chapter 4, during middle childhood the frontal structures supporting executive control processes are myelinating. Thus, the brain at this developmental stage is increasingly able to benefit from instructional cueing that prompts it to develop self-regulation strategies. On the one hand, teacher modeling of components of the comprehension process by thinking aloud is an instructional tool for showing students how to think on their own (Kucan & Beck 1997) and how to self-monitor their own comprehension processes (Oakhill & Yuill 1996). On the other hand, dialogue may also be an effective tool for achieving the transition from other- to self-regulation (Anderson et al. 1997, 1998; Kucan & Beck 1997; Palincsar & Brown 1984; Wong, 2000). For example, in reciprocal teaching (Palincsar & Brown 1984; Palincsar 1986), the teacher first models dialogue with a small group of students. Four executive control processes are modeled in an interactive setting: summarizing text, questioning and answering, seeking clarification of a portion of text, and making predictions about text. Then the teaching and learning roles switch as students take on the role of teacher and lead the discussion. This collaborative problem-solving approach is at the core of the story discussion technique in which the teacher relinquishes the instructional role (Anderson et al. 1997, 1998): The teacher poses a dilemma, prompts, and encourages, but transfers control from himor herself to the learners, who take positions, offer evidence, listen to each others' positions, weigh evidence, offer arguments and counter-arguments, and make decisions about whether to change their positions. Explicit strategy instruction may also be effective in this transition from other- to self-regulation. Effective strategies may be specific to reading or more generally directed to executive control processes. For example, Lovett, Lacerenza et al. (2000) and Lovett, Steinbach et al. (2000) demonstrated the value of both phonologically-based and metacognitive strategies in regulating the reading process. Carlson and Das (1997) showed that teaching general executive strategies for planning and attentional resource allocation that are not specific to reading may

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transfer to improved word reading skills, even though word reading is not explicitly taught. Students may also benefit from learning executive control strategies for keeping track of discourse structures in working memory. For example, for readers to construct a mental model of narrative text in comprehending stories, they need to keep track of event structures (Oakhill et al. 1998), and in comprehending expository text they need to keep track of discourse schema (e.g., for tying arguments and evidence together).

Reading-Writing Connections In the developing brain, Language by Eye and Language by Hand learn to work cooperatively with one another. For example, students write about single sources they read; for example, a book report about a novel or a written assignment based on a science or social studies text. They also learn they have to integrate information they have read from multiple sources in writing a report. Language by Ear also has to learn to work with Language by Hand and by Eye. For example, students listen to lectures or collaborative problem-solving discussions and take notes, which may then be read at a later time to study for a test or prepare a report. Language by Mouth also has to learn to work with the other language systems during collaborative problem solving discussions and formal oral presentations in front of a group. The executive control system that has to manage all this cross-talk and coordination is kept very busy. See Nelson and Calfee (1998) for a historical overview of pedagogical techniques for fostering reading-writing connections.

Affect and Habit Developing brains continue to rely on the limbic system to maintain interest, motivation, and emotional disposition toward reading. Gaskins (2000) offered a number of effective approaches for motivating older students to continue to work on reading skills. These included ensuring a sense of success and intellectual competence (e.g., through daily feedback on accomplishments and progress), cognitive engagement (content matter and words that are intellectually stimulating at their level of cognitive development), a sense of control over the learning process (e.g., through choice of reading material), and a sense of belonging to a community of learners (e.g., through collaborative group activities). IMPLEMENTING RESEARCH-SUPPORTED INSTRUCTIONAL DESIGN PRINCIPLES Tables 8.7 through 8.9 and 8.11 to 8.12 illustrate how we implemented the recommended approach of teaching close in time to all levels of language and

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both low-level and high-level components of the reading system--so that these become functionally interconnected in working memory. Table 8.7 summarizes the instructional components for supplementary, small-group instruction in reading for first graders, second graders, and third graders identified for early intervention on the basis of teacher referral and standardized tests that indicated they were at risk. Half the children were brought up to grade level in the four-month first grade intervention. The following year those children were monitored and maintained their gains during second grade. The half of the sample that was still not up to grade level received continuing tutoring during second grade (Table 8.8) and read in the average range by the end of the year (Berninger, Abbott, Vermeulen et al., 2002). The third grade sample was a new one. Table 8.9 outlines the instructional components in an extended day model developed for second-grade students at-risk for passing state assessment standards in reading. Table 8.11 describes a group intervention for upper elementary students with dyslexia, based on processes our research is showing are the most genetically constrained. Table 8.12 offers an example of an instructional approach for delivering directed reading instruction in content subjects for students in fourth through twelfth grade. The directed reading activity has evolved from the collective experience of many teachers, which is a form of fieldbased research, in teaching reading. Table 8.12 is included because students require explicit instruction in reading for the content areas throughout schooling. Collectively, Tables 8.7-8.9 and 8.11 to 8.12 illustrate the instructional design principle that there is more than one way to organize instruction to implement research on reading instruction, all of which are consistent with current understanding of functional brain systems. Nor are these tables exhaustive of the possibilities; they are shared to inspire teachers to develop their own ways of packaging instructional components aimed at the necessary components of a functional reading system and then to evaluate student learning based on that implementation. Just as students learn in alternative ways, there is more than one effective way to implement research-supported instructional practices.

F R O M DEBATES T O WARS T O C O L L A B O R A T I V E PROBLEM SOLVING From the beginning of free, public education in the nineteenth century, American educators argued about how to teach reading and tried a variety of approaches to teaching word reading (Aaron & Joshi, 1992). In the mid twentieth century, Chall (1996) urged that the debate be resolved on the basis of scientific research in which alternative hypotheses are empirically tested. Science did not resolve the dispute before the Great Debate (Chall 1996) broke out into a Great War over Whole Language versus Phonics. As with most wars, the issues cannot be reduced to good versus evil. On the one hand, one issue at stake was whether beginning reading should emphasize meaning, which is motivating for wanting to read (see limbic

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system in Chapter 5), or emphasize the code, which research shows is effective for learning word reading (e.g., left posterior occipital, temporal, parietal regions in Figure 5.1; also see Chapter 5). On the other hand, another issue was whether scientific research is relevant to instructional practice in real world classrooms that are messy environments compared to the controlled conditions of research laboratories. Some educational researchers lost faith in the relevance of scientific research. We write at a time of change. Researchers and practitioners (school systems and teachers) are beginning to develop partnerships to find more effective ways to teach literacy in an increasingly technological society with a global economy (see Chapter 11). There is growing recognition among practitioners that scientific research has something to offer in helping schools improve accountability for student achievement. In the second chapter we recalled how the dispute between the brain hypothesis and heart hypothesis raged for 2000 years and was not resolved until the technology permitted the question to be put to scientific test. Fortunately, the dispute over reading instruction does not seem to be lasting that long and appears to be resolving in about 150 years. A recent review of research on classroom reading instruction by a government panel showed that effective beginning reading instruction should include instruction aimed at linguistic awareness, word recognition and phonological decoding, reading fluency, and comprehension (National Reading Panel 2000). This conclusion, Which supports both a code- and meaning-based approach to reading instruction, is consistent with the brain-based approach in this chapter that emphasizes a system of interrelated components in reading. Undoubtedly, as scientific research on reading instruction continues, more of the details on effective instructional approaches will be added. We believe that research offers two insights that are relevant as we enter an era of implementing evidence-based instructional approaches in the classroom. The first insight is that students do vary in how easily they abstract general principles, relationships, and patterns inductively, and in how much they require that these be brought to their attention through explicit instructional cues so that they can apply them deductively. Students who have ample home literacy experiences and excel at inductive learning can learn reasonably well from reading instruction that does not provide much explicit word work and capitalizes on implicit word knowledge. However, those who rely exclusively on school instruction for learning to read will struggle if explicit word work is not provided, and their struggle will be magnified if they learn better deductively than inductively. By providing both explicit word work and meaning based instruction, as we have in our research, within the same instructional session, we showed that the instructional needs of diverse students can be met m regardless of how much home literacy experiences they have had or their profile of cognitive, linguistic, and neurodevelopmental processes relevant to literacy learning. Some of the arguments fueling controversy over methods of reading instruction may not have been asking the fight question m i t is not a matter of meaning versus decoding. The question is how best, from a classroom system perspective and a brain system perspective, to

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design and implement instruction aimed at these and other relevant processes (see Part IV). The second insight is that, just as we cannot remember our early experiences because the brain structures for conscious retrieval from memory were not myelinated then (see Chapter 5), teachers, who have automatized their word recognition, may no longer have access to their earlier awareness of word forms and parts of word forms before the codes for various word forms and their parts were connected in their brains (Figure 8.1 and Table 8.4). They have implicit but not explicit knowledge of these word forms and parts, which are now in circuits for automatic processes in their own brains. Yet, to provide instructional hints for beginning and developing readers, teachers need to regain this explicit awareness of word forms and their parts. To deal with this issue of disconnecting well integrated orthographic-phonological mappings in skilled readers, linguists devised a novel transcription system for the sounds of the language that uses a different set of visual symbols than conventional orthography and can be applied across orthographies to any spoken language (Venezky 1999). Thus, researchers might investigate how teacher trainers can help future teachers, whose word recognition is automatized, regain linguistic awareness of the word forms and their parts that are needed to provide for beginning and developing readers' instructional hints to help wire the word reading component of their functional reading systems. The fundamental issue is not that English is an irregular language, but that preservice teachers have not been given sufficient training in how to provide instructional hints to help beginning and developing readers find the regularities that do exist. At the same time, recognition should be given to the important advances made over the last two decades of the twentieth century in teaching as a profession. The meaning-based movement in reading instruction was about more than a method of teaching reading. It was also about teachers having a voice in defining how they go about doing their professional work. All the scientific research in the world will not change the quality of reading instruction in classrooms unless teachers become part of the process of investigating how best to implement scientific research findings in real classrooms as teachers go about their professional work (Calfee, Norman & Wilson, in press). We return to this important point in Chapter 12 with a plea for more teacher-researcher partnerships, in the spirit of collaborative problem-solving discussions (Anderson et al. 1997, 1998). We believe that teachers have an important contribution to make to behavioral research on the reading acquisition process. Full understanding of how the brain works depends on an integration of the neuroanatomical, computational, and psychological/behavioral planes of analysis (Mesulam 1990). Brain imaging and computational modeling studies might be validated against beginning and developing readers' individual growth curves (e.g., Beminger, Abbott, Vermeulen et al., in press. Compton, 2000) in response to different instructional protocols in order to gain a fuller understanding of how the brain is wired in learning to read.

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RECOMMENDED READING

History of Reading Instruction Aaron, P.G. &Joshi, R. M. 1992. Reading problems, Consultation and remediation. New York: Guilford.

Balanced Approach to Reading Instruction Thompson, G. B. & Nicholson, T., eds. 1999. Learning to read. Beyond phonics and whole language. Newark: DE: International Reading Association and NY: Teachers College Press.

Language Processes in Reading Moats, L. 2000. Speech to print. Language essentialsfor teachers. Baltimore: Paul H. Brookes. Mahoney, D. & Mann, V. 1992. Using children's humor to clarify the relationship between linguistic awareness and early reading ability. Cognition, 45: 163-186.

Layers of Word Origin Balmuth, M. 1992. The roots of phonics. A historical introduction. Baltimore: York Press.

Phonological Awareness Adams, M. 1990. Beginning to read. Thinking and learning about print. Cambridge, MA: MIT Press.

Orthographic-Phonological Connections Berninger, V., Abbott, R., Brooksher, R., Lemos, Z., Ogier, S., Zook, D. & Mostafapour, E. 2000. A connectionist approach to making the predictability of English orthography explicit to at-risk beginning readers: Evidence for alternative, effective strategies. Developmental Neuropsychology. 17: 241-271.

Morphology Beeler, D. 1988. Book of roots. A full study of ourfamilies of words. Homewood, IL: Union Representative. Carlisle, J. 1995. Morphological awareness and early reading achievement. In L. Feldman, ed. Morphological aspects of language processing, 189-209. Hillsdale, NJ: Erlbaum. Carlisle, J. & Stone, C. In press. The effects of morphological structure on children's reading of derived words. In E. Assink & D. Santa, eds. Reading complex words: Cross-language studies. Dordrecht, The Netherlands: Kluwer.

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Fowler, A. & Liberman, I. 1995. The role of phonology and orthography in morphological awareness. In L. Feldman, ed. Morphologicalaspects of languageprocessing, (157-188). Hillsdale, NJ: Erlbaum. Leong, C.K. 1989. Productive knowledge of derivational rules in poor readers. Annals of Dyslexia. 39: 94-115. Nagy, W., Diakidoy, I. & Anderson, R. 1993. The acquisition of morphology: Learning the contribution of suffixes to the meaning of derivatives.Journal of Reading Behavior. 25: 15-170. Nagy, W., Osborn, J., Winsor, P. & O'Flahavan, J. 1994. Structural analysis: Some guidelines for instruction. In F. Lehr & J. Osborn, eds. Reading, language, and literacy, (45-58). Hillsdale, NJ: Erlbaum.

Reading Comprehension Kintsch, W. 1998. Comprehension. A paradigmfor cognition. Cambridge, UK: Cambridge University Press. Kucan, L. & Beck, I. 1997. Thinking aloud and reading comprehension research: Inquiry, instruction, and social interaction. Review of Educational Research. 67:271-299. Oakhill,J., Cain, K. & Yuill, N. 1998. Individual differences in children's comprehension skill: Towards an integrated model. In C. Hulme & Joshi, R., eds. Reading and spelling: Development and disorder. Mahwah, NJ: Lawrence Erlbaum Associates. Palincsar, A. & Brown, A. 1984. Reciprocal teaching of comprehension-fostering and comprehensionmonitoring activities. Cognition and Instruction. 1:117-175. Wittrock, M. 1974. Learning as a generative process. Educational Psychologist. 11:87-95. Wittrock, M. 1990. Generative processes in comprehension, Educational Psychologist. 24:345-376.

Fluency Kuhn, M. & Stahl, S. 2000. Huency. A review of developmental and remedial practices. Center for the Improvement of Early Reading Achievement. Stahl, S., Heubach, K., Cramond, B. 1997. Fluency-oriented reading instruction. National Reading Research Center. Reading Research Report No. 79. Wolf, M., & Katzir-Cohen, T. (2001). Reading fluency and its intervention. Scientific Studies of Reading, 5:211-238.

MAKING

CONNECTIONS

Questions p r e c e d e d by * may be most appropriate for graduate students. 1. H o w do the various w o r d forms and their parts play a role in learning to read words? W h a t are the advantages o f r e d u n d a n t representations o f w o r d forms in learning to read? 2. W h a t is reading c o m p r e h e n s i o n ? If the brain recognizes words, will it automatically understand the text it is reading? W h y or w h y not? C a n the brain c o m p r e h e n d text if it does not recognize the words in text? W h y or w h y not? W h a t c o m p o n e n t s in the functional system benefit from automatization? W h i c h c o m ponents best remain n o n a u t o m a t i c ?

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3. How do the attention, memory, and executive systems work together in building a reading brain? What role does myelination (Chapter 4) play in learning to read? 4. How do the different language systems work together at different stages of reading development? What are the instructional implications of multiple language systems? Are all brains, to some extent, multilingual? 5. What aspects of brain structures and functions should be taken into account in designing how instructional components are packaged during reading instruction? *6. Why, from the perspective of the novice reading brain, might teachers need an explicit conceptual model of necessary components of the reading system for planning, organizing, and delivering reading instruction in the classroom? Why, from the perspective of the developing reading brain, might teachers need such a conceptual model? *7. If scientific research already exists on teaching reading, which was not informed by brain research, do we really need research on reading instruction from a brain perspective? How might brain-based research change conceptual models of the process of learning to read? How might brain-based research change instructional practices in reading? How might instructional research in reading change the questions addressed in brain research?

Building a Writing Brain Pedagogically

Based on numerous research studies (see National Reading Panel 2000, for a recent review), the need for early intervention in reading is widely understood. Unfortunately, the critical developmental period for writing and the need for early intervention in writing are not as widely understood. Many still believe that if children learn to read in the early grades, there will be ample time to teach them to write in the later grades (see Chapter 6). Indeed, most of the large body of research on teaching composition has focused on grades 6 and above (Hillocks 1986). Longitudinal research showed, however, that poor beginning writers continue to be poor writers (Jue11988). In Chapter 4 we reported evidence for a critical developmental period for reading in the early primary grades. Considering that Language by Hand begins to develop during the preschool years, postponing explicit instruction until the upper grades may miss a critical developmental window in writing development, which may also be in the early primary grades. Thus, the initial impetus for the research at the University of Washington on the prevention of writing disabilities was to identify (a) which skills are most critical in beginning writing, especially at school entry, and (b) which instructional components are most effective in improving early writing acquisition. Over a decade of research in this systematic line of investigation demonstrated that transcription skills (handwriting and spelling) compromise early writing development in children Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

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whose language skills are otherwise normal. In addition, this research identified (a) neurodevelopmental skills that contribute to poor transcription, and (b) effective instructional components for treating these transcription problems, which are most easily remediated early in schooling. More importantly, this research and research that followed by other groups showed that improving transcription skills increases beginning writers' composing--they write longer compositions within the same amount of time after treatment than before (Berninger et al. 1997, 1998; Graham, Harris & Fink 2000, 2001; Jones & Christensen 1999). This transfer effect is important because two notable characteristics of older poor writers are that they write little and they write slowly when they compose (Graham, Harris, MacArthur & Schwartz 1991; Wong 1997). Thus, the major goal in building a novice writing brain is developing its ability and willingness (in that order) to express its ideas in writing (see Table 9.1). The novice brain is more willing to do so if it has the necessary transcription skills (see Chapter 6). If these transcriptions skills are sufficiently developed during the primary grades-- a critical developmental period--there will be sufficient time later in schooling to develop and refine other processes in composing. Thus, in this chapter we first explain how, from the perspective of pedagogy, to wire the brain for transcription

TABLE 9.1

Instructional Design Principles for Creating a Writing Brain

Novice S t a g e - - G o a l is ability and willingness to express ideas in writing 1. Model writing process for children by teacher thinking aloud as she plans what to say, how to say it, sounds out the words phoneme by phoneme, and uses card with phoneme-spelling correspondences to write the words on the chalkboard. Teach strategies, "What I Think I Can Say, What I Can Say, b I Can Write, ''a and "What I Can Say Sound by Sound I Can Write." 2. Provide guided assistance as children apply these strategies to their own self-generated writing using student cards with key with pictures and letters for translating sounds in pictured words into letters. 3. Teach grapho-motor planning, control, and execution for letter formation; then develop automaticity of letter production through daily practice in writing each letter once from memory and pairing name cues with letter forms in the production process. Always follow letter practice with composing on provided topics. 4. Teach spelling explicitly, systematically, and daily by (a) teaching linguistic awareness at all levels of language, (b) making multiple connections between input and output channels "visible," especially alphabetic principle, (c) writing from dictation spoken sentences with spelling patterns and high frequency words, and (d) using a strategic approach to spelling words that relies on phoneme-spelling correspondences on student clue cards during the composing process. Always follow spelling instruction with composing on provided topic. 5. Teach introductory keyboarding skills. 6. Provide guided assistance in composing for authentic communication goals. a. Read daily compositions to other children. b. Revise, illustrate, and publish final drafts.

(continues)

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(continued)

7. Foster a positive affect toward writing and create an instructional environment that eliminates writing resistant behaviors. Encourage multiple drafts and evaluate the final, not the first, draft. 8. Use simple prompts (e.g., for writing more text) and introduce simple strategies for self-regulation of the writing process and teaching specific text genre (see Table 9.6).

Developing Stage--Goal is transition from other-regulated to self-regulated writing 9. Transition from strategies to automaticity in spelling (direct retrieval of written word forms). 10. Continue to develop linguistic awareness of word forms, especially morphological and morphophonological awareness, and engage in word study. 11. Provide explicit instruction in each subprocess of composing: planning, translating (including advanced keyboarding and spelling), reviewing/revising. 12. Provide explicit instruction in executive control and metacognitive strategies for transition from other-regulation to self-regulation of the writing process. 13. Create writing-reading connections for varied goals and develop writing-to-learn strategies. 14. Continue to foster positive affect toward writing and create a writing habit. 15. Integrate writing instruction and practice with technology. 16. Teach more sophisticated strategies for self-regulation and specific genre (see Table 9.6).

Implementation of Instructional Design Principles 1. Teach to all levels of language (subword, word, sentence, and text) close in time so that connections can form in temporally constrained working memory. 2. Avoid habituation by teaching low-level skills for brief periods and for the purpose of automatizing them. 3. Teach for transfer a. from low-level skills (tools) to high-level skills (constructing meaning). b. from other-regulation to self-regulation. 4. Encourage the high-level, nonautomatic thinking and meaning-making process of writing. 5. Teach for success a. by modeling. b. by scaffolding (guided, constructive assistance and feedback to writer). 6. Teach all the necessary components at the student's developmental level in an integrated fashion so that the writing system becomes functional. 7. Assess frequently using developmentally appropriate criteria and multiple modes (see Chapter 12). ajenifer Katahira demonstrated that this strategy can be used effectively to teach beginning writing within an integrated reading and writing framework to at-risk kindergarteners and first graders (unpublished data and Traweek & Berninger 1997) bSunshine Cards (see Traweek & Beminger 1997) or Talking Letters (Berninger 1998b)

and transfer of transcription to beginning composition in the early grades. As discussed in Chapter 6, teaching transcription involves the grapho-motor component, but primarily language skills; so transcription instruction should be aimed at language as well as motor skills. Beginning writing can be taught effectively in the context of

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integrated reading-writing (Clay 1982). Other-regulation in the form of explicit instructional cues (Englert, Raphael, Anderson, Anthony, Feary & Gregg 1988, Englert, Raphael, Andersen, Anthony & Stevens 1991) is very important in wiring the novice brain, which may not be sufficiently myelinated for frontal self-regulation (see Chapter 4). Writing for communication goals (Britton 1978) in a natural social context is also important at the beginning of the writing development (Graves 1975). We then turn to pedagogical approaches for developing the writing brain further during the intermediate grades and beyond. Further writing development depends, in part, on automatization of low-level transcription skills (Beminger, Yates, Cartwright, Rutberg, Remy & Abbott 1992), especially letter production (Graham et al. 1997), so that limited resources in working memory are available for the multiple processes that must be juggled to accomplish writing goals (McCutchen 1996, 1997). However, transcription skills are not fully automatized during the intermediate grades when linguistic awareness continues to play an important role in spelling development (e.g., morphological awareness, Carlisle 1988). Both cognitive and metacognitive processes contribute to writing development during middle school and high school (Wong 1997). See Alamargot and Chanquoy (2001) for current models of the cognitive subprocesses involved in writingm all of which may benefit from explicit instruction aimed at bringing them to the awareness of writers. From a brain perspective, the frontal regions that house the executive functions are still myelinating and synapses are still being formed and pruned in these frontal regions (see Chapter 4); the brain is increasing in its ability to support executive functions that are required for the planning and reviewing/revising functions (see Chapter 6). However, it is unlikely that brain maturation alone without explicit instruction in self-regulation strategies will help middle school and high school students develop and apply executive functions productively to writing. The major pedagogical goal at this stage of development is to guide the Writing Brain in becoming more self-regulated (Zimmerman & Risemberg 1997). A major research-supported technique for accomplishing this goal is teaching explicit strategies for regulating the writing process, some of which are genrespecific and all of which should be coordinated with curriculum (De La Paz & Graham 1996; Feretti, MacArthur & Dowdy 2000; Graham 1997; Graham, MacArthur & Schwartz 1995; Graham, MacArthur, Schwartz & Page-Voth 1992; Harris & Graham 1996; Page-Voth & Graham 1999; Wong 1997). Four kinds of neurodevelopmental profiles may affect composing at this stage of writing development. Instruction may need to be modified to accommodate the learning differences of those students who exhibit any one of the following profiles of writing disabilities: (a) combined language and fine motor deficits, (b) visual spatial deficits, (c) attention and memory deficits, and (d) sequencing deficits (Sandier et al. 1992). However, unless researchers begin to identify these learning differences in their samples for instructional studies for writing, teachers will not have a body of research knowledge for linking instruction to individual learning differences for individual students.

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PEDAGOGY FOR CREATING A NOVICE WRITING BRAIN

Modeling, Strategies, and Guided Assistance As with reading instruction, writing instruction varies in its relative emphasis on inductive versus deductive learning (see Chapter 8). For example, one approach favors "natural" writing in which children engage in free writing from which they abstract their understanding of the writing process without didactic instruction. A contrasting approach relies on teacher-dominated instruction on knowledge about writing and students' deductive application of this teacher-provided knowledge. Hillocks (1986) conducted a metaanalysis of empirical research on these two approaches and a third approach that combined elements of the first two. His results favored the third, more balanced approach that included both students' reflection about their own writing and teacher provision of instructional cues and guided assistance. Englert, Raphael, and colleagues (Englert et al. 1988, 1991) demonstrated the effectiveness of that third approach for beginning writers. In their studies, teachers provided modeling and instructional hints via think-plan-write sheets and guided assistance as children generated both narrative and expository-informative texts in a social context with authentic communication goals. Thus, a justifiable way to begin to wire the writing brain is for the teacher to model the subprocesses of planning, text generation, and transcription, and to teach explicit strategies for applying these subprocesses to one's own writing (see Table 9.1). Two strategies for beginning writing that the University of Washington team investigated are (a) "What I Think, I Can Say, What I Can Say, I Can Write," and Co) "The Sounds in What I Say Are Related to Letters in the Words I Write." Jenifer Katahira, a teacher who is an expert in integrated reading-writing instruction, implemented these strategies in a research project that she initiated, and the university research team provided the evaluation component in this partnership (Traweek & Berninger 1997). She modeled the first strategy in this way at the start of each day: (a) by thinking out loud her ideas as she discussed what she planned to write about and what she planned to say about her ideas, (b) by modeling out loud the different ways that she might say these ideas, and (c) by modeling how she could say each word sound by sound and change those sounds into letters using her clue sheet. Her clue sheet was a Sun Shine Card (Traweek & Berninger 1997) with letters for single consonant and short vowel sounds and pictures of words beginning with each sound. Each child also had a clue card to help the teacher generate the sound to spelling translations. Ms. Katahira wrote the letters on the chalkboard as she completed each sound-to-letter translation, providing feedback for students assisting her. The second step was for the teacher to circulate and assist individual children as they applied the two strategies to generate ideas, spoken text, phonologically segmented words, and written spellings. The class was full of Language by Mouth

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and by Ear as children generated spoken text and further analyzed it into its sound parts using their clue cards. It is natural for children to talk as they compose (Hillocks 1986). The compositions in progress were full of invented spellings that reflected the children's emerging knowledge of how the orthography reflects the phonology of the language (see Treiman & Bourassa 2000). The teacher stopped by each child's desk to see the writing in progress and to assist if help was needed. Following the writing time, children shared by reading their own writing to others who listened, just as professional writers read their works. As the year progressed and children learned more about letter-sound correspondences, they read each other's writing. They also selected their favorite compositions to rewrite, illustrate, and publish in books exhibited at a book fair at the end of the school year. By using the first two steps in Table 9.1, Ms. Katahira combined child-generated writing and teacher-provided explicit instructional cues in a natural language context to help her kindergarteners transition from Language by Mouth and Ear to Language by Hand and Eye. By the end of kindergarten, all the children in a low income, culturally diverse inner city school were writing and reading in a classroom in which the teacher skillfully combined reflective, inductive student learning and explicit, deductive teaching. Their first grade teacher the following year complained that these children preferred to stay in at recess and write and read with each other. As the Writing Brain becomes more skilled, the kinds of strategies children are taught should become progressively more sophisticated. That is, teachers should adjust the amount and nature of guided assistance provided as children progress along the zone of proximal development (Vygotsky 1986). Although the expository genre may take longer to learn than the narrative genre (Kellogg 1994), even young children can benefit from using think-write-plan sheets (Englert et al. 1988, 1991) and prompts and strategies (Harris & Graham 1996) to learn specific discourse genre, including expository. An example of a narrative prompt is "What happens next?" An example of an expository (informative) prompt is "What is another fact?" A general prompt for children who quit writing prematurely is "What else can you think of?." Student clue cards can be modified to reflect more sophisticated understandings of phoneme-spelling relationships, including all the most frequent spelling-phoneme correspondences in primary grade material, two-letter as well as oneletter spelling units, long as well as short vowels, and syllable types (Berninger 1998b). Students can refer to these cards throughout the day whenever they are reading or writing and need to decode or encode an unknown word using a strategy to apply alphabetic principle.

Instructional Design Principles for Transcription In addition to daily strategy-assisted, teacher-guided practice in composing, building a writing brain requires daily explicit instruction in two transcription skills--

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handwriting and spelling. Two instructional design principles apply to both transcription skills. The first instructional design principle is keep the explicit, teacherdirected instruction and practice brief (5 to 10 minutes at most). The reason is that the brain of the young child has a short attention span and rapidly habituates (see Chapter 4), which means that after habituation sets in, the brain no longer benefits from the familiar and will respond only to novelty. Children whose attention span is even shorter than age peers may engage in novelty seeking behaviors before the brief lesson is done! Typically in our instructional research we liken this brief period of explicit instruction and practice to the warm-up of the athlete or musician before the game or concert. The second instructional design principle is that transcription practice should always be followed by composing. Follow-through composing after transcription instruction has two advantages. First, transcription is perceived as a tool for the higher-level goal of composing for communication purposes. Second, transcription skills taught in isolation are more likely to transfer to transcription skills in context during composing. We have found that reluctant writers are more likely to compose following transcription if given one- or two-word topics to write about than if asked to do free writing. For beginning writers, these composing sessions can be brief (five minutes works well). Initially, it is more important that there be opportunity to share the composition by reading it to another child, the teacher, or the class than to do long stretches of extended writing. The length of the composing episode can be increased as the Writing Brain matures. Each transcription skill has its own instructional design principles, which are discussed next. For handwriting, two instructional design principles have emerged from our research. First, always teach along this developmental trajectory-- accuracy of letter formation first and automaticity of letter production second. Second, teach manuscript letters first, and then keyboarding. The goal of accuracy of letter formation requires integration between the orthographic coding system and the grapho-motor system; three components of the grapho-motor system must become functional-planning, motor control, and execution (see Chapter 6). Rubell (1995), who is a physical therapist and occupational therapist with neurodevelopmental training, developed a teacher-friendly, comprehensive, systematic program of handwriting instruction that draws on principles of neurodevelopment for teaching the motor planning, control, and execution processes involved in letter production. Once letter formation is reasonably accurate (legible), the goal should be to transition to automaticity (legibility within time constraints) so that letter-forms are now directly retrieved for production; see Chapter 6. Three factors appear to contribute to automatization of letter production. First, because of habituation, practicing each letter in the set of 26 symbols once is more beneficial than practice of a few letters over and over again. Second, teacher-directed practice that combines visual attention to the order of component strokes (through numbered arrow cues) and writing letters from memory is more effective than conventional approaches in which letters are just copied repeatedly (Berninger et al. 1997). Third, for reasons

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we discuss next, frequent naming of the letter during instruction and practice may assist in the production process by attaching a verbal label to an orthographic symbol. In keeping with the brain's coding of information in redundant formats in memory (see Chapter 3), letter forms are coded verbally in the left hemisphere and visually (in reference geometric spatial coordinates) in the right hemisphere (Kosslyn 1988). Because the motor output system for letters and speech is in left frontal areas (see Chapters 3 and 5), activating a verbal code (name) for a letter-form on the left may result in faster letter production than activating a visualspatial code for a letter on the fight. Boys produce letters from memory more slowly than girls in the primary grades (Berninger & Fuller 1992) and in the intermediate and junior high grades (Berninger 1998a); this relative difficulty in automatizing letter production appears to affect how the quality of their writing is often judged (Berninger 1998a) and may be related to the greater incidence of writing problems in boys than girls in the middle school years (Hooper et al. 1993). Thus, for boys, in particular, pairing letter names with writing letters early in writing development may increase automaticity of letter production. Further research is needed to determine if this gender difference is related to boys tending to have better access to the fight hemisphere than left hemisphere codes for letters. Manuscript letters are usually taught first for two reasons. First, they are easier motorically for children to produce than cursive letters. Second, they provide more transfer to printed words in books than do cursive letters. However, as technological advances have made computers more available in schools, the transition to cursive, which is a letter format that had utility prior to the typewriter, may not still make sense. We believe that it might make more sense to introduce beginning keyboarding skills no later than first grade and to focus on advanced keyboarding skills for computer word processing programs in the third grade when the transition to cursive writing is normally made. However, research is needed to evaluate such a change in instructional practice for normally developing students, as well as to identify those students for whom cursive may be a better written-output mode than manuscript writing. For spelling, four instructional design principles are important. The first is to develop linguistic awareness--not only orthographic and phonological but also morphological (Carlisle 1996; Treiman & Bourassa 2000) and syntactic (Bryant, Nunes & Bindman 1997) awareness. The second is to teach correspondences between spoken and written forms explicitly (Foorman, Francis, Novy & Liberman 1991). See Berninger et al. (1998) for ways to make corresponding phonological units and orthographic units of different sizes "visible" in explicit instructional cueing, and Berninger (1998b) for the alternations of alphabetic principle in teaching spelling (optional spelling units for the same phoneme). The third is daily spelling of dictated sentences that are designed to provide repeated practice in spelling of the same words and application of developmentally appropriate spelling patterns (Berninger, Vaughan et al. 2000). See Dreyer, Luke, and Melican (1994) for

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the importance of repeated practice with the same words for long-term retention of word spellings. Because spelling sentences from dictation has working memory requirements more comparable to those of composing than does spelling single words from dictation, spelling dictated sentences may generalize more to spelling during composing; further research is needed on this issue. The fourth is teaching spelling words that are appropriate for students' instructional (Morris, Blanton, Blanton, Nowacek & Perney 1995) and developmental (Bear & Templeton 1998) levels rather than grade placement level.

Authentic C o m m u n i c a t i o n Goals

Initially children should read their compositions to o t h e r s - an individual partner, small group, whole class, or teacher, just as they learn Language by Mouth and Ear in a social interactive context. Even children whose transcription skills are not sufficiently developed for others to decipher their written communications can tell the text they generated orally and intended to transcribe. Eventually most children will have sufficiently developed transcription skills for others to read their written communications. Children can both listen to and read other's written productions, choose their favorite ones, and prepare them for publication in a book or class newspaper.

E m o t i o n a l and Motivational Context

The major change in writing instruction over the past two decades has been from a product to a process approach (Hillocks 1986). The prior emphasis on evaluation of the final product (often a first draft) has given way to a process approach, more typical of professional writing, in which a draft is revised many times and only the final draft is evaluated. This process approach, which allows for constructive feedback to the author at many points in the process of constructing the text and opportunities for revision, has a more positive affect on motivation to write than does the product approach in which the first draft is evaluated with no opportunity for further revision. The process approach is more hkely than the product approach to lead to positive affect about writing. The process approach also places more emphasis on the content of the composition than the mechanics. Capitalization, punctuation, handwriting, and spelling are taught but are not the main criteria for evaluating the quality of the written product. Placing too much emphasis, too early in the process of learning to write, on these surface features in grading writing can lead to a negative affect toward writing. Put another way, handwriting and spelling should be taught explicitly and children should be given constructive feedback on improving these as they revise drafts. However, surface feature should not become the major criteria on which written

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compositions are judged and mechanics should be evaluated separately from the content of the written communication. The Writing Brain, like the Reading Brain, has many connections with the limbic system (see Chapter 3). Success leads to positive affect and motivation to write more, but lack of success leads to avoidance of writing. The less a skill is practiced, the less it will develop successfully, leading to a self-perpetuating cycle of writing avoidance. In our instructional studies, we have noted that (a) the social aspects of writing can be very motivating for reluctant writers, and (b) if the social context is restructured to convince the reluctant writers that they can succeed in at least some aspect of writing, they may become less writing avoidant and more willing to practice writing. Cognition, emotion, and motivation are highly interconnected in the Writing Brain.

P E D A G O G Y FOR. C R E A T I N G A D E V E L O P I N G WRITING BRAIN Once the novice writing brain begins to express itself via Language by Hand, the process of becoming an expert writing brain will require years of additional instruction and practice. As explained in Chapter 4, Language by Hand matures over a longer developmental trajectory than the other language systems. According to Kellogg (1994), professional writers report a range of 10 to 20 years of deliberate practice in honing writing skills before they reach expert levels. Critical to this practice is a willingness and ability to revise w w h i c h may be the hallmark of the expert writer. James Michener put it this way (Kellogg 1994, p. 120): "I have never thought of myself as a good writer. Anyone who wants reassurance of that should read one of my first drafts. But I'm one of the world's great rewriters." Creating "know how" and "want to" for revising is a major instructional challenge in developing writing brains. That is because writing is a process. Not only is the first draft never perfect but also there are likely to be many drafts in between the first and final one. One reason that writing has not been studied as much as reading is that it is more difficult to study the complexities of its internal processes and its written output reliably and validly (Humes 1983). The complexity only increases as the novice writer turns into a developing writer. For example, planning may occur prior to translating during the prewriting phase, or online as writers recursively translate, review, and revise (Hayes & Flower 1980). Pedagogy for planning during prewriting is more straightforward--for example, use of graphic organizers to organize ideas before beginning to write w t h a n during online planning when instruction would interrupt and disrupt ongoing composing processes in working memory. Likewise, revising may occur after translation is complete, at least for a draft at hand, or online as writers recursively plan and translate; pedagogy can more directly aim at posttranslation revising than online revising. Furthermore, revising and editing are not the same (Fitzgerald 1987). Revising is the meaning-driven changes in text that

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involve deeper features of the text construction. Editing changes surface features such as grammar, spelling, capitalization, and punctuation. As in novice writing, too much attention to editing too early in the writing process may interfere with both motivation and quality of the final draft in developing writers. Nevertheless, it is important to model and teach explicit strategies for planning and reviewing/revising (Harris & Graham 1996; Wong 1997). However, it is also important to recognize that it takes a long time to master each of these processes. Teachers should not use the standard of skilled writers in evaluating the planning and revising of developing writers. The developing writing brain undergoes functional reorganization. To begin with, the low-level transcription skills become increasingly automatized, but not completely--there is still a role for conscious reflection for word spelling. In addition, high-level executive functions emerge to orchestrate the many different goals and processes that must be coordinated during composing. Despite the widespread belief that planning, translating, reviewing, and revising are cognitive processes (Hayes & Flower 1980; Humes 1983), from a brain perspective, they involve executive control functions that are on a slower developmental trajectory than other brain functions because they are housed in frontal regions, which are the last to myelinate and to form and prune synapses (see Chapters 4 and 5). Kellogg (1994) also noted, based on psychological studies, that metacognition for controlling the writing process develops more slowly than the cognitive processes in writing. Metacognition is one kind of executive function and is also supported by frontal regions that myelinate late in development. In sum, the developing writing brain becomes a functional system with many different components that must learn to work together to achieve writing goals. Both instructional and writing experience and brain maturation between middle childhood and adolescence play a role in the functional reorganization of the developing functional writing system.

Monitor Automatization of Transcription

As discussed in Chapter 6, practice of a grapho-motor function usually leads to automatization and a change in the brain circuitry supporting that function. By the mid elementary grades, handwriting skills have been practiced for a long time; thus, they should be automatized, with a related shift from the cognitive, strategic pathway to the behavioral, automatic neural pathway (see Chapters 4 and 6). If developing writers are still drawing their letters, producing illegible letters, or writing very slowly, they should be referred for evaluation by a multidisciplinary team that includes a physician (developmental pediatrician or child neurologist), physical or occupational therapist, and psychologist. Sometimes, because of underlying brain anomalies in the neural pathways for automatization, practice alone does not lead to automatization of letter production.

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Failure to automatize letter production should be taken seriously and treated because it can compromise further writing development. In general, if manuscript letter-production is not automatized by the end of second grade, teaching another production system may only confuse children, who tend to revert to the first mode of written production they learn. Indeed, good writers in fourth through ninth grade often revert to manuscript letters, probably because they were learned first (Graham, Berninger & Weintraub 1998). It is more important to establish one system of automatic letter production than to have two nonautomatic ones. The instructional procedures for developing automaticity of letter production in the novice writer (see earlier in this chapter) or developing writer (see Table 9.3) should be implemented and evaluated. Substituting the keyboard for the pencil is not necessarily the solution; in the first author's clinical experience, writers who have difficulty with automatization of letter production with a pencil often also have trouble in using a keyboard. The same brain anomalies may contribute to difficulties in automatizing written output regardless of peripheral tool used.

Encourage Linguistic Awareness and Word Study Developing writers also transition from strategic approaches to spelling to direct retrieval of orthographic word forms, which have been created by linking letters with phonological word forms and their parts (see Chapters 5, 6, and 8; and Steffter et al. 1998). However, spelling will not become fully automatized during the intermediate grades. Students still benefit from conscious reflection about words and word study because the kinds of words they are expected to use in their writing changes, just as the words they encounter in their reading material are changing-especially on the dimension of word origin (see Chapter 8). Word study and reflection about words of R.omance and Greek origin are especially important during the intermediate grades (Bear & Templeton 1998). Throughout schooling, specialized vocabulary for content areas should be taught--its spelling as well as pronunciation and meaning. Some developing writers never fully switch spelling to automatic pilot, possibly because of the same brain anomalies interfering with automaticity of letter production--a possibility that requires research. However, they may remain strategic spellers whose spelling can be deciphered by others because it reflects the phonology of the language even if it does not do so in conventional orthography. Ability to spell conventionally should not be confused with ability to think or compose--create meaning through written language. Allegedly, when editors complained to Mark Twain that he had spelled the same word fifteen different ways in the same manuscript, he retorted that he felt sorry for anyone who could not think of fifteen different ways to spell the same word! We do not know for sure that he said this, but think the story makes an important point that language is generative (the same symbols can be combined in many different ways to generate open-ended

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outcomes) at the level of spelling words as well as in constructing sentences. The important instructional implication is that composing ability should be evaluated separately from spelling ability. Although spelling is an important transcription component of the translating subprocess of composing, spelling is separable from other translating subprocesses and is not the only important subprocess. Morphological awareness and connections among morphology, phonology, and orthography (see Figure 8.1) should be incorporated in spelling instruction and the feedback provided for spelling in written composition; see Carlisle (1988, 1994), Leong (2000), and Treiman and Bourassa (2000). Reflective word study (e.g., word sorts; see Bear & Templeton 1998) continues to be important in developing not only spelling but also word consciousness and vocabulary knowledge (see Graves 2000), all of which contribute to composing. Explicit instruction in grammatical awareness is also important. However, intensive training in grammar at the sentence level does not transfer to improved quality at the discourse level (Hillocks 1986). Instruction should be aimed at all levels of language to ensure quality compositions. Discourse structures for specific genre also need to be taught explicitly (eg. Wong, 1997).

Stimulate Cognitive Subprocesses Kellogg (1994) emphasized meaning-making as the unique cognitive activity of writing, but the instructional implication of this model for teaching writing to developing writers has not been thoroughly investigated. In Kellogg's model, based on the experiences of skilled writers, two states of consciousness contribute to the cognitive processes in writing--semiconscious dream states and a conscious, directed thinking state. Metaphors used by writers to refer to the first cognitive process include stream of thought, train of thought, lost in thought, and the creative flow of ideas. This stream is connected with the idea center in the mind that exists independently of a language system for expressing ideas. This stream is vulnerable to stoppage if interrupted in time. In contrast, the conscious, directed thinking is characterized by mindfulness and use of language to bring semiconscious thoughts into consciousness. This mindful state accounts for the common experience skilled writers encounter as a result of writing--they discover what they think! So, like a form of psychotherapy, writing may help uncover unconscious thoughts, and like a form of education, writing may help transform those thoughts to create new knowledge. The first process--the flow from the idea generator--may be temporally constrained--it is easy to get lost in the mind's time travel during the free flow of thought; but the second process--the mindful taming of the free-flow--may be space constrained. Working memory has limited workspace for conscious attention to cognitive tasks like writing. How the cognitive flow interacts with imagination is unknown. In contrast to memory, which is time travel to the past, imagination is

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time travel to the future m envisioning or imaging that which did not exist before. Imagination m the mind's p l a y ~ is another relatively underinvestigated cognitive process in writing. Given the lack of research on these cognitive processes in developing writers, we speculate what the instructional implications might be. Free writing, as is often recommended, for example, in journal writing, may be an outlet for the free flow of thought and self-expression, but it does not encourage developing writers to subject that free flow of ideas to the scrutiny of mindful reflection. Effective writing instruction should both stimulate the cognitive flow and the self-discipline needed to tame the flow. Effective pedagogy might include the following components: (a) instructional time devoted to automatizing the low-level skills to free up workspace in working memory for high-level, mindful writing tasks, (b) instructional activities aimed at imagination ~ playfulness of mind, (c) instructional activities aimed at mindfulness and conscious reflection in problem solving, and (d) instructional activities aimed at learning explicit strategies for executive management of all these processes. If this analysis is correct, daydreaming is not necessarily a bad use of mental time and may be a necessary way of staying in touch with the idea flow. Children should be allowed to devote s o m e time to the mind's play without being punished. At the same time, the developing brain also benefits from teacher-provided cognitive tasks that channel the idea flow for mindful mental work in which ideas and knowledge are transformed through problem-solving writing activities into new knowledge. Language by Hand has the unique potential for bringing mental play and mental work together. This merging of play and work is most likely to occur in seamless fashion when (a) the low-level transcription skills are automatized and do not take up too much workspace in working memory; and (b) other-regulation by teachers supports the meeting of the playful and working minds until self-regulation can manage this coordination.

Transition f r o m Other- to Self-regulation

Speakers and listeners must transition from highly supported other-regulation during conversation in early language development to the decontextualized aural/ oral language of the first grade classroom. Likewise, developing writers must transition from the high degree of other-regulation for written language in the primary-grade writing program to greater self-regulation of the writing processes in the middle grades. Developing writing must become increasingly self-initiated, selfsustained, self-monitored, and self-disciplined (Zimmerman & Risemberg 1997). It is unfortunate that the highly contextualized, other-regulated social supports of early conversation and early writing have been labeled as natural, which implies that the later more decontextualized, self-regulated language functions of older children are unnatural. For both aural/oral language and reading/writing, there is a natural

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progression from other-regulation to self-regulation of the components of the language system and their interactions with other brain systems like attention and memory. For both the aural/oral and reading/writing systems, explicit cueing in the context of other-regulation in a highly supportive social context is natural in the early stages of development, and a transition to greater self-regulation is natural later in development of the language systems. The kinds of instruction that facilitate this transition to self-regulation include both strategy instruction for specific writing processes (Harris & Graham 1996) that are coordinated with the school curriculum (Wong 1997) and explicit instruction in procedural skills (how to write) rather that declarative instruction about writing (Hillocks 1986). In addition, ten effective techniques have been identified for making the transition to greater self-regulation of the writing process (Zimmerman & Risemberg 1997). These fall in three categories: environmental (creating a conducive setting and selecting from social sources a model to imitate), behavioral (selfmonitoring, contingent reinforcers, and self-verbalizations used to mediate the composing process), and personal (time planning and management, goal setting, self-evaluation, cognitive strategies like outlining, and mental imagery to facilitate the verbal translation process). The transition to self-regulation is more likely to occur if adults explicitly prepare developing writers for it through instructional cueing. These strategies can be taught (see Table 9.6) and are unlikely to emerge solely on the basis ofmyelination of the frontal lobes. However, no matter how much instruction the teacher provides, students may not be able to self-regulate their writing completely independently until the frontal regions mature. The teacher plays an important role throughout writing development, first serving as the frontal executive functions for novice and developing writers (see Chapters 3 and 4), and then as the mentor who motivates writers to assume responsibility for managing their own executive functions to self-regulate the writing process. Once again, nurture and nature interact in wiring the Writing Brain.

Writing-Reading and Writing-Learning Connections Not only must the developing writing system learn to manage its own components but also it needs to learn to work with the other language systems. It writes about what it reads. It writes about the lectures it listens to and later uses those notes to study for exams in which it will write about what it has learned. During oral presentations, it shares the reports it has written. Writing thus becomes an integral part of the learning process; and the ways in which writing facilitates school learning are being investigated (Klein 1999). How writing-reading connections form over the course of development is a topic of research as well (Berninger, Abbott, Abbott et al., 2002; Fitzgerald & Shanahan, 2000; Nelson & Calfee 1988; Shanahan & Lomax 1986, 1988). Because the discourse genre of writing varies across different

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content subjects in the academic curriculum, content subject teachers need to provide instruction in the genre-specific writing forms for the subject they teach (Young & Fulweiler 1986). Throughout schooling, the developing writing brain not only reorganizes internally but also creates many external connections with other brain systems and brain-based knowledge structures to achieve rapidly expanding academic goals. Over the course of development, the cross-talk among Language by Hand, Language by Eye, Language by Ear, and Language by Mouth increases as these language systems learn to work cooperatively with each other and other brain systems to support academic learning.

Emotion and Motivation Limbic connections with the cognitive and executive government systems increase throughout writing development. Affective and motivational variables move writers to write or block their efforts to write. Writing is not a purely cognitive or metacognitive activity. Writing may elicit strong emotional responses because the cooperative principle in conversation, by which conversational partners help repair each others' faulty communication (Grice 1989), does not often transfer to written communication. The writer's audience may be unforgiving andjudgrnental because the product is relatively permanent, public, and memory-proof m it can be inspected. In contrast, conversational output typically results, by default, in forgiveness for its imperfections because the form rapidly vanishes without a visible record to inspect. The forgiving audience in conversation may lead to greater motivation to talk in informal contexts, whereas the unforgiving audience in written composition may decrease motivation to write. As with novice writers, the less motivated the brain is to write, the less it practices the craft and develops the necessary expertise. Throughout writing development, emotions and motivation are intricately linked with cognitive and executive functions in neural circuitry that spans subcortical and cortical regions of the brain. Teachers whose instructional program in writing is aimed at developing positive affect and motivation to write, along with instructional cues and guided assistance in the cognitive and metacognitive areas, are most likely to help the Writing Brain move along its zone of proximal development to become an expert writer.

Integration of Technology with Writing In an increasingly technological society, instruction aimed at the Writing Brain needs to be integrated with technology (MacArthur & Graham 1987; MacArthur, Schwartz & Graham 1991a). However, as Kellogg (1994) pointed out, the cognitive

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processes of writing (the author role) should not be confused with the technology that is merely a supportive tool for external output (the secretary role). Research with skilled writers has shown that quality of writing performance generally does not vary depending on whether a pen, pencil, keyboard, or other output device is used; however, internal cognitive processes may be orchestrated differently depending on what the external output device was (Kellogg 1994). Cognitive operations play a greater role in composing than does mode of output--handwriting or dictation (Gould 1978) E at least for the skilled writer. In general, computers have added value in the revision process (at least the editing of mechanics and reorganization of text) but not in planning and translating (e.g., word choice and discourse structure and content); however, computers may increase motivation, affect, attitude, and engagement (Kellogg 1994). Devices that translate speech into written language are not likely to eliminate completely the need to use Language by Hand. For example, dictated written products may not result in quality writing because the syntax of spoken language may lag behind the syntax of written language in the older developing writer (O'Donnell, Griffin & Norris 1967). Computers do not necessarily serve as a bypass for transcription problems. Although some assume that technology eliminates motor requirements for output, that assumption is not valid--motor planning, control, and execution processes of some sort are still involved in using a keyboard. Spell checks cannot be used until the program recognizes the user's spelling, which usually is when the writer's spelling reaches a fifth grade level. Rate of letter naming predicts individual differences in keyboarding efficiency (Bowers 2001). Clearly, research on adapting computer technology to needs of individual writers is a timely research topic and will be for years to come (Berninger to Amtmann, in press).

IMPLEMENTATION OF RESEARCHSUPPORTED INSTRUCTIONAL DESIGN PRINCIPLES As in the case of reading, our instructional research on writing is based on neuro developmental theory (Berninger 1994): We deliver intervention aimed at all levels of language close in time and teach for transfer from low-level to high-level component skills. Table 9.2 illustrates this instructional design principle in four early intervention studies we d i d - - all of which included one transcription skill and a composition component. Table 9.3 outlines a model in which we aimed instruction to both handwriting automaticity and spelling, and the planning, translating, reviewing, and revising components of composing in individual tutorials for children at the critical developmental transition from third to fourth grade when writing requirements increase (see Chapter 6). Table 9.4 outlines a similar intervention with third and fourth graders who were diagnosed with dyslexia, a severe

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TABLE 9.2 Teaching to Multiple Levels o f Language and C o m p o n e n t s o f Writing in Supplementary Writing Instruction (Pull-Out from R e g u l a r Program for Children Most At Risk in Writing) First Grade Model a Handwriting + Composition (20 minutes twice a week, one tutor per child trio for four months) SUBWORD

Handwriting lessons, b Practice each of 26 letters once in a lesson rather than a few over and over. Use numbered arrow cues and writing from memory to create precise representations of letter forms in memory and retrieval routines. Name letters frequently: Look carefully at numbered arrows in (name letter). Now make (name letter). Now cover (name letter). Now compare your letter to the model (name letter).

TEXT

Compose about provided topic b and share by reading to peers.

Second Grade Model c Spelling + Composition (20 minutes twice a week, one tutor per child pair for four months) SUBWORD

Teach. a~habetic principle in direction of phoneme to spelling unit.

WORD

Apply whole word and onset-rime strategies to model correspondences between spodken and written words of varying sound-spelling predictability.

TEXT

Compose about a provided topic using the "What I Think I Can Say and Write" strategy and share by reading to peers.

Third Grade Model for Spelling e + Composition (20 minutes, twice a week, one tutor per child) SUBWORD

Teach. aa~phabetic principle in direction of phoneme to spelling unit.

WORD

Teach set of 10 words selected to represent each of 7 syllable typesfTutor says word; child says word syllable by syllable and then counts phonemes in each syllable using colored tokens; child categorizes each syllable by type; child spells each word from dictation.

TEXT

Use "What I Think, I Can Say and Write" strategy to compose and share by reading to peers.

Third Grade Model for Composing g + Spelling (20 minutes, twice a week, one tutor per child pair) SUBWORD

Teach alphabetic principle deductively in direction ofphoneme to d /i spelling unit and inductively through word sorts.

WORD

Apply alphabetic principle to structure/content words

(continues)

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TABLE 9.2 (continued) TEXT

Explicit instruction in planning, translating, and reviewing/ revising two kinds of expository essays (3 lessons on each of 4 topics for informative essays and on each of 4 topics for persuasive essays).

aBased on Berninger et al. (1997). bBerninger (1998c); can be adapted for a multimodal approach to learning to name letters by having child look at letter, name it, and write it from memory. CBased on Berninger et al. (1998a). dTalking Letters Program (Berninger, 1998b). eBased on Berninger, Vaughan et al. (2000). fClosed, open, silent-e, vowel team, r-controlled, -le, schwa. gBased on Berninger, Vaughan et al. (in press). hBear, Invernizzi, Templeton, and Johnston (2000). TABLE 9.3 Model for Individual Tutorial to Help Children Transition from Third to Fourth Grade Writing Expectations a (14 one-hour tutorials during summer between third and fourth grade) SUBWORD

Letter production automaticity: Inspecting named letters with numbered arrow cues and writing letters from memory; recording times.

WORD

Multiple strategies for spelling:

Orthographic imaging (visualizing and spelling words in the "mind's eye"). Alphabetic principle and its alternations. Structural analysis of syllable and subsyllabic patterns. TEXT

Compose using P W R R strategy (Plan, Write, Review, Revise), which teacher models and then guides child in the writing process on different engaging topics.

aBased on Berninger, Abbott, Whitaker, Sylvester, and Nolen (1995). TABLE 9.4

Teaching to All C o m p o n e n t s in Writing for Students with Dyslexia a

SUBWORD

Handwriting Automaticity - - writing letters that come before or after designated letter (see Orthographic Awarenessa).

WORD

Spelling instruction based on Bear and Templeton's (1998) stages of spelling development.

TEXT

P W R R Strategy (Plan, Write, Review, Revise). Compose using P W R R strategy, which teacher models and then guides child in the writing process on different engaging topics, a

aWriting Lesson Frames and Orthographic Awareness in Berninger (1998a) based on research in UWLDC. r e a d i n g d i s o r d e r in w h i c h w o r d - r e a d i n g skills d o n o t d e v e l o p n o r m a l l y or as easily as v e r b a l r e a s o n i n g a n d c o n v e r s a t i o n a l skills. T a b l e 9.5 p r o v i d e s an o v e r v i e w o f a similar a p p r o a c h u s e d in b e f o r e a n d after s c h o o l clubs for f o u r t h graders at risk

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for passing the state standards in writing. This i n t e r v e n t i o n was u n i q u e in i n c l u d i n g a m o r p h o l o g i c a l awareness and w o r d play c o m p o n e n t to d e v e l o p the affective component

o f w o r d consciousness (Graves 2000) in d e v e l o p i n g w r i t i n g skill.

Table 9.6 summarizes r e s e a r c h - s u p p o r t e d w r i t i n g instructional strategies d e v e l o p e d a n d validated b y o t h e r research groups to teach the c o m p o n e n t processes o f w r i t i n g , the self-regulation o f the w r i t i n g process, and genre-specific c o m p o s i n g strategies. TABLE 9.5

CLUB S C H E D U L E a

First 5 minutes: Children give password to enter room and share riddles and jokes for the "YOU GOT TO LAUGH" contest; folders passed out. Next 15 minutes: Writers' Warm-Up ORTHOGRAPHIC AWARENESS (letters on automatic pilot) PHONOLOGICAL AWARENESS (sound games) PHONEME-SPELLING CONNECTIONS (alphabetic principle and its alternations taught in direction of sound to spelling and with automaticity goals). Next 25 minutes: Writers' Work First 12 sessions: Composing and sharing (topic starters). Last 24 sessions: Informative and persuasive essays (4 each with a planning, drafting, and reviewing/ revising lesson for each essay). PUBLISHING: Long-term composing projects for a newspaper published three times, KIDS WRITING FOR KIDS. b Next 10 minutes: Word Play. Last 5 minutes: Clean-up. aExtended day model of before or after school clubs that met twice a week for one hour at a time; for students identified by school district as at risk for passing assessment for state standards; club children improved significantly more in composing on both a standardized test and the state assessment of writing than did controls. bMOMMY LONGWORDS CONTEST: On a variable reinforcement schedule with DR. MRS. SEUSS-GOOSE, wearing a Dr.Seuss top hat and bowtie and a Mother Goose feather mask and wings, showing up with treats for children who brought in long words that met criterion of week (e.g., foursyllable word with accent on second syllable). Board games such as BINGO with structure words. TABLE 9.6 Strategies for Developing Executive Functions for Self-Regulating the Writing Process and Composing Specific Genre I.

POWER Strategy (Englert 1992) using teacher think-aloud plan sheets A. Plan B. Organize C. Write D. Edit E. Rewrite/Revise

(continues)

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(continued)

Six Kinds of Self-Instruction (Harris & Graham 1992) A. Problem definition--sizing up nature and demands of the task B. Focusing attention on task at hand and generating a plan C. Strategy implementation D. Self-evaluation and error detection and correction E. Coping and self-control n dealing with difficulties and emotional arousal F. Self-reinforcement (reward)

III.

Explicit Self-Regulation (Sawyer, Graham & Harris 1992) A. Teachers work collaboratively with students and gradually transfer responsibility for writing to the student. B. Teachers model application of strategy by composing while thinking aloud, provide students with self-instruction statements to regulate strategy application, and scaffold (give assistance and feedback) during individual conference with student. C. Goal-setting D. Independent practice in applying strategy for story grammar to own writing 1. Who is the main character? Who else is in the story? 2. When does the story take place? 3. Where does the story take place? 4. What does the main character want to do? 5. What happens when he or she tries to do it? 6. How does the story end? 7. How does the main character feel?

IV.

Three-Step Essay Writing Strategy (Sexton, Harris & Graham 1989) with teacher modeling A. Think who will read this and why I am writing it. B. Plan what to say using TREE (Topic Sentence, Reasons, Examine, Ending). C. Write and say more.

V.

PLANS strategy (Graham, MacArthur, Schwartz & Page-Voth 1992) A. Pick goals. B. List ways to meet goals. C. Make notes. D. Sequence notes. E. Write and say more. F. Test goals.

VI.

STOP Strategy for Planning (De La Paz & Graham 1996) A. Suspend judgment m stop, reflect, and plan before beginning to write; brainstorm ideas for each side of an argument. B. Take a side. Choose the side that has the strongest arguments. C. Organize ideas. Select ideas that provide the strongest support and that provide the best counter-arguments to refute the opposite side. Set goals for how many supporting and refuting arguments will be generated.

(continues)

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TABLE 9.6 (continued) D. Plan more as you write, adding and modifying, as necessary, to each part of opinion essay (DARE). 1. Develop topic sentence. 2. Add supporting ideas. 3. Reject arguments for one side. 4. End with conclusion. VII.

Revising Goals (Graham, MacArthur & Schwartz 1995) A. Write personal narrative based on writing prompt in pencil. B. Read and revise story with a ballpoint pen using revising goals on a plan sheet: 1. Add things that happened. 2. Add descriptions. 3. Add details. C. Rewrite story using goals on plan sheet

VIII.

Goal Setting in Essay Writing (Page-Voth & Graham 1999) A. Explaining and giving examples for each part of a good essay: premise or stated belief, reasons that support the premise, refutation of counter-arguments that refute the premise, and conclusion. B. Prewriting conference with the teacher to plan goals and strategies for achieving goals, drafting, and postwriting conferences to evaluate whether goals met: goals for number of reasons to support premise, and goals for number of counter arguments. C. Six-step strategy for meeting the goal: identify author's opinion (premise), brainstorm ideas, write essay, review essay to make sure all ideas included, add new ideas or modify existing ideas, and check whether goal is met.

IX.

Reciprocal Peer Revision Strategy (MacArthur, Schwartz & Graham 1991b) with students taking turns in role of peer editor A. In first meeting the peer editor (a) listens and reads along as author reads composition aloud and tells author, (b) summarizes what it is about and what is best about it, (c) rereads paper silently and makes notes about revision questions--Is there anything not clear? Where could more details be added?, and (d) makes suggestions for revision. B. In second meeting after author works independently at word processor to make revisions, students meet again to discuss revisions and use a checklist to edit for mechanical errors (complete sentences, capitalization, punctuation, and spelling).

X.

Genre-Specific Strategies for Essay Writing with Adolescents (Wong 2000) using teacher-student discussions and think-alouds, plan sheets, prompt cards, peer feedback, revising, teacher guidance, checklist for self-monitoring, graphs for recording grades for each taught component. A. Opinion Essays: (see Wong, Buffer, Ficzere & Kuperes 1996). B. Compare and Contrast Essays: (see Wong, Buffer, Ficzere & Kuperes 1997)

FUTURE DIRECTIONS R e s e a r c h e r s f i n d it difficult to assess w r i t i n g r e l i a b l y a n d v a l i d l y ( H u m e s

1983).

B e c a u s e w r i t i n g skill is a m o v i n g t a r g e t o v e r a r e l a t i v e l y large t i m e w i n d o w , w r i t i n g

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assessment should be developmentally appropriate. Researchers have studied the normal developmental trajectory for specific discourse genre (Crowhurst 1987, 1990; M c C a n n 1989), but more research is needed on this topic. In sum, m u c h work remains to be done to develop assessment of writing for the purposes of evaluating w h e t h e r students meet writing standards as defined by different states (De La Paz 1997). Such assessment needs to be reliable, valid, and developmentally appropriate. The field of writing instruction has evolved from an exclusive focus on the writing product and a myopic obsession with sentence grammar to a focus on the writing process that generates the product and an interest in all levels of language in writing, especially discourse or text structure. O f all the language systems, the functional writing system takes the longest to mature, but educators have been helping children to begin the j o u r n e y to skilled writing early in schooling instead of delaying it until the middle school years. This change in instructional practice should prevent many writing problems by c o m m e n c i n g instruction during a developmentally sensitive time window. Although writing researchers have learned a great deal about effective instruction for automatizing transcription skills and self-regulating the planning and revising processes, they have m u c h to learn yet about helping writers enter the free flow of the idea generation process and channeling and transforming that flow into well-crafted discourse.

RECOMMENDATIONS

FOlK FURTHER

READING

Handwriting Rubell, B. 1995. Big Strokesfor Little Folks. San Antonio, TX: Therapy Skill Builders. Berninger, V. 1998c. Handwriting Lessons Program, Process of the Learner (PAL) Intervention Kit. San Antonio, TX: The Psychological Corporation.

Spelling Berninger, V., Vaughan, K., Abbott, R., Brooks, A., Abbott, S., Reed, E., Rogan, L. & Graham, S. 1998. Early intervention for spelling problems: Teaching spelling units of varying size within a multiple connections framework..Journal of Educational Psychology. 90:587-605. Bryant, P., Nunes, T. & Bindman, M. 1997. Children's understanding of the connection between grammar and spelling. In B. Blachman, ed. Foundations of reading acquisition and dyslexia. Mahwah, NJ: Erlbaum. Carlisle, J. 1994. Morphological awareness, spelling, and story writing. Possible relationships for elementary-age children with and without learning disabilities. In N. Jordan & J. Goldsmith-Phillips, ed. Learning disabilities. New directionsfor assessment and intervention, (123-145). Boston: Allyn and Bacon. Foorman, B., Francis, D., Novy, D. & Liberman, D. 1991. How letter-sound instruction mediates progress in first-grade reading and spelling.Journal of Educational Psychology. 83:456-469. Fry, E. 1995. Spelling book: Words Most neededplusphonicsforgrades 1-6. Phoenix: Learning Resources. Treiman, R. & Bourassa, D. 2000. The developmentof spelling skill. Topics in Language Disorders.20:1-18.

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Composition Harris, K. & Graham, S. 1996. Making the writing process work: Strategiesfor composition and self-regulation, 2nd ed. Cambridge: Brookline Books. Hillocks, G. 1986. Research on written composition: New directions for teaching. Urbana, IL: National Conference on Research in English. Kempfer, D., Nathan, tk. & Sebranek, P. 1995. Writers Express: A Handbook for Young Writers, Thinkers, and Learners. Wilmington, MA: Write Source/D.C. Heath. Wong, B. 1997. Research on genre-specific strategiesfor enhancing writing in adolescentswith learning disabilities. Learning Disability Quarterly. 20:140-159.

Integrating Writing Instruction and Technology MacArthur, C. & Graham, S. 1987. Learning disabled students composing with three methods: Handwriting, dictation, and word processing.Journal of Special Education. 21:22-42. MacArthur, C., Schwartz, S. & Graham, S. 1991a. A model for writing instruction: Integrating word processingand strategyinstruction into a process approach to writing. Learning Disabilities Research and Practice. 6:230-236.

Self-Regulated Writing Process Zimmerman, B. & Kisemberg, R. 1997. Becoming a self-regulatedwriter: A social cognitive perspective. Contemporary Educational Psychology. 22:73-101.

MAKING

CONNECTIONS

Questions preceded by * may be most appropriate for graduate students. 1. If children learn to talk and listen through play with language during the preschool years (Garvey 1977), should play with language also be an important part of learning to write (and read) during the early school years? H o w can teachers foster a playful, affective disposition to language during writing development? (Hint: see Tables 8.1 and 8.2 with Victor Borge's inflationary language; Graves 2000; and Mahoney & Mann 1992). 2. H o w might the composing talents of a beginning writer be undetected because they are masked by transcription problems in handwriting and/or spelling? What might a teacher do to stimulate development of composing skills despite a student's impaired transcription skills? Why, from the perspective of the attention and working memory systems, should low-level skills be automatized? H o w might transcription skills be automatized as efficiently as possible? H o w might failure to teach transcription skills explicitly compromise writing development? H o w might students with impaired transcription skills be unfairly penahzed in assessment of whether they meet state standards for writing?

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3. How can self-regulation of the writing process be taught explicitly? 4. How can a classroom program be structured so that students get sufficient instruction in skills like spelling but also in creative writing and problem-solving writing (a) in the primary grades, and (b) in the intermediate grades? Why is timing important in delivery of instruction? Why does it matter, from a brain perspective, how long a lesson or practice session lasts? Why does it matter, from a brain perspective, how skills are sequenced within a block of time? What are some of the advantages of computers in writing instruction? What are some of the advantages of pencils/pens in writing instruction? Should writing instruction aim to create children who are bilingual with pencil and keyboard? Why or why not? If so, how? *5. Are the concepts of natural learning and explicit instructional cues mutually exclusive? Are these variations on the theme of inductive versus deductive learning? From the perspective of the developing brain, do inductive and deductive learning both involve the child's brain interacting with other brains (adult and child) in a social context? Is decontextualized language (language used outside a conversational format) unnatural or just a different social registrar than used in formal schooling and literacy learning? *6. What cognitive processes should be taught inductively in the instructional program in writing (a) for beginning writers, and Co) for developing writers? How? What cognitive processes should be taught deductively in the instructional program in writing for (a) beginning writers, and (b) developing writers? How? How might a writer's attentional system interfere with the cognitive processes in writing? How might the writer's attentional system facilitate the cognitive processes of writing? *7. If writing researchers have difficulty assessing writing reliably and validly (e.g., Humes 1983), why should states also be cautious in developing assessment tools for evaluating whether students meet state standards in writing? From a developmental perspective, what aspects of writing should be assessed at the primary grade, intermediate/middle school grades, and high school levels? How can the grapho-motor, language, cognitive, and metacognitive processes be assessed separately in a reliable and valid way? Given that it normally takes 10 to 20 years to become a skilled writer, how can developmentally appropriate criteria be established for written output?

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Building a Computing Brain Pedagogically

In contrast to language that is a code, math is a conceptual domain. As discussed in Parts II and III, the language code can team up with different senses and motor systems to help the brain understand and express concepts and even transform concepts to create knowledge. Nevertheless language is not a concept in the way that math is a domain of concepts. Concepts are abstract ideas in the mind. Since ancient Greek civilization, philosophers have debated whether these concepts are innate (programmed in the mind at birth) or created by experience. Piaget (1970) integrated these two contrasting views in a model that captured the nature-nurture interactions of conceptual development. He argued, and demonstrated with evidence based on observation of child development, that concepts are not prewired in the infant's brain, but rather are constructed as the neural machinery operates (acts) upon the world; hence thought that generates concepts is operational. The cornerstone foundation (see Counting and N u m b e r Line Concept in Figure 7.1) of the mathematical domain is the concept of number. From this concept, the brain, as it interacts with the world, constructs more concepts that are represented as mental models in distributed neural networks. Both the quantitative dimension and logical structures (Piaget 1952, 1970) contribute to how these mental models are constructed and represented in the brain. The brain draws on both inductive Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

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thinking (e.g., in discovering patterns) and deductive logic (e.g., in applying prooi~) in constructing this conceptual domain. During the construction process the brain also uses multiple codes to represent and understand this emerging conceptual domain (see redundancy as an organizing principle in Chapter 3 and the codes for math in Chapter 7). The hand, the memory system (short-term, long-term, and especially working memory), the attention system, the executive function system (see Chapters 5 and 7, and Table 7.1), and the language system are important parts of the functional math system that must be taken into account in planning instruction for wiring the Computing Brain. The hand, which is instrumental in development of the written language system (see Chapter 9), also plays a major role in development of conceptual representations of the world. The hand manipulates objects in the world, and from these experiences, constructs the initial mental concepts. As discussed in Chapter 7, at first concrete objects are manipulated and their sensori-motor features are represented in primary cortex (see Chapter 4); later the more abstract features of the manipulated objects are represented in the secondary and tertiary association areas (see Chapter 4). Even later in development, the objects become symbols that stand for objects and the symbols are manipulated mentally; but the hand, operating a pencil or keyboard, participates in the functional system for mental manipulations of the symbols. From the perspective of cognitive development, the hand and the brain systems (e.g., grapho-motor) that support hand function play a role in acquiring and using mental concepts in the math domain. Working memory also plays an important role in conceptual development in the math domain. Like the Writing Brain, the computing brain also develops from both play and conscious work (see Chapter 9). From play with objects, young children inductively abstract knowledge of the physical world and its quantitative dimensions. From play with ideas during the school years, children inductively abstract relationships between numbers to other conceptual variables in the mental models they construct about the world. At the same time, not all mathematical knowledge is gained inductively. Development of the Computing Brain requires guided assistance in translating implicit knowledge based on experience into explicit knowledge that can be used for the hard work of math problem solving that is conducted in resource-limited, temporally constrained working memory (see Chapters 5 and 9). While computing in the problem-solving space, working memory draws on incoming information stored in short-term memory buffers, on activated information stored in long-term memory, and on executive control processes for coordinating the problem-solving activity (see Table 5.3 and Chapters 5 and 7). Swanson (2000), who has a long-standing research program on working memory during reading, writing, and math (e.g., Wilson & Swanson, 2001) in school-aged children, grapples with the issue of whether working memory puts limits on how easily a skill like math is learned or whether learning to do a skill like math increases the capacity of the working memory for that skill. In other words, in which

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direction are causal relationships exerted-- from working memory to math or from math to working memory? Or, is the relationship bidirectional with both exerting influences on the other? Although it is hard to tease apart influences from brain maturation, instruction, and practice, more research is needed to reach resolution on these issues. Late developing frontal circuits may exert constraints on math learning via the supervising executive component of working memory or the anterior component of attention (see Chapter 5). However, despite the need for further research on these issues, it is probably a good idea to include in math instruction a component devoted to increasing working memory capacity for high-level math problem solving. One way to do this is to emphasize mental arithmetic, beginning with counting and manipulating numbers inside one's head. As with reading and writing, instructional design principles are proposed for developing the Computing Brain. These are based on the research literature, the University of Washington's Math Trek research project (see Acknowledgements), and from the Dr. Berninger's experience in teaching math to normally developing elementary school students, assessing students with math problems, and consulting with schools. The first instructional design principle is that the teacher has an important, but complex, pedagogical role to play in wiring the Computing Brain, a task that should not be left for the student to accomplish unaided. One might think this principle is self-evident, but some math curricula are not as clear as they could be about the teacher's role in explicit instruction, especially from the perspective of the developing brain. Although children arrive at school with informal knowledge of math, teachers play an important role in building the Computing Brain through formal, explicit instruction designed to promote deductive reasoning as well as teacherguided instructional activities designed to promote inductive reasoning. Some approaches to math education mainly emphasize that children construct knowledge from their inductive experiences, whereas others emphasize didactic teaching of math facts and arithmetic algorithms for deductive application. From the perspective of a developing brain, exclusive reliance on either approach alone is likely to prevent the Computing Brain from developing optimally. Children left alone to discover (jargon from the sixties) or construct (jargon from the nineties) their computing brain on their own may fail to do so, just like some children do not abstract the patterns for decoding written words when these are not explicitly taught in reading (see Chapter 8). At the same time, children who are only taught math in a didactic, rote fashion are never likely to learn how to think about numbers, the relationships among numbers, and the relationship of the quantitative domain to solving a variety of problems. The teacher also contributes by carefully observing errors children make, which provide a lens for examining their thought processes and gaining clues for how to guide their thinking (Brown & Burton 1978; Ensight 1990; Russel & Ginsburg 1984). Both the novice and developing math brains need a healthy mix of play and work, induction and deduction, construction of knowledge, and explicit instruction--all orchestrated by an expert teacher.

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The second instructional design principle is to keep the distinction between math concepts and codes clear. This distinction is important for two reasons: (a) so that any difficulty with codes is not confused with difficulty in understanding concepts and vice versa, and (b) so that explicit instruction is aimed at both concepts and codes. Each of the codes (i.e., the representational systems in the brain for concepts) should be taught explicitly. One code is verbal, which is the same as the language code discussed in Chapters 5, 6, 8, and 9. Other codes are quantitative, visual-spatial, and visual symbols (see Chapter 7). An example of a quantitative code is the mental number line. Examples of visual spatial codes are line drawings with and without arrows. Examples of visual symbols are arabic numerals that represent numbers (mental concept) and signs that represent operations performed on numbers (e.g., + for addition a n d - for subtraction) or relationships among numbers ( e . g . , - , >, or <). The third instructional design principle is that the more automatic or efficient certain kinds of mental activity are, the more working memory workspace there is for the nonautomatic and reflective kinds of mental activity. This principle applies not only to the Computing Brain but also to the reading (P, esnick & Ford 1981) and the Writing Brain (McCutchen 1996) (see Table 1.1 and Parts II and III). This principle may seem paradoxical to some because it conflicts with a prevailing philosophy of math education that emphasizes reflection and construction of mathematical thinking and concepts rather than rote calculation; but it is grounded in current research on the role of automaticity of lower-order skills in higher cognitive functions. Normally developing brains, given appropriate and sufficient practice, automatize low-level skills, freeing the limited resources of working memory for high-level thinking, which becomes more efficient with practice. Three components in the arithmetic module need to be practiced m m e m o r y retrieval of math facts (relationships among three numbers), grapho-motor production of visual notation, and application of calculation algorithms. Although such practice is needed to automatize the first two processes and make the third one more efficient, practice of these low-level skills alone is not sufficient. Also needed are (a) teacher-guided instruction in math procedures, concepts, and problems solving strategies, (b) teaching for transfer of low-level skills to high-level skills, and (c) practice in applying concepts and strategies to high-level problem solving. The fourth instructional principle is that there may be two instructional routes to practicing low-level counting and calculating skill. The first route is mental arithmetic m practice in the internal workspace of working memory. The second route is paper and pencil arithmetic m practice in the external workspace that overcomes the capacity limitations when internal operations exceed workspace available. Combined practice in the internal workspace and the external workspace may result in greater efficiency than mental practice or external practice alone, but research is needed on this topic. Combined practice may also result in more efficient processing due to less internal working memory space being used for the computing job. Combined practice may expand the capacity of working m e m o r y ~ increase

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the actual amount of the workspace available for different jobs. Failure to use the workspace in working memory may have the opposite effect--reducing its capacity. Combined practice may also increase efficiency by using less neural energy to fuel the same job in the workspace in working memory. The fifth instructional design principle, which follows from the fourth, is equal time for mental arithmetic as for paper and pencil arithmetic. Mental arithmetic, which was in vogue at the beginning of the twentieth century, fell by the wayside for many reasons, but from a brain perspective is important for the working memory component of a functional math system. External supports like paper and pencil or calculators are not always handy; and speed of mental computing may be necessary for advanced mathematical thinking (Hoffman 1998; Royer, Tronsky, Chan, Jackson, & Marchant, 1999). Likewise, paper-and-pencil arithmetic should not be dismissed as worthless rote exercise in the era of hand-held calculators. As with computers in writing, the calculator is only an external, hand-held device that supports the brain's internal cognitive activity in working memory. The sixth instructional design principle is to teach and evaluate math learning from a developmental perspective; that is, from the perspective of the novice or developing brain and not from the perspective of the expert brain, just as novice writers are not scaled-down versions of skilled writers, novice mathematicians are not scaled-down versions of expert mathematicians (see Chapters 6 and 9). The Computing Brain, like the reading and writing brain, normally is wired over a long developmental window of approximately 10 to 20 years, corresponding to the usual period of formal schooling across many cultures. Math should be taught in a developmentally sensitive manner from the perspective of a child making the journey, the budding mathematician, rather than from the perspective of the expert, who has completed the journey. Also, because of individual differences due to normal variation, all computing brains cannot be expected to reach the same level of expert knowledge, any more than they can reach the same level of expert reading and writing skill. In Part IV we return to this issue in regard to developing reasonable educational policy that is in the best interests of individuals and society. The seventh instructional design principle is to wire computing brains within a social community. This principle may be startling to those who during their own schooling experienced math as a private activity engaged in at one's own desk or home. Erdrs, a world-renowned mathematician, who was talented with numbers (cortical and subcortical activity, see Chapter 7) and who loved them (limbic activity, see Chapter 3)~especially counting them in working m e m o r y ~ illustrates this principle. He learned, practiced, and created number concepts with others ~ often in their homes. It was such a privilege for other mathematicians to work with Erd6s that they began counting how many people worked directly with him or knew someone who did or knew someone who knew someone who did and so on. In fact, one's Erd6s n u m b e r - - h o w many people one is removed from someone who actually did math with E r d r s ~ i s a mark of honor within the community of mathematicians. In the last two decades, Asian educators have taught

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an important lesson to American educators in how to teach the thinking part of math n not by leaving children on their own to discover math concepts from "hands-on" activities, but rather to solve problems jointly in open-ended classwide discussions in which children propose solutions, justify solutions, weigh the evidence for alternatives, and reach informed conclusions based on the best evidence. The process of thinking is important but so is reaching a defensible answer. There is often more than one way to solve a problem, but not all solutions are defendable, let alone correct. This collaborative problem-solving discussion approach for math has many similarities to that for reading and writing (Anderson et al. 1997, 1998). Before discussing how to wire a novice computing brain and a developing computing brain, we summarize the general instructional approach based on current understanding of the brain. The general approach emphasizes inductive and deductive learning, explicit instruction in the multiple codes used for representing math concepts, and computational operations in both internal working memory and the external environment. This approach is developmental in how instruction is packaged and learning outcomes are evaluated. We emphasize that school-age children are on a math trek toward skilled computing, but should not be judged against the standards of the expert until they complete the journey. Like reading and writing, the journey takes time and the brain is not fully wired for expert performance early in the process. Other brain systems, like attention, executive functions, and working memory, must be taken into account. Just as the reading and writing brain must learn to work with many different brain systems, so must the Computing Brain. Indeed the Computing Brain is a social accomplishment in which multiple components, both within the mental world and the external environment, learn to work together in a society of mind(s) (Minsky 1986).

PEDAGOGY FOR CREATING A NOVICE COMPUTING BRAIN

Encourage Strategies Initially Prior to first grade, typically children have mastered the first three skills in Table 10.1 n rote counting, counting objects with one-to-one correspondence, and internalized counting along a mental number line. However, even though they have quantitative representations in their mind, first graders are likely to use visible strategies, like counting on their fingers, when solving basic math facts (Jordan & Hanich 2000; Siegler 1988a). Sometimes children use the strategy of count all, and sometimes they only count on from one of the numbers (Siegler 1988a). Strategies are important in learning number facts because they allow children to develop a conceptual understanding of number and number operations. One strategy that helps the Computing Brain build a mental representation of the number line

Building a C o m p u t i n g Brain Pedagogically TABLE 10.1

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Novice StagemGoals: (a) acquire multiple representations (codes) for concept of number and place value (verbal, sensori-motor, visual-spatial, and quantitative), and (b) create a functional arithmetic module in external memory and internal working memory. 1. Count by rote (attach verbal names to numbers as objects). 2. Count with one-to-one correspondence (attach number names to quantitative attributes of objects). 3. Represent numbers in visual notation (arabic numerals or digits). a. Naming numerals (expressive phonology-visual representations) b. Writing numerals (visual-grapho-motor representations) 4. Count along a number line represented visually in external memory while touching each numeral as it is named m in forward and reverse direction by variable increments (1, 2, 3, etc.) (sensory [touch, visual, auditory], fine motor, verbal/expressive phonology, and quantitative codes for the concept of number). 5. Count with inside voice along a mentally imaged number line in internal working memory in forward and reversed directions by variable increments (1, 2, 3, etc.) (create mental visual, verbal, and quantitative codes for number). 6. Create multiple representations of the place value concept using concrete manipulatives (e.g., color-coded bars or beads to represent units, tens, and hundreds) and using the visual notation system (naming and writing numerals). 7. Create multiple representations in memory and automatic retrieval routines for addition, subtraction, multiplication, and division number facts (visual, verbal, grapho-motor, conceptual/ semantic codes for the number concept). 8. Create multiple procedural representations in internal working memory (mental codes) and external memory (visual notation system) for addition, subtraction, multiplication, and division algorithms (steps of operations performed on number facts). 9. Coordinate the computing system with language systems (specialized vocabulary, word problems). 10. Reason with numbers, visual-spatial representations, and verbal codes to solve math problems. Developing Stage m Goals: (a) acquire multiple representations and applications of part-whole concept, (b) increase speed of operations in the arithmetic module (fact retrieval and application of algorithms), (c) learn strategies for self-regulating number fact retrieval and arithmetic operations (e.g., self-monitoring and self-correcting strategies and magnitude estimation in problem solving), (d) transition from concrete to abstract operational thought in problem solving, and (e) learn the logical structures in the conceptual domain of math and apply them to problem solving. 11. Create conceptual representations of part-whole relationships and apply to operations involved in fractions, measurement (time and money), geometry, and algebra in (a) internal working memory and (b) external memory using the visual notation system. 12. Automatize math fact retrieval and arithmetic algorithm in both internal working memory and paper and pencil calculations. 13. Use external props like calculators to increase speed of arithmetic operations in math problem solving or to bypass problems in producing the visual notation system.

(continues)

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TABLE 10.1

(continued)

14. Learn strategies for self-checking answers to arithmetic computations. 15. Learn strategies for justifying approach to problem solving. 16. Create abstract symbolic representations of variables for mathematical thinking. 17. Solve math problems of increasing difficulty.

Implementation of Instructional Design Principles 1. Teach to multiple codes for representing a concept (number, place value, or part-whole) close in time so that connections can form in temporally constrained working memory. 2. Teach for transfer a. from automatic low-level skills (tools of arithmetic) to nonautomatic high-level skills (problem solving) within the same session. b. from other-regulation to self-regulation over development. c. from classroom to everyday life. 3. Teach for success a. by teacher modeling through think alouds. b. by scaffolding (guided assistance). c. by class discussion of alternative ways of solving the same problem and of justifying approach. d. by emphasizing the thinking process and not just whether the answer is correct. 4. Teach all the necessarycomponents at student's developmental level in an integrated fashion so that computing system becomes functional. 5. Assessfrequently using developmentally appropriate criteria and multiple modes (see Chapter 12).

(see C h a p t e r 7) from outside-in (see Table 10.2) is for students to have on their desk a n u m b e r line, w h i c h they can touch as they count aloud the numerals they see, thereby forming sensori-motor and verbal coding in cortex o f the quantitative dimension. Manipulating objects or using pictorial representations o f objects during p r o b l e m solving may help in understanding math concepts, but some children will n e e d help in interpreting pictures (Fleischner & M a n h e i m e r 1997; Garnett 1998).

Foster T r a n s i t i o n to A u t o m a t i c i t y O v e r the course o f elementary school, children will progress from using strategies to direct, automatic retrieval o f math facts (Garnett & Fleischner 1983; Jordan & H a n i c h 2000). This normal progression parallels the transition from strategies to automaticity o f low-level skills in reading and writing (see Table 1.1 and Chapters 5, 6, 8, and 9). By fourth grade, most students show habitual automaticity in retrieving math facts (Kaye, de Winstanley, C h e n & Bonnefil 1989).

Building a Computing Brain Pedagogically TABLE 10.2

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Lessons Aimed at Developing Functional Computing System

I. Teacher Explaining the Same Concept in Multiple Representational Systems in One Lesson (Outside to Inside the Mind) A. External Memory

Use concrete props to represent and solve a problem in visual-spatial format (concrete-operational math using manipulatives). Use notation system to represent and solve the problem in visual symbols (paper and pencil or calculator math). Use language to represent the verbal part of a quantitative problem (word problems). B. Internal Working Memory (Mental Math) Use mental imagery to represent and solve the problem. II. Teaching Arithmetic from Low-Level to High-Level Components Number Warm-Up: With novices, vary sensory-motor input and output combinations for fact

retrieval: Hear---* Say, Hear-~ Write, See -~ Say, See --~ Write. With developing students, do mental math (arithmetic algorithms or other calculation operations in internal working memory). (Engage Arithmetic Module in Figure 7.1) Number Work: (Engage Problem Solving Space in Figure 7.1)

Explicit instruction (deductive) about computational algorithm, math concept, or problem solving or self-monitoring strategy using concrete manipulatives, visual notation system, and/or verbal word problem in external memory. Inductive Problem Solving (teacher-guided, student problem-solving discussion of possible solutions and justification with evidence). Number Play: To encourage a positive affect toward numbers and quantitative thinking, provide opportunites for playing board games, puzzles, and computer games involving high-level problem solving (not eye-hand coordination).

Learning m a t h facts helps the C o m p u t i n g Brain transition from true c o u n t i n g (by increments o f one) to p e r f o r m i n g operations on n u m b e r s because it allows faster c o u n t i n g along the n u m b e r line in increments (mostly larger than one) and in either a forward (addition) or backward direction (subtraction), and even faster c o u n t i n g t h r o u g h repeated addition (multiplication) or repeated subtraction (division). O n c e n u m b e r facts can be manipulated strategically or automatically, arithmetic operations can be p e r f o r m e d on m o r e c o m p l e x quantitative a m o u n t s along the n u m b e r line than are possible w i t h c o u n t i n g by increments o f one. T h e c o m p u t i n g brain probably has schema-based (conceptual) representations, as well as n e t w o r k m e m o r y associations, for m a t h facts (Baroody 1994). O n e way to acquire the c o n c e p t o f n u m b e r facts is to have varied experience in c o u n t i n g along an external and internal n u m b e r line. For the reasons discussed earlier in this chapter, and t h r o u g h o u t this book, automatization is a critical process in skill acquisition. A widespread practice for automatizing m a t h facts is m i n u t e m a t h (writing as m a n y answers as one can in one

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minute). To avoid habituation due to using the same approach over and over (see Chapter 4), four other approaches might be used as well, especially for those students who have difficulty forming associations between the visual symbols and grapho-motor output, but who may be able to make connections on other sensorimotor pathways. These approaches might be used with addition and subtraction facts in the first two grades and with multiplication and division facts in the third and fourth grades. To avoid habituation (see Chapters 4 and 8), practice time for all these approaches should be brief, and followed by other activities that require transfer of math facts to math problem solving. First, automatize the visual notation system for writing digits by (a) studying models of digits with numbered arrow cues and write them from memory (see Chapter 9), and (b) writing single and double digits from dictation (their verbal name cues). Second, practice different combinations of input-output retrieval routines for the basic math facts and time performance for completion of the set: (a) look-say, (b) look-write, (c) listen-say, and (d) listen-write. Third, students refer to their own Go Figure! Math Fact Card, which contains all the addition facts displayed on one side and all the subtraction facts displayed on the other side (or multiplication on one side and division on the other side), while they compute. In the process of referring to these, associations may form on the implicit memory networks (see Chapter 5). Fourth, teacher-led games in which children visualize, in their mind's eye, and name, with their inside voice, the numbers in a number fact equation may lead to more automatic activation of number facts in working memory. Additional research is needed to explain why some children automatize math facts with minimal effort and a little practice, but others require considerable effort to do so.

Model Place Value in Multiple Codes Children need to understand why more than one numeral is needed to represent most numbers. The concept should be taught using a multimodal approach so that redundant representations are created in the brain. For example, two kinds of manipulatives illustrate the concept of place value: First, when more than ten beads accumulate on the wire for the units place, they convert to one bead on the wire for the tens place; when more than ten beads accumulate on the wire for the tens place, they convert to one bead on the wire for the hundreds place, etc. Second, cuisenaire rods (Magarian-Gold & Morgenson 1990) use the same color for blocks of the same size, and portray how ten of the unit-size blocks equal one of the tensize blocks, and ten of the ten-size blocks equal one of the hundred-size blocks. For visual notation, children write digits to teacher dictatation of multiplace numbers that vary in how many places have values (or zero for a place holder). For verbal coding, numerals that vary in how many places have values (or zero for a place holder) are named as multiplace numbers.

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Teach Arithmetic Algorithms Instruction should first provide a conceptual understanding of the operations that can be performed on number facts (addition, subtraction, multiplication, and division): (a) order of operations does not matter for addition and multiplication but does for subtraction and division; and (b) addition can be used to undo subtraction, and multiplication can be used to undo division. Procedural strategies should also be taught (verbal rules), with attention to the temporal-sequential steps and visual-spatial requirements: In North America these strategies often include moving from fight to left and top to bottom for addition, subtraction, and multiplication, but from left to fight, top to bottom, and bottom to top in long division. These procedural strategies or algorithms (rules of thumb) for arithmetic calculation (see Figure 7.1) need to be linked to the attention system and the executive system (see Figure 7.2). If attention is not paid to the sign that indicates the operation to perform, the wrong operation will be applied. If self-monitoring strategies are not applied, obviously wrong answers will be missed. If self-checking strategies are not learned m for example, adding in reverse direction to check subtraction problems or multiplying in reverse direction to check division problems m then mistakes cannot be repaired.

Link Computing with Language Systems The Computing Brain is very dependent on the aural/oral language systems because math instruction is usually delivered in spoken language that the child must process aurally and, if asked questions, the child must answer orally. The problem-solving discussion method discussed earlier in this chapter requires that children understand their peers' speech and language and translate their own thoughts about numbers into words. Math also has specialized vocabulary that needs to be taught. Many forms of math problems are expressed in verbal format, with or without pictorial representations. For example, four kinds of operations, each with its own special vocabulary--change, equalize, combine, and compare--are common in beginning word problems (Jordan & Hanich 2000). For change, the initial quantity is increased or decreased by a constant. For equalize, one quantity is changed to equal another. For combine, two quantities are put together. For compare, the difference between two quantities is computed.

Facilitate Math Problem Solving Early math instruction should build upon the informal quantitative knowledge children bring to school and should make ample use of math problems based on everyday events of the world children live in (Anno 1987; Ginsburg 1989, 1997;

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Kamii 1985, 1989, 1994; Scieszka & Smith 1995; Waxman, Robinson & Mukhopadhayay 1997). These math problems can be presented orally or in written format and can be based on activities, using concrete objects designed to promote reflective discussion. Teachers can pose the problems by asking questions rather than telling information. Multiple solutions and justifications for solutions should be encouraged. Formal problem solving can be supplemented with a variety of number games, including board games like Monopoly that require application of quantitative knowledge. During choice time, students can select from among these.

PEDAGOGY FOR CREATING A DEVELOPING COMPUTING BRAIN

Explore Part-Whole Relationships As discussed in Chapter 7, understanding the part-whole concept is necessary for grasping fractions, time, decimals, money, measurement, probability, and even algebra. Once the concept of number and place value is understood, the concept of variable parts for a constant whole should be demonstrated through concrete operational activities and not just rote performance of calculations involving fractions, time, decimals, and so forth.

Find Patterns Another conceptual advancement as the Computing Brain develops is the discovery of patterns in numbers. For example, for teaching and testing purposes, a series of numbers is given with the last one missing. The task is to figure out what the missing one is, indicating that the underlying rule generating the series has been abstracted. Mathematicians tackle a harder task--figuring out heretofore undiscovered number patterns in the infinite number line containing positive, negative, prime, real, and imaginary integers (and parts of integers), which can be expressed in different bases and subjected to different mathematical transformations. From predictable relationships in such number patterns, much has been learned about the mathematical structure of the physical universe (see Greene 1999; Hoffman 1998).

Increase Speed of Operations The faster the Computing Brain computes, the more etticient it is at problem solvingmprobably because capacity of working memory space for computing is increased, as discussed earlier in this chapter. See Royer et al. (1999) for evidence that faster processing is associated with advanced math problem solving. Practice is

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essential for increasing efficiency of computing in working memory. An analogy to reading is instructive. Word reading may lead to automaticity of word-level processes and practice in text reading may lead to efficiency (fluency) of textlevel reading (see Chapter 8). Likewise, practice with math facts may lead to automaticity of math fact retrieval, but practice in applying arithmetic algorithms to math facts and in math problem solving, may lead to efficiency of problem solving (faster processing). Although automatic processing is faster than nonautomatic processing, speed alone does not differentiate automatic and efficient processing. Automatic processing involves direct retrieval and is supported by the behavioral pathway, whereas efficient processing, which is also fast, may involve cognitive schemata (relational concepts) and may be supported by the cognitive pathway (see Chapters 3 and 5). No doubt, maturation of the frontal areas, which continues throughout middle childhood and adolescence (see Chapter 4), plays some role in achieving math efficiency, but instruction also plays an important role. Two key instructional design principles are likely to lead to increased automaticity and efficiency of number processing. The first is practice; that is, repeated experience in math fact retrieval, performance of arithmetic operations, and application to problem solving. The second is engagement during practice of both the internal working memory environment and the external visual-spatial environment. For both the internal and external environments, the kind of practice has to be developmentally appropriate. For example, for mental practice, the novice brain may be prompted to count on an imaginary mental line inside the head with the inside voice m forward and backward and by variable increments (one, two, three, four, five, etc.) and forward or backward to represent the basic number facts. The developing brain may be prompted to apply arithmetic algorithms to solve math problems that require reasoning. Some math problems, but not all, require arithmetic. For external practice with the notation system, novice brains might write number facts and arithmetic computations, whereas the developing brain might use paper and pencil to support solving multistep word problems. Production of the visual symbols and signs used in math thinking may lead to representation of these symbols and signs in cortex, where they may be activated during mental problem solving. However, children who have impairment in grapho-motor or visual skills needed to produce the visual symbols in the external notation system may not derive the same benefit from practice as children without those impairments; adapatations may be needed in helping them derive benefit from practice with paper and pencil. The developing functional math system, in addition, may become more efficient through practice, with hand-held calculators that provide external support for internal computation. In sum, the Computing Brain benefits from a healthy mix of practice in mental and written arithmetic and math problem solving. A pedagogical challenge is to make sure that the brain gets sufficient, but not more than needed, practice to automatize math fact retrieval and to develop efficient arithmetic calculation. Given

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limited time in the school day, teachers need to achieve the right balance between practice of low-level skills and high-level math thinking.

Transition to Abstract Thought Developing brains become increasingly capable of abstract thinking. Both maturational factors and environmental experiences (Piaget 1970) play a role in this increasing ability to deal with concepts, which are not represented directly in terms of features in the external environment. One consequence of this developmental change (and probably functional reorganization of the developing computing system) is that math learning becomes less dependent on concrete representation (manipulatives and pictorial representations of objects) and more dependent on abstract mental symbols that stand for objects and operations that can be performed on objects. The developing computing brain acquires the ability to use these symbols both to represent problems and to solve problems; for example, in algebra equations or geometry proofS. However, there are individual differences among learners in (a) how early this transition to abstract thinking emerges, (b) how easy it is for them to make the transition to abstract mathematical thinking, (c) what levels of abstract thinking they can eventually achieve, and (d) how abstract they become in thinking in each of the different domains of school curriculum. One educational challenge is how to nudge individual developing computing brains along their zone of proximal development. Another educational challenge is to determine when they are ready to cross the bridge from concrete into abstract thinking; that is, to enter the world of mental models of their own and others' invention.

Foster Self-Regulation, Motivation, and Positive Affect Like music, themes repeat in the wiring of the learning brain. For the novice and developing functional math system, self-regulation, motivation, and affect are as important as they are for the functional reading and writing systems (see Chapters 8 and 9). From a brain perspective, explicit instruction aimed at the executive functions helps the functional math system to learn to self-regulate. For example, Naglieri and Johnson (2000) taught planning strategies for arithmetic computation to middle school students with diagnosed planning problems. Those students improved in arithmetic performance, but the students who had other problems (for example, in attention) did not because the executive function taught was not one that they needed. Such aptitude-treatment interactions are rare in educational research, which sorely needs more research on how best to teach students with specific kinds of executive function problems so that they can better self-regulate academic learning during the middle school and high school years.

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Another topic on which research is sadly lacking is instructional approaches to improving motivation for practicing math skills (and other academic skills) until they reach suffcient automaticity and efficiency to support self-regulated high-level thinking. A first step in this direction is a study by Logan and Skinner (1998), who found ~that the most effective way to motivate students to do math is to mix challenging, "stretch the mind" tasks (lower probability for success but within the zone of reasonable development) and "easy success" tasks (ones with high probability of success). Students were more willing to work hard if they experienced success as well as challenging work in the process. As for affect, it is well known that math causes anxiety in many students and liking for math may decrease as students become older. Part of the problem may be the way math is taught in middle and high school--primarily geared to collegebound students and rites of passage in gaining college entry. For students who do not fit neatly into this developmental path, math curriculum may be an unmotivating recycling of what was taught in the past rather than a fresh approach that capitalizes on teacher-provided situational motivation for creating interest in the subject (Hidi & Harackiewicz 2000). A large part of the problem, however, may be the lack of teaching for transfer from mathematical thinking in the classroom to everyday living and job tasks. More research has been devoted to everyday math thinking for young children, as discussed earlier in this chapter, than to everyday math thinking for adolescents and young adults, both college-bound and not.

Consider Curriculum Sequence for Logical Structures in Math Subdomains In contrast to reading and writing, knowledge acquired by the Computing Brain is considered a content subject area of the curriculum. That is, there is a specific body of knowledge associated with it, and this content-specificity has instructional implications. Math, as a discipline, developed over time by a community of mathematicians, who (a) jointly defined problems, (b) acknowledged when a problem was solved, and (c) then proceeded to new problems. Although many different problems were worked on at the same time within the larger community, work on a specific problem has a sequential history. As a result, within math subdomains (e.g., algebra, trigonometry, geometry, and calculus) there are logically unfolding sequential steps toward understanding specific topics within the body of knowledge. Understanding one concept is a prerequisite for understanding a subsequent concept, and each new concept builds on prior ones, much as ladders are climbed one step at a time to reach new heights. Unfortunately curriculum does not always reflect this logical sequential structure. Experts, who have both cognitive and metacognitive knowledge about each subdomain, understand the emergent relationships among the subdomains that transcend the sequential relationships. Their expert, metacognitive knowledge has

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sometimes been used to restructure math curriculum. Unfortunately, this approach that superimposes the perspective of the expert on the developing mind is confusing to some students, who learn best in a hnear fashion, with each new goal building upon past knowledge. Sometimes the structure of the curriculum fails to make clear the logical progression within a subdomain. From the perspective of the developing brain, the brain structures that support cognitive processing (Luria's second functional unit) mature before those that support metacognitive processing (Luria's third functional unit) (see Chapter 4). The structure of curriculum should reflect learning from the perspective of the developing brain in a forward direction rather than the perspective of the expert brain that looks backward and reflects. This approach does not mean that reflection is not an important part of the journey toward expert skill; it simply acknowledges that the learner, who does not have access to all the knowledge the expert has, may be confused by an approach that is generated by the experts and makes sense to them but not to those still on the journey. Other students are confused by a hodgepodge approach that emphasizes meaning-making with a vast array of learning activities at the expense of a curriculum that does not make the sequential logic of a knowledge base transparent. To many, the beauty of mathematics is not that there is a correct answer but rather that the conceptual structures that lead to an answer are logical and elegant. Too much focus on constructing an individual's own meaning rather than on learning from the tradition of a socially constructed body of knowledge may actually interfere with learning mathematics, which has an inherent logic that needs to be mastered before one can construct knowledge that can be truly considered new knowledge in this domain. Math instruction that focuses on personal meaning-making may wire a computing brain to reinvent the wheel, whereas math instruction that teaches socially constructed tradition as a springboard for math problem solving within a social community may wire a computing brain that may someday advance knowledge.

IMPLEMENTING RESEARCH-SUPPORTED INSTRUCTIONAL DESIGN PRINCIPLES Wiring the Computing Brain requires instruction aimed at all the necessary instruc-tional components for building a computing brain at its specific stage of development. These components include both low-level skills, like computing, retrieving math facts, and applying arithmetic algorithms, and high-level reasoning to solve problems. In this way, the tools of the math thinker transfer to the work and play of the math thinker. Within a large group instructional session, developmentally appropriate low-level skills should be practiced for short warm-up periods, and then developmentally appropriate high-level problem solving skills should be put to work. For the cool down, developmentally appropriate math play is recommended

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to create positive affect toward math. Within individual or small-group sessions, students can practice math with multiple modes of representation and external supports. All students receive a mix of large group, small group, and individual sessions. Peer-assisted practice may be more effective in helping with the computational than conceptual part of math (Fuchs, Fuchs, Phillips, Hamlett, & Karns, 1995; Fantuzzo, King & Heller 1992), whereas the teacher is probably more effective in explaining new conceptual and procedural knowledge, guiding collaborative problem solving through question posing, teaching self-regulation of math thinking, and creating an interesting, motivating learning environment (Hidi & Harackiewicz

2000). The warm-up, just like athletes' push-ups before the game or musicians' tuneup before the concert, does not last too l o n g ~ or the brain will habituate and there will be no time for the important work (and even play) of the functional math system. The warm-up should include both a mental math component and a visual notation component. The reason is math facts need to be automatized and counting procedures, arithmetic algorithms, and problem solving need to become efficient ~ both in internal working memory and in an environment in which internal working memory is supported by external supports like electronic calculators. The problem-solving component should include a mix of teacher explaining (even telling or transmitting) and teacher-guided oral problem solving in which teachers pose questions and children explain, justify, and defend their thinking and try to reach a group consensus. Some problem-s01ving sessions should be devoted to introducing new procedures, concepts, or strategies (see Figure 7.1), and some should be devoted to application of this procedural, conceptual, or strategic knowledge to real world or domain-specific math problems (math as a formal discipline of knowledge). Group problem solving should be supplemented with individual problem solving completed independently with paper and pencil. The cool-down should include opportunities to play around with math ideas just for the fun of it. Overall, math includes a blend of hard work for sense-making and of playfulness within the context of social tradition (knowledge discovered in the past) and social context (the community of learners who reflect, discuss, justify and defend, and conclude). We illustrate how this model, based on our understanding of how the brain may support math learning, might be implemented instructionally at both the novice and developing stages. In keeping with the instructional design principle that allows for alternative pathways in learning (see Chapter 5), Table 10.2 offers a slightly different way of implementing the conceptual scheme discussed next for teaching to all components of the functional math system.

Lessonfor Novice Computing Brain The group lesson begins with a two-part warm-up. For the first warm-up activity in working memory, children are instructed to close their eyes and image a number

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line, like the one on their desk, and to do the following exercises with their inside voice. First, count forward from one by ones to 24. Then, count forward by two's from 24 to 48. Then count forward by three's from 48 to 72. Then count backward from 72 by one's to 48. Then count backward from 48 by two's to 24. Then count backward from 24 by three's to one. For the second warm-up activity, children are asked to listen and use their pencil and paper to write answers for the following math facts that the teacher says (without looking at their number fines or Go Figure! cards with math facts)" "One plus three e q u a l s One plus six equals . One plus nine equals . Two plus three e q u a l s . Two plus six e q u a l s . Two plus nine equals . Three plus three e q u a l s Three plus six equals . Three plus nine equals Nine minus one e q u a l s . Nine minus three e q u a l s . Nine minus six e q u a l s . Six minus one e q u a l s Six minus three equals .Six minus six equals Twelve minus one equals Twelve minus three equals . Twelve minus six equals ." For the two-part problem-solving session, the teacher first poses the question of how the numbers 361,360, 301, 1230, and 603 can be represented in multiple ways using the beads on wires, cuisenaire rods, paper and pencil, and language for naming numbers. The goal is for children to understand the multiple ways the concepts of place value and zero, which had been introduced in a prior session, can be represented. The teacher then poses a real world math problem for the group to solve through collaborative problem solving. Here is a hypothetical example of such a word problem for the reader to solve, too. If there are only 360 minutes in the school day, and reading takes 60 minutes, writing takes 60 minutes, math takes 60 minutes, lunch takes 60 minutes, morning and afternoon recess take 30 minutes, science takes 45 minutes, and social studies takes 45 minutes, how much time is left for the teacher to teach the test for assessing state standards in reading, writing, and math? Children explain the steps they would go through to solve the problem and discuss why. Each child tries to solve the problem using external aids if necessary. Children compare answers. If children get different answers, they go to the board and show their calculations. The teacher poses questions to guide them through calculation procedures that involve regrouping related to place value, which also had been introduced in a prior lesson. We return to the answer to this problem in discussing policy issues in Chapter 12. For the cool down, children select from a variety of math games to play for 15 minutes in small groups. Some are commercially available and some have been designed by the teacher to reinforce place value concepts. For individual practice, children are given individually tailored assignments to practice the place value and regrouping concepts in subtraction calculations and word problems requiting subtraction of two- and three-place numbers. Results are graphed for an individual record of accuracy and time for completion. Children who do not complete the work take it home for homework but do not miss recess.

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That would create negative affect for math, and there are many reasons for not completing work besides lack of effort or motivation (see Part IV).

Lesson for the Developing Computing Brain For the warm-up, the teacher directs the mental math, which students perform in their internal working memory work spaces: (a) Count forward from 37 by three's until I say stop (after 30 seconds). Count backwards from 93 by three's until I say stop (after 30 seconds). Now, add 123 and 237 (pause 5 seconds); now subtract 200 from that sum (pause 5 seconds); now divide that difference by 40 (pause 5 seconds); now multiply that quotient by 4 (pause 5 seconds). What answer did you get? N o w here is a real-world mental arithmetic problem. You are in line and want to rent three videos, and the sales manager announces on a storewide speaker a special m if you purchase a previously viewed video for $10, you can rent two videos at a special price (one is free and one is half price). If videos are normally rented for $4.50 apiece, should you buy one and rent two videos at the special price or rent three at the regular price? You have one minute to decide before you will be waited on. For the problem-solving, the group discusses collaboratively multiple possible solutions for the following problem. The school day is organized into seven periods. Except for the initial h o m e r o o m period of fifteen minutes and the lunch period of forty-five minutes, all the other periods are fifty minutes long, with ten minutes to switch between classes. The school district policy requires five periods a week in each of four major subjects: language arts, math, science, and social studies. The district also requires two periods a week of physical and health education, one period a week of art, one period a week of music, and one period a week of guidance (study skills, vocational guidance, etc.). The state legislature just passed a law that all students must take a year-long course in social skills, to prevent bullying and violence in the schools. Moreover, there is a regulation in the law that the amount of time spent in this course must be determined by a formula (square root of time in minutes required for two course periods a week). The question is how much time should this school devote to the new course? The goal is to solve the multistep problem by first finding out how much time remains in the schedule for any new courses, review the concept of negative numbers, and introduce the concept of imaginary numbers. If any students object that it is not possible to add one more one requirement to what already exists, then the teacher might consider reading Alice in Wonderland to them and discuss how the real world sometimes makes less sense that the fantasy world of numbers, which exist only in mental models. For the cool-down, students work in small groups, each of which is keeping and updating a diary of all the ways in which they experience numbers or need to think with or about numbers in a typical week. Next week they will begin a project to design a slot machine or lottery game that defies the laws of probability.

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RECOMMENDATIONS FOR FURTHER READING

Conceptual Foundations Gelman, R. & Gallistel, C. 1978. The child's understandingof number. Cambridge, MA. Harvard University Press. Huttenlocher, J., Jordan, N. & Levine, S. 1994. A mental model for early arithmetic. Journal of Experimental Psychology: General. 123:284-296. Mayer, R. & Hegarty, M. 1996. The process of understanding mathematical problems. In R. Sternberg & T. Ben-Zee, eds. The nature of mathematical thinking, 29-53. Hillsdale, NJ: Erlbaum.

Automatization Garnett, K. & Fleishner, J. 1983. Automatization and basic fact performance of learning disabled children. Learning Disability Quarterly. 6:223-230.

Constructive Processes Kamii, C. 1985. Young children reinvent arithmetic. New York: Teachers College Press.

Integrating Verbal and Quantitative Systems Lewis, A. & Mayer, R. 1987. Students' miscomprehension of relational statements in arithmetic word problems. Journal of Educational Psychology. 79:363-371. Resnick, L. 1982. Syntax and semantics in learning to subtract. In P. Carpenter, J. Moser & T. Romberg, eds. Addition and subtraction:A cognitiveperspective. 136-155. Hillsdale, NJ: Erlbaum. Riley, M. & Greeno,J. 1988. Developmental analysis of understanding language about quantitites and of solving problems. Cognition and Instruction. 5:49-101.

Error Analysis and Assessment Brown, J. & Burton, R. 1978. Diagnostic procedural bugs in basic mathematical skills. Cognitive Science. 2:379-342. Ensight, B. 1990. Mathematics assessment tips: A checklist of common errors. Diagnostique. 16:45-48. Russell, R. & Ginsberg, H. 1984. Cognitive analysis of children's mathematical difficulties. Cognition and Instruction. 1:217-244.

Research-based Teaching Tips Baroody, A. & Hume, J. 1991. Meaningful mathematics instruction: The case of fractions. Remedial and Special Education. 12:54-68.

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Fleishner, J. & Manheimer, M. 1997. Math interventions for students with learning disabilities: Myths and realities. School Psychology Review. 24:604-620. Ginsburg, H. 1989. Children's arithmetic: How they learn it and how you teach it. TX: Pro-Ed. Ginsburg, H. 1997. Mathematics learning disabilities: A view from developmental psychology.Journal of Learning Disabilities. 30:20-33. Ginsburg, H., Greenes, C., & Balfanz, R. (2002). Big math for little kids. New York: Dale Seymour. Hiebert, F. & Wearne, D. 1996. Instruction, understanding, and skill in multidigit addition and subtraction. Cognition and Instruction. 14:251-283. Maccini, P., Mc Naughton, D., & Ruhl, K. (1999). Algebra instruction for students with learning disabilities: Implications from a research review. Learning Disability Quarterly. 22:113-126. Resnick, L. & Ford, W. (1981). The psychology of mathematics for instruction. Hillsdale, NJ: Erlbaum. Waxman, B., Robinson, N. & Mukhopadhayay, S. 1997. Teachers nurturing math-talented young childen. R M 96228. Storrs, CT: National Research Center on Gifted and Talented.

MAKING CONNECTIONS The questions within each item that are preceded by an asterisk are most appropriate for graduate students. 1. If students have trouble with one mode of representation of quantitative knowledge, what are some of the instructional approaches that can be used to help them develop alternative modes of internal representation and external expression of math knowledge? *How could brain imaging studies inform instructional strategies related to multiple codes of representation and expression? 2. How might attentional problems interfere with learning to do arithmetic? Learning to think mathematically? Performing in the classroom? What instructional strategies might be used to help students with attentional problems learn to do math? *How might brain imaging tease apart attentional and computing processes? 3. How might working memory problems interfere with learning to do arithmetic? Learning to think mathematically? Performing in the classroom? What instructional strategies might be used to help students with working memory problems learn to do math? *How might brain imaging tease apart working memory and computing processes? 4. How might executive function and self-regulation problems interfere with learning to do arithmetic? Learning to think mathematically? Performing in the classroom? What instructional strategies might be used to help students with executive function problems learn to do math? *How might brain imaging tease apart executive functions and computing processes? 5. How might instructional strategies in math incorporate visual spatial reasoning? Quantitative reasoning? Verbal reasoning? *How might brain imaging contribute to our understanding of how these different kinds of reasoning may contribute to math problem solving? 6. Here is a word problem for math educators. Years ago after the Russians sent up Sputnik, the federal government encouraged schools to improve the teaching of

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math and science so that the United States could compete in the new frontier of space exploration. One school district designed a special program for students who might benefit from accelerated math and science training. Periodically members of the military, for example, Admiral P, ickover, appeared to inspire fear that if the country did not improve in math and science the communists would take over the world. During the junior high grades the gender ratio in the accelerated math/ science program was about equal. By high school, the number of female students in the program dwindled, presumably because of a scheduling problem. Nevertheless two girls were assigned to a section of advanced placement, taught by the chairman of the department, who had the responsibility of grading all the midterm and final exams for the math department. He explained that grading these papers was a burden, put the girls in an annex with the tests, and gave them the assignment of grading them instead of participating in the class. He assured them there was no reason to worry because he would make sure that they got a B, and anyway, girls did not need to do math. The questions are (a) Have schools made progress over the past forty years in equity of access to math education for all students? Females, as well as, males? Minority, as well as majority, students? High-income, as well as low-income, students? All students, as well as those who are academically talented? (b) W h y would a chair of a math department think that half the population does not need to know about math? Do those who are trained in math as a domain discipline fully recognize its rich connections to daily living across life span for the whole population? 7. Here is another math word problem. A male student from the same high school program as the female students in the sixth problem graduated with honors in math and engineering from one of the world's most prestigious universities. After years of education, he discovered that he had a degree that was mainly of use to the military, for example, for building stars wars defense systems. He wanted to use his math expertise for nonmilitary purposes. The questions are (a) H o w can the nonmilitary, pro-social uses of math during peacetime be emphasized in math education? (b) H o w can experts in the math domain be attracted to and enticed to stay in the schools to improve the quality of math education for peacetime purposes?

P A R T IV

EDUCA TIONAL A P P L I C A T I O N S OF B R A IN- LI T E R A C Y LINK, S

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Toward the end of the twentieth century, a school reform movement began in the United States. Schools were made the scapegoats for the ills of society, but schools did not cause the problems. Schools are merely a mirror of the problems confronting society. Choice of the word reform was unfortunate because it implies that schools are bad and need to be punished or rehabilitated, as when a naughty student is sent to reform school. Schools are no less perfect than any human institution. A better term would have been school evolution. Schools need to develop as organizations to help the new generation best prepare for the changing problems society faces. Evolution is a neutral, no-fault concept that recognizes that humans and their organizations have adapted to changes in the environment throughout history. Schools are not the cause of society's problems, but might be part of the solution if their role in making evolution happen were framed more positively. It is trendy now to quote statistics of doom and gloom about how students in the United States are losing their competitive edge compared to other countries. Some of this negative hype is undoubtedly due to the collective fears of how America will fare in a more global economy and how well the population will be prepared to use the rapidly expanding information technology. In a 1997 presentation to the Washington State Educational Measurement Association, R o b e r t Calfee reported a wealth of statistics to support a more optimistic case of h o w America's schools are Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

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doing (also see Calfee & Patrick 1995, for a positive approach to evolution of educational practices). In this chapter we argue that one key to future evolution of the schools is to draw less on political rhetoric and business metaphors (like accountability) and more on the scientific foundations of educational practice based on a rapidly expanding research base. A related key to evolutionary progress is to replace teacher bashing with teacher respect and appreciation. High stakes tests are not the best mechanisms for evolution. Evolutionary mechanisms require a dual approach: (a) improved preservice teacher education and continuing professional development; and (b) education of politicians and the business community about the nature-nurture interactions in learning, the complexities of assessing learning, and the importance of diverse abilities for the workforce. In all cases, this education should be grounded in scientific research relevant to educational practice. Free public education has only been available in the United States since the middle of the nineteenth century. In the past 150 years considerable controversy persisted over the best methods for teaching reading, writing, and math. In the middle of the twentieth century, Chall (1979, 1996) invited the nation into a Great Debate on the best way to teach reading, and recommended resolving the debate on the basis of research. By the end of the twentieth century, a great deal of research on effective instructional practices had accumulated. Some of this research is scientific in that competing, alternative methods are compared; that is, controlled experiments are conducted in the laboratory or classroom. Some of this research is design experiments (Brown 1992; Calfee, Norman & Wilson, in press) in which teachers learn what is effective by practicing their craft in clinics or classrooms (Liberman & Miller 2001). Both kinds of research have contributed to current instructional practices. Much of the research did not receive financial support from the government or other agencies. However, since the middle of the twentieth century, the federal government has funded instructional research--first the US Department of Education, and more recently, the National Institutes of Health (NIH) and the National Science Foundation (NSF) have provided grant support. Within NIH, scientific research on learning to read, write, and compute competes successfully with basic and applied health-related biomedical research for the grant dollars (which are as limited as the resources in working memory). As the research knowledge has expanded, so has the interest in disseminating the results to practitioners and basing educational policy on the research. Experts in educational policy recently conducted a survey of educators throughout the country (McDaniel, Sims & Miskel 2001) and reported that the most influential leaders in the movement to develop research-supported instructional practices are in rank order of influence: Reid Lyon (Chief Human Learning and Behavior Branch, National Institute of Child Health and Human Development [NICHD] at NIH), Bill Goodling (Chair, Education and Work Force Committee, US Congress), Bob Sweet (staffmember in the same congressional committee), Louisa Moats (author of Speech to Print; see references), and Marilyn Adams (author of Beginning to Read; see

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references). Dr. Lyon's career ladder toward this influential role is noteworthy in that it included classroom teaching, clinical experience in neuropsychology, and publication of peer-reviewed research prior to becoming involved in government review of scientific research and government policy on education. In this chapter we discuss what the implications of brain research are for educational policy. We highlight nine educational issues that should be informed by research on the brain and nature-nurture interactions: age of entrance to schooling, grade repetition, early identification and intervention, progress monitoring, educational diagnosis, teacher education, dealing with diversity, the educational pendulum, and systems wide interventions. Relevant to the last issue, at the end of the chapter we showcase the teaching and research career of Barbara Foorman, as a role model for systems-level translation of research into practice. Her story sets the scene for the transition to the final chapter on the impact of brain-based educational research for the teachers at work in their classrooms.

SCHOOL ENTRANCE

AGE

Teachers who have received no training in the brain may stereotype children as mature or immature for their age and believe that chronological age is an index of maturity; thus, children with later birthdays are apt to be perceived as less mature than those with earlier birthdays. However, considerable research on child development during the preschool years, begun with the pioneering work of Nancy Bayley at the University of Washington, has shown that developmental trajectories in the following domains show variability across individuals of the same age and within the same individual: gross motor, fine motor, receptive language for understanding speech, expressive language for communicating, cognition and thinking, social-emotional, executive functions, and attention. The intraindividual developmental variation should not be surprising given that different brain systems are involved in each domain (see Chapter 5). Thus, children enter school with profiles of relative strengths and weaknesses (hills and valleys) across developmental domains. A child may be immature in one or more domains (e.g., fine motor or attention) but average or even superior in other domains (e.g., language and cognition). Most children's profiles show such intraindividual variation, that is, normal variation within the individual across developmental domains. Children also show interindividual variation; that is, normal variation among individuals along the continuum for a specific developmental domain and normal variation in their profiles across all developmental domains. Children may be relatively immature in some areas but relatively mature in others, and each child has a unique profile that does not reduce to a single summary score or subtype (mature or immature). Delaying school entrance is problematic because of the importance of nature-nurture interactions: Without formal school experiences, neither the strengths nor the weaknesses may continue to develop as much as they could.

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Morrison and colleagues took advantage of the cut-offs for school entrance to do "natural experiments" on the effect of age at entrance to school. For example, Morrison, Griffith, and Alberts (1997) compared three groups. The first group was the younger first graders who just made the cut-off. The second group was the older first graders who just missed the cut-off for kindergarten two years earlier and thus were nearly a year older than the first group when they finally entered first grade. The third group was the older kindergartners who just missed the cut-off the same year as the first group. Comparison of the first and third group shows the effect of first grade instruction compared to waiting a year for first grade instruction for children who are not significantly different in age (on average less than two months different). Comparison of the first and second group shows the effect on first grade performance of not delaying versus delaying school entrance. Growth (change scores from pretest in early fall to posttest in late spring) was compared and showed the same pattern of results for reading and math. The change scores were not significantly different for the first and second groups. Regardless of age, these children showed the same relative amount of growth during first grade. However, both first grade groups showed significantly more growth than the kindergartners. The young first graders (first group) who just made the cut-off learned more than those who just missed it (third group) but did not differ significantly in age. Formal instruction in reading and math benefited learning to read and write more than the pre-academic kindergarten curriculum did for children who were comparable in age. These data tell an important story about the role of instruction in learning to read and do math at the stage ofdeveloment when the Cross-Talking Computers of Mind in tertiary cortex are just beginning to turn on (see Chapter 5).

GRADE REPETITION Retention is a popular practice in the United States where (a) 15 to 19 percent repeat at least one grade, compared to 1 percent in Japan; and (b) boys, minorities, poor children, short children, and children with late birthdays are most likely to be retained. Retention increases the cost of educating retained children by at least 8 percent (Carstens 1985; Smith & Shephard 1987, 1988). The popularity of retention persists despite lack of research support for it. Research touting the beneficial effects of retention suffers from serious design problems such as lack of a control group (Overman 1986). Without a control group who are promoted, it is not possible to evaluate what progress the retained child may have made if promoted. In a meta-analysis of research using control groups, Holmes and Matthews (1984) found that promoted children scored, on average, 0.34 to 0.38 standard deviation units higher than retained students on a variety of outcome measures, including academic achievement, personal adjustment, and attitude toward school. Some children made progress during the year the grade was repeated, consistent with teachers' anecdotal observations, but not as much as similar children

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who were promoted. There was no evidence that retained children make relative gains during the repeated or subsequent years (move from the bottom to the top of the class). Teachers who retain children do not have access to children's performance or socioemotional adjustment in later years; although not evident to teachers or parents during the repeated year, significant shame and emotional problems surface in subsequent years (Holmes & Matthews 1984; Smith & Shephard 1988). Next to bhndness and death of a parent, children rate the thought of retention as the most stressful thing that could happen to them (Byrnes & Yamamoto 1984). Reviews of research on the effects of retaining children find no benefits in either academic achievement or social emotional factors from retaining immature children (Gredler 1984; Shephard & Smith 1986, 1988). Transitional (K to 1) programs are no more successful than retention (Smith & Shephard 1988). Students who received remedial instruction made greater grains than either promoted or retained students who received no help (Smith & Shephard 1988). A National Association of School Psychologists (NASP) task force (1988) issued a position statement, based on retention research, that retention had not been shown to be effective and can even be harmful, leading to increased probability of school drop out. NASP strongly supported seeking alternatives to retention. Viable alternatives to retention include (a) promotion with assessment and special services, (b) early intervention that supplements the regular program, and (c) development of teachers' tolerance and appreciation of diversity (Smith & Shephard 1988). Teachers who value diversity do not recommend retention. Despite fears that children will be socially stigmatized by special services, these do not impair children's self-concept or peer acceptance (Vaughn, Haager, Hogan, & Kouzekanani, 1992). Table 11.1 summarizes the problems with retention and viable alternatives to it that are consistent with research on the development of brain systems.

T A B L E 11.1

Problems w i t h R e t e n t i o n and R e c o m m e n d e d Alternatives to R e t e n t i o n Problems

1. Well-designed research does not support it. 2. It costs more money for the extra year of education. 3. It has harmful social emotional effects subsequent to the year of retention. 4. It discriminates against boys, minorities, poor children, short children, and children with late birthdays. 5. It increases the probability that children will later drop out of school before graduation, with unknown costs to society. 6. It does not identify why the child did not respond well to the curriculum and does not offer anything substantially different from what was offered the first time.

(continues)

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TABLE 11.1 (continued) Alternatives 1. School-wideearlyidentificationof students at riskin reading,writing,and math-- basedon researchsupported assessmentpractices. 2. School-wideearlyintervention (supplementaryinstruction) for at-risk students. 3. Summerschool. 4. Beforeschool and after school tutoring. 5. Specialeducation. 6. Developmentof teacher's tolerance and appreciationof diversityand skillsfor dealingwith it in the classroom.

EARLY IDENTIFICATION AND INTERVENTION Early identification of a reading problem is associated with better reading performance five years later (Muehl & Forell 1973). Researchers have developed a variety of normed-based, criterion-referenced, and curriculum-based measures for early identification (for review, see Berninger, 2002; Berninger, Stage, Smith & Hildebrand 2001). Early, intensive, and continuing intervention during the primary grades kept student achievement within normal limits even in high-risk urban schools (Slavin et al. 1991). "Success for All" (Slavin, Madden, Karwait, Livermon, & Dolan, 1990), "Reading Recovery" (Pinnell, Fried & Estice 1990), and explicit code instruction (Foorman, Francis, Fletcher, Schatschneider, & Mehta, 1998; Iverson & Tunmer 1992; Vellutino, Scanlon & Tanzman 1998; Williams 1980) are effective early intervention approaches, but explicit instruction in alphabetic principle is necessary (Rayner, Foorman, Perfetti, Pesetsky, & Seidenberg, 2001).

PROGRESS MONITORING Evaluating response to early intervention is as important as providing early intervention (Vellutino et al. 1998). Students whose progress is frequently monitored make more progress than those whose progress is not frequently monitored (Fuchs 1986; Fuchs & Fuchs 1986; Fuchs, Deno & Mirkin 1984). Many researchers have developed multiple modes of assessment for progress monitoring (see Chapter 10, Berninger 1998a). A technological advance in assessing response to intervention is growth curve modeling, which both classroom teachers and children find informative (Compton 2000). Schools that implement progress monitoring along with early intervention within a collaborative problem-solving model have reduced the number of students needing special services (see research by Stage in Berninger et al. 2001).

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EDUCATIONAL DIAGNOSIS Even with early intervention and progress monitoring, some children will fail to make progress at some point in schooling. In general, children want to succeed and do not fail to learn on purpose. The reasons for poor progress are many, but three factors often contribute: nature-nurture interactions, mismatch between child's profile of abilities and delivery of curriculum components, and/or missing instructional components (Berninger 1994, 1998a). As explained in Chapter 4, brains exhibit incredible normal variation in neuroanatomy and, as a result, in function. Consequently, they may respond differently to the same instruction. For an individual child, the way instruction is delivered in a particular curriculum may not be a good match with an individual's profile; and the goodness of fit between child and curriculum can vary at different points in development. Other times, not all the necessary instructional components are provided for developing a functional system for reading, writing, or math. Thus, the first step in educational diagnosis is to assess the curriculum rather than the student. Educators should assess whether all the necessary instructional components are provided for developing all the components of a functional reading, writing, or math system. If they are not, they should be added and the student's progress monitored. If the components are there, the way they are delivered might be modified. However, sometimes problems persist despite early intervention and curriculum modification. In those cases, the next step should be to refer the student to the multidisciplinary team for evaluation of the student's unique profile of skills across developmental and academic domains, and of a possible specific learning disability, developmental disability, or other educationally handicapping condition (see Table 12.3). For further discussion of these issues, see Berninger (1998a).

TEACHER EDUCATION Early in the twentieth century, medical education faced similar problems to those that teacher education confronts now (Goodlad 1990). Preservice training of physicians was practitioner-oriented rather than grounded in conceptual understanding of the disease and healing process. Flexner (1910), in a report commissioned by the Carnegie Foundation for the Advancement of Teaching, recommended that medical education include course work in basic sciences like biology and chemistry. His recommendations were implemented and credited with the improved effectiveness of medical practice and a consequent increased status for the medical profession. For example, surgeons who once received the same training as barbers rose from the bottom to the top of the medical hierarchy when their science-based practices became more effective. Like medical education early in the last century, preservice teacher education early in the twenty-first century would benefit from course work in the basic

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sciences such as developmental psychology, cognitive science, neuroscience, and linguistics. Such coursework would give future teachers a better understanding of the complexity of factors that can constrain the learning process and a conceptual framework for generating alternative instructional approaches if the one(s) being used are not effective. A conceptual framework is different from a menu with a list of different instructional options from which to select. A conceptual framework takes into account the system of interacting components, all of which must be orchestrated to achieve literacy and numeracy goals. During the past decade, research on teacher education and professional development has progressed beyond documenting that current approaches are inadequate. There is an emerging consensus that one- and two-day workshops at the inservice level are inadequate for providing what teachers did not learn at the preservice level. A number of research studies are empirically comparing alternative models of developing teacher knowledge. See the 1999 special issue in Learning Disabilities. Research and Practice, which was guest edited by Candace Bos and David Chard, leaders in research on teacher knowledge needed for scientifically supported instructional practices. The article by McCutchen and Beminger (1999) summarizes a conceptual model for teacher knowledge in reading and writing that can be used at both the preservice and continuing education levels and that McCutchen has investigated empirically in the U W L D C (see Acknowledgments) and found to be effective. Inservice teachers are more likely than preservice teachers to understand the importance of explicit literacy instruction (Mather, Bos, & Babur, 2001). To the extent that teachers can apply conceptual models to help all students reach reasonable levels of learning outcome for their individual developmental profile, the status of the educational profession will be elevated (see Foreword). With funding from the Hello Friend/Ennis William Cosby Foundation, Joanna Uhry, a Professor at Fordham University, has pioneered a novel approach to continuing education for inservice teachers in New York City. Called "Young Readers at Risk", this program combines rigorous graduate study at Fordham University Graduate School of Education with supervised teaching experience. The goal is to prepare kindergarten, first grade, and second grade teachers to meet the needs of urban children whose special needs may stem from a combination of factors: living in poverty, learning English (the language of instruction) as a second language, and/or genetic/environmental risk factors that affect school learning. The six-course curriculum draws on research from federally funded studies and reviews of research (e.g., the National Research Council, the National Reading Panel, the Center for Improvement of Early Reading Achievement, and the New York State Task Force on Quality Inclusive Classrooms). A unique two-component practicum is ongoing throughout the program. The first component provides supervised tutoring in an after school program in an urban school near the university. The second component offers supervision of teaching in participants' own classrooms. Teachers learn to teach phonological awareness, spelling-sound correspondences, automaticity of word reading skills, application of skills to texts written to reflect

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phonics regularities at the word level or narrative structures at the text level, spelling, written expression, use of multiple cues and strategies, and monitoring strategy us e. P m evaluation is based on both teacher and student performance. At-risk, lower performing students who were tutored after school have caught up with higher achieving, untutored classmates (Uhry, 2001). Assessment of teacher performance, that is, application of research-supported instructional strategies, to teaching practice is still underway.

DEALING WITH BIOCULTURAL

DIVERSITY

Teaching does not eliminate diversity. In fact, learning may increase diversity. To the normal variation derived from brain differences, family differences, and cultural differences, add the generative processes for constructing individual meaning ~ i t trock 1974). Normal variation is a fact of life that brain research validates. Delaying school entrance or repeating a grade will not eliminate biodiversity and create classrooms of homogeneous learners. Teachers cannot undo the diversity that nature has woven into our genetic constitutions and social environments. The first step in dealing with biodiversity is to frame it as a desirable phenomenon of nature that allows humans to deal with the complex needs of society. Nature built in diversity to ensure that a species can adapt to the current environment and a constantly changing environment. Consider what the world would be like if there were no diversity. If everyone were a star athlete, music maestro, or chief executive officer, society would not function. There would be no one to grow the food, transport it, process it, serve it, collect the garbage, build houses and other facilities, keep facilities clean, fight fire, provide health care, teach the next generation, invent new technology or generate new ideas or information, manufacture products and other commodities necessary for everyday living, govern, defend from other societies that intrude, meet spiritual needs, or create visual beauty. Society depends on a supply of many different kinds of talents. Even in the information age society cannot function with a work force that has only computer skills. Once diversity is valued, the next step is to figure out practical ways to optimize development of each individual's profile of talents. For public schools, the primary task has been to develop those talents in the areas of literacy and numeracy. This task is not an easy one because individual students begin at different entry levels and with different profiles of relevant skills. Biological and environmental constraints affect students' rate of growth and pattern of growth as well as their eventual outcome l e v e l - - w h e r e their skills plateau when they are fully developed. Research is sorely needed to help teachers develop effective ways of dealing with this immense normal variation within every classroom. Legislation that sets as the goal creating the same, very high-level standards of achievement in all areas of the curriculum for all students is not realistic. Neither is this "reversed limbo" mentality (How high can you go?) likely to achieve the intended goal. Likewise, attaching to that goal

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negative consequence for students ~ no high school degree, and for teachers, no r a i s e s ~ d o e s not mesh with what we know about effective motivation (Hidi & Harackiewicz 2000). In Chapter 12 we return to ways in which classroom teachers might deal with diversity in making evolution happen and how this process might be self-monitored.

THE EDUCATIONAL PENDULUM EDUCATING THE PUBLIC

AND

Over the past century, both philosophy and politics have kept the educational pendulum in m o t i o n - - s w i n g i n g from one extreme to another. For reading (see Chapters 5 and 8), writing (see Chapters 6 and 9), and math (see Chapters 7 and 10), at one extreme was inductive learning (whole language, process writing, discovery math), and at the other extreme was deductive learning (basic skills). Most children benefit from a mix of both: teachers carefully and skillfully structuring learning experiences from which knowledge can be inferred and providing explicit instructional cueing of patterns that can be applied deductively. The recent infusion of scientific research into the educational scene will hopefully slow down the swinging of the pendulum from one fad to another as educators look to evidence-based teaching practices. We are less certain how the political influences on the educational pendulum can be eliminated. W h e n our forefathers had the foresight to separate church and state in the US constitution, they overlooked separating education and politics. Deep in the brain m the part that sustained survival early in evolution m are primitive structures for defending territory. Despite the emergence of the forebrain more recently in evolution, the primitive brain lives on in all people. In a democracy, politicians are elected to serve this primitive function of defending our turf. Scientists are not immune from the activation of the lower, more primitive centers and have been known to judge data on whether it fits with their pre-existing beliefs and philosophies rather than objectively evaluating whether it adequately supports or rejects the hypotheses tested. There are more scientists alive today than ever before in the history of mankind ~ so there is a lot of territory to defend! Ultimately politicians m e l e c t e d government officials and legislators m p l a y as important a role as scientists or philosophers in regulating education as an institution and thus its pendulum and politics. Like the blood that supplies the fuel for the neurons and the frontal executive system, they pass laws that fund and regulate educational practice. Just as feedback travels from cortical and subcortical processing units to the executive government centers of the brain, and from those centers back to the other cortical and subcortical processors (see Chapter 5), educators need to educate these politicians who set educational policy about the scientific foundations of their profession. That is, our student body must extend beyond our individual classrooms where we practice our craft to those who influence educational policy.

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Otherwise, the educational policy they set will not be based on the advances that have been and will be made in science. And the pendulum will just keep swinging ... and swinging.., and swinging. We turn now to the sto W of a teacher/researcher doing just t h a t . . , extending her classroom outside a single school to the government officials at the state level. It is an important lesson about how research can inform educational practice.

IMPACT OF AN IN VIVO TEACHER RESEARCHER: F R O M H E R O W N C L A S S R O O M T O ALL T H E C L A S S R O O M S I N T H E STATE

Conceptual Approach to Teaching Reading Barbara Foorman's career illustrates teaching that has conceptual foundations grounded in research. Her teaching career began during the summer between her junior year and senior year at Stanford University. Through Volunteers in Asia, she taught English as a second language to students in Japan. In the process she developed a profound interest in how languages have different mapping systems for representing the spoken language in the written orthography. Japan has a language whose syntax is not as word-order driven as English. Japan, which is one of the most literate countries of the world, has three different writing systems: kanji, hiragana, and katakana. Kanji are the morpheme-based characters borrowed from the Chinese that must be memorized; hiragana and katakana form the syllabaries. Hiragana are used to signal grammatical features, whereas katakana are used to represent foreign words. The 46 characters of hiragana and katakana and their diacritical marks are easily learned. However, kanji are learned over the course of primary and secondary schooling in Japan, with approximately 2000 required by high school graduation. However, educators estimate that passive recognition of 4000 kanji is required to read a newspaper and up to 10,000 to read scholarly texts. Barbara was so intrigued by the experience of teaching English to Japanese students and learning to read Japanese herself that following graduation she returned to Japan to teach another year. That experience led to a desire to obtain teaching credentials, through the Masters of Art in Teaching at Harvard Graduate School of Education. There she took coursework in clinical diagnosis of reading from Jeanne Chall and did a teaching internship in a junior high school in a working class neighborhood in the Boston area. She was intrigued why obviously bright adolescents still could hardly decode words sufficiently well to read with comprehension. She did not understand why there had to be a Great Debate m it seemed like common sense that readers needed to be able both to decode the orthography and use meaning cues. To her the challenge was to engage the students in print without drilling them in phonics or rules so that she could maintain their

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motivation. She set up her own clinic in the school to help these struggling readers. By the end of the year, the students had made progress. Barbara concluded that a curriculum issue was at stake and she wondered what could be done to teach the children to read more effectively the first time around in early elementary school. However, every teacher she talked to had her own "magic bullet." Barbara thought that objective, systematic research might sort out, in a more efficient way than each teacher having to figure it out in her own classroom, which approach to prevention might be most effective. Thus, Barbara was "hooked on research" early in her teaching career and moved on to the University of California at Berkeley to pursue research training. There she completed a Ph.D. in Education with a specialization in Language and Reading Development. After becoming Dr. Foorman, Barbara directed the Early Childhood Education Center at the University of California at Davis for one year before being recruited by the University of Houston.

Researcher and Teacher Trainer Barbara spent the next 20 years at the University of Houston as a Professor of Educational Psychology where she conducted research and taught teachers in graduate courses. She did not abandon her life-long interest in language and spent some time away from Houston as a Visiting Lecturer at the University of Guadalajara, as a Visiting Scholar at Oxford, and a Visiting Professor, Miyazaki, Japan. Barbara's next move was across town to the University of Texas-Houston Medical School where she is a Professor of Pediatrics and Director of the Center for Academic and Reading Skills. Throughout the nearly a quarter of a century in Texas, Barbara has been investigator or coinvestigator on numerous grants from N I C H D and participated in research that is generating research knowledge for teacher practice, especially on culturally diverse, low income schools. One of these projects, "Early Interventions for Children with Reading Problems," began in Houston but gained a second site in Washington D.C. From this vantage point, Barbara can reflect on what she has learned about teaching from systematic research. From the first wave, she learned that curriculum matters, but so does children's initial level of skill, which interacts with progress made in response to curriculum. Explicit code instruction is effective, but children's initial phonological awareness (ability to analyze sound patterns in spoken words) affects how they respond to curriculum. From the second wave, she learned that teachers' behaviors matter, too. In some classrooms in poverty schools, a student's initial score predicts learning outcome, but in other ones it does n o t - - t h e teachers' behaviors (e.g., percent of time spent

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on word work and oral language) predict learning outcome. As a result, the research team is now working with teachers on how best to use the curriculum materials available to them and how best to prioritize their limited time. In sum, they have become interested in instructional leadership issues. From the third wave in progress, she is learning that, although some think that children have fixed, brain-based learning styles, that the brain is trainable. Her team, which includes Andrew Papanicolaou, Akis Simos, Jack Fletcher, and David Francis, imaged brains of children at risk for reading problems and normal controls in the summer between kindergarten and first grade. Preintervention brain imaging was a good predictor of who was at risk for reading problems (Papanicolaou et al., in press). These at-risk children received supplementary pullout reading instruction during first grade and were reimaged. From this project, Barbara gained a renewed appreciation of the role of the brain in learning. All learning is brain-based!

Impact on a State

Shapiro (2001) argued that psychologists should work on the big problems in academic skill development m l i k e organizational change at the systems level m rather than on little problems one student at a time. Barbara's career as a teacher/researcher is unique because it has had this kind of systems-level impact. The Texas partnership is unique among state education agencies because business, higher education (Colleges of Education), teacher certification agencies, and researchers are working together to accomplish organizational change. The first step was to develop and validate an instrument for screening to identify high-risk students for early intervention. This instrument, The Texas Primary Reading Inventory, has both English and Spanish versions, and is used in 92 percent of the Texas schools. From this experience, Barabara concluded that early identification instruments should be tied to local teacher practice rather than accountability. The second step has been, therefore, to become active in teacher training. Her team uses a differentiated staff model in which master level teachers train trainers in state regional service centers who train local teachers. Her team has also partnered with Professor Sharon Vaughn of the University of Texas at Austin to develop Reading Academies. Each summer all the teachers in the state at a particular grade level attend a four-day workshop in which research-supported instructional practices are disseminated. Although it has been a long time since Barbara's first teaching experiences, she still has empathy for what beginning teachers experience. From the vantage point of seasoned researcher and teacher trainer, Barbara remains convinced that research has something valuable to offer the beginning teacher.

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RECOMMENDATIONS

FOlK F U R T H E R R E A D I N G

School Entrance Morrison, F., Griffith, E. & Alberts, D. 1997. Nature-nurture in the classroom: Entrance age, school readiness, and learning in children. Developmental Psychology. 33:254--262.

Grade Retention Shepard, L. & Smith, M. 1989. Flunking grades: Research and policies on retention. New York: Falmer Press.

Prevention of Academic Difficulties though Early Identification and Intervention and Progress Monitoring and Diagnosis Berninger, V. 2002. Best practices in reading, writing, and math assessment-intervention links: A systems approach for schools, classrooms, and individuals. In A. Thomas &J. Grimes, eds. Best practices in school psychology IV. National Association of School Psychologists. Bethesda, MD. Beminger, V., Stage, S., Smith, D. & Hildebrand, D. 2001. Assessment for reading and writing intervention: A three-tier model for prevention and remediation. In J. Andrews, D. Saklofske & H. Janzen, eds. Handbook of Psychoeducational Assessment. Ability, achievement, and behavior in children, (195-223). New York: Academic Press. Busse, J, Berninger, V., Smith, D. & Hildebrand, D. 2001. Assessment for math talent and disability: A developmental model.. In J. Andrews, D. Saklofske & H. Janzen, eds. Handbook of Psychoeducational Assessment. Ability, achievement, and behavior in children, (225-253). New York: Academic Press.

Teacher Knowledge American Federation of Teachers 1999. Teaching reading Is rocketscience. What expert teachers of reading should know and be able to do. McCutchen, D. & Berninger, V. 1999. Those who know, teach well: Helping teachers master literacyrelated subject-matter knowledge. Learning Disabilities Research & Practice. 14:215-226. And other articles in this special issue.

MAKING CONNECTIONS Questions preceded by * may be most appropriate for graduate students. 1. Business is run on the accounting model. The fundamental question is whether income exceeds o u t g o - - t h a t is, what the bottom line is in financial expenditure. Based on what you have learned about the brain, normal variation within and across developmental domains, and the multiple dimensions of instruc-

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tion, is the business model a good one for evaluating and self-regulating education? Ifbiodiversity constrains the eventual learning outcome to some degree, can a single standard apply meaningfully to the whole student population? A teacher reported that in her state any student who could hold a pencil had to take the state standards test in writing, even if the student was mentally retarded or had a severe physical handicap or a learning disability. Is this an enlightened, brain-based approach to assessment and improving accountability? Who should be accountable m students, teachers, or legislators? What might be a better model for linking assessment and instruction? 2. Is the real reason for politicians and the business community losing faith in the public schools that graduates cannot read or that employees have problems in job performance? Might the young employees' executive functions play a role in their job performance as well? What role might late maturing frontal areas play in on-job performance (see Chapter 4)? Is teaching executive skills for self-regulation the responsibility of the public schools alone or also of the business community? Might failure to teach specific job skills on-site also play a role in how recent graduates perform on the job? Should high schools have more enlightened pre-vocational training programs developed in partnership with industry? How have other cultures at different points in history prepared the next generation for the world of work? What lessons might be learned from these other approaches? 3. The answer to the math word problem for the developing computing brain in Chapter 10 is an imaginary number (the square root of a negative number). If there simply is not enough time for schools to do everything expected of them, how can they do a quality job, even if the objectives are desirable; for example, improving social skills and reducing violence? Is there a limit to doing more and more with less and less? If so, what are the policy implications? Based on what you have learned about the brain, what do you suppose the brain does when processing demands exceed resources (glucose and oxygen)? How might teachers respond under similar circumstances? 4. If normal variation is the rule and not the exception, can we really expect everyone (except those living in Lake Wobegon) to be above average? If norm referenced tests define grade level as the fiftieth percentile-- the point at which half the population falls above and half the population falls b e l o w - - t h e n will half the population always be below grade level even if the mean (most average score) increases? If expectations for achievement are not developmentally appropriate, what negative effects might that have on motivation and affect? What kind of assessment systems could be developed to promote motivation, affect, and executive functions and not just cognitive learning and performance? What assessment practices might strive to optimize all students' performance profiles, yet acknowledge diversity, normal variation, and alternative pathways? *5. How would you educate the following legislators and teacher trainer, who expressed their views in a local newspaper? (Hint: see Chapters 5 and 8).

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a. A former head of the education committee for the state legislature released a statement that everyone could learn to read if only teachers would teach syllables. b. A current co-head of the education committee for the state legislature stated to the press that it is no big deal to assess reading because obviously children can either read accurately and fluently or they cannot. c. Another politician stated that he did not know what the fuss was about in trying to assess reading comprehension because a test of reading comprehension is a test of reading comprehension. d. A professor of education released a statement that there is no point in teaching phonics because English is a hopelessly irregular language. *6. Should the educational profession have a larger role in defining its professional standards and self-regulating its practices, like medicine, law, and psychology do, instead of having state government agencies define these for educators? Medicine and psychology have training standards that ensure that the scientific foundations of practice are part of the training program. What might be done to make sure the same is true for preservice teacher training programs? *7. Should enhancement of the status of the teaching profession be the cornerstone for educational evolution? How can putting teachers on the pressure block and constantly criticizing them help them do a better job in the classroom? Why do so many of the success stories in the classroom go unsung? Given the complexity of biodiversity, family diversity, and cultural diversity, is it amazing that so many students can read, write, and compute pretty well?

Implications for Classroom Practice

Science generates knowledge and conceptual frameworks that teachers can draw upon in planning their instructional programs, but science does not specify exactly how to implement that knowledge on a daily basis. The chemical, pharmaceutical, and biotechnology industries face the same challenge in translating research into practice. Their research and development divisions generate new knowledge, but their product development division translates that knowledge to "take it to market." Implementation of knowledge requires a different set of skills than generation of new knowledge. Consequently, businesses are most likely to succeed if research and development create partnerships with product development. Increasingly, research efforts are being directed to teachers' practice of their craft in the classroom (Pressley et al. 2001) and factors that support sustained use of research-based instructional practices (Gersten, Chad & Baker 2000). We propose that schools are most likely to succeed if scientists, who generate new knowledge about learning, partner with teachers, who evaluate the best way to implement that new knowledge in their classrooms. The analogy is similar to that of the scientist and the engineer who do basic and applied research, respectively. To make the point that this partnership is ideally bidirectional, we begin this final chapter with two stories that illustrate teacher-researcher partnerships, and that there is more than one effective way to apply science to instructional practice. Brain Literacyfor Educators and Psychologists Copyright 9 2002, Elsevier Science (USA). All Rights of reproduction in any form reserved.

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Then, in order to synthesize the prior eleven chapters succinctly for the classroom teacher, we define what a brain-based educator is. Next, we summarize instructional design principles that are consistent with current knowledge of the brain. Finally, we encourage teachers to apply their brain literacy to observing students and thinking about their learning problems. We provide a brief overview of the kinds of learning differences teachers are most likely to confront in the classroom. We present this synthesis not as the final word but rather as a work in progress because the newly emerging field of educational neuropsychology is rapidly expanding. We end with a gentle suggestion that the nation may want to rethink, from the perspective of brain-based education and pedagogy, whether the current approach to testing and accountability is most likely to achieve the intended goal.

TEACHERS IMPLEMENTING SCIENTIFICALLY SUPPORTED TEACHING PRACTICES Jeannie Patten, like Jenifer Katahira (see Chapter 9), initiated her own instructional innovation, and sought out university researchers to evaluate it. Jeannie's innovation was to break down the barriers in her local school between general education and special education in preventing reading problems. She convinced the first grade and second grade teachers to teach reading during a two-hour language arts block every morning. Students were placed in reading groups formed on the basis of their instructional levels, as determined by informal reading inventories, rather than their grade levels. During the language arts block children left their regular classes and went to the classroom where their group was meeting. Jeannie taught the first graders who were the lowest at the beginning of the year and were at great risk for qualifying for special education m but she taught them in a regular reading group at the same time everyone received reading instruction. Her reading program had all the necessary instructional components for beginning reading mphonological awareness, orthographic awareness, alphabetic principle, and repeated experience in reading specific words in highly engaging text, and comprehension instruction (see Chapter 8). She used some of the phonological awareness programs listed at the end of this chapter, some of the materials from the U W L D C (Berninger 1998a,b,c), and the Early Success program discussed in Chapter 8. Just as importantly, she encouraged a reflective approach to written language, in which she constantly encouraged the children to be reflective, ask questions, and even challenge each other and her. Her readings program was not only balanced but cognitively engaging. She suspected she was doing something right when children in the higher achieving groups asked to come join her group. Our empirical evaluation of her students' achievement at the end of the year, compared to the beginning of the year, confirmed her perception. These children who started out at the bottom of the first grade were by the end of first grade above

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the mean on nationally normed tests and not significantly different in achievement from the first graders as a whole in this urban school. WithJeannie's guidance, these children had overcome the Matthew Effect. Instead of the poor readers becoming poorer, they had become richer. For further details about this teacher-implemented success story, see Berninger (2002). Robert Femiano is also committed to breaking down the barriers between special and general education and applying research on literacy, which he reads on a regular basis, to teaching. He welcomes into his second grade classroom children whose first language is not English, those who qualify for special education, as well as the general education students. He applied an innovative approach, which also had all the necessary components for beginning reading, to teaching this diverse mix of students. For phonological awareness, his approach emphasized the vowel sounds, which pose the greatest challenges in decoding written words, by elongating them. He immersed children in written words, which were carefully selected to develop in a systematic way the patterns of spelling-sound correspondence in primary-grade reading material. These were used in daily teacher-directed activities and independent activities, which integrated the spelling-sound patterns with meaning cues in sentences. The teacher-directed activities were unique because of the high level of teacher-student interaction in fast paced literacy activities in small groups with ample opportunity for students to respond by writing on their individual slate boards. On a yearly basis Robert evaluated the student progress of each student with nationally normed tests and other assessment measures and had evidence that each student knew more at the end of the year than at the beginning. He shared with the teachers in the U W L D C Teacher Training Institute how multiple modes of assessment can be used to evaluate the effectiveness of research into practice. For more information about Femiano's instructional methods and multiple modes of assessment, see Berninger (1998a).

DEFINING BRAIN-BASED EDUCATION First and foremost, brain-based educators are not like the robot in the Calvin and Hobbes cartoon reproduced in this chapter as Figure 12.1. Teaching is not a onetime knowledge transplant from the teacher's brain to the student's brain, as Calvin thinks. Teachers cannot directly program students' brains. However, the teacher's instructional cues (behavioral level) interact in complex ways with the neural architecture of the student's brain at the microlevel (Chapter 2) and macrolevel (Chapter 3), and with the computational processes of the student's brain (Chapters 2 and 5), to make changes in the brain. Table 12.1 summarizes hallmark characteristics of educators who have conceptual knowledge of basic neuroscience. They understand these fundamental concepts: normal variation (within and across student profiles and in student learning outcomes), nature-nurture interactions, learning as a developmental process over

FIGURE 12.1 Calvin and Hobbes cartoon. CALVIN AND HOBBES 0 1993 Wattenon. Reprinted with permission ofUNIVERSAL PRESS SYNDICATE. AU rights reserved.

Implications for Classroom Practice T A B L E 12.1

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Brain-Based Educators

1. Acknowledge, tolerate, and celebrate normal variation a in students. 2. Recognize that learning is the result of nature-nurture interactions.

b

3. Understand that learning is a long developmental process from novice to expert and is charactenzed d e by alternative learning pathways and different levels of learning outcomes in specific domains and components. 9

-

c

.

.

.

4. Draw on the multiple codes and modes of representation f (redundancy) in designing instructional hints and clues and alternative instructional approaches g if the current ones are not effective. 5. Recognize that functional systems involve many different components h that have to be orchestrated i and thus the complexity of the learning process. 6. Use multiple modes of assessment j to document and evalutate student progress and keep up to date on k research literature. 7. Have patience / and compassionativity, m aThis variation that falls within the normal range occurs (a) within each developmental and academic domain, (b) in profile patterns across developmental and academic domains, and (c) in each component of functional systems for reading, writing, and computing. bThe brain constrains leaming, but also changes as the result of learning. The teacher matters! cIt takes a long time to wire a brain. Cortex changes but changes slowly and changes in functional organization over development; some changes are driven by biological maturation processes but environmental stimulation including teaching drives the changes too. dAt the brain level, different neural circuits may perform a task. At the behavioral level, students may follow different routes to the same learning outcome. At the pedagogical level, there may be more than one effective way to teach a skill. eLeaming outcome will plateau (level off) within a learning profile at different stages and levels of achievement. It is unrealistic to expect every student to reach the same standard of performance any more than all students can make the junior varsity or varsity sports teams. fThe brain codes information in multiple ways (see Chapter 5) and has different modes of representation for expressing information (see Chapter 7). Teachers can capitalize on this redundancy in designing instructional approaches. gThere is more than one way to teach the same skill and more than one way to learn the same skill. hThe brain has many different parts that are organized to performed different functions. Different systems draw on some of the same parts as well as unique parts specific to their system. The same system may draw on different parts at different stages of development. ~The complexity of the functional systems illustrates that overly simple ideas like learning styles based on.sensory modality or side of the brain cannot explain brain function underlying learning. JTeachers should assemble a portfolio of multiple assessments of each student across the year, including daily and weekly teacher-administered assessment and student work-products, group-administered nationally normed tests, group-administered criterion referenced measures, and individually administered nationally normed measures (see Chapter 10, Berninger, 1998a). kBy reading the research published in peer-reviewed journals and/or taking university-based courses on site or online. /It takes time to wire the brain; a no-fault policy that avoids blaming the child, the family, or the teacher is wise when learning comes slowly. mThis word coined by Saturday Night Live combines the importance of both compassion toward students and play with words (Graves 2000) in fostering literacy. See this Chapter.

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time, alternative pathways in learning, multiple codes and modes of representations (redundancy), potential for constructing multiple and alternative connections within the brain (alternative pathways), alternative instructional approaches for accomplishing the same learning outcome (alternative pathways), and functional systems with multiple, interacting components. As a result, they appreciate the complexity of teaching and learning and the need to use multiple approaches for teaching and assessing learning. Given normal variation in neuroanatomy and neural function and the complexity of functional systems, there is unlikely to be a single explanation for any school learning problem or a single magic bullet for fixing it. They recognize the importance of keeping up to date on research as a source to draw upon in refining their instructional practices. They also understand the necessity of multivariate thinking in translating research findings to practice. Brain-based educators are patient. They understand that the brain changes, but cortex changes slowly over time. Learning is a developmental process. There are no quick fixes even in the age of instant communication and instant gratification. At the same time, they do not just wait for maturation to kick in; brain-based teachers realize that their instructional cues are an important part of the process, even when it is not immediately evident. Although sometimes students appear to know something, at other times they may appear to have forgotten. That is normal and to be expected. Sometimes learning does not become observable until later in development. Brain-based educators are slow to blame when learning is not proceeding as hoped. They do not blame the parents or home. They do not blame the student m children do not choose not to learn or to fail. If teachers need to make an external attribution and place blame somewhere, better to blame the fusiform gyrus, inferior temporal gyrus, insula, or BA 37 (or left posterior circuits in occipital, temporal, and parietal lobes) for reading problems, the lenticular nucleus and parietal lobes for math problems, and the cerebellar-striatal-frontal circuits for writing problems. Or, electrochemical activity at the microlevel may be momentarily out of phase. It is unlikely, as Flip Wilson flippantly claimed, that the devil makes them do it (fail to learn). Brain-based educators have compassionativityma profound sense of caring for the students they teach. The word compassionativity, which was coined by Saturday Night Live to advertise for its 2001 Mothers' Day Special, is a pseudomorph, like the ones we created for morphological word play (see Table 8.3). In contrast to Calvin who thought a one-time knowledge transplant to his brain would free him of school and open up a world of play, brain educators recognize that true learning at all stages of development is a blend of work and play (see Part III). Although the Heart Hypothesis lost out to the Brain Hypothesis as the seat of learning (see Chapter 2), students learn best when the limbic system, as well as the cerebrum, is well nurtured (see Chapter 5) by teachers with heartfelt compassionativity. Above all, brain-based educators know that they can make a difference in students' lives. Middle childhood may be a peak time for cognitive development and the brain may be sculpted by instructional experience well into adolescence and

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even adulthood (see Thompson & Nelson 2001, the work of Chugani and colleagues, and, that of Scheibel and colleagues, discussed in Chapter 4). Teachers have an important role to play in the wiring of the Cross-Talking Computers of Mind, which seem to turn on across all cultures around age six (see Chapter 4). Without a doubt, poverty, home variables, and biologically constrained cognitive and language variables (Bay&r, Brooks-Gunn & Furstenburg 1993; Eckert et al. 2001) place some students more at risk than others and increase the challenges educators face. However, Frank McCourt's memoirs as both a student and a teacher (see Recommended Reading at the end of this chapter) pay elegant tribute to both the power of teaching and the resilience of the human spirit m neither of which should be underestimated.

BRAIN-BASED INSTRUCTIONAL DESIGN PROCEDURES Unlike computer programmers, who have to program only one computer at a time, teachers are given the monumental task of teaching 20 to 30 minds simultaneously. Moreover, unlike the computer programmers who can directly program the computer with the instructions they input, teachers cannot directly program any of the minds directly and have the daunting task of teaching the minds to self-program themselves! Given this daunting task, teachers must wonder how they can package instructional components to accomplish their job as efficiently as possible. Brain research and literacy research offers clues to instructional design principles that might be implemented in the classroom. Table 12.2 elaborates on these: (a) teach to all components in a functional system that are appropriate for the stage ofskiI1 development; (b) teach to maximize transfer across components of a functional system; (c) teach in a way to overcome the temporal and capacity limitations in working memory; (d) teach to coordinate the behavioral and cognitive pathways; (e) teach multiple codes and modes of representation; and (f) serve as the chief executive functions (for other-regulation) while teaching strategies for the transition to selfregulation. The American Federation of Teachers (2000) claims that teaching is rocket science, but that organization may have underestimated the job at hand. A large team of highly educated, well-funded technical specialists work together to launch o n e rocket. In contrast, the individual teacher, working alone, is expected to launch 20 to 30 minds in the journey to skilled literacy. Moreover, all these minds show normal variation in their profiles and some show biologically constrained learning differences outside the range of normal variation (see Table 12.3). Thus, we believe that exemplary teaching is harder and just as important as rocket science. Teachers must have extraordinary executive functions to accomplish the task expected of them by society. Managing diversity effectively in building 20 to 30 reading, writing, and computing brains simultaneously requires skillful use of large group, small group, and even individual instruction. See Jenkins, Jewell, Leicester,

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TABLE 12.2

Brain-based Pedagogy (Instructional Design Principles)

1. Teach to all the components of a functional system at the relevant developmental stage, not only the cognitive but also the motivational components (limbic system) in a way that combines situational interest, extrinsic and intrinsic motivation (see Part III, and Hidi & Harackiewicz 2000). 2. Maximize transfer across the components of functional systems. Teach tools (low-level skills) in same session as goals (high-level skills) (see Table 1.2). 3. Overcome the temporal and capacity limitations of working memory. Teach low-level skills close in time to high-level skills (see Part III). 4. Coordinate the behavioral and cognitive pathways (see Chapters 3, 4, and 5). Provide sufficient practice in the low-level skills so that they become automatic but not so much that the learner habituates. Provide ample opportunities to examine relationships among elements (schemes), to problem solve, and to engage in inquiry. 5. Teach multiple codes in input and output units so that learners can form connections in hidden units (see Chapters 5, 6, 8, and 9). Use naming and other strategies to direct attention to units of different size in the input layers (written words) and output layers (spoken words). 6. Provide opportunities to use multiple modes for representing a problem (see Chapters 7 and 10). 7. Provide other-regulation for executive functions but also teach explicit strategies for self-regulation so that the transition from other- to self-regulation will eventually happen (see Parts II and III).

TABLE 12.3

Learning Differences in the Classroom a

Learner Disorder Down's Syndrome

Nature of Biological Constraint Genetic

Fragile X Syndrome

Genetic

Spina Bifida

Neural tube deficit (neural tube fails to close early in development; may be related to missing nutrients in diet)

Hydrocephalus

Cerebral spinal fluid bleeds from the ventricles

Fetal Alchohol Syndrome or Fetal Alcohol Effect

Ingestation of alcohol by mother during pregnancy

Drug-addicted Babies

Ingestation of alcohol and/or other substances by mother during pregnancy

Cerebral Palsy

Damage to the motor system in cerebral cortex during pregnancy or during birth; not genetic or progressive.

Muscular Dystrophy

Genetic disease; progressive neuromuscular degeneration

Mental Retardation

Significant impairment in all developmental domains; cause unknown in two thirds of cases; may occur along with other disorders

(continues)

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TABLE 12.3 (continued) Pervasive Developmental Disorder

Significant impairment in two or more developmental domains

Autism

Significant impairment in communication and socioemotional domains; often but not always occurs along with mental retardation

Specific Learning Disability

All development domains within normal limits but specific skill(s) in functional reading, writing, or computing domains not developing normally

Dyslexiab

Word reading skills impaired significantly

Dysgraphia

Handwriting and/or spelling skills impaired significantly

Other

Other specific component academic skills are impaired

Communication Disorders Articulation Disorder

Genetic or result of brain injury Impaired speech

Specific Language Impairment receptive

Difficulty in understanding language

expressive

Difficulty in producing language

Attention Deficit/Hyperactivity Disorder inattentive subtype

All developmental domains are within normal limits except attention/executive functions; genetic influences Only self-regulation of attention impaired

hyperactivity subtype

Only self-regulation of behavior impaired

mixed subtype

Both kinds of self-regulation impaired

Closed Head Injury

Severe trauma to the head that often impairs frontal areas

Prematurity

Wide range of causes and outcomes, which are very dependent on nature-nurture interactions

aMost common ones in addition to the normal variation related to fluctuations in patterns across learner profiles. 6Genetic influences; a language disorder with changing phenotype over development; hallmark is not letter reversals (see Berninger 1998a).

O ' C o n n o r , Jenkins, and T r o u t n e r (1994) for creative solutions to dealing with diversity and the i m p o r t a n c e o f evaluating such implementations empirically for a particular local school. Dealing effectively with diversity requires a detailed, carefully r e c o r d e d and m o n i t o r e d assessment system for individual progress, and a t h o r o u g h understanding o f the normal d e v e l o p m e n t a l progression, and its side paths and meanderings. Truly, classroom teachers are the chief executive officers for developing brains in c o m p l e x sociobiological systems. See Table 12.4 for the kind o f detailed planning and assessing required for ensuring each student makes optimal progress.

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BRAIN-BASED EDUCATIONAL ASSESSMENT AND INTERVENTION As a result of training in the brain and complexities of the teaching and learning processes, teachers become astute observers of students during the learning process. They begin to take notice of student behaviors for clues to where bottlenecks in learning may be occurring, and where instructional assistance is needed (see Chapters 8, 9, and 10). They begin to start thinking diagnostically to troubleshoot why the learning process is breaking down. The examples at the end of this chapter in the section, "Making Connections" offer opportunities for diagnostic thinking about learning and teaching. TRUE ACCOUNTABILITY Teaching the high-level workstations of the mind to network with each other to learn to read, write, and compute is complex because there are All Kinds of Minds (as captured by the name ofMel Levine's Institute at the University of North Carolina at Chapel Hill). Succeeding at the task requires knowledge of Mind in Society (Vygotsky 1978) as teachers interact with students and the biological complexities of Societies of Mind in the learner's brain (Minsky 1986). To create a mindful society that is prepared to adapt effectively to a changing environment, schools should base instruction and assessment practices on scientific knowledge of normal developmental trajectories, normal variation in those trajectories, and learning differences that fall outside the range of normal variation. The current approach to improving education through high stakes testingm setting one set of standards for each academic domain for all to attain--may be shortsighted because it assumes that students are a homogeneous population. What our research team has witnessed is enormous anxiety (even panic) on the part of teachers and students alike. In our experience, the students being flagged as below standards are the ones living in poverty, the ones whose development falls outside the normal range, the ones whose development falls within the lower limits of the normal range but below the mean (which is the most average score), and the ones for whom English is not their first language. We also observe an increasing amount of time spent on teaching tests that have not yet stood the test of time for being developmentally appropriate and sufficiently reliable and valid. We wonder if this obsession with one test is at odds with the legislation mandating that educational decisions for students with handicapping conditions cannot be based on a single test instrument. Should not those whose development and learning fall within the normal range have those same civil fights? We are concerned about this obsession with reducing teaching to accounting. Just as the brain has multiple codes for representing information and for expressing its thinking processes, we recommend that assessment for raising standards of educational performance make increasing use of multiple modes of assessment (see Table 12.4). Multimodal assessment combines nationally normed measures with

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TABLE 12.4 Brain-based Instructional Planning and Assessment W o r k s h e e t for Each Student in Classroom on Each Literacy and N u m e r a c y Skill a' b, c, Skill

Norm-Based

Criterion-Based

Curriculum-Based

Portfolio-Based

Linguistic Awareness Reading 1. keading real words 2. Reading nonwords 3. Reading pseudomorphs 4. single word automaticity 5. oral text fluency: rate 6. oral text fluency: prosody 7. Comprehension vocabulary meaning text Silent speed

Writing Handwriting legibility automaticity Spelling Composing d Fluency Quality

Math concepts number facts e arithmetic algorithms e problem solving e

Self-regulation (exeuctive functions) aSets as a goal demonstration that a student knows more at end of school year than at beginning in each of the developmentally appropriate skills in his or her profile; compares pretest and posttest scores, uses growth curves" and provides evidence of qualitative changes. b Employs multimodal assessment (all four column headings) (see Berninger, 1998a, Chapter 10). CUses a healthy mix of teaching and assessment (too much executive monitoring will not allow sufficient time for learning; focusing only on learning without assessment will not allow sufficient executive monitoring to modify instruction when necessary). dLength within specified time limits. eEvaluate separately for mental arithmetic in working memory, paper and pencil expression, and external, hand-held calculator support.

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demonstrated reliability and validity, criterion-referenced measures, curriculumbased measures, and portfolio assessment (Valencia 1998). The purpose of assessment is to assess whether teachers need to modify instructional approaches for individuals or groups, or whether the school system needs to modify its curriculum. Assessment is linked to instructional decisions for the purpose of improving instruction rather than to student graduation or teacher salary. Ultimately, educational achievement and post-graduation performance depends on the interaction of many components in the many systems, ranging from the brains of individual learners to groups of students and a teacher in classrooms, to schools in school districts, to school districts in states, countries, and the earth. No one component in this complex of many interacting systems can alone be accountable for the overall functioning of the systems. Just as the executive functions in the brain send self-monitoring messages to other components of a functional system, we propose in Table 12.5 a high-stakes test that might be given to state and federal government officials who are devising high-stakes tests for school-age students. We note that the brain strives to deal with these three same fundamental issues on a daily basis. Perhaps as we learn more about how the brain manages conflict that arises, allocation of limited resources, and orchestration of diversity, we will find clues for creating a mindful society. In the meantime, we encourage teachers not to teach the test, but rather to continue to teach children information about the world discovered by past generations and to teach children how to think to solve problems they will face as they inherit the earth. The answer to the word problem for novice computing brains in Chapter 10 is that there is zero time left for teaching the test if the necessary time is TABLE 12.5 High-Stakes Testing for the Mindful Society Question I a Can the peoples of the earth learn to resolve conflict without destroying each other? How? Question 2 b Can procedures be developed for equitable sharing of the limited resources of planet earth among all its

peoples? How? Question 3c Can we develop and monitor instructional practices to promote optimal development of each individual's unique profde of strengths and weaknesses and capitalize on this pool of diversity for meeting society's needs? aThe anterior cingulate is a brain structure that continually has to manage the conflict that arises within the societies of the mind (see Chapter 5). bThe brain constantly has to negotiate how to allocate the limited resources of working memory and the limited glucose and oxygen supply for fueling neural activities. ' CThe brain has to organize into functional systems very diverse processes to accomplish a variety of goals. The brain has a self-monitoring and governing system that improves over the course of development during the school age years to manage this diversity.

Implications for Classroom Practice

325

spent on teaching all the other necessary parts of the curriculum. Miss Bonkers, the heroine of Dr. Seuss's last book, refused to teach to the test and the children passed anyway. According to Dr. Seuss (1998): "Miss Bonkers rose. 'Don't fret!' she said. 'You've learned the things you need to pass that test and many more m I'm certain you'll succeed. We've taught you that the earth is round, that red and white make pink, and something else that matters m o r e - - w e ' v e taught you how to think.'"

RECOMMENDATIONS

FOR FURTHER

READING

Hope and Cautious Optimism Calfee, R. & Patrick, C. 1995. Teach our children well: Bringing K- 12 education into the 21st century. Stanford, CA: Stanford Alumni Association. McCourt, F. 1996. Angela's ashes. A memoir. New York: Simon & Schuster. McCourt, F. 1999. 'Tis. A memoir. New York: Simon & Schuster. Thompson, R. & Nelson, C. 2001. Developmental science and the media. Early brain development. American Psychologist. 56:5-15. Seuss, Dr. & Prelutsky, J. 1998. Hurray for diffendoofer day! New York: Alfred Knopf, Inc.

Learning in a Social Context Wertsch, J. 1985. Vygotsky and the social formation of the mind. Cambridge, MA: Harvard University Press.

Motivation Hidi, S. & Harackiewicz, J. 2000. Motivating the academically unmotivated: A critical issue for the 21st century. Review of Educational Research. 70:151-179.

Teachers at Work in Their Classrooms Liberman, A. & Miller, L. 2001. Teachers caught in the action. Professional development that matters. New York: Teachers College Press.

Teaching Students with Learning Differences Clark, D. & Uhry, J. 1995. Dyslexia: Theory and practice of remedial instruction, 2nd ed. Baltimore, York Press. Wong, B., ed. 1998. Learning about learning disabilities, 2nd ed. New York: Academic Press. Birsh, J., ed. Multisensory teaching of basic language skills. Baltimore: Paul H. Brookes.

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Teaching Phonological Awareness Adams, M., Foorman, B., Lundberg, I. & Beeler, T. 1988. Phonemic awareness in young children. Baltimore: Paul H. Brookes. Blachman, B., Ball, E., Black, R. & Tangel, D. 2000. Road to the Code. A phonological awareness program for young children. Baltimore, MD: Paul H. Brookes. Jenkins, J., Vadasy, P., Firebaugh, M. & Profiler, C. 2000. Tutoring first grade struggling readers in phonological reading skills. Learning Disabilities: Research and Practice. 15:75-84. Order Sound Phonics from Washington Research Institute, 150 Nickerson Suite 305, Seattle, WA 98109. O'Connor, R., Notari-Syverson, A. & Vadasy, P. 1998. Ladders to Literacy. Baltimore: Paul H. Brookes.

Teaching Word Skills Bear, D., Invernizzi, M., Templeton, S. & Johnston, F. 2000. Words their way. Word study for phonics, vocabulary, and spelling instruction, 2nd ed. Upper Saddle River, NJ: Merrill (Prentice Hall). Beck, I., & Hamilton, R. 1996, 2000. Beginning reading module. Washington, DC: American Federation of Teachers. Henry, M. 1990. Words. Integrated decoding and spelling instruction based on word origin and word structure. Austin, TX: pro-ed. Lovett, M. 1998. The P H A S T Program: The phonological and strategy training program. The Hospital for Sick Children. Toronto, CA. Moats, L. & Foorman, B. 1998. Scholastic Spelling. New York: Scholastic, Inc.

Teaching Reading Fluency Cooper, T., Pikulski,J. & Au, K. 1997. Early Success. An Intervention Program. Boston: Houghton Mifflin. Read Naturally. 1997. St. Paul, MN: Turman Publishing. Mercer, C. & Campbell, K. 1997. Great leaps reading. Gainesville, FL.

Portfolio Assessment Valencia, S. 1998. Literary portfolios in action. Fort Worth, TX: Harcourt Brace.

MAKING

CONNECTIONS

1. The principal in a high school committed to educational reform, because of Sputnik, focused narrowly on test scores in tracking students and advising them for future careers. Students trekked past his desk as he told them, one at a time, their SAT results and inquired what their career goals were. Those with high scores who expressed an interest in teaching were encouraged to pursue other paths. Many ignored him and went into teaching anyway. What could state educational agencies

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do to increase the likelihood that the best students pursue teaching? What could schools do to ensure that the best teachers remain in teaching as a profession? What nongovernmental policies instituted by the teaching profession might help teaching evolve as a profession? 2. Two children from the same family showed a very different pattern of learning to read. One seemed like a natural reader and could read before coming to school. The other just could not decode until the second grade teacher taught him phonics very explicitly and systematically. Based on what has been presented in this book on brain literacy, how could one account for these two alternative pathways to learning to read in siblings who did not differ in any other remarkable way? 3. A third grader is in danger of not passing third grade because he did not hand in a required assignment to write twenty book reports during the year. In every other way he appears to be learning. The teacher has recommended that he repeat third grade so that he can mature. Is this a good idea? Why or why not? What are some of the reasons, from the perspective of a developing brain, that he might not be completing the written assignments? What might be some of the alternatives to retention? 4. A second grader keeps reversing digits when he does written calculations, but he always comes up with the correct answers when he does mental calculations. Why, from the perspective of brain literacy, might this be? What might the teacher do to help him? 5. A fifth grader has been diagnosed with attention deficit disorder-inattentive subtype. The family pediatrician recommended a clinical trial of stimulant medication to evaluate whether this medical intervention might help. He has asked the classroom teacher to make behavioral observations on a daily basis and report to him what the child's behavior is like on a daily basis. What behaviors should be teacher observe? How can change in behavior be evaluated to decide if the child's behavior improves or not? 6. A first grader is struggling with learning to decode written words. What alternative teaching strategies might be used? A fourth grader is struggling with reading comprehension. What alternative teaching strategies might be used? A second grader is struggling with handwriting and spelling. What alternative teaching strategies might be used? A fourth grader is struggling with written composition. What alternative teaching strategies might be used? A third grader just cannot learn the multiplication tables. What alternative teaching strategies might be used? A fifth grader is struggling with fractions. What alternative teaching strategies might be used? 7. For each of the same learning problems in the sixth question, what behaviors should a teacher observe as the student engages in the process for clues as to why the student is having trouble? How should the teacher decide whether the student is responding to the alternative interventions?

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Glossary

A c q u i r e d disorder A previously acquired function is lost. All or n o n e potential Nerve impulse that fires in digital (on-off) fashion as a result of change in the balance between positive and negative charges on the inside and outside of the membrane surrounding a neuron. Association fibers Neural pathways on the same side of the brain. A s y m m e t r y Structure on the left is larger or smaller than the homologous or corresponding structure on the right. A x o n Receives information from dendrites in axon hillocks where it summates and transmits information to other neurons across a synapse; has terminals. A t t e n t i o n deficit disorder (ADD) ADHD-inattentive subtype; problem in self-regulation of internal attentional processes. A t t e n t i o n deficit hyperactivity disorder (ADHD) -Inattentive, -hyperactive, or -mixed subtypes; hyperactivity disorder, problem in self-regulation of overt behavior.

A u t o m a t i z a t i o n Process is on automatic pilot; direct retrieval or function that does require conscious effort. Basal ganglia Nuclei lying under the front regions of cortex; part of the motor system. B i n o c u l a r vision Coordination of two eyes in creating a stable visual percept. Brain s t e m Midbrain, pons, medulla oblongata, reticular activating system. B r o d m a n n areas Regions defined by a German neurologist on the basis of common structural properties of neurons, and identified with a number. Calcarine fissure lobe (BA 17).

Located in the occipital

CeU differentiation Generation of different kinds of neuronal cells with different structures and functions. Cell m i g r a t i o n Newly generated neurons travel to their final destination in the brain's neural architecture. Cell proliferation Rapid generation of neuronal and glial cells between the fifth and twentieth week of gestation.

329

330

Glossary

Cell p r u n i n g Selective elimination of synapses in an overconnected brain to reduce metabolic needs and increase energy efficiency in neural functioning.

Central sulcus Divides the parietal and frontal lobes and the primary somatosensory and motor areas, respectively. Cerebellum Situated below the occipital lobe in the cerebral cortex; involved in motor control, learning, and automatization of functions. Cerebrum Two hemispheres divided by longitudinal fissure into a right side and a left side, both of which have an outer cortex further divided into four lobes occipital, temporal, parietal, and frontal. Chemical pathway Neurotransmitters that facilitate neural transmission. CNS

Central nervous system.

Commissures Pathways connecting the right and left hemispheres (sides of cortex or cerebellum). Controlled processing Requires conscious effort and involves cognitive strategies and/or schemata (specify relationships among elements). Cranial nerves Twelve nerves that keep the CNS in contact with the external world. Critical periods Developmental windows during which skills are learned most easily and after which they may not be learned, or may be learned only with great difficulty.

Dendrites Branching structures of neurons that have spines and collect information from other neurons. Developmental axes Bottom-to-top, right-to-left, or back-to-front directions of development.

Developmental disorder Struggle to acquire a function during development (preschool and school years). Developmental trajectories Changes in a skill or system of interacting components over time. Dysplasias Disordered layers of cerebral cortex in which excessive numbers of large cells distort the normal organization of cerebral cortex into columns and layers. Ectopias Neurons end up in the wrong layer of cerebral cortex during neural migration. Excitatory neurons Increase the probability that a downstream neuron will fire. Fibers Fissure

Assemblies of fibers. Very deep area between gyri.

Fixations Pauses during eye movements in which written words are processed in foveal vision (rods in the retina that are sensitive to visual detail). Forebrain Limbic system, basal ganglia, cerebral hemispheres and cortex, lateral ventricles. Functional system Total set of brain structures and component processes activated in time to perform a task. Common brain structures and functions may be orchestrated differently to perform different tasks within one stage of development; they may be reorganized across development. Glial cell Cell that provides support and nourishment. Graded potential Voltage change in response to upstream neural activity that summates spatially and/or temporally in analog fashion along a continuous quantitative gradient.

Glossary

Gray matter

Maturation

Gyrus

Meninges

Assemblies of cell bodies of neurons where capillary blood vessels may be found. Folds or convolutions rising above the surface; plural gyri.

Hindbrain

Cerebellum, brainstem, fourth ventricle, and cranial nerves.

Hypothalamus

Very small structure involved in the regulation of basic lifesustaining body functions such as foodintake, body temperature, endocrine functions.

Inhibitory neurons

Decrease the probability that a downstream neuron will fire.

Language by Ear

Functional system for listening/aural language.

Language by Eye

Functional system for

reading.

Language by H a n d

Functional system

for writing. L a n g u a g e by Mouth Functional system for expressive language/oral language; involves more than articulation or speech.

Limbic structures

Hippocampus, septum, cingulate cortex, parahippocampal gyrus in the temporal lobe, amygdala, mammillary bodies, olfactory lobe, fornix; involved in emotion, motivation, and other functions.

Lateral fissure

Sylvian fissure that divides the temporal from parietal and frontal lobes.

Lexicon

Mental dictionary.

Longitudinal fissure

Divides the cerebrum into a right and left side.

Macrolevel

Analysis of very large units that draw on many small units.

M a g n o c e l l u l a r A fast system for processing rapidly changing stimuli.

331

Genetically preprogrammed instructions guiding synaptogenesis, dendrite branching, and myelination.

Three membranes-outer dura mater, middle arachnoid, inner pia m a t e r - - that protect the brain under the skull.

Microlevel

Analysis of very small units.

Midbrain

A structure in the brain stem between the forebrain and other structures in the hindbrain.

Multivariate thinker Ability to think about more than one variable or dimension.

Myelin sheath

Glial cells that wrap around a neuron's cell membrane to increase speed of neural transmission.

Myelination

Genetically and maturationally influenced process of the formation of the myelin sheath by glial cells around axons. This occurs over a long developmental period from birth to young adulthood with specific brain regions myelinating at different times of development, and results in more efficient and rapid transmission of neural signals.

Name code

Expressive phonological code for a word unit.

Nativist view

Behavior is genetically determined and experience cannot alter developmental trajectories. This view was popularized by Gessell early in the twentieth century, but not supported by neural science at the end of the twentieth century; see naturenurture interaction for an alternative view.

Nature-nurture interaction

Learning and behavior are jointly influenced by biology and the environment rather than by biology or the environment alonc.

332

Glossary

Nerve g r o w t h factor (NGF) Chemical that influences the synaptogenesis and learning processes.

P o l y m i c r o g y r i a Many small gyri as a result of excessive folding and absence of columnar organization.

Neurulation Development of the brain and nervous system beginning 19 days after fertilization.

Prefrontal association cortex The most recently evolved region of cerebral cortex; BA 46.

Neural pathways Sequential connections from one neural unit to another.

Prelexical Subwordlanguage code before itisrecodedatwordlevelorgains access to the internal lexicon (mental dictionary).

Neuron Nerve cell that consists of a cell body, dendrites, and an axon. Neurotransmitters Chemicals that affect neural transmission.

Prelinguistic Sensory code before it is recoded as language.

Node o f Ranier Exposed gaps in axons between glial cells that are unmyelinated and therefore less efficient in energy consumption.

P r i m a r y projection pathways Unimodal neural pathways for the sensory and motor systems that have direct connections with the outside world; see Table 3.2.

Nucleus Large number of cell bodies with a characteristic gray color.

P r o p r i o c e p t i o n Sense of position and movement of the body and limbs.

Nuclei Assemblies of more than one nucleus.

Pyramidal cells Star-shaped neurons in the cerebral cortex.

O r t h o g r a p h i c codes Representations of written words that are specific to visible language and not to visual stimuli in general. Parvocellular A slower system for processing slower changing stimuli. P h o n e Sound unit in speech; several phones may belong to the same phoneme category. P h o n o l o g i c a l codes Represent sounds in words, either receptively for incoming words or expressively for producing words (name codes).

Phoneme An abstract class of sound that makes a difference in meaning without having meaning of its own.

Reticular activating system Relatively small net of grayish brown cell bodies mixed with white myelinated axons in the core of the brain stem that has many connections with the rest of the brain stem, spinal cord, cerebellum, thalamus, cortex. This system regulates arousal and consciousness. Saccades Rapid eye movements from one fixation point to another m forward and backward saccades occur although readers are not consciously aware of them. Saltatory c o n d u c t i o n Nerve impulse jumps from one unmyelinated Node of Ranier to another one along an axon.

P l a n u m t e m p o r a l e A triangular region spanning regions of the temporal and parietal lobes that typically is larger on the left than on the fight.

S e c o n d a r y association areas Heteromodal pathways that receive and integrate inputs from primary projection areas but do not communicate directly with the external environment.

PNS

S e m a n t i c codes

Peripheral nervous system.

Represent meaning.

Glossary

Somatosensory

Sense of touch, temperature, pressure, pain, and proprioception.

Spinal cord Transmits sensory and motor information in between the external environment and the brain. S t r i a t u m Receives information from all over the cortex; involved in automatization of some functions. Sulcus Shallow area or valley between gyri (pl., sulci). Sylvian fissure Lateral fissure that divides the temporal from parietal and frontal lobes. S y m m e t r y Homologous or corresponding structures on the right and left are the same size. Synapse Electrochemical functional connection between spatially separated neurons; a nerve impulse is carried across the gap that separates the neurons.

333

Synaptogenesis Forming potential synaptic connections in the cortex between gestation and adolescence; based on chemical cues from nerve growth factor and experience.

Tertiary association areas

Heteromodal pathways that receive and integrate sensory- and motor-free inputs from secondary association areas.

T h a l a m u s Way station (especially for incoming information) that receives from and projects to many brain areas.

Tract

Large number of axons that are white if myelinated.

Ventricles Four brain cavities fLLledwith colorless cerebral spinal fluid. Vestibular sense The sense that perceives body movement and degrees of balance in reference to gravity and motion and helps to maintain smoothness of action. White matter myelin.

Axons covered with

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Index

Abstract thinking, see Cross-Talking Computers of Mind Action potentials, 26-28 All-or-none potentials, 26-27, 30-31, 35 Alphabetic principle, 218-219, 224, 227-231, 254, 264-266 Alternative pathways, 57-58 Arcuate fasciculus, 40, 42, 83 Articulatory skills, 119-120 Assessment developmentally sensitive, 271 of curriculum rather than student, 303 progress monitoring, 302 Association areas, 44, 49, 54, 58, 84, 91-94, 130, 137, 174-175, 178 Association fibers, 40 Asymmetry, 50, 98 Attention and attentional system, 52, 91, 95, 130-134,156,180, 224-227, 271 Attention and instruction, 224-227 Attention Deficit (ADD), 7, 92, 321,327 Attention Deficit/Hyperactivity Disorder, (ADHD), 7, 321,327 Auditory system, 41-42, 119-120, 124, Automatic processing, 125, 129-130, 134, 141, t65, 173, 179-184, 187, 223-234, 245,249, 253-254, 271,280 Axial slice, 65-68 Axon, 21-23, 27, 30, 35, 82

Basal ganglia, 47-49, 59-60, 62, 82 Behavioral pathway, see Automatic processing. Biodiversity and biocultural diversity, 11, 17,103, 305-306 Biological determinism, 10, 104 Blood-brain barrier, 34 Blood Oxygenation Level Dependent (BOLD) response, 64, 71, 136, 137 Brain as independent and dependent variable, 10-11 Brain axes, 14, 34, 37, 40-44, 46, 50, 57,87, 88, 110 Brain literacy definition, 6 Brain stem, 46, 59-60, 62, 82, 89, 93 Brain vs. heart hypotheses, 19 Brain vocabulary, 45-46 Brain-based education, 16 assessment and intervention, 321,323-325 definition, 315-319 instructional design principles, 319-321 pedagogy for reading brain, 218-241,246 pedagogy for writing brain, 247-269 pedagogy for computing brain, 275-291 Broca's area, 40, 54, 84, 88, 122-123, 125, 127-128, 138, 143, 145, 178, 203-205 Brodmann, 36-38, 51, 53

Cell body, 21, 26 Cell differentitation, 78, 80 Cell membrane, 25

369

370

Index

Cell proliferation, 78-80 Cell/synaptic pruning, 78, 82, 91,164 Cerebellar layers, 36-37 Cerebellum, 36, 46, 59-60,62, 82, 89, 125, 127-128, 130, 138, 143, 145, 158, 178-184, 207 Cerebral cortex, 28, 37-40, 46-49, 52-53, 60, 62-63, 80-89, 91, 93-95, 97, 100-101, 125, 127-128, 131,133,137-153, 156-158,178-184, 201-204, 205-207 Cerebral hemispheres, 36, 48 Chromosomes, 22 Cingulate, 132-134 Clinical cognitive neuropsychology, 51 CNS, 26, 46 Cognitive neuroscience models, 51 Cognitive pathway, see Controlled, non-automatic procesing. Colored lenses, 115 Commissures, 40 Composing, 170, 180, 247-272 Computational processes, 26, 30, 153-155, 164 Computer-assisted tomography (CAT or CT), 64, 69-70 Connectionist models, 153-154 Constraints on learning, 10-13, 85, 91, 96-104 Controlled, non-automatic processing, 125, 129-130, 134, 141,165, 179-184, 234, 238-239, 245, 249 Convolutions/folds, 37, 46, 48, 49, 78 Coronal slice, 65-68 Corpus callosum, 40, 57 Cortical layers, 36, 48, 92 Cranial nerves, 46-47, 157 Critical developmental periods, 88-89, 92, 105, 247 Crossing principle, 42-43 Cross-Talking Computers of Mind, 54, 55, 63, 87, 91-93, 95, 105, 153 Cytoarchitectonic studies, 36, 51, 55, 96-98

Deduction, see induction vs deduction. Dendrites, 21-22, 30,78, 81, 85-88, 90, 95, 164 Diffusion tensor magnetic resonance imaging (DT-MRI), 64, 70, 100 Distributive function, 54-55, 154 Division of labor, 51-52

DNA, 22, 86 Double deficit, 120

Early identification and intervention, 302 Electroencephalography (EEG), 64, 72 Embryonic and fetal stages, 78-80 Event-related potentials (ERPs), 64, 73, 101-102 studies on computing,201 studies on reading, 136, 152 Evoked potentials (EPs), 73, 101-102 Excitatory postsynaptic potential, 27, 57 Executive functions, 60, 88, 129, 134, 138-140, 151,157, 172-173, 181,186-188, 190, 227, 256-257 Explicit memory, See implicit versus explicit memory. Eye movements, 114-115 Eye patching, 88, 114 Eye training, 116

Finger movements 178-184 Fissure, 37, 48-49, 110 Flexible orchestration, 95-96, 109 Fluency, 233-234 Functional magnetic resonance imaging (fMRI), 64, 72, 101 studies on reading, 135-137, 152-153 studies on writing, 179 Functional magnetic resonance spectroscopic imaging (fM1LS), 64, 72, 152-153 Functional maps, 51 Functional organizing principles, 35, 51-61 Functional system(s), 14, 59, 77, 93-96, 109, 121-123, 185-187, 193-196,200-201

Genes and gene expression, 82, 84, 86, 103-104, 164 Glial cells, 20, 26, 34-35,79-80 Grade repetition, 300-302 Graded potentials, 26, 30-31, 35 Grapho-motor system, 180 Gray matter, 35, 83 Gyrus (pl. gyri), 37, 40, 45, 49, 53, 81-82, 84, 93, 97, 110, 124-125, 127-128, 133-134

Index Habituation, 90-91,249,253 Handwriting, 168-169, 174-175, 180, 184, 187, 247-248, 252-255 Heteromodal association areas, See association

371

Localization of function, 52, 54-55 Long-term memory, 127-128, 158, 173, 181,186 Low-level skills, 9 Luria, 8, 54, 93-95, 174

areas.

High-level skills, 9 Hippocampus, 47-48, 52, 62, 80, 83, 89, 93, 95, 127-128 Human Brain Coloring Book, The (Diamond, Scheibel, & Elson), 21, 24, 25, 26, 27, 36, 37, 40, 46, 49, 78, 80, 85 Hypothalamus, 47, 58, 62

Idea generation, 180, 259-260 Imaging studies of developmental dyslexia in adults, 141-144 developmental dyslexia in children, 146-153 math, 201-204 normal, skilled reading in adults, 137-141 oral vs. silent reading, 144-146 writing, 178-185 Implicit versus explicit memory, 216-217, 220-222, 224-227,242, 251-252, 271 Induction versus deduction, 216, 251,271, 273-274 Inhibitory postsynaptic potential, 27, 57 Instructional scaffolding, 9 Insula, 49-52, 132-133, 142, 151,207 Interindividual differences, 12, 50 Intraindividual differences, 12, 50

Jackson, Hughlings, 54 Language systems, 96, 121-126, 156, 165, 185-187, 271 Lateralization of function, 55-57 Learning mechanisms, 90-91, 219-220 Learning styles, 15, 17, 46, 57, 105, 110, 165 Left dorsal prefrontal cortex (LDPFC), 88, 129, 138, 180-181,206 Levels of analysis, 13-14, 31,54, 59,121-123, 125, 153-155, 165, 171-172, 189 Limbic (emotional/motivational) system), 37, 47, 52, 82-83, 93, 95, 207, 234-235,240, 255, 262, 271,286-287 Linguistic awareness, 220-222, 236-237,243, 254, 258-259

Macrostructure, 13-14, 33-75 Magnetic Source Imaging (MSI), 151-153 Magnetoelectroencephalographcy (MEG), 64, 73, 138, 144 Mapping functions and relationships, 54, 165 Mass action, 52 Math development, 196-199, 208-209,287-288 Maturation, 83, 95, 106 Memory, 126-130, See short-term, working, and long-term memory. Meninges, 35 Microstructure, 13-14, 19-31 Middle childhood, 16 Minsky, 63, 102, 126 Motor pathways and systems, 41-42, 62, 87, 89, 91, 95, 117-120, 127-128, 174, 178-185, 205 Motor theory of speech perception, 119, 145, 150 mRNA, 22 Multiple connections, 222-227 Myelin or myelin sheath, 20-21, 27, 35, 82, 100 Myelination, 78, 82-84, 88, 91, 95,164, 188, 190

Nature-Nurture interactions, 9-11, 17, 31, 83-86, 90-92, 106, 164, 168,204-208, 215,273 Necessary and sufficient conditions, 60 NGF, 86 Nerve impulse, 27 Neural connections, 30, 35,58, 91, 95 Neural migration, 12, 78, 80 Neural tube, 78 Neuron vs. nerve net hypotheses, 19 Neuronal electrical activity, 26 Neuronal energy, 26, 34, 82, 89, 152-153 Neuronal gates, 25, 28 Neuronal receptor sites, 25, 28 Neurons, 20, 21-26, 30, 79, 85 Neurotransmitter, 20, 22-24, 27-28, 31,36, 127-128 Neurulation, 78 Nodes ofRanier, 26-27

372

Index

Normal variation, 12, 15, 50, 58, 84

Oral vs. silent reading, 158, 227-238 Orchestration of mind, 55 Orthographic coding and orthographic word forms, 112,134,138-140, 146, 157, 171, 174-178, 180,222-224, 227,254

Parallel processing, 59, 154 Phonological coding and phonological word form, 124, 138-140, 146, 157, 171-172, 176-178, 180, 222-224, 227, 245-254 Phonological training with articulatory feedback, 119-120 without articulatory feedback, 119-120 Pineal body, 46-47 Plasticity, 60 PNS, 26, 80 Positron emission tomography (PET), 64, 71 studies on reading, 135-137, 141 studies on writing, 179, 182, 184 studies on computing, 201-202 Primary projection areas, 41-42, 44, 93-94, 130, 137 Projection maps, 41-42, 51, 53

alternatives to a business model, 311-312, 322-325 implementation of research in classrooms, 313-315 research-classroom connections, 307-309 separating education and politics, 306-307 Second messenger systems, 28 Self-regulation, 7, 60, 88, 92, 173, 227-233, 239-240, 249-250, 260, 267-269, 271,286 Semantic coding and semantic word form, 124, 138-140, 157, 171-172,222-224, 227, 245, 254 Sensory pathways and systems, 41-42, 62, 87, 89, 91, 95, 111-117,118-120, 127-128, 185, 205 Short-term memory, 127-128, 158,173, 181,186 Social-emotional behavior, 62, 82, 92-93 Spelling, 169-170, 180, 185-187,247-248 Spinal cord, 39, 41-42,46,60,68, 82 Split-brain experiments, 55-57 Striatum, 48-49, 127-128, 130-131,156, 158, 178-184 Structural organizing principles, 35-44 Substantia nigra, 35, 49, 59, 131 Sulcus (pl. sulci), 37, 45, 49, 84, 110, 124 Symmetry, 50, 97-98 Synapse, 23-24, 57, 62, 80-83, 87 Synaptogenesis, 78, 91, 95, 164 Systems approach, 7-8, 18, 60, 80-82, 87-89, 192, 235, 246, 249, 273-274, 283, 309

Reading development, 155-165 Reading lexicon, 157 Redundancy, 57, 193-194, 254, 282 Regional cerebral blood flow (rCBF), 64, 70-71 studies on reading, 136-137, 141 Relationships between reading and writing, reading and math, and writing and math systems, 157-158, 167-175, 240, 261-262, 210 Resting potentials, 26-28 Reticular activating system, 35, 46, 49, 52, 62, 83, 156, 180, 205 rRNA, 22

Teacher education Continuing education, 16 Evolution in preservice training, 303-305, 312 Technology for brain research, 15, 20-21, 24, 29, 33-35,50,63-74, 135-137 Thalamus, 28, 47-49, 60, 62, 82, 89, 127-128, 131,138, 143 Thinking, 126, 173, 186-187,271,283, also see Cross-Talking Computers of Mind Timing in reading, 144, 152 Topographic maps, 53, 132

Sagittal slice, 65-68 School entrance age, 299-300 School evolution, 297-298

Variable structure-function relationships, 52 Ventricles, 35, 46-47 Vestibular system, 41-42, 116

Index Visual system, 41-42, 111-116, 156 Vygotsky, 9

Wernicke's area, 40, 52, 54, 83, 88, 97, 122-123, 139, 142

White matter, 35, 83 Word origin, 169, 235-236 Working memory, 2, 127-128, 131, 134, 158, 172-173, 181,186-187, 250, 274 Writing development, 175-178, 187-192

373

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