ASSOCIATIVE LEARNING AND CONDITIONING THEORY
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Associative Learning and Conditioning Theory Human and Non-Human Applications
Edited by
Todd R. Schachtman Steve Reilly
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Oxford University Press, Inc., publishes works that further Oxford University’s objective of excellence in research, scholarship, and education. Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam
Copyright © 2011 Oxford University Press Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 www.oup.com Oxford is a registered trademark of Oxford University Press All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Associative learning and conditioning theory: human and non-human applications/edited by Todd Schachtman and Steve Reilly. p. cm. Includes bibliographical references and index. ISBN 978-0-19-973596-9 (hardback) 1. Learning, Psychology of. 2. Classical conditioning. 3. Human behavior. 4. Paired-association learning. I. Schachtman, Todd R. II. Reilly, Steve. BF318.A85 2011 153.1’526—dc22 2010037600
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Printed in the United States of America on acid-free paper
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For Ariel and Becky (T.S.) For Elaine (S.R.)
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CONTENTS
Contributors
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PART I: OVERVIEW
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Things You Always Wanted to Know About Conditioning But Were Afraid to Ask Todd R. Schachtman and Steve Reilly
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PART II: APPLICATIONS TO CLINICAL PATHOLOGY
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Fear Extinction and Emotional Processing Theory: A Critical Review Seth J. Gillihan and Edna B. Foa Fear Conditioning and Attention to Threat: An Integrative Approach to Understanding the Etiology of Anxiety Disorders Katherine Oehlberg and Susan Mineka Behavioral Techniques to Reduce Relapse After Exposure Therapy: Applications of Studies of Experimental Extinction Mario A. Laborda, Bridget L. McConnell, and Ralph R. Miller
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79 104
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Learning and Anxiety: A Cognitive Perspective Peter F. Lovibond
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Trauma, Learned Helplessness, Its Neuroscience, and Implications for Posttraumatic Stress Disorder Vincent M. LoLordo and J. Bruce Overmier
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Aberrant Attentional Processes in Schizophrenia as Reflected in Latent Inhibition Data Robert E. Lubow
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CONTENTS
8. Discrimination Learning Process in Autism Spectrum Disorders: A Comparator Theory Phil Reed
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PART III: APPLICATIONS TO HEALTH AND ADDICTION
9. Conditioned Immunomodulation
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Jennifer L. Szczytkowski and Donald T. Lysle
10. Learning, Expectancy, and Behavioral Control: Implications for Drug Abuse Muriel Vogel-Sprott and Mark T. Fillmore
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11. Applications of Contemporary Learning Theory in the Treatment of Drug Abuse Danielle E. McCarthy, Timothy B. Baker, Haruka M. Minami, and Vivian M. Yeh
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12. Internal Stimuli Generated by Abused Substances: Role of Pavlovian Conditioning and Its Implications for Drug Addiction Rick A. Bevins and Jennifer E. Murray
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13. Learning to Eat: The Influence of Food Cues on What, When, and How Much We Eat Janet Polivy, C. Peter Herman, and Laura Girz
14. Conditional Analgesia, Negative Feedback, and Error Correction
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Moriel Zelikowsky and Michael S. Fanselow
15. Incentives in the Modification and Cessation of Cigarette Smoking
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Edwin B. Fisher, Leonard Green, Amanda L. Calvert, and Russell E. Glasgow
PART IV: APPLICATIONS TO COGNITION, SOCIAL INTERACTION, AND MOTIVATION
16. Social Learning and Connectionism
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Frank Van Overwalle
17. Application of Associative Learning Paradigms to Clinically Relevant Individual Differences in Cognitive Processing Teresa A. Treat, John K. Kruschke, Richard J. Viken, and Richard M. McFall
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18. Evaluative Conditioning: A Review of Functional Knowledge and Mental Process Theories Jan De Houwer
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19. Instrumental and Pavlovian Conditioning Analogs of Familiar Social Processes Robert Ervin Cramer and Robert Frank Weiss
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20. The Impact of Social Cognition on Emotional Learning: A Cognitive Neuroscience Perspective Andreas Olsson
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CONTENTS
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21. Effects of Conditioning in Advertising
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Todd R. Schachtman, Jennifer Walker, and Stephanie Fowler
22. Applications of Pavlovian Conditioning to Sexual Behavior and Reproduction Michael Domjan and Chana K. Akins
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23. Hot and Bothered: Classical Conditioning of Sexual Incentives in Humans Heather Hoffmann Index
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CONTRIBUTORS
Chana K. Akins, Ph.D. Department of Psychology University of Kentucky Lexington, KY
Michael S. Fanselow, Ph.D. Department of Psychology University of California, Los Angeles Los Angeles, CA
Timothy B. Baker, Ph.D. Center for Tobacco Research & Intervention, and Department of Medicine University of Wisconsin School of Medicine and Public Health Madison, WI
Mark T. Fillmore, Ph.D. Department of Psychology University of Kentucky Lexington, KY
Rick A. Bevins, Ph.D. Department of Psychology University of Nebraska-Lincoln Lincoln, NE Amanda L. Calvert, A.M. Department of Psychology Washington University St. Louis, MO Robert Ervin Cramer, Ph.D. Department of Psychology California State University, San Bernardino San Bernardino, CA Jan De Houwer, Ph.D. Department of Psychology Ghent University Ghent, Belgium Michael Domjan, Ph.D. Department of Psychology The University of Texas at Austin Austin, TX
Edwin B. Fisher, Ph.D. Global Director, Peers for Progress American Academy of Family Physicians Foundation Professor, Health Behavior & Health Education Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill, NC Edna B. Foa, Ph.D. Center for the Treatment and Study of Anxiety University of Pennsylvania Philadelphia, PA Stephanie Fowler, M.A. Department of Psychological Sciences University of Missouri Columbia, MO Seth J. Gillihan, Ph.D. Center for the Treatment and Study of Anxiety University of Pennsylvania Philadelphia, PA xi
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CONTRIBUTORS
Laura Girz, M.A. Department of Psychology University of Toronto Toronto, ON, Canada
Robert E. Lubow, Ph.D. Department of Psychology Tel Aviv University Tel Aviv, Israel
Russell E. Glasgow, Ph.D. Institute for Health Research Kaiser Permanente Colorado Denver, CO
Donald T. Lysle, Ph.D. Department of Psychology University of North Carolina at Chapel Hill Chapel Hill, NC
Leonard Green, Ph.D. Department of Psychology Washington University St. Louis, MO C. Peter Herman, Ph.D. Department of Psychology University of Toronto Toronto, ON, Canada Heather Hoffmann, Ph.D. Department of Psychology Knox College Galesburg, IL John K. Kruschke, Ph.D. Department of Psychological and Brain Sciences Indiana University Bloomington, IN Mario A. Laborda, M.S. Department of Psychology State University of New York— Binghamton Binghamton, NY Universidad de Chile Santiago, RM, Chile Vincent M. LoLordo, Ph.D. Department of Psychology Dalhousie University Halifax, Nova Scotia, Canada Peter F. Lovibond, Ph.D. School of Psychology University of New South Wales Sydney, Australia
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Danielle E. McCarthy, Ph.D. Department of Psychology, Rutgers The State University of New Jersey New Brunswick, NJ Bridget L. McConnell, M.S. Department of Psychology State University of New York— Binghamton Binghamton, NY Richard M. McFall, Ph.D. Department of Psychological and Brain Sciences Indiana University Bloomington, IN Ralph R. Miller, Ph.D. Department of Psychology State University of New York— Binghamton Binghamton, NY Haruka M. Minami, M.S. Department of Psychology Rutgers, The State University of New Jersey New Brunswick, NJ Susan Mineka, Ph.D. Department of Psychology Northwestern University Evanston, IL Jennifer E. Murray, Ph.D. Department of Experimental Psychology University of Cambridge Cambridge, UK
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CONTRIBUTORS
Katherine Oehlberg, M.S. Department of Psychology Northwestern University Evanston, IL Andreas Olsson, Ph.D. Karolinska Institutet Department of Clinical Neuroscience Psychology Unit Stockholm, Sweden
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Jennifer L. Szczytkowski, Ph.D. Department of Psychology University of North Carolina at Chapel Hill Chapel Hill, NC Teresa A. Treat, Ph.D. Psychology Department University of Iowa Iowa City, IA
J. Bruce Overmier, Ph.D. Department of Psychology University of Minnesota Minneapolis, MN
Richard J. Viken, Ph.D. Department of Psychological and Brain Sciences Indiana University Bloomington, IN
Frank Van Overwalle, Ph.D. Department of Psychology Vrije Universiteit Brussel Brussels, Belgium
Muriel Vogel-Sprott, Ph.D. Department of Psychology University of Waterloo, Waterloo, ON, Canada
Janet Polivy, Ph.D. Department of Psychology University of Toronto Toronto, ON, Canada
Jennifer Walker, M.A. Department of Psychological Sciences University of Missouri Columbia, MO
Phil Reed, D.Phil. Department of Psychology Swansea University Swansea, Wales, UK
Robert Frank Weiss, Ph.D. Department of Psychology University of Oklahoma Norman, OK
Steve Reilly, D.Phil. Department of Psychology University of Illinois at Chicago Chicago, IL
Vivian M. Yeh, M.S. Department of Psychology Rutgers, The State University of New Jersey New Brunswick, NJ
Todd R. Schachtman, Ph.D. Department of Psychological Sciences University of Missouri Columbia, MO
Moriel Zelikowsky, M.A. Department of Psychology University of California, Los Angeles Los Angeles, CA
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PART I
Overview
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CHAPTER 1 Things You Always Wanted to Know About Conditioning But Were Afraid to Ask Todd R. Schachtman and Steve Reilly
This chapter describes significant developments in the field of conditioning and associative learning over the last 40 or so years (e.g., the emergence of cognitive views, potential associative structures, and the role of contextual factors) to clarify some issues that may confuse psychologists and other researchers or practitioners. Along the way, we shall illustrate some important and, in most cases, ongoing findings that explore the processes underlying associative learning and conditioning. Our intention is to help readers better appreciate the more detailed, application-focused chapters that follow in this volume.
INTRODUCTION This chapter has two primary aims, each of which will help expose those readers unschooled in conditioning and associative learning to this still-flourishing and significant area of research. First, we will describe some of the most important and exciting developments over the last 40 years or so, including the cognitive orientation of the field, the possible associative structures underlying conditioning, and the role of contextual factors in conditioning. Second, we will clarify some specific issues that often plague psychologists and students of psychology. These issues include the following: (1) distinctions and similarities between classical (or Pavlovian) and instrumental (or operant) conditioning (e.g., when an animal [human or non-human] exhibits fear—is it an instrumental response or a classically conditioned response?); (2) the types of associations involved in the various forms of learning, for example, S-O (stimulus-outcome), R-O (response-outcome), and S-R (stimulusresponse) associations; (3) the similarities and differences among procedures such as avoidance,
punishment, and omission training; (4) differences between phenomena such as extinction, habituation, dishabituation, spontaneous recovery, and pseudoconditioning in classical conditioning. As might be expected due to the dynamic and evolving nature of the field, these two purposes are intertwined with one another in the present chapter. A few disclaimers are warranted. We will report on those issues that strike us as most interesting; and this may be, in fact, because we are more familiar with these issues. Moreover, our own theoretical predilections will be exposed. For instance, much of our chapter focuses on classical conditioning. It should be clear that the discussion in this chapter is not intended to exhaust all of the topics that are of current interest in the associative conditioning literature. The present chapter, for these and others reasons, should not be taken as a substitute for a textbook on learning and conditioning because such a text would include many more issues (e.g., Bouton, 2007; Dickinson, 1980; Domjan, 2009; Frieman, 2002; Gluck, Mercado, & Myers, 2007; Mackintosh, 1974; Pearce, 2008); and each of the
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topics covered here requires much elaboration to fully do them justice. The present chapter aims to merely whet the appetite for the more detailed and often application-focused expositions found in the later chapters of this volume. Comments About Recent Developments in the Field of Conditioning
For many researchers, the field has become increasingly more cognitive in its orientation and conceptual development (e.g., Bolles, 1972; Dickinson, 1980; Klein & Mowrer, 1989a, 1989b; Mackintosh, 1994; Mowrer & Klein, 2001; Rescorla, 1988; Wagner, 1976). There are several findings and theoretical advances that have shaped these changes. First, the field has been largely dominated by the concepts of expectancy and prediction. The importance of expectancy was really thrust into the limelight with the discovery of the phenomenon of blocking (Kamin, 1968, 1969), and publication of the Rescorla-Wagner model (Rescorla & Wagner, 1972; Wagner & Rescorla, 1972) nearly 40 years ago. To say that after CS-US (conditioned stimulus-unconditioned stimulus) pairings, the CS allows an organism to expect the US1 or to be able to predict that the US will occur seems fairly obvious to many of us today; but, for many decades, conditioning was not often discussed within the framework of information processing and its closely related concepts of prediction and expectancy. Before discussing specific issues, we will point out two developments in the last 30 years that have greatly contributed to the changes in conditioning that we mentioned earlier. First, Rescorla’s work in the 1970s (to be discussed later) often used manipulations administered after an initial conditioning phase but prior to a final test phase. This work by Rescorla was presumably inspired in part by Rozeboom (1958), who posited that treatments administered between conditioning and testing served to help determine the contents of the learned information (for examples of such findings, see sections on “The Role of Representations in Conditioning” and “Postconditioning Representations of the Unconditioned Stimulus”). A second issue that
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has been valuable is the distinction between what an organism knows and what the organism reveals that it knows by its performance. In other words, an organism does not always fully express what it actually knows. This learningperformance problem is an old one. Seventy or so years ago, Tolman’s work on latent learning showed that rats can be exposed to a maze without food present, and that they will explore the maze despite the absence of any obvious motivation to do so (Tolman & Honzik, 1930). During exploration, the rats do not reveal much evidence that they have learned about the floor plan of the maze. However, once these rats start true maze training in which food is presented at the end of the maze and the rats are now hungry, they show considerable “positive transfer” (beneficial effects of earlier experience on a current task) and reveal that they learned a lot during the earlier, nonrewarded exposure to the maze. Another example of this kind of (i.e., unexpressed) learning is that which occurs during the initial phase of a sensory preconditioning experiment as will be discussed later (see section on “Compound Conditioning”). Relatedly, there is a distinction between poor conditioned responding that occurs due to an association never being formed, and poor conditioned responding that occurs because the association is formed and intact but is poorly retrieved or expressed at the time of test. Tulving (e.g., Tulving & Pearlstone, 1966) noted this same distinction while discussing human memory research when he distinguished between an unavailable memory (one that is not present or no longer present in memory) and an inaccessible memory (one that is located in memory but unable to be retrieved at a given point in time). How might one experimentally distinguish between an acquired, intact but unexpressed association and one that was never acquired? Several decades ago, researchers such as Ralph Miller, David Riccio, and Norman Spear administered conditioning trials to rats in which the animals had an opportunity to learn about, for example, a CS which predicted a US; but the animals did not show any evidence of such learning when tested (i.e., did not show evidence of the acquired CS-US association as demonstrated by
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THINGS TO KNOW ABOUT CONDITIONING
a lack of a conditioned response [CR] to the CS). Then, in a second phase, the animals were given a treatment that presumably enhanced retrieval of the already existing association. This treatment facilitated retrieval, but it did not allow for any additional learning per se. When the rats were then tested, a CR did occur, showing that the retrieval facilitation procedure (sometimes called a “reminder treatment”) allowed a previously unexpressed association to then become evident in performance (e.g., Miller, Kasprow, & Schachtman, 1986; Spear, 1978; Spear & Riccio, 1994). When animals show no evidence of learning, and then some treatment—which of itself does not allow new learning to occur— causes this learning to be expressed, one can say that the association was latent or unexpressed prior to this reminder treatment. This finding illustrates several things. First, it shows how postconditioning manipulations can be important to reveal the content of learning as Rescorla and Rozeboom posited. By “content of learning” we mean the type of association that was acquired or whether an association was acquired at all. Second, it shows that conditioning research now uses very cognitive expressions such as the retrieval of information when discussing conditioned responding. Retrieval has been discussed in the work of many distinguished learning theorists, including Bouton, Hall, Hearst, Miller, Riccio, Spear, and others. Content and Structure of Learning
An instructor at a large Midwestern university was once giving a presentation on what it was like to teach an Introductory Psychology course to hundreds of students packed into a large lecture hall. The speaker made two main points. The first one had to do with energy—using an animated voice, in-class demonstrations, and entertainment. The second point contained more shock value: He claimed, “You have to be able to lie.” He said that freshmen and sophomores are not ready to hear all the limitations of the theories that are presented, such as those of Piaget’s writings or the strengths and limitations of the various views of learned helplessness theory or many other ideas that have required
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complicated revisions over the empirical tests of time. The lesson: When starting out, one needs to keep things simple. Similarly, when lecturing on conditioning or discussing it in a textbook, one feels a need to keep things simple. However, most researchers recognize that in any given situation where conditioning is possible, there is the potential for both classical and instrumental conditioning to occur on the same conditioning trial. One’s task might be to give classical conditioning trials to a dog in which a tone (the CS) is followed by food (the US). This, of course, is the same type of procedure used by Pavlov when he discovered classical conditioning (Pavlov, 1927). The food, professors tell their students, promotes classical conditioning to occur for the tone CS; but the food is also capable of instrumentally reinforcing a response that may happen to occur around the time that the food is delivered. Thus, when the dog makes a salivation response, it is not entirely clear whether the salivation is a classically conditioned response occurring to the tone or whether the dog happened to be salivating at the time the food was first delivered (and in the presence of the tone) such that instrumental conditioning occurred. That said, because it might be unlikely that salivation would happen to occur at the same time that the food was delivered (assuming the dog can’t smell the food coming), any salivation arising from the first conditioning trial was probably not due to operant conditioning. However, if the dog makes a salivation response to the tone on the second classical conditioning trial, then the food delivery could reward that response as instrumental learning as well as further the classical conditioning to the tone. In this way, it is easy to see that a conditioning episode does not always render itself to a pure classical conditioning analysis or a pure instrumental analysis. Indeed, the overlap between the two types of conditioning may be even more pronounced. Some associative learning theorists (e.g., Mackintosh, 1983) have noted a “symmetry” of the potential associations underlying the two types of conditioning (i.e., the two associations having some similarity in their structure). Classical conditioning is viewed as involving a
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stimulus-stimulus (S-S) association (i.e., an association between the CS and the US), such that pairings of a tone and food result in a tone-food association. Instrumental conditioning in which a response (e.g., a rat pressing a lever) is followed by food can be said to result in a responsestimulus (R-S) association in which the lever press response is associated with a food stimulus. In the literature, this food stimulus in both types of conditioning is sometimes called an outcome. Hence, a response-outcome (R-O) association is formed in which the outcome is a food stimulus; from this perspective, classical conditioning may be termed stimulus-outcome or S-O learning (see Table 1.1, which lists some of the more common nomenclature, terminology, and potential underlying processes thought to be involved in these two forms of associative learning). The symmetry view of conditioned associations acknowledges that classical conditioning using the example provided results in a tone-food association and instrumental conditioning results in a response-food association; and so the mechanisms underlying these two associations may be similar, with the exception that one type of conditioning involves a CS that is associated with food and the other involves a response that is
associated with food. According to this view, these associations may interact. This interaction could result in the two types of associations being in competition with each other on a given trial and producing effects such as “blocking,” which will be discussed later (also see Williams, 1999). We mentioned that instrumental conditioning can occur on a trial that uses a classical conditioning procedure and classical conditioning can occur on a trial that uses an instrumental conditioning procedure. If instrumentally and classically conditioned associations can compete, then it means that learning an association between a cue and the outcome will reduce the potential for R-O learning and vice versa. There is another parallel that exists between instrumental and classical conditioning. In instrumental conditioning, a stimulus (e.g., a tone) is often present at a time when a schedule of reinforcement is in place. In the case of positive reinforcement, the subject can learn that responding leads to reward when the tone is present. If the tone is absent, then responses have no consequences. The tone, in this example, is referred to as a “discriminative stimulus” in that it signals when the schedule of reinforcement is operating. A stimulus which signals that the response will
Table 1.1 Some Features of the Two Types of Associative Learning Classical Conditioning
Instrumental Learning Early in Training
After Extensive Training
Also called:
Pavlovian conditioning or Stimulus learning or Type I learning or Respondent conditioning
Operant conditioning or Response learning or Type II learning
Habit learning
Associations are commonly known as:
CS-US or S-S or S-S∗ or S-O
R-O or R-S∗ or A-O
S-R
Potential underlying processes:
Ability to predict future events
Modification of voluntary behavior
Elicited by antecedent stimulus
A, action; CS, conditioned stimulus; O, outcome; R, response; S, stimulus; S∗, biologically significant stimulus; US, unconditioned stimulus.
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lead to a certain outcome is sometimes referred to as an SD or S+. Skinner (e.g., 1938), who coined the expression “SD,” also stated that the stimulus in this case “sets the occasion” for the response to result in the reward. When a stimulus signals that the response will not lead to the outcome (extinction), then such a stimulus is referred to as an S∆ (pronounced: S delta) or “S–.” These definitions illustrate the conditional relationships that events can have: A response can lead to a reward in the presence of the SD, but a response will not lead to a reward in the absence of the SD or, alternatively, in the presence of an S∆. Parallel phenomena exist in classical conditioning. A great deal of research in the past 25–30 years has focused on the conditional relationship among stimuli in classical conditioning. These phenomena are called “occasion setting” stimuli (Holland, 1992; Schmajuk & Holland, 1998). If a tone leads to food when it is accompanied by a light but the tone does not lead to food when the light is absent, then the light is said to “set the occasion” for when the tone will be followed by food. The light is referred to as a positive “occasion setter.” To reiterate for clarity, if lighttone-food trials occur along with tone–no-food trials, then the light becomes a “positive occasion setter” for the tone–food relationship. On the other hand, if light-tone–no-food trials occurred with tone-food trials, then the light could be said to be a “negative occasion setter.” The associative processes underlying classical and instrumental conditioning may extend into other conditioning phenomena. A pairing of the CS and US results in a CS-US association, and then extinction (a postacquisition phenomenon) involves the presentation of the CS without the US. Some conditioning theorists (and this issue will be briefly discussed again later; see section on “Postconditioning Representations of the Unconditioned Stimulus”) have posited that such CS-only presentations after conditioning result in the learning of a CS-noUS association (an association reflecting that the CS is no longer being paired with the US) or, using other symbols, an S-noO associations (e.g., Bouton, 1993; Konorski, 1967; but see Rescorla, 2007). When instrumental conditioning occurs in which
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a response produces an outcome, an R-O association is formed; and extinction involves the occurrence of response without the outcome. One can say that a response–no-outcome (R-noO) association is formed during extinction in instrumental conditioning. Hence, parallel associative processes may occur for the two types of associations (classical and instrumental). Many other important conditioning phenomena (particularly those that stem from work using classical conditioning) such as blocking, overshadowing, potentiation, spontaneous recovery, and outcome preexposure effects may produce comparable empirical effects in instrumental and classical conditioning as well as similar underlying associative mechanisms. The extent that such parallels hold true will be determined by research findings, but mention of these issues at this time helps one appreciate the potential similarities of the different types of associative learning. Rescorla and Solomon (1967) showed that one can separately train a classically conditioned response and an instrumental response for a group of subjects and then combine the two training events together to observe what happens with this combination of the two types of training. Their ideas have been termed the “twoprocess theory” of conditioning. A subject (e.g., a dog) can learn that a tone leads to food, and thus the occurrence of the tone causes the dog to expect food. The same dog can also be trained to press a lever to produce food, and this dog will press the lever frequently because it expects that the lever press will produce food. Subsequently, when placed in front of the lever and presented with the tone, the dog will have two expectancies: one from the tone (classical conditioning) and one from the lever press (instrumental conditioning). The summation of expectancies can produce even greater lever pressing because the dog has an extremely high expectancy of food. Similar interactions can be examined for all combinations of predictors of the US and even for events that predict no US (e.g., a CS that signals the absence of the US) combined with a response that produces outcome avoidance. Two-factor theory (e.g., Mowrer, 1951) is also concerned with how classical and instrumental conditioning can interact. Mowrer pointed out
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many years ago that if a subject must make a response to avoid an aversive event (called “active avoidance” or “negative reinforcement” as we will discuss in the next section), then at least two things can be learned on such a trial. First, the subject receives classical conditioning pairings of the environmental cues (i.e., the apparatus within which the experiment is conducted or, perhaps, a small light within the apparatus that is illuminated at the beginning of each trial signaling that an aversive event will occur if the target response does not occur) with the aversive outcome. That is, a CS-O association is potentially formed. These cues (the CS) become capable of producing fear in the subject. Thus, a fear CR occurs to the cues. Second, the subject learns to make the instrumentally conditioned response (such as fleeing from the CS or the place that has been paired with shock) to prevent the aversive event from occurring. Therefore, subjects run because they have fear of the CS, which is due to classical conditioning; and they are making the response that will remove them from the aversive cues—the avoidance response (instrumental conditioning). Two-factor theory states that both classical and instrumental conditioning can occur during avoidance training. This example also reveals again how classical conditioning and instrumental conditioning can be intertwined—a CR to a cue (classical conditioning) can resemble and avoidance response (instrumental conditioning). Since classical and instrumental conditioning may overlap in terms of their associative mechanism (S-O and R-O associations, respectively), there has been interest in pitting the two types of conditioning against each other to document the roles of the two processes. The omission training procedure, which involves overlaying an instrumental contingency upon a classical contingency, has been useful in this regard (e.g., Sheffield, 1965). In omission training, a cue (CS) is presented and the subject receives a US (e.g., food) only if it does not respond to the CS. To repeat, if the subject responds to the CS, the US is omitted. Thus, a CS-US pairing occurs only when the subject makes no response to the CS. As an instrumental procedure, however, the subject is rewarded for not responding. Again, if
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the subject does not respond to the cue, then the food is delivered and, consequently, a CS-US pairing is experienced. Thus, performance can be guided by both classical and instrumental conditioning in the omission procedure. As a concrete example, we can consider one of the original omission experiments done with pigeons in which the CS was a colored keylight that the birds can peck (Williams & Williams, 1969; also see Williams, 1981). When pigeons receive presentations of a keylight followed by food, they quickly learn through classical conditioning to respond by pecking that keylight (a classical conditioning phenomenon termed autoshaping or sign-tracking in which the CR to a light is a peck response; for reviews, see Hearst & Jenkins, 1974; Locurto, Terrace, & Gibbon, 1981). However, the operant (omission) contingency gives them food for not responding; and when they don’t respond they get a keylight-food pairing that makes them respond on the next trial. Both classical and operant conditioning will exert an effect on behavior in such situations: Responding is not as high as it might be because the omission contingency keeps responding low, but the CS-US pairings cause the animal to sometimes respond and, therefore, lose food presentations when they respond to the CS (the CS occurs without the US). Sixty or seventy years ago discussions of conditioning focused on S-R (stimulus-response) associations. Many readers of this chapter may have heard this expression and wondered about its contemporary status. Indeed, these are the associations that were purported to underlie conditioning when Thorndike began investigating associative learning in non-human animals at the turn of the previous century (e.g., Thorndike, 1898, 1911) and they were alleged to underlie both classical and instrumental conditioning throughout, at least, the 1950s. S-R associations involve an association between a (usually) environmental stimulus (such as a light, tone, or contextual stimulus) and the response. We say “usually” because one could have an “internal” stimulus (such as hunger pangs, moods and emotions, or states induced by the ingestion of a drug) as the stimulus of the S-R association, but we will keep things simple for this discussion.
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It is important to be clear as to what this association involves and how it differs from the contemporary view of associations (S-O and R-O) described earlier. An outcome is not part of, or encoded into, the S-R association. According to this older S-R view of conditioning, when an outcome (e.g., food) follows the occurrence of response (e.g., lever press) in the presence of a stimulus (e.g., the apparatus within which the experiment is being conducted), an association is formed between the stimulus (apparatus) and the response (lever press). That is, an S-R association is formed. The outcome (food) serves to facilitate the formation of the S-R association. According to this S-R association view of conditioning, the representation of the outcome in memory is not a part of the acquired association. Once the outcome has promoted the formation of the S-R association, its function is accomplished and it no longer has a role in further processing or performance regarding the S-R association. Work by Anthony Dickinson and Robert Rescorla and their colleagues and others challenged this view experimentally (e.g., Colwill & Rescorla, 1986; Dickinson, 1985; Dickinson & Balleine, 1994; Rescorla, 1991). If the outcome is no longer important after the instrumental association is acquired, then changing the value of the outcome for the subject after conditioning and prior to testing for the learned response should have no influence on performance. Work in Dickinson’s and Rescorla’s laboratories showed that changing the value of the outcome between conditioning and testing produced a marked change in the performance of the learned response (e.g., Adams & Dickinson, 1981; Colwill & Rescorla, 1985). This makes some intuitive sense. If you train a hungry rat to press a lever to obtain food pellets, it will learn to respond at a high rate to obtain the desired food. However, if you now devalue the food pellets (e.g., by conditioning a taste aversion to them) such that the rat dislikes them, it is not surprising that the rat will not press the lever much to obtain a disliked food. Yet earlier S-R theories did not predict such a change in behavior. A similar situation exists for a dog that might salivate to a signal for food—changing how the organism values
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the food after such training will attenuate the salivation response. The outcome seems to be part of the association. The subject has acquired an R-O association because the response activated the food representation in memory. Despite the strong focus on the S-O and R-O associations in this chapter, Dickinson and Rescorla have shown that S-R associations can be formed during conditioning in certain situations. Research in the last 30 years or so has shown that instrumental learning involves R-O associations early in training and can be said to be “goal directed” (the organism makes a response to obtain a desired goal or outcome). Moreover, this voluntary (i.e., intentional), goal-directed behavior is occurring in a specific context or in the presence of a specific stimulus (S). In certain circumstances, this stimulus, because it is invariably and repeatedly paired with the target response, may become associated with the response and an S-R association is also eventually acquired. Thus, presentation of the S triggers an activation of the response representation and the subject then responds habitually (i.e., automatically and usually without any conscious active representations), independent of the status (valued or devalued) of the outcome (Dickinson, Balleine, Watt, Gonzalez, & Boakes, 1995; but see Holland, 2004). By this analysis, habit (S-R) formation develops after, and overlays or masks, the initial goal-directed (R-O) behavior. Both associations involve the same instrumental response. As noted earlier, the S-R association, unlike the R-O association, is impervious to outcome devaluation (so clinical treatments aimed at changing behavior via some form of outcome devaluation may have limited utility if that behavior has become habitual; e.g., smoking or drinking). There is, moreover, evidence that each type of instrumental association (R-O and S-R) is subserved by an anatomically distinct brain system (Balleine, 2005; Balleine, Liljeholm, & Ostlund, 2009; Delamater, 2004; Yin & Knowlton, 2006). If these systems can be dissociated with drugs, there may be some hope that, with pharmacological intervention, extreme habits (S-R associations) can be alleviated.
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Instrumental Contingencies
We mentioned the terms avoidance and negative reinforcement in the preceding section. These and other instrumental contingencies (see Table 1.2 for a description of the four basic procedures and their effects on behavior) can be a source of confusion, especially since textbooks are not always consistent with the nomenclature applied to the various instrumental-conditioning manipulations. As most psychologists know, positive reinforcement occurs when there is a positive relationship (or positive contingency) between the production of a response and the presentation of an appetitive (pleasurable) outcome. When the expression “positive contingency” is used, it should be viewed as comparable to the expression “positive correlation” (and conversely for the expression “negative contingency”). That is, when the subject makes the response, something pleasurable (like food) reliably occurs. This positive contingency causes the response to increase in the future. Punishment involves a positive contingency between the response and an aversive outcome (something painful or unpleasant). When the subject makes this response, an aversive event occurs and, not surprisingly, this causes the response to decrease in frequency.
There is a caveat to mention that readers will likely wish that we did not bring up (this certainly should be spared from an undergraduate lecture on this topic). We make this cumbersome point because it will help when we make related points later. With positive reinforcement in which a lever press produces a pleasurable outcome, one could imagine what it would be like for the subject that has a lot of experience with this contingency to momentarily refrain from making a response and to therefore get no pleasurable outcome. That is, with positive reinforcement: No response leads to no reward. It involves a positive contingency between no response and no reward. One can think of it as an intrinsic, necessary feature of the positive contingency involving the response and reward occurrence. In truth, for any behavioral contingency (a response leads to an outcome), there are a total of four contingencies related to it because nonresponses and no outcomes can be viewed as events: A response has a positive contingency with the outcome, a nonresponse has a positive contingency with no outcome, a response has a negative contingency with no outcome, and a nonresponse has a negative contingency with the outcome. Four contingencies exist for a simple positive reinforcement contingency! But it is best to simply focus on
Table 1.2 Basic Instrumental Contingencies of Reinforcement and Punishment and Their Usual Effect on Behavior Outcome
Positive
Appetitive
Aversive
Reward (or positive reinforcement)
Punishment (or positive punishment)
Increase in behavior
Reduction in behavior
Example: Receive a kiss after complementing partner
Example: Drive too fast and get a speeding ticket
Omission (or negative punishment)
Avoidance (or negative reinforcement)
Reduction in behavior
Increase in behavior
Example: Driving license suspended following conviction for drunk driving
Example: Open umbrella to avoid getting rained on
Contingency
Negative
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the fact that positive reinforcement is a positive contingency between producing the response and the presentation of a pleasurable outcome (and let the positive contingency between nonresponses and nonrewards just rest wherever they might rest and these other contingencies can rest there, too). One can say the same thing about punishment. Nonresponses will lead to the nonoccurrence of the aversive event. This is a positive contingency between nonresponding and no outcome. Nonresponses are preferred for the organism that is experiencing a punishment contingency. But again we will let these nonresponses and nonrewards slip away without further discussion and simply say that punishment involves an association between the occurrence of a response and the presentation of an aversive outcome. Avoidance involves a negative contingency between the response and the subsequent occurrence of an aversive outcome. If the subject makes a response, then the aversive event will not occur. This is referred to as negative reinforcement since the contingency increases the frequency of the behavior (the subject makes the response to prevent the aversive event from occurring). One key note is that whenever one sees the word reinforcement (either negative reinforcement or positive reinforcement), it means that the contingency causes the frequency of the response to increase. Hence, rewarding a behavior is positive reinforcement and avoidance is negative reinforcement, but the former is a positive contingency between the behavior and an appetitive outcome while the latter is a negative contingency between a behavior and an aversive outcome.2 Omission training is a negative contingency between the occurrence of a response and the presentation of an appetitive outcome. Thus, as mentioned before, a subject will receive food if it does not make a certain response. Avoidance and omission training are both negative contingencies, but the former is between a response and an aversive outcome and the latter is between a response and an appetitive outcome. Avoidance increases the rate of responding, whereas omission decreases response rate. It can be easy to get avoidance and punishment confused since they both involve an aversive
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outcome, and in both cases the subject is motivated to not experience the aversive event. It is good to remember that these two types of instrumental training involve different contingencies: negative and positive. That is, with avoidance the subject must make a certain response to prevent the aversive event from occurring; whereas with punishment the subject must not make a certain response to prevent the aversive event from occurring. Also, similar to the cumbersome digression expounded earlier, one can be tempted to think of an avoidance procedure as follows: A nonresponse produces an aversive event (a positive contingency between a nonresponse and an aversive event). This is true because it is the necessary converse of the following: A response prevents an aversive event—a negative contingency. But we encourage the reader to simply focus on the “occurrence of a specific response” when working through these contingencies. It is also easy to get confused about the difference between escape and avoidance. Escape does not fit nicely into the scheme outlined earlier. In escape, the organism receives the aversive event no matter what, but it can terminate the existing aversive event by making a response. Hence, with a successful escape response, a response and an aversive event occur on the same trial (which suggests that a positive contingency is occurring as in punishment, but this is not true since the response certainly did not produce the aversive event). Indeed, since the response terminates the aversive event, it makes the escape contingency share properties with an avoidance contingency—the response gets rid of the aversive event. Another realm of confusion concerns the procedure termed “passive avoidance” (which is also known as “inhibitory avoidance”), a procedure that has become extensively used in recent neuroscience research. It is a task in which the subject receives an aversive event (e.g., a footshock) if it makes a certain response (e.g., step into one side of a two-compartment shuttle box). It should be clear at this point that this is a punishment procedure—there is a positive contingency between making a response (moving into the side of the chamber) and the occurrence
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of an aversive event. Although the names “passive avoidance” and “inhibitory avoidance” have been around for a long time, they are misnomers. They are not avoidance procedures. When scientists decided perhaps 25 years ago to favor the name “inhibitory avoidance” over “passive avoidance,” they did not correct the part of the name—avoidance—that has caused the greatest degree of confusion in the previous 70 years. While discussing instrumental work, we felt it was a good idea to illustrate some important and interesting issues with respect to schedules of reinforcement that might not always be fully appreciated. Nearly all psychologists have been exposed to the “classic” schedules of (intermittent) reinforcement: fixed ratio, fixed interval, variable ratio, and variable interval (Ferster & Skinner, 1957; see Table 1.3 for a description and some characteristics of performance on these four simple schedules of reinforcement). These are all schedules of positive reinforcement for the purposes of our exposition. Punishment contingencies can also be applied using these
schedules, but we will not consider them at this time. However, we want to describe a few of the properties of responding on these schedules, which many psychologists may not be aware of. First, an outcome is delivered on an interval schedule for making the first response on the trial after the interval times out.3 We will assume it is a fixed interval schedule for our discussion. Earlier responses (before the designated time interval elapses) are not necessary and, thus, a waste of energy. Hence, the most efficient form of responding on a fixed interval schedule is one in which only one response occurs per reinforcement. If there is a less-than-perfect positive contingency between responding and the reinforcement, it is because of the nonrewarded response “mistakes” on the part of the subject. The subject, in essence, determines the number of responses per reward based on how early in the trial he or she begins to respond (e.g., to what extent the responses are “early” and therefore not particularly functional). It is interesting that although a high response rate on an interval
Table 1.3 Characteristics of the Four Basic Types of Reinforcement Schedules Basis for Reward
Response
Fixed
Variable
Fixed ratio (FR)
Variable ratio (VR)
Reward delivered after fixed, predictable number of responses
Reward delivered after variable, unpredictable number of responses
High rate of responding and pause after reward delivered
High, steady rate of responding and little or no pause after reward delivered
Example: Working on commission or on “piecework”
Example: Gambling (e.g., slot machine)
Fixed interval (FI)
Variable interval (VI)
Reward delivered for first response after fixed, predictable time period
Reward delivered for first response after variable, unpredictable time period
Increasing rate of response and pause after reward delivered
Moderate, steady rate of responding and little or no pause after reward delivered
Example: Study patterns of students when examinations scheduled at regular intervals
Example: Trying to call someone on the phone when line is busy
Criterion
Time
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schedule can be interpreted as indicative of a strong R-O or S-R association, it is highly inefficient. On ratio schedules, every response “counts for something” and the subjects are free to respond at their own rate: quickly or slowly. From a “responses per outcome perspective,” all responses are valuable, all responding is equally efficient, and all subjects end up with a comparable response/reinforcer ratio. Time is irrelevant in terms of the contingency. Thus, from a time perspective, an organism can be inefficient on a ratio schedule but all responses count; but on a fixed interval schedule an organism can be inefficient from a response-wasting or energy-wasting standpoint, but time is only wasted if the subject is slow to respond after the interval times out. Burgeoning interest in the etiology, treatment, and neural underpinnings of addiction (and related issues) has rekindled interest in the motivation value of drugs and other highly desired stimuli. Although the schedules of reinforcement discussed earlier have been used in this enterprise, comparisons of the relative value of different outcomes may sometimes be problematic. As noted previously, rates of responding do not always provide an accurate index of the value of an outcome, particularly for stimuli such as psychoactive drugs, where ceiling effects in response rate may obscure detection of the true value of a reward. In this context, the progressive ratio schedule has proven to be a highly successful method to use (e.g., Hodos, 1961). These schedules differ from those just discussed in that progressive ratio schedules can have both a response and a time requirement. Although there are many variations, the essence of the progressive ratio schedule is this: As trials progress, more responses are required to obtain each successive outcome until the subject eventually fails to obtain the outcome within a specified time limit. For example, on a progressive ratio 3, three responses are required to obtain the first outcome, six for the second, nine for the third, and so on. The number of responses produced to obtain the final outcome (the final outcome is the ratio value—it is the final one because, after that trial, the ratio value is so high that the
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subject quits responding) is termed the “break point” and is an index of the value of the outcome (i.e., how hard the animal is willing to work to obtain the outcome). Progressive ratio schedules have been used to assess the “value” of outcomes such as gustatory stimuli (Reilly, 1999; Sclafani & Ackroff, 2003), electrical brain stimulation (Depoortere, Perrault, & Sanger, 1999; Keesey & Goldstein, 1968), and drugs of abuse (Ranaldi & Wise, 2000; Roberts, Morgan, & Liu, 2007). Another finding should be noted. We mentioned in an earlier section that research has found that changing the value of the instrumental outcome after conditioning and before testing can produce a marked change in the vigor or probability of occurrence of the instrumental response. It was noted that if you train a rat to press a lever to obtain food pellets and then you change the degree that the rat values the food pellets (i.e., pair it with a toxic drug to produce an aversion to the food pellet) such that the rat now dislikes food pellets, then the rat does not bar press much after this manipulation. Dickinson and colleagues (e.g., Adams, 1982: Dickinson, Nicholas, & Adams, 1983) found this to be more true for ratio than interval schedules (see Balleine, 2009, for a review of this literature). The Role of Representations in Conditioning
As mentioned earlier, the field of conditioning and associative learning has become very cognitive in its focus in the past several decades. Given that the word representation is integral to a cognitive approach, the event-memory model put forth by Rescorla in the 1970s (e.g., Rescorla, 1973, 1974) also encouraged, we believe, conditioning to move in a cognitive direction by promoting the idea that an event can activate the mental (and neural) representation of itself and other associated events (see, e.g., Holland & Wheeler, 2009). The event-memory model stated that when pairings of a CS and US occur for an organism, the animal learns at least three things: (1) it forms a representation of the CS in memory; (2) it forms a representation of the US in
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memory; and (3) it forms an association between these two representations in memory, a CS-US association. Once a CS is associated with a US, then presentation of the CS will activate a representation of that CS in memory and this will, via the CS-US association, cause an activation of the US representation. A dog salivates to the sound of a tone because of a previously acquired tone– food association. That is, detection of the CS activates the US representation and the dog salivates in anticipation of, and in preparation for, the expected food. In many cases, like the present example, the CR is similar, if not identical, to the unconditioned response (UR) triggered by the US. The UR to food is salivation (a dog salivates when food is placed in its mouth) and the CR to a CS that predicts food is also salivation. However, in other cases, particularly where the US is a drug state, the CR may actually be opposite of the UR (for further discussion, see Dworkin, 1993; Eikelboom & Stewart, 1982; Siegel & Ramos, 2002). Rescorla’s event-memory research focused a great deal on extinction (e.g., Rescorla, 1973; Rescorla, 1974; Rescorla & Heth, 1975). Once a CS-US association is formed as a result of CS-US pairings, CS-alone presentations (i.e., extinction trials) will result in a loss of conditioned responding to the CS. Rescorla stated that CS-alone presentations degrade the representation of the US because the US is no longer being presented at a time that it is expected. If a sufficient number of extinction trials occurred, then not only was the US representation degraded, but the CS-US association will lose strength. Rescorla has conducted a considerable amount of research on extinction phenomena over the past few decades. His views of the underlying mechanisms of extinction in classical conditioning have changed greatly over the years (e.g., Rescorla 2001, 2004). In particular, although providing evidence that extinction does not involve unlearning of the original CS-US association (e.g., Rescorla, 1996), Rescorla may be less convinced than some contemporary theorists that extinction involves an association between the representation of the CS and the representation of the absence of the US (or no US; e.g., Rescorla, 2007).
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Two CSs can also be associated if they are presented together (two or more CSs presented together are collectively known as a stimulus compound or compound CS; this stimulus compound may be either simultaneous or serial depending, respectively, on whether the two CSs occur together at the same time or if one CS terminates before the other begins). Rescorla referred to these as within-compound associations (e.g., Rescorla & Cunningham, 1978; Rescorla & Durlach, 1981). Within-compound associations have important implications for many conditioning phenomena. For instance, let’s call one CS “A” and a second CS “B.” If A and B are each associated with the same US and A and B are also associated with each other (via a withincompound association), then what degree of conditioned responding might one expect if, say, A is presented? Well, A is associated with the US and so A can activate the US representation; therefore, we expect a CR to A based on this association. However, A also has the ability to activate a representation of B, and B has the ability to activate a representation of the US, and so A can produce a CR based on this sequence of activations as well (the A representation activates the B representation, this latter representation activates the US representation, and an extra vigorous CR results; e.g., Rescorla & Durlach, 1981). Within-compound associations can influence other conditioning effects. What if we have four CSs: A, B, C, and D? Let’s say that A is associated with B such that A has the ability to activate a representation of B in memory. Assume also that C and D are associated such that C has the ability to activate D in memory. Recent work by Holland and Sherwood (2008) and Dwyer, Mackintosh, and Boakes (1998) has found that if A and C are presented together, then an association can be formed between B and D because A has the ability to activate a representation of B on the AC trial and C has the ability to activate the D representation on the AC trial. Therefore, both the B and D representations are active in the organism’s memory at the same time on the AC conditioning trial. This is a very impressive finding because it shows two events becoming associated when neither is present on the trial
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that is responsible for their associative formation. Simply put, B and D become associated with each other even though they were never paired together. Along with phenomena like sensory preconditioning and higher order conditioning (to be discussed later, see section on “Compound Conditioning”), within-compound associations indicate a level of complexity and scope to classical conditioning that is not widely appreciated today. Postconditioning Presentations of the Unconditioned Stimulus
In support of his event-memory model, Rescorla (e.g., 1974) showed that one can change the representation of the US after conditioning and prior to testing. Rats were given CS-US pairings (e.g., tone paired with medium-intensity footshock) in phase 1. The CS was therefore associated with a representation of footshock which was moderate in intensity. Then in phase 2 Rescorla gave some rats a low-intensity footshock (just shocks presented in the experimental chamber— no CS was presented during this phase) in order to change the US representation from the previously formed “medium-intensity” to a new, low-intensity shock representation. These rats showed a smaller CR—which one would expect if the tone activated a low-intensity shock representation (the low-intensity shock representation replaced the older medium-intensity shock representation). Other rats received a higher intensity shock presented alone without the CS in phase 2. These rats now had a strong US representation associated with the tone (the strong shock representation replaced the medium one), so a stronger CR was expected; and, indeed, a stronger CR was observed. These results are fundamentally important because they demonstrate that, postconditioning, the representation of the USs can be changed (either deflated or inflated) in the absence of the CS. These findings reveal manipulations in classical conditioning that have yet to be fully exploited outside the learning laboratory for their clinical relevance. Another classical conditioning phenomenon has been used to document such processes. Counterconditioning is a treatment in which a
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CS is paired with a US of one affective valence (e.g., appetitive) in phase 1; and then during phase 2 (the counterconditioning treatment) the CS is paired with a US of the opposite affective valence (e.g., aversive). Readers may recognize that counterconditioning is one of the techniques used to deter alcoholics from drinking by pairing alcohol with an aversive experience (e.g., Revusky, 2009). Counterconditioning was also used to change the US representation associated with a CS for Rescorla’s rats that had received CS-footshock pairings earlier. Counterconditioning results in a lower CR to the CS. However, it is not clear what kind of associations underlie counterconditioning, that is, what the “contents” of learning might be. If a tone is first paired with footshock and, subsequently, the tone is paired with food, then it is easy to imagine that the tone has an association with footshock as well as an association with food. If this is the case, then it is not clear that a footshock representation would truly be modified by the second US representation. Readers might ask whether these various postconditioning treatments (US representational deflation via counterconditioning and extinction) for decreasing the CR possess any similarities. As mentioned earlier, the associative structures underlying them are likely different. However, at least one similarity can be mentioned for counterconditioning and extinction: They both appear to be sensitive to context manipulations, as Bouton’s work demonstrates (e.g., Bouton, 1993, 2002). We will note one more finding before discussing context effects. This finding involves administering CS-alone extinction presentations after the CS-US conditioning pairings. Of course, extinction presentations will decrease the CR to the CS. Research conducted in the laboratories of Bouton, Miller, and Rescorla, among others, has examined the influence of the administration of USs after extinction and prior to testing the CS on performance. Similar to the ideas inherent in Rescorla’s event-memory model, extinction can be said to weaken (or deflate) the US representation. The US presentations occurring after extinction, then, can be said to increase (reestablish or reflate) the US representation.
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This treatment is called “US reinstatement” and it does serve to increase the CR for a CS that has received extinction training. However, other interpretations of this effect exist. It has been argued that CS-US associations are learned during conditioning, a CS-noUS association is learned during extinction and the latter association causes interference with the former association, thus rendering it less retrievable. In other words, some have claimed that extinction reduces the retrievability of the CS-US association (and enhances the accessibility of a new CS-noUS association that is presumably learned during extinction); and that the US presentation during reinstatement causes rehearsal of the CS-US association, thereby making this CS-US association retrievable once again. Alternatively, it has also been pointed out that the US presentations can result in context conditioning (i.e., the formation of context-US associations) as a result of the context-US pairings at the time of the US reinstatement phase. If subjects are tested on the CS in the same context where the US reinstatement presentations occurred, then they are being tested on two events at once—the CS and the contextual cues, and both events are associated with the US, so a stronger CR is expected compared to subjects that did not receive US presentations in the context (where testing will occur) prior to the test (Bouton & Bolles, 1979). For the latter condition, only the CS has a strong association with the US. The former subjects are tested on two cues that are associated with the US. Of course, if all subjects are tested on the CS in a neutral context (a context where no other experimental treatments occurred although subjects may be previously acclimated to this context), then both groups are only tested on one event associated with the US. The Role of Contextual Cues During Conditioning
The importance of the conditioning of contextual cues has received a great deal of attention since the 1980s (see, e.g., the edited volume by Balsam & Tomie, 1985). We do not have space here to review the large amount of research findings on the role of these cues, but we will note
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two things. First, since pairings between a CS and US (i.e., classical conditioning) as well as instrumental learning do not occur in a vacuum, all types of conditioning procedures have the potential to produce associations involving contextual cues. These associations may involve context-CS associations (akin to withincompound associations mentioned earlier) or context-US associations. Second, there has been a lot of attention focused on the possibility that contextual cues can influence the retrieval of CS-US associations. The laboratories of Bouton, Miller, and Schachtman (Bouton, Garcia-Gutierrez, Zilski, & Moody, 2006; Chelonis, Calton, Hart, & Schachtman, 1999; Gunther, Denniston, & Miller, 1998) have shown that extinction is context specific. That is, if CS-US pairings occur in one context (Context A) and the CS-alone presentations (extinction) occur in a second context (Context B), then performance during those extinction trials will depend on where the CS is tested. After the extinction phase, if the CS is presented in the context where extinction occurred (Context B), then poor conditioned responding will occur. If the CS is tested in a context other than the extinction context (i.e., Context A or a third, neutral context), then the extinction treatment will not be expressed there and a strong CR to the CS will be seen (e.g., Bouton, 2004). This finding has a number of clinical implications not least of which concerns the efficacy of treatments for phobias that utilize a desensitization treatment in a context other than that in which the original fear learning occurred or where the person will encounter the cue in the future (for further discussion, see Bouton, Westbrook, Corcoran, & Maren, 2006; Hermans, Craske, Mineka, & Lovibond, 2006; Redish, Jensen, Johnson, & Kurth-Nelson, 2007; Sotres-Bayon, Cain, & LeDoux, 2006). Another phenomenon that also shows great context specificity is latent inhibition (also known as the “CS preexposure effect”). Latent inhibition is the poor learning to a CS that is paired with a US after preconditioning exposure to the same CS in the absence of the US (Lubow, 1989, 2009; Lubow & Weiner, 2010). That is, in phase 1 the CS is presented several times on its
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own. Then the learning that stems from CS-US pairings in phase 2 is poor because of the phase 1 CS-alone trials. We should also point out that context-CS associations can presumably be formed even when a CS is presented alone in a context, and these associations can be important (Wagner, 1976). Finally, there has also been a lot of research examining the extent to which contextual cues function just like another CS or have distinctive properties of their own (Balsam & Tomie, 1985). Compound Conditioning
Another area of research that has produced a great deal of attention since the 1970s is compound conditioning—conditioning in which more than one CS is presented on a trial. Blocking was discovered in the late 1960s, and this finding stimulated an abundance of research that (along with other findings) gave rise to the RescorlaWagner (1972) model of classical conditioning. Blocking occurs when a weak CR is elicited by a CS (the “target CS”) that had been conditioned in the presence of a second CS (the “blocking CS”) that had been previously paired with the US. That is, in phase 1, the “blocking CS” (CS A) is paired with the US (A-US trials). In phase 2, the blocking CS and target CS (CS X) are presented together and paired with the US (AX-US trials). When the target CS (X) is tested, a weak CR occurs. The target CS does not produce a CR because the information that it provides (signaling the occurrence of the US) is redundant with the information already provided by the blocking CS (A). In phase 1, the subjects learn that the US is predicted by the blocking CS, A. In phase 2, the subject already expects the US because of the presence of CS A, and so no evidence of learning to CS X occurs. CS X is redundant and learning about it is “blocked.” The Rescorla-Wagner model highlights the important idea that CSs can compete for learning and predicts a large number of conditioning phenomena (Miller, Barnet, & Grahame, 1995). This model predicts another phenomenon in which CSs compete for learning— overshadowing,4 as well as many phenomena involving conditioned inhibitors5—stimuli that predict that the US will not occur.
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Some researchers have pointed out that CSs and instrumental responses may compete for learning. If a CS and a response both occur on a conditioning trial along with an outcome, then blocking or overshadowing of one of these events by the other event may occur (see Schachtman & Reed, 1998 for a review). If such competition occurs, then it is valuable to consider this effect because organisms are typically responding in some way at the time that a classical conditioning trial occurs, such that this behavior may compete with the CS for learning and attenuate the degree of manifest classical conditioning to the CS. Moreover, stimuli are typically present during instrumental conditioning (minimally, the cues of the experimental apparatus within which the subject is located), and these cues may compete with the response for learning and undermine the degree of expressed instrumental conditioning. Second-order conditioning and sensory preconditioning are two compound conditioning phenomena. Second-order conditioning involves pairing of a CS (A) with the US in phase 1 (A-US pairings) and then in phase 2 the target CS (X) is presented along with A but without the US (XAtrials; notice that a minus symbol is used to designate the absence of the US). A conditioned response occurs to X even though it had never been paired with the US. One early interpretation of this phenomenon by Rescorla (1980a, 1980b) is that the CR might occur to X because X is associated with A and A is associated with the US. Hence, X is capable of activating a representation of A in memory (via the A-X association acquired in phase 2) and this activation of A is capable of activating a representation of the US in memory (via the A-US association acquired in phase 1). X evokes a CR since the US representation has been activated when X is presented (via this sequence of associative linkages). Note that the association between A and X is similar to the within-compound associations mentioned earlier. Sensory preconditioning is similar in many ways to second-order conditioning. The primary difference is that the two training phases are reversed. In sensory preconditioning, then, XA– is presented in phase 1. Note that no US is
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presented in this phase—just two CSs. In phase 2, A is paired with the US (A-US) and then, in the test phase, the occurrence of the CR to CSX is examined. A CR to X demonstrates that X can generate a CR without ever having been paired directly with the US. Instead, X was paired with A and an association was formed between them. CSA was paired with the US. Hence, a CR to X could occur if X activates a representation of A and A activates a representation of the US; and the active US representation results in a CR. However, other mechanisms may exist for why the CR to X occurs following sensory preconditioning training. These findings and related phenomena reveal interesting effects in classical conditioning and provide a glimpse at the theoretical approaches to understanding their underlying processes and structures. Distinctions Among Procedures in Which Single Conditioned Stimuli Are Presented
Researchers can have a difficult time distinguishing among the many procedures that, for the most part, involve single-stimulus presentation. Habituation, pseudoconditioning, dishabituation, latent inhibition, disinhibition, sensitization, and extinction can easily be confused. Habituation and sensitization are easy to discuss because they involve the presentation of stimuli in a situation in which no conditioning trials occur. Habituation is the progressive decrease in responding to the stimulus if it is repeatedly presented. Sensitization is the increase in responding to a stimulus if it is repeatedly presented. Thus, in terms of behavioral responding, sensitization is the opposite of habituation. However, sensitization, unlike habituation, occurs when the subject, for any of a number of reasons, is in a state of high arousal. Habituation may be characterized as learning to ignore stimuli that have proven to be of no biological consequence. By helping us not to become distracted by harmless and meaningless stimuli, habituation is one of the most pervasive phenomena in our life. Sensitization, on the other hand, involves heightened vigilance to stimuli that might be dangerous. Indeed, it has been suggested that sensitization may contribute to a number of
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clinical conditions, including phobia and generalized anxiety disorder (e.g., Marks, 1987), as well as posttraumatic stress disorder (e.g., Dykman, Ackerman, & Newton, 1997). Although published many years ago, the dual-process theory of Groves and Thompson (1970; also see Thompson, 2009), which views habituation and sensitization as independent processes that function in parallel, remains one of the more prominent explanations of these two phenomena. Dishabituation is also easy to explain. This occurs when a stimulus (let’s say a tone) has already received habituation training so that the subject is no longer making a strong response to it. But some other “extraneous” stimulus occurs (say, a light) and this produces dishabituation to the tone in that a response suddenly occurs to the tone when it is presented again (especially if it is presented shortly after the light occurred). We have already discussed extinction—the decrease in CR when a CS (that has been previously conditioned using CS-US trials) is presented alone for many trials. Hence, a tone could be paired with a US and then given tone-alone extinction trials. If another “extraneous stimulus” occurs (say, a light), then the light might cause the CR to return to the CS. This increase in conditioned responding is called disinhibition. Therefore, dishabituation and disinhibition both involve an extraneous stimulus causing an increase in responding to a stimulus that had previously lost response-producing ability. Dishabituation involves a response that was not previously conditioned, whereas disinhibition involves a previously conditioned cue. Both habituation and extinction involve a decrease in responding (in the case of extinction, it is a CR that decreases; in the case of habituation, it is the waning of an unconditioned response to a stimulus). It is not clear if the mechanisms underlying extinction and habituation (as well as for disinhibition and dishabituation) are similar; however, it seems unlikely that the two phenomena are comparable. Latent inhibition, as we mentioned, involves CS-alone presentations prior to CS-US pairings, and the effect of these initial presentations of the CS is poor learning when the CS is paired with
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the US. Latent inhibition shares features with a few of the other phenomena presented in this section. The procedure for latent inhibition is the opposite of extinction: CS-alone presentations precede CS-US pairings for the former, whereas CS-alone presentations follow CS-US pairings for the latter. Despite this similarity (and difference) it seems unlikely that extinction and latent inhibition possess identical mechanisms; although, as mentioned earlier, both effects involve dependence on the contextual cues present as to whether the learning that occurs during the CS-alone phase will be expressed. Both phenomena may involve retrieval processes (Miller, Kasprow, & Schachtman, 1986). The first phase of a latent inhibition procedure is similar to a habituation experiment in which the stimulus is simply presented repeatedly. Of course, a habituation experiment focuses on the response to the stimulus during this exposure phase, while latent inhibition focuses on conditioning to the stimulus in a subsequent phase; but, nonetheless, investigators have been curious as to whether similar processes may occur during the stimulus-alone phases during habituation and latent inhibition (e.g., a loss of attention). Pseudoconditioning is a procedure in which subjects receive presentations of the CS and US (e.g., a tone paired with shock) and it appears as though a CR is occurring to the CS due to conditioning (i.e., it appears as though learning has occurred), but what has really happened is that the US presentation on a previous trial has influenced the subject in such a way that they make a response (e.g., a startle response such as freezing) to the CS that looks like, but is not, the CR (e.g., a fear response such as freezing). Hence, conditioning has not occurred, but it appears as though it has occurred. Researchers sometimes use control conditions to identify (and therefore potentially rule out) the contribution that pseudoconditioning is making to manifest performance during conditioning. One reason that we have discussed the differences (and similarities) among these (primarily) single-stimulus treatments is that researchers and clinicians may become confused when administering a treatment. For instance, if someone has
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a fear of an untethered, growling, snarling dog and seeks treatment for this fear, then the clinician may administer a form of exposure therapy for the treatment. These therapeutic interventions (exposure therapy) are often viewed as extinction treatments. They are described this way for good reason in many cases. However, was the fear response to the dog acquired through conditioning or not? If the fear was acquired through conditioning, then certainly the use of flooding or systematic sensitization should be considered extinction. But if there was no conditioning or learning of the fear (if the person has always had it), then the treatment could be viewed as habituation training. In both cases, we know that recovery of the fear can occur through spontaneous recovery (via time) or disinhibition/dishabituation (via an extraneous salient event) for the extinguished response. Both habituation and extinction can be reversed by a retention interval or an extraneous event. We also know that extinction (Bouton, Garcia-Gutierrez, Zilski, & Moody, 2006; Chelonis, Calton, Hart, & Schachtman, 1999; Gunther, Denniston, & Miller, 1998) and habituation (Marlin & Miller, 1981) are context specific; if you change contexts, the response can return. This chapter covered numerous instances of conditioning. The chapter sought to clarify some issues that may confuse psychologists and other researchers or practitioners, but it also illustrated some important findings that explore the processes and structures underlying associative learning. The following chapters examine the role of conditioning, based on contemporary research, in a number of domains, including connectionist modeling, psychoneuroimmunology, social inference and attribution, incentive learning, fears and phobias and anxiety, addictive behaviors, social learning, marketing, schizophrenia, learned helplessness, autism, analgesic responses, sexual behavior, evaluative conditioning, and consummatory behavior. These chapters were written by many of the premiere investigators in the field, and we hope they illustrate that conditioning and associative learning theory and research continues to flourish and be influential in many areas of our discipline.
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ACKNOWLEDGMENTS Preparation of this chapter was supported in part by National Institutes of Deafness and Other Communication Disorders grant DC06456 to Steve Reilly. Thanks to Stephanie Fowler, Oskar Pineno, Jen Walker, and Jian-You Lin for their comments on this chapter.
NOTES 1. Note that the abbreviation “US” is used currently by most animal conditioning researchers rather than the more archaic “UCS” that one occasionally sees in some elementary textbooks. 2. It is a negative contingency because of the occurrence of the response means that the aversive event has not occurred and the occurrence of the aversive event means that the response did not occur. 3. A trial is defined as the events that occur prior to each reward presentation. 4. Overshadowing, like blocking, results in weaker learning to one of the two elements of a compound CS. However, overshadowing is empirically defined as the difference in conditioning to a CS that is paired with the US in the presence of a second CS relative to a group that receives conditioning to a CS paired with the US in the absence of a second CS. 5. A conditioned inhibitor is a cue that signals the absence of an otherwise expected US.
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PART II
Applications to Clinical Pathology
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CHAPTER 2 Fear Extinction and Emotional Processing Theory A Critical Review Seth J. Gillihan and Edna B. Foa
The process of fear extinction in animal studies bears strong resemblance to the process of reduction of pathological anxiety in humans via exposure therapy. Thus, findings emerging from experiments of extinction can inform us about the mechanisms of exposure therapy, which may lead to modifying the manner in which therapists conduct exposure and thereby improve treatment outcomes. In this chapter we use emotional processing theory as a framework to organize the knowledge about both exposure therapy and extinction. We examine whether hypotheses and suppositions derived from the theory are consistent with knowledge emerging from extinction experiments and from treatment studies of anxiety disorders; conversely, we examine how emotional processing theory can inform the questions that extinction research needs to address. We include an examination of the neural correlates of the reduction of pathological fear, which may allow us to expand our knowledge of mechanisms of exposure therapy by adding brain processes to the existing behavioral mechanisms implicated in extinction.
INTRODUCTION We view a parallel between exposure therapy and extinction paradigms in animals and humans and therefore take the position that findings emerging from experiments of extinction can inform us about the mechanisms of exposure therapy. It is our hope that this information will lead to modifying the manner in which therapists conduct exposure and thereby improve outcomes. As clinical researchers have long noted (Baum, 1970; Dollard & Miller, 1950; Stampfl & Levis, 1967), the process of fear extinction in animal studies bears strong resemblance to the process of reduction of pathological anxiety via exposure therapy, including the amelioration of symptoms of anxiety disorders. Indeed, patients with anxiety disorders tend to show deficient fear extinction in laboratory studies (Lissek et al., 2005). Research into the phenomenon of fear
extinction therefore may have great relevance to the understanding of the learning processes that facilitate effective cognitive-behavioral treatment for anxiety disorders. In this chapter we use emotional processing theory (Foa & Kozak, 1985, 1986) as a framework to organize the knowledge about both exposure therapy and extinction. This approach will allow us to examine whether hypotheses and suppositions derived from the theory are consistent or in contradiction with knowledge emerging from extinction experiments and from treatment studies of anxiety disorders; conversely, we can examine how emotional processing theory can inform the questions that need to be asked by extinction research. Also, the review of the extinction literature may allow us to expand our knowledge of mechanisms of exposure therapy by adding brain processes to the existing behavioral mechanisms implicated in extinction.
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EMOTIONAL PROCESSING THEORY Foa and Kozak’s (1985, 1986) emotional processing theory presented a heuristic model for understanding pathological anxiety and the mechanisms involved in treatment of anxiety disorders. Expanding on Lang’s (1977, 1984) bioinformational model, the starting point of emotional processing theory is the supposition that fear is represented in memory as a cognitive structure that includes information about the fear stimuli, the fear responses, and their meaning. In contrast to Lang who emphasized the role of response representations, emotional processing theory places particular emphasis on the meaning representations of the stimuli and the responses. For example, a combat soldier in Vietnam may have a fear structure that includes representations of stimuli such as persons moving through the jungle and representations of responses such as heart beating fast and muscle tension. Of particular importance, however, is the meaning of the persons moving through the jungle as “those are Viet Cong soldiers and my life is in danger”; and the meaning of heart beating fast and muscle tension as “I am afraid.” The representations of the stimuli, responses, and their meaning in the structure are related to each other such that when a stimulus and/or response in the environment matches those represented in the fear structure, the entire structure is activated. Thus, seeing an enemy soldier in the jungle will activate the representation of a moving person, the meaning associated with that representation (“I’m in danger”), and the behavioral and physiological fear responses. In addition to emphasizing the crucial role of meaning representations, Foa and Kozak (1986) outlined the distinguishing features of normal and pathological fear structures. In the example mentioned earlier, the soldier’s fear structure is normal if it is restricted to the jungle setting during wartime; in these circumstances, activation of the fear structure will lead to adaptive responses such as staying low and avoiding enemy fire. In contrast, the fear structure is pathological if it is activated by safe stimuli, for example when, upon returning to the United States
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the Vietnam veteran experiences fear and “takes cover” when seeing a person walking through the woods while vacationing with his family in the Great Smoky Mountains National Park. In other words, the pathological fear structure produces overgeneralization when safe stimuli are perceived as dangerous. Pathological fear structures also comprise excessive response elements (e.g., hypervigilance). According to emotional processing theory, two conditions are required for the modification of pathological fear structures. First, the fear structure must be activated through exposure to information that is sufficiently similar to the information embedded in the fear structure. Second, information that is incompatible with the pathological elements of the fear structure must be available during exposure and incorporated into the fear structure. In the example of the combat veteran, the fear structure was activated when the veteran was exposed to persons moving through the woods; remaining in that situation and discovering that the persons do not harm him constitutes information that is incompatible with his perception that all moving persons in the woods are dangerous. The incorporation of this information into the existing pathological fear structure modifies the pathological elements in the structure. This modification, which is the essence of emotional processing, underlies the reduction in pathological fear. While it is not possible to directly observe fear structures and their modification, Foa and Kozak (1986) postulated three indicators of successful emotional processing: activation of the fear structure, as indicated by both subjective and objective measures of fear; within-session habituation,1 or the reduction of anxiety within the course of a treatment session; and betweensession habituation, that is, lower peak anxiety to fear-related stimuli during successive treatment sessions. As Foa and Kozak (1986) suggested, many sources of data can be brought to bear to test the prediction that these three indicators of emotional processing are associated with the reduction of pathological fear. The original formulations of the theory incorporated existing research, which included behavioral and
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psychophysiological studies conducted primarily with human subjects; an update of the theory (Foa, Huppert, & Cahill, 2006) examined the degree to which the latest research lent support to the predictions of emotional processing theory, again focusing on clinical findings from human studies. The current chapter aims to evaluate the validity of emotional processing theory in light of basic research in learning processes. Learning research offers a powerful experimentally testable model of psychopathology and of treatment. It is particularly relevant for anxiety disorder because the biology and psychology of fear have been the focus of animal studies for decades; as such, findings from the learning literature may inform theoretical models of pathological anxiety and its treatment. Since the work of Ivan Pavlov (e.g., 1927), learning research has shed light on the processes that underlie the acquisition and retention of associations between stimuli and responses. Learning research has played a key role in our understanding of anxiety disorders, including panic disorder (Bouton, Mineka, & Barlow, 2001), posttraumatic stress disorder (PTSD; Rothbaum & Davis, 2003), and simple phobia (Davey, 1992). The applicability of learning principles to the study of anxiety is particularly salient in the context of PTSD because of the clear etiological factors associated with the development of the disorder. Indeed, PTSD is the sole anxiety disorder with diagnostic criteria that require an identifiable event that preceded the onset of the disorder. Much animal research has been done to model the fear responses seen after the experience of a trauma. Fear conditioning as an animal model of PTSD was discussed at length by Foa, Zinbarg, and Rothbaum (1992). Briefly, Foa and colleagues argued that the behavioral responses seen in animals that are exposed to uncontrollable and unpredictable aversive stimuli parallel the clusters of symptoms seen in PTSD. For example, the heightened fear and arousal seen in fear-conditioned animals may be analogous to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criterion of persistent arousal; similarly, animals tend to show avoidance of the fear-related stimuli, just
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as PTSD patients tend to avoid trauma-related stimuli. The authors also make the case that animal models of PTSD can be used to generate hypotheses about human trauma-related psychopathology. For instance, based on the animal literature they posit that traumatic events that lead to PTSD are perceived not only as more life threatening (e.g., Dunmore, Clark, & Ehlers, 1999; Resnick, Kilpatrick, Dansky, Saunders, & Best, 1993) but also as unpredictable or uncontrollable. Most individuals who experience a traumatic event experience fear-related symptoms that overlap with those of PTSD, including reexperiencing the event in response to reminders of it, hyperarousal, and avoidance of trauma-related stimuli (e.g., Breslau, Reboussin, Anthony, & Storr, 2005; Rothbaum, Foa, Riggs, Murdock, & Walsh, 1992). These symptoms ameliorate over time in most trauma survivors. When such reduction does not occur, chronic PTSD develops. Thus, PTSD can be viewed as a failure to extinguish conditioned fear responses. This perspective on the tenacious fear reactions in PTSD is supported by studies showing that patients with PTSD demonstrate deficient fear extinction relative to controls (Blechert, Michael, Vriends, Margraf, & Wilhelm, 2007; Lissek et al., 2005; Peri, Ben-Shakhar, Orr, & Shalev, 2000). Earlier conceptualizations viewed extinction as a passive process of undoing learned associations, and little research was devoted to understanding the processes that facilitate or inhibit extinction. More recent research revealed that fear extinction is an active process that is distinct from the process of fear acquisition. Accordingly, an extinguished fear response (conditioned stimulus [CS]) may show spontaneous recovery (e.g., Leung & Westbrook, 2008), is “reinstated” when the unconditioned stimulus (US) is presented in the absence of the CS (e.g., Rescorla & Heth, 1975), and reemerges in contexts other than the extinction setting (e.g., Alvarez, Johnson, & Grillon, 2007). These findings suggest that extinction is not simply the erasure of the acquired fear response. Instead, extinction is thought to involve the inhibition of a fear association through the formation of new stimulus–response associations. As noted by
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Barad, Gean, and Lutz (2006), “extinction gates the expression of fear” (p. 326). This new conceptualization influenced Foa and McNally (1996) who updated emotional processing theory by suggesting that exposure therapy generates new fear structures rather than weakening the associations of the existing pathological fear structure. As discussed earlier, emotional processing theory posits three indicators that emotional processing of the fear structure has occurred (i.e., the pathological elements of the fear structure were modified): activation of the fear, and within-session and between-session habituation to fear-related stimuli. The hypotheses that are generated by the proposed indicators can be tested in fear extinction paradigms. First, the degree of fear activation during extinction training will be positively associated with a greater reduction in fear responses. Second, the degree of fear reduction within a fear extinction session will be associated with greater reduction at testing. Finally, lower peak responses in successive sessions of presentation of the previously conditioned stimulus will be associated with fear reduction at testing. We will address each of these hypotheses by examining the relevant research findings from human and animal studies from basic behavioral research to neural localization (i.e., identifying a brain area that is associated with a particular behavior or cognitive process) studies across a range of methodologies. We will focus on fear, although clearly PTSD comprises many other emotions such as anger or guilt; nevertheless, the majority of PTSD sufferers experience debilitatingly high levels of unrealistic fear. The fear is evident in the common symptoms of PTSD such as avoidance of trauma-related stimuli, stress reaction to trauma reminders, hypervigilance, and being easily startled. A Note About Terminology
For the sake of clarity we distinguish between the extinction phase, which involves presenting the CS in the absence of the US to previously fear conditioned organisms, and the test phase, which occurs at some point in time after the extinction phase.
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Evaluation of Prediction 1: Fear Activation During Extinction Behavioral Studies
During fear extinction paradigms, fear is activated by definition because the organism has been conditioned to experience fear when confronted with a CS that was associated with an aversive US. The fact that fear is activated as part of the extinction paradigm results in the restriction of range (i.e., low variability) in initial fear, which in turn renders the examination of the effects of level of fear activation on subsequent fear extinction problematic given that the detection of a significant relationship between level of activation and degree of extinction requires variability in initial fear. Despite this inherent limitation, there is evidence that the level of fear activation during fear extinction learning predicts fear responding at test. Animals who were administered a chemical agent known to reduce fear activation (i.e., barbiturates) during fear extinction showed greater fear responses to later presentations of the CS during the test phase when the animals were not drugged (e.g., Barry, Etheredge, & Miller, 1965). Similarly, Bouton, Kenney, and Rosengard (1990) found that benzodiazepines commonly used in the treatment of anxiety disorders (chlordiazepoxide and diazepam) interfered with fear extinction learning. Bouton et al. paired a chamber with footshock and subsequently exposed the animals to the chamber in the absence of shock. Although rats in these experiments showed fear extinction during the extinction learning phase of the experiment irrespective of whether they received benzodiazepines, animals that had received the benzodiazepines during extinction showed significantly greater fear responses during the test phase when the animals were exposed to the chamber in an undrugged state. Given that benzodiazepines and other sedating drugs reduce fear-related behavior, these data support the premise that fear activation during the extinction of conditioned fear is associated with greater reductions in fear at test. However, these studies did not specifically address variability in the animals’ fear responses during extinction learning as a predictor of
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eventual fear extinction; rather, the studies focused on the differences among groups that received different drugs. Moreover, other interpretations of these results are possible, such as state-dependent learning—that is, that optimal recall during the test phase depends on the animal’s being in the same physiological state as during the learning acquisition phase (see Overton, 1985). Nevertheless, results from these and similar studies are consistent with the hypothesis that greater activation during exposure therapy is associated with greater reductions in anxiety. More direct behavioral evidence for the role of activation in fear extinction comes from later research demonstrating that direct pharmacologic manipulation of fear responses during extinction affects fear responding during the test phase. Cain, Blouin, and Barad (2004) administered yohimbine or propranolol to mice prior to fear extinction; yohimbine blocks the α2-receptor, resulting in increased adrenergic activity and anxiety, while propranolol blocks the β-receptor, leading to decreased adrenergic activity and anxiolysis. In light of the role of the adrenergic system in anxiety disorders (Bremner, Krystal, Southwick, & Charney, 1996) and the strong evidence for the role of norepinephrine in PTSD (Southwick et al., 1999), manipulations of this neurotransmitter system have important implications for the extinction of conditioned fear. Results from Cain et al. indicated that animals treated with yohimbine showed enhanced fear extinction during the test phase; in contrast, animals treated with propranolol exhibited impaired extinction. These findings again are consistent with the hypothesis that fear activation during exposure is associated with better treatment outcomes. In addition, these results provide compelling evidence that state-dependent learning per se cannot account for the observed effects of activation on fear reduction, given that neither the propranolol- nor the yohimbine-treated mice were in the same physiological state during the test phase as during the extinction learning phase. Morris and Bouton (2007) also performed multiple experiments to test the effect of yohimbine on conditioned fear extinction, measuring
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freezing response during extinction training under yohimbine versus saline administration. For the purpose of this discussion, the amount of freezing is taken to indicate level of fear activation during extinction training. Results indicated that a 1.0 mg/kg dose of yohimbine was associated with less fear activation during extinction, as well as less fear responding during the test phase. Given that yohimbine produces fearlike symptoms, the finding that the yohimbine group showed less fear during the test phase provides additional evidence that pharmacologically induced activation is associated with greater reductions in conditioned fear. However, because the yohimbine group did not show more fear response during the extinction learning phase than did the control group, this study does not present evidence that it was the greater initial fear activation which was responsible for the greater extinction learning. The activation hypothesis of emotional processing theory appears to be supported not only by extinction of conditioned fear but also of conditioned appetitive responses. In an elegant series of experiments, Rescorla (2000) paired various CSs with the delivery of food pellets. Animals that showed the greatest response during the extinction phase also showed the most profound degree of extinction during the test phase. These animals had been presented with compound CSs (noise plus light) during the extinction phase, which produced the greatest mean response during this phase; when presented subsequently with a single extinguished CS (noise or light) at test, the rats in this condition exhibited the lowest level of conditioned responses. These results were replicated and extended to a fear conditioning paradigm (Rescorla, 2006), further confirming the importance of activation in extinction learning. Taken together, results from extinction studies demonstrate that greater activation during extinction is associated with greater decrements in the conditioned behavior. It follows that without activation of the relevant representations, extinction is unlikely to occur; this seems true for both fear and appetitive conditioning. Given the importance of activation, optimal extinction depends on the identification of factors that promote
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activation. On the basis of Rescorla’s findings (2000, 2006), the presentation of multiple CSs that are associated with the US are most likely to produce enhanced activation and thus greater subsequent extinction of the CS. Implications of this observation for exposure therapy will be discussed later. Comparatively little research has been conducted to test for the effects of pharmacologically manipulated activation on fear extinction in humans. Contrary to the findings for animals in which the β-blocker propranolol was associated with impaired fear extinction (Cain et al., 2004), Orr et al. (2006) found no significant effect of propranolol on fear extinction among a group of males with PTSD. Conclusive evidence for the role of pharmacological agents in augmenting activation and thereby enhancing extinction awaits future research. Of note, a process complementary to extinction also demonstrates that alteration of fear memory requires activation. Several studies have shown that learned fear can be disrupted by blocking the reconsolidation of CS-US associations. In a typical experiment in this area, animals that had learned a CS-US pairing were presented with the CS alone some time after fear training. The retrieval of the fear memory in response to the CS presentation temporarily renders the memory labile and sensitive to disruption; evidence for this phenomenon comes from studies showing that interruption of the processes that are required for reconsolidation of the retrieved memory leads to a decrease in fear responding to the CS (e.g., Lee, Milton, & Everitt, 2006; Nader, Schafe, & LeDoux, 2000). Importantly, activation of the learned fear association through presentation of the CS must precede administration of the chemical agents that interrupt reconsolidation; without activation of the fear, there is no effect on the CS-US association (Nader et al., 2000). These findings provide further evidence for the critical role of fear activation in altering learned fear associations. Neural Studies
In addition to the behavioral data summarized earlier, many studies examining neural function have demonstrated that the extinction of
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conditioned fear is associated with the activation of brain structures that are involved in the representation of fear. The most widely studied brain structure in this context is the amygdala. The amygdala is an almond-shaped mass that is located in the limbic system; it plays a key role in the processing of emotional information, including emotional memory (Phelps, 2005) and making judgments about emotional facial expressions in humans (for a review see Sergerie, Chochol, & Armony, 2008). Importantly, the amygdala has been implicated in the acquisition of fear conditioning, both in animals (LeDoux, 2000) and in humans (LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998). Of direct relevance to the current chapter, patients with PTSD tend to show hyperactivity of the amygdala (e.g., Rauch et al., 2000; Shin et al., 2004), indicating the chronically heightened fear and arousal experienced by these individuals. Numerous experimental paradigms have demonstrated the involvement of the amygdala in fear extinction. In animals, the blockade of N-methyl-D-aspartate (NMDA) receptors by injection of NMDA antagonists, either systemically (Baker & Azorlosa, 1996) or into the amygdala, leads to poor extinction learning (Falls, Miserendino, & Davis, 1992; SotresBayon, Bush, & LeDoux, 2007). Conversely, infusion of the NMDA agonist D-cycloserine (DCS) either systemically or directly into the amygdala enhances the extinction of conditioned fear (Walker, Ressler, Lu, & Davis, 2002). The involvement of the amygdala in fear extinction has been demonstrated through tests of other neurotransmitter receptor systems, including the cannabinoid receptor type 1 (Chhatwal, Davis, Maguschak, & Ressler, 2005) and L-type voltage-gated calcium channels (e.g., Cain, Blouin, & Barad, 2002). In addition, fear extinction is associated with depotentiation of excitatory thalamic neurons that terminate at synapses on the amygdala (Kim et al., 2007). These studies indicated that amygdala activity is recruited during the extinction of conditioned fear. However, they do not tell us definitively whether the amygdala activity represents the activation of the existing fear structure, the modification of the existing fear structure, or the
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creation of an inhibitory extinction representation. A recent study by Herry et al. (2008) provides striking evidence that the basal amygdala is involved in both conditioned fear and its extinction. This research demonstrated that conditioned fear leads to the activation of “fear neurons” in response to the presentation of a CS. When the CS is presented in the absence of the US, “extinction neurons” increase their firing rate; fear neurons subsequently reduce their firing rate. Importantly, these neuronal changes preceded changes in freezing behavior. Crucially, the fear neurons continue to fire at the beginning of extinction (see Herry et al., Fig. 3c), with extinction neurons starting to fire just before fear neurons switch off. These results demonstrated that the neurons representing fear are active during the extinction of fear. Findings from the literature on fear memory reconsolidation also demonstrate the essential role of neurons in the amygdala in the modification of learned fear associations. For example, reconsolidation can be inhibited by blocking protein synthesis in the amygdala (Nader et al., 2000); blocking NMDA receptors in the amygdala (Lee et al., 2006); blocking noradrenergic transmission in the amygdala (Debiec & LeDoux, 2004); the infusion of cannabinoid CB1 receptor agonists (Lin, Mao, & Gean, 2006); and by genetic disruption of transcription in the amygdala (Mamiya et al., 2009). Again, these effects hold only when presentation of the CS precedes administration of the chemical agents, that is, after activation of the fear memory. Thus, there is ample evidence that amygdala activation underlies the activation of fear memories and subsequent modification of these memories. Human neuroimaging studies provide an essential test for the relevance of the animal findings for patients (Rauch, Shin, & Phelps, 2006). A related line of research has shown the involvement of the amygdala in human fear extinction. LaBar et al. (1998) found significant amygdala activation during the early phase of fear extinction. Later studies replicated and extended these findings. Amygdala activation was found when the association between the US and the CS was altered, both during fear acquisition and extinction (Knight, Smith, Cheng, Stein, & Helmstetter,
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2004; Milad et al., 2007); moreover, the degree of fear extinction was strongly correlated with amount of amygdala activation (Phelps, Delgado, Nearing, & LeDoux, 2004). These data are consistent with Foa and Kozak’s (1986) emotional processing theory, which states that activation of the fear structure is necessary for its modification and the subsequent alleviation of pathological fear levels present in anxiety disorders, including PTSD. If one can view exposure therapy as analogous to extinction and the reduction in different measures of PTSD as indicators of extinction, then there are several clinical studies that also lend support to the activation hypothesis (e.g., Foa, Riggs, Massie, & Yarczower, 1995; Kozak, Foa, & Steketee, 1988; Lang, Melamed, & Hart, 1970; Pitman et al., 1996). These studies assessed level of fear activation using different measures of fear (e.g., facial fear expression, heart rate, subjective ratings), thus demonstrating the robust effect of activation on extinction. The data from the fear extinction literature in animals and humans presented earlier provide a window into the mechanisms that underlie fear reduction and suggest a line of research that will combine fear extinction paradigms and studies on exposure therapy. Evaluation of Prediction 2: Within-Session Fear Reduction
According to Foa and Kozak (1986), the second indicator of emotional processing is withinsession fear reduction. It can be argued that within-session habituation would enhance extinction in the test phase. A small number of studies have tested this prediction, with mixed results. One approach that researchers have used is to compare the effects of massed versus spaced presentation of the CS during extinction training on fear responding during extinction and later during recall of extinction—that is, during presentation of the CS without the US some time after the extinction learning. Studies have demonstrated that massed presentations produce greater within-session fear extinction; at test 1 week later, massed CS presentations were associated with lower fear responding when animals were again presented with the CS alone
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(Cain, Blouin, & Barad, 2003). This finding is consistent with the emotional processing theory prediction. Two similar studies that examined appetitive extinction produced inconsistent results. Rescorla and Durlach (1987) obtained similar results to those of Cain et al. (2003), finding that extinction in pigeons was more durable when extinction trials were massed (intertrial interval of 10 sec) versus spaced (2 min). In contrast, Moody, Sunsay, and Bouton (2006) found no significant effect of 60- versus 240-sec intertrial intervals; although the shorter interval produced greater within-session extinction, there was no significant effect of interval at the test phase. Other studies demonstrated that the relationship between within-session fear reduction and extinction of conditioned fear during the test phase is more complicated. In a context conditioning paradigm, Li and Westbrook (2008) found that although spaced extinction trials produced less within-session fear reduction than massed extinction trials, the spaced trials resulted in greater fear extinction during the test phase. However, this study differed from that of Cain et al. (2003) in several important ways. First, Cain et al. used presentation of a discrete CS, whereas Li and Westbrook studied context conditioning, with each stint in the conditioning context representing a “session” of fear extinction learning. This distinction renders the Li and Westbrook design a test of between-session fear reduction, given that the animal was removed from the conditioned context for varying intervals between trials. Second, Cain et al.’s intertrial intervals ranged from 6 sec to 6 min, whereas those of Li and Westbrook were 4 min versus 24 hr. Therefore, the study by Li and Westbrook appears to apply to the spacing of exposure sessions rather than to the reduction of fear within a session. In summary, Foa and Kozak’s (1986) view that within-session habituation is an indicator of emotional processing has not received as strong support from studies of fear extinction as did their view of fear activation as an indicator of, as well as a necessary condition for, emotional processing. Similarly, exposure therapy studies with human beings also do not lend strong
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support to the role of within-session habituation in promoting emotional processing (e.g., Kozak et al., 1988; Riley et al., 1995; van Minnen & Foa, 2006). Thus, although patients often experience within-session habituation during exposure sessions, the degree of within-session habituation is not a consistent predictor of outcome. Evaluation of Prediction 3: Between-Session Fear Reduction Different Brain Mechanisms Involved in Within- and Between-Session Habituation
The third indicator of emotional processing in Foa and Kozak’s (1986) theory is betweensession habituation of fear. Emotional processing theory proposes that although betweensession fear reduction is related to within-session fear reduction, the two involve separate processes. In extinction paradigms, between-session fear reduction can be viewed as analogous to the retention of fear extinction between the extinction phase and the subsequent test phase, as distinct from the initial acquisition of fear extinction learning, which is akin to within-session fear reduction. Foa (1979) posited that different brain mechanisms are involved in within- and betweensession habituation in exposure therapy. Building on findings by Groves and Lynch (1972), she argued that a low-level brain structure (brain stem reticular formation) is involved in within-session habituation while between-session habituation relies on forebrain structures. Foa further argued that if forebrain structures are necessary for retention of habituation (extinction) across time, then higher level cognitive processes seem to be necessary for consolidation of extinction learning. Support for the view that two distinct learning processes are involved in short- and longterm extinction comes from studies of evaluative conditioning. In these studies there is a distinction between expectancy learning and evaluative learning; in the former, the organism learns that a certain event (stimulus) predicts another event, while in the latter the organism assigns a negative or positive valence to the CS (see Chapter 18; for a review see De Houwer, Thomas, & Baeyens, 2001). For example, an animal will
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learn that a red light paired with a subsequent electric shock predicts the onset of the shock (expectancy learning); assigning a negative valence to the red light itself represents evaluative learning. Interestingly, Vansteenwegen et al. (2006) note that evaluative learning is more resistant to extinction than is expectancy learning. Accordingly, a subject may stop producing an electrodermal response following extinction of a previously conditioned stimulus that predicted electric shock but still will have a negative evaluation of the conditioned stimulus (e.g., Hermans, Vansteenwegen, Crombez, Baeyesn, & Eelen, 2002; Vansteenwegen, Francken, Vervliet, De Clercq, & Eelen, 2006). It is reasonable to assume that evaluative conditioning involves higher level cognitive operations and that those operations are more resistant to extinction, given the involvement of higher order brain areas (especially the prefrontal cortex; Zysset, Huber, Ferstl, & von Cramnon, 2002) in evaluative judgments. Indeed, there is evidence that evaluative conditioning is intact even in the absence of the amygdala (Coppens et al., 2006). These observations may suggest that extinction of expectancy learning is more similar to within-session extinction and that extinction of evaluative learning is more akin to between-session extinction. The distinctions between expectancy learning and evaluative learning may have important implications for exposure therapy (see Discussion). Taken together with Groves and Lynch (1972), the results from evaluative conditioning studies reinforce the view that separate brain areas and cognitive processes underlie within- and between-session habituation. Brain Mechanisms Underlying Retention of Fear Extinction
A large body of studies has confirmed that distinct neural regions underlie the retention of fear extinction versus the acquisition of fear extinction. The vast majority of these studies have implicated the medial prefrontal cortex (mPFC) in the retention of extinguished fear in both animals and humans. In animals, many experimental paradigms have been used to examine the role of mPFC in extinction retention (for a review,
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see Quirk, Garcia, & González-Lima, 2006). In lesion studies, the destruction of mPFC structures is associated with normal within-session extinction, but the animals do not retain the extinction learning 24 hours later at test (e.g., Lebrón, Milad, & Quirk, 2004; Quirk, Russo, Barron, & Lebron, 2000). Disruption of the targets of inhibitory mPFC projections within the amygdala also has been shown to interfere with extinction recall (Chhatwal, Stanek-Rattiner, Davis, & Ressler, 2006; Likhtik, Popa, AspergisSchoute, Fidacaro, & Paré, 2008). Additional research has provided more precise estimates of the temporal course of mPFC involvement in extinction retention. Reversible pharmacological disruption of mPFC activity (by injection of PD098059, a mitogen-activated protein kinase inhibitor) immediately after fear extinction learning also produced deficient extinction retention during the test phase (Hugues, Deschaux, & Garcia, 2004). Similar results were obtained via the infusion of an NMDA receptor antagonist immediately after extinction training (Burgos-Robles, Vidal-Gonzalez, Santini, & Quirk, 2007). Both of these studies found no significant deficit in extinction retention at test if the chemical agents were applied 2 hours after extinction training, suggesting that mPFC activity during this period is critical for extinction retention. Complementary paradigms have produced corroborating results; rather than examining the loss of function associated with destroying mPFC, Milad and Quirk (2002) measured neuronal activity in intact mPFC during fear conditioning, extinction training, and recall of extinction at test. They found that neurons in mPFC showed increased firing rates only during the 24-hour recall of extinguished fear conditioning, again supporting the involvement of this region in extinction retention. Similar studies in humans have corroborated the results from animal studies showing that the mPFC is involved in extinction retention. Kalisch et al. (2006) paired presentation of faces (CS) with electric shock during the conditioning phase and then extinguished the CR by presenting the faces without shock. The following day during the test phase, presentation of the
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extinguished CS produced significant activation of mPFC. Milad et al. (2007) replicated these results using a slightly different paradigm, pairing electric shock with different visual contexts. In addition to finding significant mPFC activations during recall of extinction, their results showed that the degree of extinction retention at test was positively correlated with percent signal change in mPFC. Phelps et al. (2004) also found that mPFC activity increased during the recall of extinction, and they reported a positive correlation between mPFC activity during extinction recall and prior success of extinction; that is, subjects who showed greater extinction during extinction training also had greater activation of mPFC during the recall of extinction at test. In addition to the functional associations between mPFC and extinction retention, structural variability within this region was found to be correlated with recall of extinction. Milad et al. (2005) found a positive correlation between thickness of mPFC and extinction retention. Retention of extinction learning has been consistently shown to be context dependent; that is, a CS that is extinguished in one context may provoke the CR when presented in a novel context at test (e.g., Bouton & Bolles, 1979). The hippocampus appears to play a crucial role in evaluating context in recall of extinction learning. Indeed, disruption of the hippocampus following fear extinction prevented the return of conditioned fear responses in a novel context (Corcoran & Maren, 2001), suggesting that hippocampal input is necessary for context sensitivity in extinction recall. Furthermore, the hippocampus has been shown to play a similar role in the contextual modulation of human extinction recall during the test phase (Kalisch et al., 2006; Milad et al., 2007). That is, if conditioning occurs in one context and extinction in another context, then the CR ordinarily would return if the subjects are tested in a novel context (Bouton & Bolles, 1979); such return of the CR does not occur in those with hippocampal lesions. Convergence Between Experimental Laboratory and Clinical Treatment Findings
In summary, it seems that between-session habituation (i.e., extinction retention) relies on
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higher cognitive processes more than does within-session habituation (extinction learning), given the higher level cortical areas that support between-session habituation. Supporting this conclusion are the findings that different brain structures are involved in each type of habituation, suggesting a distinction between extinction learning and extinction retention at test. The laboratory findings that extinction retention and extinction learning comprise different processes may explain why between-session habituation, which is akin to extinction retention, is related to treatment outcome (e.g., Kozak et al., 1988; Lang et al., 1970; Rauch et al., 2004), whereas within-session habituation generally is not. Moreover, the findings summarized earlier, suggesting the involvement of higher cognitive functioning in extinction retention, may further explain why between-session habituation is crucial for recovery from pathological anxiety. After all, to recover, patients need to remember the outcome of their previous experiences— namely, that their feared consequences were disconfirmed—and such memory relies on higher cognitive functioning.
DISCUSSION Implications of Fear Extinction Literature for Emotional Processing Theory
In this chapter our goal was to reexamine and extend emotional processing theory by considering three core tenets of the theory—activation, within-session habitation, and between-session habituation—in light of experimental extinction paradigms. Specifically, we have attempted to examine the body of information about extinction learning and retention and the related brain structures and functions in light of emotional processing theory with the goal of furthering our understanding of the mechanisms that support the reduction of pathological fear. Our review indicates that there is strong evidence from the extinction as well as from the treatment literature that initial fear activation is important in achieving extinction of conditioned fear and in reducing pathological anxiety after exposure therapy. These findings support the emotional
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activation hypothesis of emotional processing theory. Behavioral, neural, and pharmacological data converge to indicate that the fear structure must be activated in order for the conditioned fear to be extinguished. These consistent findings from diverse areas will serve as a strong foundation for further research that will help us understand the basic mechanisms underlying fear extinction and exposure therapy. There have been fewer studies in the extinction literature that can shed light on the second hypothesis of emotional processing theory, that within-session fear extinction is related to outcome. This tenet of the theory has accrued less support in the clinical literature (for a review, see Craske et al., 2008), suggesting that withinsession fear reduction may not be required for fear to be extinguished or for successful exposure treatment to reduce pathological anxiety. However, the relative paucity of data argues for further examination of this hypothesis before a final verdict is rendered. There is considerable support from the animal literature on extinction learning for the supposition of emotional processing theory that between-session habitation is strongly related to outcome of exposure therapy and therefore is a valid indicator of emotional processing. Of particular interest are the findings that activity in specific neural regions, namely the mPFC, is necessary for the maintenance of extinction learning between extinction training and subsequent test sessions. These data underscore the importance of between-session fear extinction and support the third tenet of emotional processing theory. The converging evidence from the clinical literature for the importance of between-session habituation on treatment outcome (e.g., Rauch et al., 2004) reinforces the importance of fear reduction across sessions as an indicator of emotional processing. Implications of Emotional Processing Theory for Extinction Research
As we noted in the Introduction, emotional processing theory and extinction paradigms in animal research may be mutually enriching in understanding the mechanisms involved in fear
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reduction and in the treatment of pathological anxiety. The literature reviewed in this chapter provides not only a test of the predictions put forward by emotional processing theory but also provides future directions for basic fear extinction research that may more directly inform the theory and treatment of anxiety. Few of the animal extinction studies reviewed herein were designed explicitly to test the tenets of emotional processing theory; more research in this area may further solidify the empirical basis of exposure treatment. Although the role of fear activation in extinction learning has been studied extensively and its importance is clearly supported by existing data, fewer studies have investigated the role of withinsession fear reduction on subsequent fear extinction. Similarly, although many studies have demonstrated that between-session fear reduction is a distinct process that appears to be related to extinction learning, few behavioral studies have tested the prediction that greater fear extinction across sessions is correlated with greater fear reduction at the test phase. Future studies designed specifically to bridge basic and clinical science must address issues that will help therapists modify exposure treatment in order to increase its efficacy, such as optimal level of initial fear activation and the degree of withinsession fear reduction that is necessary for successful outcome. Treatment Implications
Findings from the fear extinction literature may have important implications for the practice of exposure therapy. As Foa and Kozak (1997) noted, the initial optimism that followed the advent of the first behavioral therapies for anxiety gave way to the recognition that many patients fail to benefit from the treatments, and that a significant percentage of those that benefit fail to maintain their gains. The authors argued that the apparent “efficacy ceiling”—the leveling off of response rates to empirically tested cognitive and behavioral treatments—may be due to “an alienation from psychopathology and experimental psychology” (p. 606). They suggest that therapy researchers have not exploited findings
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from basic research in psychopathology. In their view, greater communication between basic clinical scientists and therapy researchers has the potential to produce more powerful and more broadly applicable clinical treatments. Clinical application of the principles reviewed here in the context of emotional processing theory has the potential to raise the efficacy ceiling on the treatment of anxiety disorders. For example, the results from Cain et al. (2004), who used pharmacologic agents to enhance the extinction of conditioned fear, suggest that exposure therapy may be delivered effectively across fewer sessions if activating agents are used to enhance the inhibitory learning processes. Existing research has shown that activating agents such as D-cycloserine enhance the effects of exposure therapy for anxiety disorders (e.g., Ressler et al., 2004). Some research laboratories currently are investigating the ability of agents such as yohimbine to enhance the efficacy of exposure treatment for PTSD. If positive results are obtained, patients with PTSD may experience relief more quickly. Of particular importance is the possibility that people who might otherwise drop out before experiencing the effectiveness of the treatment might have quicker gains that encourage them to complete the treatment and reach full remittance of symptoms. Nonpharmacologic tools also may render exposure therapy more effective. For example, Powers and Emmelkamp (2008) in their metaanalysis found that virtual reality exposure (VRE) is more effective than in vivo exposure in the treatment of phobias. Virtual reality may increase the level of activation by producing a closer match with the fear structure. Effectiveness of VRE also may be driven by the ability to present multiple CSs (e.g., sounds of warfare; combat-related images) that deepen extinction (see Rescorla, 2000, 2006). In addition, VRE may help patients who are “under-engagers”; as Rothbaum (2009) points out, VR may help “patients who are reluctant to engage in recollections of feared memories or are not very good at imagining situations” (p. 210). Our current understanding of the role of within-session extinction does not provide clear guidance for the application of exposure therapy.
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Nevertheless, the finding that massed extinction sessions, which result in greater within-session extinction, are more effective in reducing fear than spaced sessions, suggests the importance of fear reduction within each exposure session. Among PTSD patients treated with exposure therapy, Pitman et al. (1996) found a trend for within-session HR habituation to be correlated with outcome. However, several clinical studies (e.g., Jaycox, Foa, & Morral, 1998; Kozak et al., 1988; van Minnen & Foa, 2006) have not found significant associations between within-session habituation and outcome. Perhaps withinsession habituation is not a necessary condition for positive outcomes in exposure therapy for PTSD; alternatively, the subjective ratings of within-session distress may lack sensitivity for detecting associations between within-session experiences and outcome. Additional animal and neuroimaging studies may help resolve the question of whether within-session changes in emotional experience are important for longterm reduction in pathological fear reactions. The findings from evaluative conditioning may have implications for exposure therapy in that the negative evaluations of trauma-related stimuli such as automobiles (for motor vehicle accident survivors) or men (for rape survivors) likely will persist longer than will the fear responses to such stimuli. That is, while a patient with PTSD may no longer report distress in response to trauma-related stimuli because the expectancy has decreased in strength, the person may continue to avoid items on an exposure hierarchy in response to a persistent negative evaluation of these stimuli. An awareness of this phenomenon on the part of the therapist may be instrumental in helping the patient to overcome factors that encourage avoidance. Finally, the importance of consolidating fear extinction in order to retain the extinction learning (e.g., Hugues et al., 2004) also has important ramifications for exposure treatment. Patients should be encouraged strongly to abstain from activities, such as excessive drinking, that may interfere with memory consolidation processes, particularly in the immediate aftermath of an exposure session. Although abstaining may be more difficult in the short term, especially for
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patients who rely on chemical substances to numb their fear and anxiety, in the long run the patients will maintain their extinction learning and will experience more rapid relief from their anxiety.
NOTE 1. In this chapter we generally use the term extinction in the context of fear conditioning studies because it is clear what CS-US association is being extinguished. We use the less specific term habituation in the context of fear reduction during exposure therapy because in most cases it is unknown how the pathological fear originated.
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CHAPTER 3 Fear Conditioning and Attention to Threat An Integrative Approach to Understanding the Etiology of Anxiety Disorders Katherine Oehlberg and Susan Mineka
In this chapter, we review the emerging literatures on conditioning of fear and anxiety and attentional biases for threat. First, we identify constructs common to both phenomena and suggest reasons for an approach that integrates both processes in the study of human anxiety disorders. We also review the current literature on individual differences in fear and anxiety conditioning. Next, we provide a current account of research on attentional biases for threat, which has focused on differentiating the components of attention affected by the threat relevance of stimuli and individual differences in anxiety. Finally, we review the growing number of studies that have combined conditioning and attention paradigms to investigate the relationships between these two phenomena. We conclude by indicating how these approaches to the study of conditioning and attentional processing in human anxiety might be integrated in the future.
RELEVANCE OF CONTEMPORARY LEARNING THEORY AND ATTENTIONAL PROCESSES IN MODELS OF ANXIETY DISORDERS Early theories of associative learning formed the basis for both research on fundamental conditioning processes as well as behavioral models for the acquisition and treatment of several human anxiety disorders (see Mineka & Zinbarg, 1996, 2006, for reviews). However, subsequent research has shifted the focus to cognitive and neurobiological variables that also play a role in both specific phobias and more complex disorders, such as generalized anxiety disorder, panic disorder, and posttraumatic stress disorder (see Chapter 6; also see, e.g., Bouton, Mineka, & Barlow, 2001; Mathews & MacLeod, 2005; Mineka & Zinbarg, 1996, 2006). Although modern research on conditioning processes continues to have theoretical importance and clinical relevance, we believe
that the continued vitality of this tradition (particularly in psychopathology) must consist of integrating conditioning theories with contemporary research on human cognition and its neurobiological underpinnings, with particular attention to individual differences (Mineka & Oehlberg, 2008). In this chapter, we explore the potential that this integrative approach may have by bringing current research findings on anxiety and fear learning to bear on one important cognitive phenomenon associated with anxiety, namely, attentional biases for threat. A great deal of research has indicated that our attentional systems are highly attuned for the rapid identification of environmental threat (e.g., Esteves, Dimberg, & Öhman, 1994). Moreover, individual differences in attentional processing of threatening stimuli—typically referred to as attentional biases for threat-relevant stimuli— have been associated with both trait anxiety, a vulnerability factor for anxiety disorders, as well
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as a range of clinical anxiety disorders such as specific phobias, generalized anxiety disorder (GAD), posttraumatic stress disorder (PTSD), and social phobias (see Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van Ijzendoorn, 2007; Mathews & MacLeod, 2005, for reviews). Such biases are typically assessed using a behavioral response-time paradigm, in which a participant demonstrates selective attention toward a threatening stimulus (e.g., a threatening word, “disaster,” or an angry face) rather than a neutral stimulus (e.g., “vacuum,” or a neutral face). Several decades of research have now established that these biases appear to be multiply determined and may not be the exclusive purview of highly anxious individuals. Rather, there is evidence that biased attentional processing of threat stimuli may depend upon the intensity of the threat stimuli (Koster, Verschuere, Crombez, & Van Damme, 2005; Mogg et al., 2000; Wilson & MacLeod, 2003). Moreover, it appears that individuals low in trait anxiety tend to direct attention away from mild to moderate threat, but like highly traitanxious individuals direct attention toward highly threatening stimuli (see Bar-Haim et al., 2007; Frewen, Dozois, Joanisse, & Neufeld, 2008, for meta-analyses). A recent computational model of attentional biases for threat versus reward (Frewen et al., 2008), discussed later in this chapter, incorporates this finding as an essential feature of the anxiety-attentional bias link. Classical conditioning is widely considered to be a fundamental mechanism by which initially neutral environmental stimuli acquire the capacity to elicit fear and anxiety, and there is a long tradition of research relating classical conditioning of fear to the anxiety disorders (see Mineka, 1985; Mineka & Zinbarg, 1996, 2006 for reviews). Indeed, conditioning research has formed the basis of behavioral therapies for anxiety disorders, with successful treatment models being based upon extinction processes facilitated by gradual or massed exposures to feared stimuli. Such treatments, particularly with the specific phobias and panic disorder, represent some of the greatest successes of modern clinical psychology (Craske & Mystkowski, 2006).
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The degree to which a stimulus is perceived as threatening, as well as the subjective experience of anxiety or fear, represent areas of construct overlap between the domains of attentional threat biases and aversive conditioning. It is plausible, for instance, that the perceived threat value of a stimulus might be a direct consequence of basic associative learning processes. Given the significance of perceived threat value in attentional biases, it makes sense to consider such processes in models of the development of attentional biases. Moreover, attention is an important moderator— if not mediator—of conditioning, and has been explicitly included in both traditional (e.g., Pearce & Hall, 1980) and current computational models of conditioning processes (Schmajuk, Lam, & Gray, 1996; Schmajuk & Larrauri, 2006). In addition, both attentional biases and learned classical associations are subject to modification, so their study can elucidate mechanisms by which diathesis-stress interactions may occur. For instance, it has been found that modification of attentional biases has little to no immediate effect on state anxiety, but that it has an effect on the amount of distress that an individual experiences when faced with a stressful event (see MacLeod, Rutherford, Campbell, Ebsworthy, & Holker, 2002; MacLeod, & Bridle, 2009; but also Amir, Beard, Burns, & Bomyea, 2009). The goal of this chapter is to review the emerging literatures in attentional biases for threat and conditioning of fear and anxiety, to identify constructs common to both phenomena (e.g., the stimulus threat value), and to suggest ways in which these approaches to the study of information processing in human anxiety might be integrated in the future. First, we distinguish between states of anxiety and fear, and briefly review recent findings on the neural systems involved in the processing of threatening stimuli and their genetic correlates. We also discuss current directions in fear and anxiety learning that have particular relevance to human anxiety disorders. Second, we discuss findings and ongoing issues in attentional bias research. Third, we critically examine the small but growing literature directly relating attention to threat and conditioning processes. Finally, we suggest some directions for future research.
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CLASSICAL CONDITIONING IN THE ANXIETY DISORDERS Starting with Watson and Rayner (1920), Pavlovian or classical conditioning has long been implicated in the origins of fears and phobias (e.g., see also Wolpe, 1968; Mowrer, 1939). Although early conditioning models assumed that traumatic conditioning experiences were both necessary and sufficient for the development of phobic fears and other anxiety disorders, subsequent theorizing has expanded considerably upon this restrictive assumption. As discussed at length elsewhere (Mineka & Zinbarg, 1996; 2006), several important criticisms of these earlier simplistic conditioning views emerged and required careful analysis. Particularly as researchers focused on additional anxiety disorders (such as social phobia, panic disorder, and posttraumatic stress disorder), the importance of other forms of associative learning in the etiology of these disorders became evident (e.g., see Mineka, 1985; Mineka & Zinbarg, 1996). In this section we review highlights of the primary themes of contemporary research and thought on learning theory perspectives on several anxiety disorders. Distinctions Between Fear and Anxiety: Ethological, Clinical, and Neurobiological Evidence
Fear and anxiety, both common in the anxiety disorders, represent partially overlapping but also distinct emotional states. Traditionally, they were distinguished by whether there exists a clear and obvious source of danger that would be regarded as real by most people. Fear was thought to be experienced when the source of danger is obvious, and anxiety was experienced when one could not specify clearly what the danger was. In the past 20–25 years, however, many prominent researchers and theorists have proposed more fundamental distinctions between fear versus anxiety based on a strong and growing body of ethological, clinical, and neurobiological evidence (e.g., Barlow, 2002; Gray & McNaughton, 2000; Grillon, 2008). Although the theories vary in their specifics, there is general agreement on there being at least two negative threat-related
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emotional states. For example, Barlow proposed that fear or panic is a basic emotion involving activation of the “fight-or-flight” response of the sympathetic nervous system. By contrast, he characterized anxiety as a relatively diffuse, future oriented state consisting of a complex blend of negative emotions and cognitions. It involves a negative affective state, worry or anxious apprehension about possible future threat or danger, and a sense of being unable to predict or control the future threat. Physiologically anxiety often creates a state of tension, chronic overarousal, and vigilance for future threat, but no activation of the fight-or-flight response (Barlow, 1988, 2002). In this section, we briefly outline several of the ways in which fear and anxiety have been distinguished. Ethological Evidence
From an evolutionary perspective, defensive systems must enable organisms to efficiently and adequately handle a variety of survival threats, including predators. A defensive fear system should motivate the animal to escape or avoid sources of imminent danger with very fast activation of defensive behaviors, but it must also allow an animal to deal with less explicit or more generalized threat cues with less effort or energy. For example, according to one prominent theory the defensive fear system in animals is organized such that different behaviors emerge depending on the animal’s psychological (and/or physical) distance from a “predator”—known as “predatory imminence” (Fanselow & Lester, 1988). That is, each type of defensive behavior has been selected through evolution to prevent movement to the next higher point on the imminence scale. If no threat exists, the animal goes about its usual daily activities. However, if predatory imminence begins to increase, even very slightly, behaviors optimal to the new situation are activated. For instance, when the predatory imminence is low but non-zero for rats, the animal’s eating patterns may change such that the animal shows heightened vigilance, and approach becomes more cautious, likely corresponding to the human affective states of anxiety and worry (Quinn & Fanselow, 2006). If a danger (like a predator) is actually detected but still at some
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distance, the animal engages in freezing behavior while its heart rate slows and respiration becomes shallower; this state seems to correspond to the human emotion of fear. As the threat proximity increases even further and attack is imminent, the animal may jump, flee, or return the attack; this may correspond to human panic attacks. Thus, the specific physiological responses and overt behaviors engaged in at different levels of threat imminence differ not just quantitatively but also qualitatively. In this context, then, anxiety behaviors are prompted by less explicit or more generalized cues, and they involve physiological arousal and increased vigilance but often without organized functional behavior. By contrast, fear behaviors are hypothesized to be optimized for moderately imminent threat, and panic behaviors for immediately imminent threat. Clinical Evidence
Phenomenological evidence as well as psychometric analyses of clinical symptoms also indicate two relatively independent clusters of symptoms—fear and/or panic symptoms on the one hand, and a more general state of worry or anxious apprehension on the other hand (e.g., Barlow, 2002; Bouton et al., 2001; Brown, Chorpita, & Barlow, 1998). For example, structural equation modeling and factor analyses have uncovered two different factors when examining symptoms of panic, anxiety, and depression in clinical populations. One is exemplified by the kinds of apprehension and worry that are characteristic of anxiety and the other is exemplified by a sense of extreme fear or terror, strong autonomic arousal, and fight-or-flight action tendencies that are characteristic of panic or fear. The interrelationships between these two clusters of symptoms are surprisingly complex, and still a topic of active study. For instance, the autonomic arousal symptoms of panic seem to be inhibited in individuals with GAD, suggesting that worry functions to suppress heightened autonomic reactivity in this disorder (e.g., Borkovec, Alcaine, & Behar, 2004; Brown et al., 1998). However, there is also evidence from studies on panic-disordered individuals that panic attacks are actually potentiated when such an individual has a high level of anxiety
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(see Bouton et al., 2001, for a review). Thus, the effect of elevated anxiety on the occurance of panic appears to vary according to perameters not yet well specified. It is not yet clear how to reconcile such apparently paradoxical findings among the anxiety disorders. Neuroanatomical Evidence
Behavioral neuroscience research in animals also supports the hypothesis of two distinct aversive motivational systems involved in conditioning, although researchers do not agree on all the details. For example, according to Davis and colleagues (e.g., Davis & Shi, 1999; Davis, Walker, & Lee, 1997; Grillon, 2008), fear is a short-term state activated by discrete Pavlovian conditioned stimuli (CSs; see Table 3.1 for a summary of conditioning terminology), whereas anxiety is a longer term state that is activated by more diffuse cues that can be either unconditioned (such as darkness for humans) or conditioned (as in contextual cues associated with threat). These states appear to be related to two distinct neural systems, namely, the amygdala and the bed nucleus of the stria terminalis (BNST), which is immediately downstream from the basolateral amygdala. Although the amygdala mediates fear responses to explicit threatening stimuli, both the amygdala and the BNST are associated with more long-lasting aversive states not clearly linked to explicit threat cues. Both conditioned and unconditioned fear and anxiety states have been modeled in research on animals, and in many cases these experimental paradigms have been extended to research in humans. One of the principal experimental models today is the fear-potentiated startle effect (Davis, 1998, 2006), which is used in both animals and humans. This effect occurs when a greater magnitude startle reflex is elicited by a loud noise occurring 3–4 seconds after an independently established light CS+ (excitatory conditioned stimulus) has been presented, compared to when the light is a novel stimulus. One model of unconditioned anxiety is the light-enhanced startle effect, which occurs when a bright light (a mild unconditioned stimulus, or US, for anxiety in rats that prefer the dark) has been turned on 5–20 minutes prior to a loud noise; this also
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48 Table 3.1
LEARNING: HUMAN AND NON-HUMAN APPLICATIONS Terminology
Construct
Definition
Current Abbreviation
Alternative Abbreviations
Unconditioned stimulus
A biologically significant stimulus or event; in this chapter, the Os of interest are drug related and include both direct effects of drug and the symptoms and signs of drug withdrawal
O
S, S∗, US
Conditioned stimulus
A previously neutral stimulus that obtains significance through association with an unconditioned stimulus
S
S, CS
Conditioned response
A response elicited by a conditioned stimulus associated with a biologically significant stimulus
R
R, CR
Pavlovian/classical conditioning
Learning in which individuals form associations between conditioned stimuli and unconditioned stimuli
S-O
S-S, S-S∗, CS-US
Instrumental/ operant conditioning
Learning in which individuals form associations between behavioral responses and outcomes
R-O
R-S
Habit learning
Learning in which stimuli come to evoke responses automatically even if the reinforcer is devalued
S-R
Incentive learning
Learning in which the value of a reinforcer is associated with certain stimuli or contexts, thus modulating the incentive value of Ss associated with the reinforcer
Occasion setter
A stimulus that signals whether a learned S-O association will hold in Pavlovian conditioning
Positive
Signals that O will follow S
S+
Negative
Signals that O will not follow S
S–
Discriminative stimulus
A stimulus that signals that a response will be reinforced in instrumental conditioning
Positive feature
A discriminative stimulus that indicates that responding will be reinforced
SD
S+
Negative feature
A discriminative stimulus that indicates that responding will not be reinforced
S∆
S–
Conditioned inhibitor
A conditioned stimulus that is associated with the absence of a biologically important stimulus. Unlike occasion setters and discriminative stimuli that are not related directly to the presence or absence of the unconditioned stimulus (O), conditioned inhibitors are directly associated with the absence of the O
S-
CS–
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results in increased magnitude of the startle reflex in rats (Walker & Davis, 1997). It is referred to as an unconditioned anxiety effect, because it does not extinguish as a source of increased startle either within or across multiple test sessions (Walker & Davis, 1997). In humans, a similar increase in startle amplitude can be induced by exposure to the dark (Grillon, Pellowski, Merikangas, & Davis, 1997). What is common to the fear and anxiety paradigms is the dependent variable (an enhanced startle response), but the paradigms reflect unconditioned anxiety when the longer duration light stimulus is used, and conditioned fear when the brief presentation of a light CS+ is used in fearpotentiated startle paradigms. Lesions of the basolateral amygdala substantially reduce or abolish behavioral and autonomic responses to conditioned fear stimuli in a fear-potentiated startle task (e.g., LeDoux, 1995). However, such lesions do not reduce a rat’s preference for covered arms in a plus maze, another commonly used measure of unconditioned anxiety in rat research (Treit, Pesold, & Rotzinger, 1993). In addition, lesions of the central nucleus of the amygdala also do not reduce the lightenhanced startle reflex (Davis et al., 1997). Conversely, lesions of the BNST have no effect on the fear-potentiated startle effect but do significantly reduce light-enhanced startle reflexes (see Grillon, 2008, for a review). Anxiety can also be a conditioned response evoked by long-duration CSs. For example, Waddell, Morris, and Bouton (2006) compared the effects of BNST lesions on aversive conditioning with short (e.g., 60 sec CS) or long (e.g., 600 sec CS) signals for shock. Prior to conditioning, some of the animals had received lesions of the BNST, while others received sham lesions. The BNST lesions had no effect on conditioning of fear with the short-duration CS, but for the long-duration CS, the BNST lesion significantly reduced conditioning of anxiety. Therefore, it appears that the BNST is not only involved in the expression of unconditioned anxiety as seen with the light-induced startle effects, but it is also involved in the conditioning of anxious apprehension (as with the 600 sec CS). Finally, another model of conditioned anxiety, to be discussed
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later, occurs when contextual cues are associated with unpredictable threat. Fear and Anxiety Conditioning Abnormalities in Anxiety Disorders
If fear and anxiety conditioning processes account in part for the development of various anxiety disorders, then individual differences in personality or past experiences may serve as diatheses and these should be detectable using classical fear or anxiety conditioning paradigms measuring the degree and speed of fear acquisition and extinction. The possibility of individual differences in acquisition (ACQ) and extinction (EXT) has been explored in both psychophysiological experiments comparing anxious and nonanxious individuals, and it has begun to be investigated in behavioral and molecular genetic studies. Ideally, a deeper understanding of these issues would greatly benefit from longitudinal prospective studies in which individual differences in conditionability of fear or anxiety could be used to predict which individuals would subsequently develop anxiety disorders following relevant learning experiences. Unfortunately, we are not aware of any such studies to date and so we will review highlights of the studies that do exist on individuals with several kinds of anxiety disorders. Conditioning Paradigms Used to Study Individual Differences
Two basic experimental designs have been employed in studies comparing conditioning in anxious and nonanxious groups (e.g., Lissek et al., 2005). In the first (simple conditioning), participants in both groups are first exposed to several CS-only trials (habituation) and are then subjected to repeated pairings of a neutral stimulus (CS) with an aversive stimulus (US). The development of a conditioned response (CR) is measured following the conditioning trials (or on nonreinforced test trials during acquisition) by measuring the CR to the CS presented alone and comparing it with responding during habituation; between-group differences are also examined. However, this simple conditioning paradigm is not ideal for measuring the amount of associative learning that has occurred because there
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is no control for possible sensitization (nonassociative) effects. In the second type of paradigm (discriminative conditioning), two CSs are used, one paired with the aversive US (the CS+) and one explicitly unpaired with the aversive US (the CS–). By using within-subject comparisons of responding to the CS+ and CS–, one can infer whether the two groups differ in developing discriminative responding to the CS+ and CS–. Such paradigms are superior to simple conditioning paradigms because they better control for possible sensitization effects that should accrue as much to a CS– as to a CS+. Discriminative conditioning paradigms also allow for at least indirect comparisons of inhibitory as well as excitatory conditioning. There are several possible ways in which individual differences in conditioning as a function of anxiety might operate. With simple conditioning paradigms, anxious individuals, relative to nonanxious individuals, might either acquire CRs more rapidly or acquire greater magnitude CRs (to either discrete CSs paired with USs, or to contexts paired with USs). Because it is known that stronger associations extinguish more slowly (e.g., Annau & Kamin, 1961), another possibility is that individual differences might also emerge in rates of extinction. Indeed, EXT might be retarded by the presence of anxiety regardless of whether ACQ responding has been affected. With discriminative conditioning paradigms the possibilities are more complex. One theory proposes that greater differences in discriminative responding to CS+s and CS–s should emerge because of superior conditioning to the CS+ in anxious individuals (e.g., Orr et al., 2000). These differences may also be manifested in slower rates of EXT. However, before considering this issue, we first describe several different theories reviewed by Lissek et al. (2005) that were developed to provide an explanation for differences in fear conditioning in anxious versus nonanxious individuals that are also relevant to predictions about potential group differences in discriminative conditioning. One theory, advanced by Orr and colleagues (Orr et al., 2000), specifically proposed that individual differences in conditionability to fear
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stimuli may help account for individual differences in anxiety. This theory predicted that anxious individuals would display stronger or more rapid acquisition of fear CRs than nonanxious individuals and therefore slower extinction. For example, GAD patients, compared with nonanxious individuals, showed similar ACQ but reduced EXT of skin conductance CRs to angry faces (Pitman & Orr, 1986). According to this theory anxious individuals, relative to nonanxious individuals, should also show a greater difference in discriminative responding to CS+s and CS–s. For example, in a study of trauma-exposed individuals, Orr et al. (2000) found that those who had developed PTSD showed greater discriminative responding during ACQ and EXT relative to those traumaexposed individuals who had not developed PTSD. Such results are consistent with the possibility that differences in diatheses for development of PTSD may be partly a function of differences in conditionability to fear cues, although a prospective design would be required to conclude this with certainty. By contrast, another theory proposed by Davis and colleagues (Davis, Falls, & Gewirtz, 2000) specifies differences in inhibitory, rather than excitatory, conditioning as most relevant to the emergence of anxiety disorders. Specifically, they proposed that it is a failure to inhibit the fear CR in the presence of safety signals (for example, a CS–, which signals the absence of the aversive US) that is one mechanism by which clinical anxiety may develop. Consistent with this proposal, some studies show that anxiety patients (relative to controls) exhibit greater fear-potentiated startle during the CS- (Grillon & Ameli, 2001; Grillon & Morgan, 1999). In other studies they showed higher magnitude electrodermal responses during the CS– (e.g., Orr et al., 2000) or greater subjective anticipatory anxiety during a CS– (e.g., Hermann, Ziegler, Birbaumer, & Flor, 2002). Thus, results using all three kinds of fear measures converge in suggesting that the anxiety patients showed smaller magnitude inhibitory CRs to a CS– than controls. However, none of these studies provide definitive direct support for this theory about differences in inhibitory learning as a function of
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anxiety status, which would require direct assessment of the inhibitory power of the CS– in the two groups. Empirical Status of These Theories
Lissek et al. (2005) reviewed and performed a meta-analysis on 20 studies comparing fear conditioning in participants with (n = 453) and without (n = 455) an anxiety disorder (including panic disorder, PTSD, and GAD) in order to assess the empirical status of these theories. It was found that anxious individuals had stronger fear CRs during ACQ and EXT compared to healthy controls, although both effect sizes were small. Interestingly, these effects were found primarily in studies using a simple conditioning procedure; patients and controls showed similar rates of ACQ and EXT when discriminative conditioning procedures were used. Similar discriminative conditioning in patients and controls suggests that, in addition to stronger excitatory conditioning, anxious individuals may be less able than nonanxious individuals to suppress a fear CR in the presence of safety cues (CS–s). In fact, a number of the reviewed studies using discriminative conditioning paradigms (e.g., Grillon & Morgan, 1999; Peri, Ben-Shakhar, Orr, & Shalev, 2000) found elevated CRs in anxious individuals to both CS+ and CS– stimuli. These findings are not consistent with the Orr et al. (2000) hypothesis (or the Orr et al. results with PTSD), but they are consistent with the Davis et al. (2000) hypothesis that anxious individuals are less able to inhibit fear responding in the presence of safety cues. Again, however, such a conclusion would require an independent direct assessment of the inhibitory power of the CS– in the two groups. Researchers have recently begun addressing this question using methodology specifically designed to experimentally distinguish excitatory from inhibitory conditioning. For example, Myers and Davis (2004) adapted a discriminative conditioning paradigm called conditioned discrimination (Wagner & Rescorla, 1972), to explicitly test the inhibitory power of a CS– in rats. In this paradigm (abbreviated AX+/BX–), the excitatory A and inhibitory B stimuli are conditioned independently of one another, in
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compound with a third stimulus X. After AX+ and BX– trials, A was excitatory, and B inhibitory. Winslow, Noble, and Davis (2008) found parallel inhibitory effects when a very similar paradigm was used in rhesus monkeys. Moreover, one study on individuals with no history of any diagnosable psychological disorder also successfully demonstrated the inhibitory effects of B using such a paradigm (Jovanovic et al., 2005). An even more recent study (Jovanovic et al., 2009) used this paradigm to examine fear inhibition in patients with fairly severe PTSD and found that they failed to show any significant fear inhibition (compared to those with mild PTSD or controls) on a compound trial. However, they were aware that a shock would not occur (i.e., they knew they were safe but they could not actually inhibit the response). It will be interesting to see whether other anxiety disorders are also characterized by failure to learn fear inhibition. In addition to the question of whether vulnerability to anxiety disorders reflects individual differences in either excitatory or inhibitory conditioning processes (or both), the question also remains as to whether individual differences in discrete-cue versus context conditioning, and fear versus anxiety conditioning, are relevant to the development of particular anxiety disorders. As discussed later, fear learning appears to be neurobiologically and behaviorally distinguishable from context conditioning (more akin to anxiety), which is the learning of associations between a US and its broader environmental context, rather than discrete cues upon which a US is contingent. If these two processes vary independently, individual differences in either or both may confer risk for specific anxiety disorders. Correlates of Individual Differences in Fear Conditioning: Personality, Neuroimaging, and Genes
If individual differences in either excitatory or inhibitory fear conditioning may act as diatheses for the development of anxiety disorders, it is sensible to ask what relationship they may have to other diatheses for anxiety. Personality factors such as neuroticism or high negative affectivity
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have long been known to predict the development of a range of both anxiety and depressive disorders (e.g., Clark, Watson, & Mineka, 1994; Hayward, Killen, Kraemer, & Taylor, 1998, 2000). In addition, in the past 10 years there has been an explosion of findings using functional neuroimaging techniques that have uncovered individual differences in responsiveness to threat and punishment cues, and progress is being made toward associating differential neural responsivity in critical brain regions with individual differences in trait anxiety and other personality factors (see Hariri, 2009, for a review, but also Vul, Harris, Winkielman, & Pashler, 2009, for a methodological critique of this literature). Moreover, very exciting (but increasingly contested) associations have been found between specific genetic polymorphisms and personality traits, as well as clinical anxiety and mood disorders. In this section, we review research that relates individual differences in fear conditioning with personality and neural activity, as well as discussing emerging research investigating the heritability of fear conditioning and the association of fear conditioning with specific genetic polymorphisms. To prepare readers for this discussion, we begin by briefly summarizing the current research on neuroimaging and genetics associated with threat responsiveness and anxiety. We focus in particular on polymorphisms in two particular genes, the serotonin transporter (5-HTT) and catechol-O-methyltransferase (COMT), which have received considerable attention in recent years for their putative relationships with trait anxiety, fear conditioning, and attentional processing of threat. Genetic and Neuroimaging Variation Associated with Anxiety: Serotonin Transporter Polymorphisms, Anxiety, and Amygdala Activation
In recent years, there has been tremendous excitement over the discovery of a possible relationship between anxious/depressive pathology and allelic differences in a variable repeat sequence of the promoter region of the serotonin transporter gene (5-HTT), particularly in relation to stressful life events. This gene encodes for a protein that regulates the reuptake of serotonin
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from the synaptic cleft, in turn affecting the synaptic concentrations of serotonin. Research has shown that having one or two short 5-HTT alleles, rather than two long alleles, is associated with as much as 50% less serotonin transporter availability, functionally increasing the synaptic concentration of serotonin (Heinz et al., 2000; Lesch et al., 1996). Initial work suggested that carriers of at least one short allele have higher levels of trait anxiety and neuroticism (Munafo, Clark, & Flint, 2004; Schinka, Busch, & Robichaux-Keene, 2004; Sen, Burmeister, & Ghosh, 2004), and increased risk for major depressive disorder after exposure to stressful life events (Caspi et al., 2003; Eley et al., 2004; Kaufman et al., 2004). With respect to the neuroanatomical correlates of these polymorphisms, healthy carriers of at least one short allele of the 5-HTT polymorphism, in comparison to those homozygous for the long allele, demonstrate greater limbic responsiveness to angry or fearful expressions (Bertolino et al., 2005; Hariri et al., 2002; Pezawas et al., 2005). Evidence also suggests an association between the presence of a short allele and stronger activation of the amygdala, together with greater coupling between the amygdala and ventromedial prefrontal cortex (vmPFC), to aversive images (Canli et al., 2005; Heinz et al., 2004). However, it is noteworthy that the analytic methodologies used in research relating neuroimaging results with self-reported mood and personality are presently under intense scrutiny (see Vul, Harris, Winkielman, & Pashler, 2009, for a pointed critique), so it is prudent to interpret such findings with caution. Enthusiasm over such findings is also currently waning in the light of several recent metaanalyses that call into question the relationship of the 5-HTT polymorphisms, particularly in interaction with life stress, with psychopathology and trait anxiety (Munafo, Durrant, Lewis, & Flint, 2009; Risch et al., 2009). However, as Monroe and Reid have argued (2008), the overwhelming majority of research on such gene– environment interactions has relied upon highly inconsistent measures of life stress across different studies, severely limiting the implications of meta-analyses on the literature to date. As this
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issue receives more attention, and the considerable research on life stress conceptualization and measurement is brought to bear on behavioral genetics research (Monroe, 2008), greater clarity will hopefully be brought to the role of this promising gene candidate. On a more positive note, there appears to be greater consistency in studies of the link between serotonin transporter genotype and amygdala activation (Munafo, Brown, & Hariri, 2008), although the authors of this meta-analysis and review warn that most studies to date are lacking in statistical power, rendering estimates of effect sizes premature. Genetic and Neuroimaging Variation Associated With Anxiety: COMT Polymorphisms, Anxiety, and Prefrontal Functioning
Another promising candidate genetic variant in anxiety is the catechol-O-methyltransferase gene (COMT), which encodes for an enzyme that degrades dopamine, epinephrine, and norephinephrine in the prefrontal cortex (Gogos et al., 1998; Tunbridege, Bannerman, Sharp, & Harrison, 2004), and hippocampus (Matsumoto et al., 2003). Variations in its functional polymorphism, COMT Val158Met, influence the levels of dopamine in these areas, with the Met allele associated with a third of the enzymatic activity of Val the allele in breaking down dopamine. Functionally, this results in the Met allele producing higher levels of synaptic dopamine in these brain areas. Although many contradictory findings have been published, preliminary evidence suggests that the Val158 allele is associated with inefficiency in cognitive control, whereas the Met allele has been related to anxiety related traits and disorders (Domschke et al., 2004; Enoch, Xu, Ferro, Harris, & Goldman., 2003; Stein, Fallin, Schork, & Gelernter, 2005). Personality and Temperament in Fear/ Anxiety Conditioning
As mentioned earlier, several stable temperamental and personality factors that are partially heritable have also been identified as diatheses for various anxiety disorders. One of these is the personality trait known as neuroticism or negative affectivity (Clark et al., 1994; Watson,
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Gamez, & Simms, 2005). In addition, trait anxiety—one of several facets of neuroticism— is also thought to be a vulnerability factor. Moreover, over the years numerous studies have shown that individuals high in trait anxiety show more rapid and stronger aversive conditioning (e.g., see Levy & Martin, 1981, for an early review; Lissek et al., 2005; Zinbarg & Mohlman, 1998). It is possible that such conditioning processes could be a mechanism through which high trait anxiety operates as a vulnerability factor for clinical anxiety disorders. In this regard it is also interesting to speculate that the partial overlap in heritability of anxiety disorders, such as specific phobias and panic disorder (e.g., Kendler et al., 1995), could be mediated by heritable differences in conditionability (see Bouton et al., 2001). Moreover, research on temperament in children indicates that young children with high levels of behavioral inhibition (a tendency to be shy, avoidant, and easily distressed by unfamiliar stimuli) in early childhood are at heightened risk for developing multiple specific phobias in childhood (Biederman et al., 1990), and social anxiety in adolescence when it is most likely to develop (e.g., Hayward et al., 1998; Schwartz, Snidman, & Kagan, 1999). Whether the effects of behavioral inhibition are mediated through differences in conditionability is not yet known, although this seems like a possibility worth investigating. Heritability and Genetic Findings in Fear Conditioning
There are several twin studies of other forms of learning, but we are only aware of one study that has specifically examined the heritability of fear conditioning. Hettema and colleagues (2003) investigated fear conditioning in 173 same-sex twin pairs (90 monozygotic and 83 dizygotic) using a standard discriminative conditioning procedure, in which pictorial stimuli (either fear-relevant [snakes and spiders] or fear-irrelevant [circles and triangles] stimuli) were paired with a mild electric shock US, measuring skin conductance (SCR) as the CR. Rates of habituation, acquisition, and extinction were all assessed in order to model the relative contributions of both associative and nonassociative processes.
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Moderate heritability was found for all three components, with additive genetic effects accounting for 34% to 43% of the total variance. Using structural equation modeling, they found that a two-factor model best explained the observed genetic variation in twin pairs. One factor accounted for variation in habituation, acquisition, and extinction (the nonassociative factor) and one factor accounted for variation in only acquisition and extinction (the associative factor). Interestingly, the investigators also found evidence that the heritability of associative fear learning using evolutionarily relevant stimuli, such as snakes and spiders, may be greater than that of fear-irrelevant stimuli, although statistical power was not sufficient to draw this conclusion with certainty. A recent and exciting genetic study investigated the specific genetic polymorphisms 5-HTTLPR and COMT for their effects on conditioned fear acquisition and extinction (Lonsdorf et al., 2009). Forty-eight college students donated blood for DNA extraction and genotyping and then underwent a discriminative fear-conditioning paradigm using facial stimuli as CS+s and CS–s. There were nine CS+ trials, each ending with a 10 ms shock, and nine CS- trials ending with no shock. Startle probes were presented after 4–5 seconds on six of nine CS+ trials and on six of nine CS– trials; SCRs were also measured but the most important dependent variable was startle potentiation. On the following day there were 18 presentations of both the CS+ and the CS–, neither followed by a US, to assess extinction performance. As shown in Figure 3.1, results were striking for startle potentiation, but not SCRs, as the dependent measure. Carriers of one or two short alleles of the 5-HTT gene showed significantly greater fear potentiation to the CS+ than did l/l homozygous carriers during acquisition, a pattern that persisted in extinction. By contrast, the two polymorphisms of the COMT Val158Met gene did not affect startle potentiation during acquisition. However, those with the homozygous met/met COMT Val158Met polymorphism showed much greater CS+ fear potentiation during extinction than those who were val-allele carriers. The participants who showed the most pronounced startle responding in extinction
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were those with the short allele(s) of the 5-HTT gene and the homozygous met allele of the COMT gene. These results suggest that polymorphisms in these two genes are differentially related to the ability to learn and maintain fear of aversive stimuli. More specifically, those with a short 5-HTT allele and two COMT met alleles are more likely to acquire fear of conditioned aversive stimuli, and less able to extinguish such learned responses. The additional finding that such results held only for the startle index of fear (and not for SCRs) is consistent with findings and ideas reviewed by Öhman and Mineka (2001) that there are two levels of fear learning: One is at a very basic emotional level and the other is at a cognitive level. Research shows that startle potentiation is a better index of a real emotional level of fear. SCRs, by contrast, are more an index of cognitive contingency learning. As Öhman and Mineka (2001) discuss, there are many examples in the literature of different parameters having selective effects on these two different indices of fear. Other Sources of Individual Differences in the Learning of Phobias and Other Anxiety Disorders Other Pathways to the Acquisition of Fear and Anxiety
So far we have only discussed traditional classical conditioning as a source of fear and anxiety. Yet not everyone developing phobias or other anxiety disorders seems to have undergone traumatic conditioning experiences. In many instances phobic fears instead may be acquired vicariously through simply watching another person (live or on a TV or movie screen) behaving fearfully with some object or situation. This is best illustrated by Mineka and Cook’s primate model of phobic fear acquisition from the 1980s (e.g., Cook & Mineka, 1991). They showed that laboratoryreared rhesus monkeys that were not initially afraid of snakes rapidly acquired an intense and long-lasting phobic-like fear of snakes after simply watching a wild-reared model monkey behaving fearfully on some trials when a toy or real snake was present and nonfearfully on other
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Startle blink magnitude (Difference from ITI; DT scores)
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Figure 3.1 Potentiation of startle-response magnitudes as a function of genotype and stimulus. Black
bars show the difference between magnitude of the startle response elicited during the CS+ (the conditioned stimulus coupled to the unconditioned stimulus) and magnitude of the startle response elicited during the intertrial interval (ITI); white bars show the difference between magnitude of the startle response elicited during the CS− (the conditioned stimulus never coupled to the unconditioned stimulus) and magnitude of the response elicited during the ITI. Results are shown for 5-HTTLPR genotype groups (a) during conditioning and (b) during extinction, for COMTval158met genotype groups (c) during conditioning and (d) during extinction, and for COMTval158met genotype groups within 5-HTTLPR s-allele carriers (e) during conditioning and (f) during extinction. Error bars represent standard errors. Asterisks indicate significant differences, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. (From Lonsdorf, T. B., Weike, A. I., Nikamo, P., Schalling, M., Hamm, A. O., & Öhman, A. 2009. Genetic gating of human fear learning and extinction: Possible implications for gene-environment interaction in anxiety disorder. Psychological Science, 20(2), 198–206).
trials when a neutral object was present. Vicarious acquisition of aversive conditioning has also been demonstrated in humans using psychophysiological responses (such as electrodermal responses) as the dependent variable (Green & Osborne, 1985). Several studies have also supported the influence of parental modeling on increasing children’s fears for at least 1 week
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after the modeling experience (e.g., Askew & Field, 2007; Gerull & Rapee, 2002). Evidence is also accumulating of modeling of social anxiety in families of those who have developed social phobia (e.g., Bruch & Heimberg, 1994; Rapee & Melville, 1997). For example, both mothers and their socially phobic offspring reported more social avoidance in the parents
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than seen in nonclinical control families. Moreover, parents of those who develop social phobia often seem to have discussed and thereby reinforced children’s avoidant tendencies in the context of threatening situations (e.g., Barrett, Rapee, Dadds, & Ryan, 1996). The Effects of Experiential Differences on Learning
In addition to genetic and personality differences having sometimes significant effects on conditioning, it is also well known that prior learning experience can have powerful effects on the acquisition, maintenance, and extinction of fear and anxiety (Mineka, 1985; Mineka & Zinbarg, 1996, 2006). Indeed, there are a multitude of individual experiential differences that affect the outcome of aversive learning experiences. For example, the amount of exposure an individual has had with a potential CS before encountering it paired with a US very much affects the outcome of the conditioning experience. This phenomenon in classical conditioning is known as latent inhibition and illustrates that familiar stimuli or situations result in weaker conditioning than do novel or strange objects or situations (e.g., Lubow, 1998; see Kent, 1997, for a naturalistic example). Moreover, Mineka and Cook (1986) showed that monkeys who first simply watched nonfearful monkeys behaving nonfearfully with snakes were immunized against later acquisition of a fear of snakes when they watched a fearful model behaving fearfully with snakes (which as discussed earlier ordinarily leads to strong and long-lasting conditioning). Finally, one study showed that rats initially exposed to escapable shocks later showed reduced fear conditioning both to a context and to a discrete CS relative to groups receiving no prior shocks or inescapable shocks (Baratta et al., 2007). Conversely, rats initially exposed to inescapable shocks later showed potentiated fear conditioning (see Chapter 6). Certain characteristics of a conditioning experience itself can also be important determinants of the level of fear that is conditioned. For example, the ability to escape (i.e., control) a traumatic experience dramatically reduces the magnitude of the level of fear that is conditioned
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to a CS+ relative to when the same intensity of trauma is inescapable or uncontrollable (e.g., Mineka et al., 1984; Mineka & Zinbarg, 1996, 2006). Animal research has also shown that social defeat (another uncontrollable stressor) leads to exaggerated fear-conditioned responses (e.g., Williams & Scott, 1989) as well as increased submissiveness to other conspecifics (e.g., Uhrich, 1938) as in social phobia. In addition, experiences that a person has following a conditioning experience may affect the strength and maintenance of conditioned fear. For example, the inflation effect, first discovered by Rescorla (1974), suggests that someone who acquired a mild fear following a minor aversive experience with a CS might be expected to develop a more intense fear when later exposed to a much more aversive experience (even though it was not paired with the CS). These few examples of experiential factors that very much influence the onset and maintenance of fears and phobias are more complex than suggested by earlier simplistic conditioning views, although they are altogether consistent with contemporary research and theory on learning (Mineka & Zinbarg, 1996, 2006). Selective Associations in Fear Learning
Finally, some sources of important differences in who acquires phobias and other anxiety disorders reside in the nature of the objects or situations that come to be paired with aversive experiences. Indeed, human and non-human primates seem to be evolutionarily prepared to rapidly associate certain kinds of objects or situations—such as snakes, spiders, water, and enclosed spaces—with frightening or unpleasant events (e.g., Mineka & Öhman, 2002; Öhman, 1986; Öhman & Mineka, 2001). This inborn tendency to rapidly associate certain objects or situations that once posed real threats to our early ancestors probably occurred because organisms that did so would have enjoyed a selective advantage in the struggle for existence. In the social realm, the kinds of cues that are most likely to become the sources of fears are cues that signal dominance and aggression from conspecifics such as angry or threatening faces (e.g., Mineka & Öhman, 2002; Öhman & Mineka, 2001).
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A large amount of experimental evidence now supports this preparedness theory of phobias. One very important series of experiments using human participants conducted by Öhman and his colleagues has found that fear is conditioned more effectively to fear-relevant stimuli (slides of snakes, spiders, or angry faces) than to fearirrelevant stimuli (such as slides of flowers, geometric objects, or happy faces) that are paired with mild electric shocks (e.g., see Öhman & Mineka, 2001, for a review). Indeed, even very brief subliminal presentations of such fearrelevant stimuli (but not fear-irrelevant stimuli) are sufficient to evoke conditioned responses (including activation of the amygdala) either when presented in acquisition or in extinction (Carlsson et al., 2004; Öhman, Carlsson, Lundqvist, & Ingvar, 2007). This subliminal activation of responses to phobic stimuli may help to account for certain aspects of the irrationality of phobias because the fear may arise from cognitive structures not under conscious control (e.g., Öhman & Mineka, 2001). Another series of experiments on observational conditioning showed that lab-reared monkeys can easily acquire fears of fear-relevant stimuli such as toy snakes or toy crocodiles but not of fear-irrelevant stimuli such as flowers or toy rabbits (Cook & Mineka, 1989, 1990). Thus, both monkeys and humans seem to selectively associate certain fear-relevant stimuli with threat or danger. Moreover, the lab-reared monkeys had no prior exposure to any of the stimuli involved (e.g., snakes or flowers) before participating in these experiments. Thus, the monkey results support the evolutionarily based preparedness hypothesis even more strongly than do the human experiments. For example, human participants (unlike the lab-reared monkeys) might show superior conditioning to snakes or spiders because of ontogenetic factors such as preexisting negative associations to snakes or spiders, rather than because of evolutionary factors. These results also demonstrate that selective associations occur not only with mild and transient conditioning as seen in the human experiments but also with intense and longlasting phobic-like fears seen in the monkey experiments (Mineka & Ohman, 2002).
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Contextual Control of Conditioned Responses
Conditioned anxiety effects have also been observed using contextual cues associated with threat. Specifically, it has long been known that when unsignaled shocks are presented to rats in a distinctive environment, the environment itself acquires the capacity to elicit conditioned anxiety. Such contextual conditioning is important for several reasons. One reason why contextual conditioning is important stems from demonstrations that patients with PTSD and panic disorder show enhanced contextual modulation of baseline startle in experiments in which they know a fear-potentiated startle paradigm with an explicit threat cue will be included at some point (i.e., including delivery of shock) (Grillon & Ameli, 1998; Grillon & Morgan, 1999). However, anxiety-disordered individuals in these studies did not show exaggerated fear responses to explicit cues for imminent threat (as would be evidenced by greater fear-potentiated startle— that is, phasic fear to explicit cues) relative to controls. Thus, at least for people with panic disorder or PTSD, this elevated contextual modulation of startle seems to represent heightened anxiety in contexts in which something threatening may occur, but this is not accompanied by greater fear to more proximal discrete cues for threat (relative to controls). Very similar results have also been observed in children at familial risk for anxiety disorders (having a parent with an anxiety disorder) who also show enhanced contextual modulation of startle, but normal fear-potentiated startle (Grillon, Dierker, & Merikangas, 1998). Finally, individuals with high levels of neuroticism—a risk factor for most anxiety disorders—show a very similar pattern of results (Craske et al., 2009), that is, enhanced contextually modulated startle, but normal fearpotentiated startle relative to those with low or medium levels of neuroticism. Contextual conditioning is also important because of the critical role it plays in reinstatement of fear to a CS+ following the full or partial extinction that occurs when the CS is no longer followed by the US. Reinstatement is important in part because it may underlie the fluctuating
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course of symptoms often seen in anxiety disorders (e.g., Bouton et al., 2001; Mineka & Zinbarg, 1996). Reinstatement occurs when animals or humans whose fear of a CS has been extinguished show a return of that fear (“reinstatement”) after one or a few exposures to the US alone (not paired with the CS) (Rescorla & Heth, 1975). Subsequent work showed that reinstatement only occurs if the US is presented in the same context as where testing for reinstatement is to be conducted (e.g., Bouton, 1984). According to this and other research by Bouton and colleagues, when the reinstating US is presented, the animal must associate it with the current context. The presence of that contextual danger when the CS is next presented is thought to trigger fear of the previously extinguished CS. Moreover, evidence from Bouton’s lab has also confirmed the prediction that lesions of the BNST significantly attenuate reinstatement of fear, presumably because they blocked or reduced contextual conditioning of anxiety that would ordinarily occur with the reinstating US (e.g., Waddell et al., 2006). Unpredictability and Anxiety
Unpredictable aversive events have long been known to be more stressful than predictable aversive events, and exposure to unpredictability has been hypothesized to play a role in the development of panic disorder, GAD (with more minor events), and PTSD (with more severe events) (Mineka & Zinbarg, 1996, 2006). The most widely cited hypothesis offered to explain these effects is the safety-signal hypothesis (e.g., Seligman, 1968; Seligman & Binik, 1977). The idea is that when organisms are presented with signaled (predictable) aversive events they not only know when the event will occur (during the CS+) but also when the event will not occur (when the CS is not present—a safety signal). Safety signals allow them to relax and feel safe. By contrast, organisms exposed to unpredictable aversive events have no knowledge of when the threatening events will occur, or when they can relax and feel safe, and thus are in a state of chronic anxiety in this context. Early studies in rats used both stress-induced ulceration paradigms (Weiss, 1971) and conditioned suppression paradigms (Seligman, 1968).
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Recently this work has been replicated and extended using other measures of anxiety in human participants. For example, Grillon et al. (2004) modeled this situation in a human laboratory with normal participants who all experienced three conditions: one in which predictable shocks were given, one in which unpredictable shocks were given, and one in which no shocks were given. Results clearly showed that in the unpredictable shock condition participants showed both greater startle magnitude and higher subjective anxiety than in the other two conditions. Other studies by Grillon and colleagues have shown that in some situations what may be most important in determining contextual levels of anxiety is whether aversive stimuli are perceived to be predictable rather than whether they actually are predictable, with more conditioning to the context when the participants do not detect the CS-US contingency. So, for example, several studies have shown that participants in a discriminative conditioning procedure who failed to become aware of the CS-US contingency showed more contextual conditioning (i.e., potentiation of baseline startle responding in the startle context) than participants who were aware of the contingency. In the latter case they showed enhanced startle to their CS+ rather than more generalized contextual conditioning (Grillon, 2002). In one ingenious experiment by Grillon and colleagues (2006), participants were passively exposed to a virtual reality environment with three virtual rooms (one with no shock, one with predictable shocks, and one with unpredictable shocks). Participants later showed potentiated startle in the unpredictable context relative to the other two contexts, presumably because of greater contextual conditioning with the unpredictable shocks. Furthermore, when allowed to freely enter the three different rooms to find monetary rewards, there was a strong preference for the no-shock context and avoidance of the unpredictable context. Recently Grillon and colleagues extended the study of unpredictability to patients with panic disorder, PTSD, and GAD to determine whether they show elevated reactivity to unpredictable aversive events. In one study on panic disorder,
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FEAR CONDITIONING AND ATTENTION TO THREAT
Grillon, Lissek et al. (2008) found that panic patients showed elevated contextually modulated startle when startle probes were delivered in a context where naturalistic unpredictable aversive events sometimes occurred (such as a white noise or a female scream) relative to a neutral context; startle responding in a predictable threat condition was intermediate. The healthy controls did not show this elevated startle potentiation in the unpredictable relative to the neutral condition. In another study the same investigators used nearly an identical paradigm but compared patients with PTSD, GAD and healthy controls (Grillon, Pine, Lissek, Rabin, & Bythilingam, 2009). The PTSD group showed elevated startle in the unpredictable context relative to the neutral and predictable contexts, but the other two groups showed elevated startle only in the predictable context. Thus, patients with both panic disorder and PTSD showed elevated reactivity to unpredictable aversive events relative to healthy controls and to patients with generalized anxiety. Summary
In this section we have briefly reviewed multiple sources of individual differences in associative learning processes relevant to furthering our understanding of diathesis-stress perspectives on anxiety disorders. Thus, we discussed how high trait anxiety and clinical anxiety both affect acquisition and extinction of conditioned fear using simple conditioning paradigms, whereas discriminative conditioning does not seem to be affected—possibly because anxiety is associated with poor inhibitory conditioning. We also reviewed results of several studies suggesting that fear conditioning is moderately heritable and that individuals who have different combinations of polymorphisms of the 5-HTT and COMT genes show more or less robust fear conditioning, and faster or slower fear extinction. In addition, evidence that many individual experiential differences occurring before, during, or following real or vicarious conditioning trials affect the outcome of those conditioning trials was also reviewed. Finally, some sources of individual differences reside in the nature of the objects or situations that are associated with
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aversive consequences rather than in the individuals themselves as seen in research on selective associations. We also reviewed some of the evidence showing how individuals with certain anxiety disorders (PTSD and PD) show enhanced contextual conditioning in situations associated with uncertain threat and greater reactivity to unpredictable aversive events than do normal controls.
THREAT-RELEVANT ATTENTIONAL BIASES Although early models of anxiety disorders were primarily behavioral, many contemporary psychological models of the etiology and treatment of mood and anxiety disorders are more cognitive in nature. They posit that biased modes of processing affectively valenced material determine important characteristics of the emotional pathology (Beck, 1976; Eysenck, 1992; Williams, Watts, MacLeod, & Mathews, 1997). Within this tradition, attentional biases for emotional stimuli have been studied for over two decades and remain an exciting source of theoretical and applied questions in psychopathology. Most research on attentional biases has focused on their association with clinical anxiety and mood disorders, elevated trait anxiety, and dysphoria (Bar-Haim et al., 2007; Mogg & Bradley, 1998; Williams et al., 1997). Indeed, attentional biases for threat have been central to several etiological models of anxiety (Mathews & Mackintosh, 1998; Mogg & Bradley, 1998; Williams et al., 1997). A smaller body of research has shown the following: (1) attentional biases in individuals at risk for emotional disorders (Joormann, Talbot, & Gotlib, 2007); (2) biases may predict negative reactions or responses to stress (MacLeod & Hagan, 1992; Mogg, Bradley, & Hallowell, 1994); (3) biases may persist beyond remission of clinical disorders (e.g., Joormann & Gotlib, 2007). Particularly interesting from a learning perspective are studies suggesting that bias modification—the use of attentional training tasks to change response patterns—may reduce negative responses to stress and thereby indirectly influence mood states (e.g., Amir et al., 2009; Dandeneau et al., 2007; MacLeod et al., 2002).
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Many of the great challenges in this research are related to developing a deeper understanding of precisely what attentional phenomena are associated with mood and anxiety pathology, the causes of these associations, their consequences for other cognitive processes, and the etiology/ maintenance of clinical disorders. MacLeod, Mathews, and colleagues, who were responsible for the first identification of the association between trait anxiety and biased attention for threat (Mathews & MacLeod, 1985), have continued to explore the relationship between biased attention to threat and stress, as well as the effects of modifying attentional biases on reactions to stress (MacLeod et al., 2002; MacLeod, Soong, Rutherford & Campbell, 2007; See et al., 2009). Drawing from classic attentional models (Posner & Petersen, 1990), Fox and others have sought to understand the specific components and time courses of emotion-relevant attentional biases (orientation/shifting, disengagement). The work of Öhman and colleagues (e.g., Öhman & Mineka, 2001) has focused on attentional processing of evolutionary fear-relevant stimuli and their relationship to the development of clinical phobias. Moreover, great strides in understanding the neural and genetic underpinnings of threat perception and attention in trait anxiety/neuroticism have been made by several research groups (e.g., Canli et al., 2005; Hariri et al., 2002). In this section, we first review the experimental paradigms typically used in attention and emotion research. Next, we summarize the most recent findings in attentional bias research, with particular focus on the concepts with potential relevance to conditioning processes. Methods of Attentional Bias Assessment
Attentional biases have traditionally been assessed using one of several experimental paradigms, including most prominently the emotional Stroop task, the dot-probe task, the exogenous spatial-cueing task, and the visual search task. All of these tasks share a common logic: Attentional biases are inferred by comparing an individual’s reaction times (RTs) on critical trials involving emotionally valenced stimuli to trials involving neutral stimuli (stimuli are variably words, photographs, or schematics of
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emotional faces, objects, or scenes). That is, individual biases are inferred when mean or median RTs to trials with emotionally valenced stimuli differ significantly from those to trials with neutral stimuli. Researchers have debated and tested which of these paradigms best captures the biased attentional processing associated with psychopathology. In the following sections, we briefly describe the most frequently employed paradigms, with particular emphasis on those that have been used in conditioning paradigms. Emotional Stroop Task
The emotional Stroop task (see Williams, Mathews, & MacLeod, 1996, for a review), although widely used in early research on attentional biases, has received substantial criticism as a pure measure of selective attention. In this task, participants are required to name the color that a word is printed in; trials in which the meaning of the word is emotionally valenced versus those in which the meaning is neutral are compared. Biased attention toward emotional words is inferred by slower reaction times to the emotional versus neutral words. Several researchers (e.g., Algom, Chajut, & Lev, 2004; Baldo, Shimamura, & Prinzmetal, 1998) have indicated that the emotional Stroop task represents a “generic slowdown,” or response bias, to emotionally valenced stimuli rather than a selective attention mechanism. Despite criticisms leveled against the emotional Stroop task, it continues to be used, and several of the conditioning studies discussed in the next section employ the measure. Dot-Probe Task
In the past 10 years, the dot-probe detection task (MacLeod, Mathews, & Tata, 1986) has emerged as the most widely used paradigm for assessing selective attentional biases. In the most common form of the task, trials consist of a neutral and a valenced stimulus briefly presented (typically 15–1,250 ms) on each side of a central fixation cross displayed on a computer monitor, followed immediately by a small dot probe that replaces one of the stimuli. The probe remains on the screen until participants indicate, by pressing a keyboard button, which side of the screen the
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probe appears on, and their latency to respond from the time of the dot probe onset is recorded as their reaction time. If a participant responds more quickly, on average, to probes replacing valenced than neutral stimuli, it is inferred that he or she was attending to the valenced stimulus at the time of the probe onset more frequently than to the neutral stimulus. Spatial-Cueing Task
Another task that is increasingly used is the exogenous spatial-cueing task, which was adapted by Fox and colleagues (2001) from the classic Posner cueing paradigm (Posner, 1980), in order to differentiate whether attentional biases associated with anxiety were due to facilitated orientation toward threat stimuli versus delayed disengagement from threat. Trials consist of a presentation of a single cue stimulus presented on the left or right side of a central fixation cross on the screen for a brief period of time (typically 200–600 ms), followed by a screen with only the fixation cross remaining, followed by a small target circle to which participants are required to respond with a spacebar press. Of these experimental trials, most (60%–75%) are valid (i.e., the target appears in the same location as the cue), whereas the remainder are either invalid (i.e., the target appears in the opposite location to the cue) or catch trials (no target appears after the cue). Equal numbers of trials are allotted to each cue stimulus type, and any particular stimulus has an equal probability of appearing in the left- and right-hand side boxes, and an equal probability of being followed by a valid, invalid, or catch trial. By comparing within-subject reaction times to threat versus neutral stimuli on validly cued trials, it is possible to infer that the speed of orientation is affected by the stimulus valence. By comparing reaction times to threat versus neutral stimuli on invalidly cued trials, it is possible to infer that the speed of disengagement is affected by the stimulus valence. At the longer stimulus exposure intervals (600 ms), this paradigm is used to assess the phenomenon of inhibition of return, in which all individuals are slower to reengage their attention toward a location or object that has been recently attended. Individual differences in the speed with which
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participants respond to a target that has been validly cued by a threatening versus neutral stimulus over this longer time frame are indicative of valence-dependent inhibition of return. Visual Search Task
The visual search task has been used less frequently in attentional bias research in clinical and trait anxiety (e.g., Fox et al., 2000; Öhman, Flykt, & Esteves, 2001; Tipples, Young, Quinlan, Broks, & Ellis, 2002), but it has been advocated as a more ecologically valid attention task than those discussed earlier (Weierich, Treat, & Hollingworth, 2008). It also has the advantage of a well-established body of normative research in visual cognition. In this task, participants view an array of visual stimuli on a computer screen and indicate whether a particular “target” stimulus is present among other distracter stimuli. The speed with which the target stimulus is detected is taken to indicate the efficiency with which attention is directed toward that stimulus. In anxiety research, arrays typically consist of fear-relevant and fear-irrelevant stimuli, and individual differences are assessed in the speed with which he or she is able to detect threatening stimuli among neutral arrays versus neutral stimuli among threatening stimulus arrays. Current Theories and Open Questions in Attentional Bias Research
In general, researchers have found statistically significant within-subject differences to valenced and neutral stimuli primarily in participants with clinical anxiety or depression, or with high levels of trait anxiety or depressive symptoms. The majority of these effects have been found among individuals with anxiety disorders using threatening stimuli and shorter dot-probe or spatial-cueing task stimulus presentations (≤500 ms). Depression-related attentional biases (generally for depression-relevant stimuli, such as sad faces), by contrast, have only been consistently reported more recently, and only at exposure durations of greater than 1,000 ms (see Mogg & Bradley, 2005, for an overview). However, there are several exceptions to the failure to find biases in nonanxious, nondepressed individuals.
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Several studies using dot-probe tasks have shown that at shorter exposure durations (100 ms), all individuals attend toward highly threatening stimuli (e.g., Cooper & Langton, 2006; Koster et al., 2005), while only anxious individuals continue to attend toward the threatening stimuli at exposure durations near 500 ms. Moreover, at least one study (Wilson & MacLeod, 2003) demonstrated that individuals both high and low in trait anxiety demonstrate a bias toward highly threatening angry faces, whereas only highly trait-anxious individuals demonstrate a bias toward moderately threatening faces. In addition, evidence has increasingly mounted for the notion that nonanxious individuals selectively attend away from mild to moderately threatening stimuli at longer exposure durations (see Bar-Haim et al., 2007; Frewen et al., 2008, for meta-analyses). Such findings point toward a rather complex interaction between individual differences in anxiety and depression, stimulus valence intensity, and stimulus exposure duration. As mentioned previously, several models of attentional biases in anxiety have been articulated by researchers (Mathews & Mackintosh, 1998; Mogg & Bradley, 1998; Williams et al., 1997). In this section, we highlight the most important theoretical issues that researchers have tackled experimentally. Time Course of Attentional Biases: Orientation, Disengagement, and Avoidance
A recent debate in attentional bias research has concerned resolving apparently conflicting findings regarding whether high trait anxiety is associated with more rapid orientation toward threat or delayed disengagement from threat. In addition, there has also been related debate about whether initial attentional engagement with threat in trait anxiety is followed by avoidance of threat at longer stimulus exposure intervals. For instance, Mogg and Bradley (1998) argued that highly trait-anxious individuals direct their attention more rapidly toward threat, but later avoid it, resulting in a failure to habituate normally to threat-relevant stimuli. By contrast, Fox and colleagues (Fox, Russo, Bowles, & Dutton, 2001; Fox, Russo, & Dutton, 2002) demonstrated
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with the spatial-cueing task described earlier that highly trait-anxious individuals do not actually appear to orient more quickly toward a singly presented threatening cue, but they do tend to dwell on such stimuli longer than do low traitanxious individuals. Other researchers have found additional evidence for the delayed disengagement, or sustained attentional maintenance, hypothesis (e.g., Batty, Cave, & Pauli, 2003; Salemink, van den Hout, & Kindt, 2007). By approaching the literature from a more rigorous vision science perspective, Weierich, Treat, and Hollingworth (2008) have provided an excellent theoretical synthesis of these apparently disparate findings—that is, whether traitanxious individuals orient their attention more quickly toward threat versus whether they take longer to disengage from it, and whether these phenomena are followed by attentional threat avoidance. Their analysis is based on conceptual clarification of covert and overt attentional processes, close evaluation of the timescale at which biases are assessed, and differentiating between paradigms that require attention to a single stimulus versus competition between two stimuli (e.g., the exogenous cueing task, which uses a single stimulus, versus the dot-probe task, which presents two stimuli). In summary, they point out that these two views are compatible if it is assumed that threatening stimuli are more likely to be selected as targets of attention when in competition with other stimuli, but that the speed of covert attentional shifts toward stimuli is not influenced by threat relevance. That is, highly trait-anxious individuals may be more likely to attend to an angry face when it is presented together with a neutral face and be more likely to dwell upon it longer, but they are not faster than low trait-anxious individuals to covertly direct their attention toward the angry face. Moreover, evidence for overt and covert avoidance following disengagement from threat stimuli comes from studies assessing individual differences at longer stimulus exposure durations, particularly in eye-tracking experiments or spatial-cueing tasks showing interrupted inhibition of return among anxious individuals (Calvo & Avero, 2005; Fox et al., 2002; Pflugshaupt et al., 2005).
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Trait Anxiety, Threat Responsiveness, and the Amygdala
Several comprehensive theories of attention and anxiety (Mathews & Mackintosh, 1998; Mogg & Bradley, 1998) have argued that the perceived intensity of a threat cue mediates the relationship between trait anxiety and attentional biases. Evidence for this has been mixed, but it receives considerable support from the studies described in the next section on attentional bias and conditioning. Although explicit measures of selfreported threat intensity do not appear to be related to trait anxiety (Wilson & MacLeod, 2003), neuroimaging data increasingly suggests that trait anxiety is correlated with amygdala reactivity to emotional stimuli, even when presented subliminally (e.g., using backward masking and <15 ms stimulus exposure durations) (e.g., Etkin et al., 2004; Etkin & Wager, 2007). In further support of this hypothesis, amygdala reactivity during an attentional dot-probe task has also been found to be associated with behavioral measures of biased attention toward threat (Amin, Constable, & Canli, 2004; Armony & Dolan, 2002; Monk et al., 2004). As mentioned earlier, a novel neural-network model of attentional biases for threat versus positive stimuli using the dot-probe task has recently been published (Frewen et al., 2008). This model explicitly incorporates intensity appraisal, among other factors, in its connectionist architecture. According to the Frewen et al. model, sufficient levels of perceived threat should receive preferred attention from all individuals. Thus, if trait-anxious individuals are more sensitive to threat stimuli, their threshold for directing attention toward a threatening stimulus should be lower than that for low trait-anxious individuals. It bears mentioning that a central assumption of this approach is that in order for low traitanxious individuals to selectively attend away, and high trait individuals to selectively attend toward, mildly to moderately threatening stimuli, it is necessary to introduce the idea that attention biases also have the normative function of orienting the organism toward potentially rewarding stimuli. Attention to threat would represent, in this model, an interruption of the
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normal operating mode. This notion dovetails nicely with theories of fear and anxiety discussed earlier (Quinn & Fanselow, 2006), which posit that the imminence of environmental threat serves to cue specific behavioral responses representing an interruption of the normal operating mode of reward-seeking behavior. Genetic and Neuroanatomical Correlates of Attention to Threat
Research on the genetics of attentional threat biases is only beginning to emerge, but recent publications have shown promising results that are consistent with the genetic findings on trait anxiety and anxiety disorders in general. As discussed earlier, behavioral genetics research has implicated both the COMT Val158 Met and 5-HTT promoter polymorphisms in reactivity in limbic and prefrontal areas of the brain, as well as anxiety and depression. To our knowledge, few published experiments have compared attentional biases for threat among individuals varying in one of these polymorphisms. In one experiment, Beevers, Gibb, McGeary, and Miller (2007) compared attentional biases for words related to anxious and dysphoric emotional states, using a standard dot-probe task, among groups of psychiatric inpatients who differed in the 5-HTT promoter polymorphism. Carriers of at least one short 5-HTT allele (the polymorphism associated with anxiety and depression) showed stronger attentional biases for anxious word stimuli as compared to carriers of two long alleles. In another recent study, Beevers and colleagues (2009) found that healthy individuals homozygous for the short allele, as well as those with a long allele variant (rs25531) that functions like a short allele, showed greater latency to disengage attention from sad, happy, and fearful faces than did individuals with the other allelic variations. Finally, Fox, Ridgewell, and Ashwin (2009) found that individuals with two long alleles (those at lowest risk for anxiety and depression) showed attentional biases away from negative and toward positive affective material. Further evidence for a possible link between genetics and attentional biases comes from the “neural endophenotype” approach, which compares neuroimaging patterns in behavioral tasks
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among preselected individuals varying in theoretically significant genotypes. Advocated by Canli (2008) and colleagues, this approach has also provided considerable insight into the mechanisms by which the phenotype of trait anxiety might relate to the phenomenon of biased attentional processing of threat. For instance, the COMT Val158 Met polymorphism has been implicated in individual differences in various cognitive functions, including attentional processes (Goldberg & Weinberger, 2004; Heinz & Smolka, 2006; Winterer & Goldman, 2003). In one functional magnetic resonance imaging (fMRI) study (Smolka et al., 2005), participants with one or more Met alleles had greater activation in limbic, prefrontal cortical regions, and regions associated with visuospatial attention when viewing negative, but not neutral or positively valenced images. Even more promising findings have been reported in regard to the 5-HTT polymorphism. As discussed earlier, presence of a short 5-HTT allele is associated with increased amygdala response to emotional faces relative to a visuospatial control task (Hariri et al., 2002). Moreover, Canli and colleagues (Canli et al., 2005; Canli et al., 2006) have provided evidence using an emotional Stroop task that greater amygdala activation to threat, relative to neutral stimuli, is associated with the presence of a short 5-HTT allele. Although this research is in its infancy, the published findings converge upon the notion that allelic variations among genes implicated in anxiety and depression may also be involved in attentional biases for emotional material. Further research is needed, however, to clarify the underlying mechanisms of these associations. Summary
Over the past 20 years, the question of how anxiety influences attention, specifically differential attention toward valenced stimuli, has received a great deal of interest and experimental investigation. In this section, we have reviewed the most recent findings in this area, which have had the benefit of several new meta-analyses, critical methodological reviews, and a computational model. In short, it can be said that anxious, relative to nonanxious, individuals (1) do not orient
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toward threat stimuli more quickly, but they are more likely to select them as objects of attention relative to neutral stimuli, (2) tend to dwell upon threat stimuli for longer than neutral stimuli, and (3) are more likely to avoid reengaging with threat stimuli after disengagement than neutral stimuli. Recent neuroimaging and genetics findings in attentional biases have been largely consistent with the broader body of research on anxiety and threat processing, with associations found between biased attention for threat and amygdala reactivity, as well as specific genetic polymorphisms. Thus, this continues to be an exciting area worthy of further investigation.
RELATING FEAR CONDITIONING AND ATTENTIONAL BIASES As the previous sections have described, both fear/ anxiety conditioning and attentional biases for threatening stimuli have been implicated in the etiology and maintenance of anxiety disorders. Only recently, however, have researchers begun to assess how these two anxiety-related processes might relate to one another. To date, the published studies we have identified have focused on whether or how newly conditioned fear CSs may influence attention in normal (average traitanxious) participants. Here, we review the current body of research on the effect of fear conditioning on attentional bias for threat, research which stems from several theoretical perspectives. Many of these studies have been designed to explore the effect that conditioning of fear has upon attention to threat in normal (average trait-anxious) individuals, although the specific hypotheses being tested vary as will be described. The majority of these studies have a common design: Participants first undergo a discriminative conditioning procedure using visual stimuli (either geometric figures or pictorial face stimuli) and an aversive US (white noise or mild electrodermal shock). They then complete an attentional bias task that tests whether the CS+ has indeed acquired the capacity to elicit a greater attentional bias than the CS–. However, as will be described later, successful conditioning of fear sufficient to elicit attentional biases has also been obtained by interspersing the conditioning
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and attentional bias assessment phases, for example, by administering CS+–US and CS-–no US trials on intermixed blocks during the attentional bias task. Conditioned Fear and Attentional Shifting and Disengagement in Nonanxious Individuals
In two early studies, Stormark and colleagues (Stormark & Hugdahl, 1996; Stormark, Hugdahl, & Posner, 1999) used Posner’s (1980) exogenous spatial-cueing task to measure differential orientation (responding on validly cued trials) and shifting (responding on invalidly cued trials) following a discriminative conditioning paradigm in which certain highlighted rectangles (CS+) were explicitly paired with aversive white noise bursts (US), while other rectangles were presented with no noise (CS–). Differential conditioning to the CS+ was confirmed in the conditioning group via electrodermal responses. Results showed that the conditioning group detected invalidly cued CS+ targets more quickly than invalidly cued CS– targets, indicating that participants shifted attention away from fearinducing stimuli more quickly than from neutral stimuli. No effect of discriminative conditioning was found for validly cued trials. These results suggest that nonanxious individuals do not tend to differ in orientation speed toward threatening versus nonthreatening stimuli, but they do appear to disengage from threat stimuli more quickly than neutral stimuli. However, it is important to note that this study used a relatively long stimulus exposure duration (600 ms). The findings provide support for the notion, described earlier, that nonanxious individuals may preferentially avoid conditioned fear stimuli; furthermore, the findings more specifically isolate this phenomenon to the disengagement stage of attention processing. Armony and Dolan (2002) adopted a somewhat different approach by combining the conditioning and attentional bias paradigms into a single task from the perspective of a participant. They compared the neural responses and reaction times of six healthy volunteers in an fMRI paradigm using an angry face CS+ paired with an aversive white noise, and an angry face
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CS– not paired with white noise. Trials consisting of a random presentation of (a) the CS– alone, (b) the CS+ alone, or (c) the CS+ with the US were followed immediately by a visual probe on either side of the screen. These conditioning trials were interspersed with typical dot-probe trials (using a 50 ms stimulus exposure duration) in which the CS+ and CS– were presented together, followed by a probe replacing either the CS+ (congruent) or CS– (incongruent trials). Participants were slower to respond to probes following incongruent trials than congruent trials, demonstrating the typical dot-probe bias effect. No reaction-time differences were observed when either the CS+ or CS– was presented singly. Thus, this study demonstrated that nonanxious individuals, at very short stimulus exposure durations, may indeed preferentially attend toward stimuli having acquired threat value from conditioning. Koster et al. (2004) followed this combinedtrials approach, using an exogenous spatialcueing task in which CSs were presented for 200 ms, rather than the dot-probe task employed by Armony and Dolan (2002). This task choice allowed Koster et al. to determine which aspects of attention were influenced by the acquisition of differential fear conditioning. Conditioning trials consisted of a white noise US paired with the CS+ and a neutral tone stimulus as a control to be paired with the CS–. Results indicated that the CS+ facilitated responding on validly cued trials and slowed responding on invalidly cued trials, suggesting that a signal of immediate threat relevance both captures attention more readily and holds it for longer than does a signal of safety in normal participants. Although these results appear to conflict with those of Stormark and colleagues described earlier, it is important to note that both the Armony and Dolan (2002) and Koster et al. (2004) studies used very short stimulus exposure durations. Thus, taken together, the studies suggest that nonanxious individuals appear to orient their attention more quickly toward threat-conditioned stimuli and disengage from them more slowly over very brief time courses, but that over a longer time course may show attentional avoidance of threat.
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In a series of replications and extensions of these findings, Koster and colleagues (Koster, Crombez, Van Damme, Verschuere, & De Houwer, 2005; Van Damme, Crombez, Hermans, Koster, & Eccleston, 2006; Van Damme et al., 2004) also found the facilitated attentional capture and delayed disengagement observed during the acquisition phase diminished during an extinction phase, in which neither the aversive nor neutral USs were presented. Further evidence for the sensitivity of attentional biases to extinction processes can be found in an interesting study (Kelly & Forsyth, 2007) that investigated attentional biases in an observational fear-conditioning paradigm including acquisition and extinction. Moreover, as shown in Figure 3.2, in a paradigm using an aversive shock US (Van Damme et al., 2006), attentional biases to threat signals reemerged after extinction in a group undergoing reinstatement of the initial contingency, but not in a control group that did not undergo reinstatement conditioning.
Reinstatement group Control group 40
Attentional bias index (ms)
35 30 25 20 15 10 5 0 –5 –10 –15 –20 Baseline
Acquisition
Extinction Reinstatement
Figure 3.2 Mean and standard error of attentional bias index (difference scores of cue-validity index for CS+ minus cue-validity index for CS–) as a function of group (reinstatement/control) and experiment phase (baseline/acquisition/ extinction/reinstatement). (From Van Damme, S., Crombez, G., Hermans, D., Koster, E. H. W., & Eccleston, C. 2006. The role of extinction and reinstatement in attentional bias to threat: A conditioning approach. Behaviour Research and Therapy, 44(11), 1555–1563).
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Automaticity of Learning and Attentional Biases
Several studies have used conditioning and attentional bias designs to test the predictions of theories that posit automatic, preattentive processing of evolutionarily fear-relevant stimuli, such as angry faces, snakes, and spiders (e.g., Öhman, Lundqvist, & Esteves, 2001; Öhman & Mineka, 2001). Frequently, these studies have made reference to the two pathways of fear processing elucidated in the work of LeDoux and others (e.g., LeDoux, 1996), which differentiates two routes of threat-relevant sensory information to the amygdala: a “quick and dirty” input from the sensory thalamus, and a slower but more veridical representation from the sensory cortex. Attentional bias researchers have taken up the question of whether attentional biases might mediate one, but not the other, of these routes, in the studies described in the text that follows. In two experiments, Beaver, Mogg, and Bradley (2005) used dot-probe tasks following a conditioning paradigm to assess whether CSs conditioned without conscious awareness could bias attention without the confound of low-level perceptual differences between threat and nonthreat stimuli. In the first study, a conditioning phase presented two masked fear-relevant stimuli (snakes and spiders) with either an aversive white noise US or a neutral tone; next, a dotprobe task presented CSs either unmasked (Experiment 1) or both masked and unmasked (Experiment 2), with fear-irrelevant flower and mushroom stimuli paired with the fearconditioned stimuli for each dot-probe trial. A forced-choice recognition test confirmed that participants were not aware of which stimuli had been paired with the aversive stimulus. Significant attentional biases for the CS+ but not the CS– on the dot-probe task were found in both experiments for unmasked stimuli presented for 200 ms, and in the second experiment on masked trials as well. In the second experiment, it was also found that explicit ratings of the aversiveness of the US were predictive of differential attentional bias for both masked and unmasked CS+ versus CS–. Taken together, these results
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suggest that attentional bias depended upon the perceived aversiveness of the US and did not require conscious recognition of CSs during conditioning. In another set of experiments (Batty, Cave, & Pauli, 2005), visual search tasks were combined with an evaluative conditioning paradigm (in which aversive pictorial stimuli were used as USs rather than a shock or noise stimulus) to assess whether stimuli conditioned without conscious awareness could bias attention without the confound of low-level perceptual differences between threat and nonthreat stimuli. In these experiments, one abstract shape was paired with a highly aversive image (e.g., mutilated bodies) and one with a neutral image. Following each trial of the visual search task (each with 1, 3, 6, or 12 distracters which were similarly shaped figures) for which either one of the abstract shapes was the correct response, either the aversive or neutral image was shown briefly. Successful evaluative conditioning was confirmed using a modified Implicit Association Task (Greenwald, McGhee, & Schwartz, 1998), a frequently used measure of associations that develop under evaluative conditioning procedures. In the first experiment, participants responded faster to threat-associated stimuli, but there was no evidence that they were detected preattentively, regardless of the trait anxiety of the participant. Similarly, the second experiment found that even individuals high in snake or spider fear showed no evidence of preattentive detection of threat-associated stimuli that had been associated with slides of snakes or spiders on conditioning trials. Although the findings of Batty, Cave, and Pauli (2005) and Beaver, Mogg, and Bradley (2005) appear to contradict one another in their findings for preattentive threat detection, it is important to underscore that Batty, Cave, and Pauli (2005) employed an evaluative rather than traditional fear-conditioning procedure. The relationship of Pavlovian conditioning with evaluative conditioning is still somewhat unclear. Indeed, this has been its own subject of investigation and debate, the scope of which is beyond the present chapter (see De Houwer, Thomas, & Baeyens, 2001, for a review). Although it has
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been found that variants of implicit association tasks are sensitive to changes in evaluative processes following evaluative conditioning, there is evidence to suggest that such paradigms may not capture the nature of threat associations resulting from classical, Pavlovian fear-conditioning paradigms. For instance, Boschen and colleagues (Boschen, Parker, & Neumann, 2007) found that despite successful discriminative conditioning as measured by SCR in a conditioning paradigm using angry face CSs and an aversive shock US, an implicit association task was not sensitive to these affective changes. However, a follow-up study (Pischek-Simpson, Boschen, Neumann, & Waters, 2009), found that the same conditioning procedure did lead to attentional biases as measured with a standard dot-probe task. Data from our own laboratory (unpublished) also suggest that the nature of the reactivity to threat associated with attentional biases is not well captured by implicit association measures. We administered a dot-probe task (using 300 ms and 500 ms exposure durations) together with self-report measures of trait anxiety and two implicit association measures (the Go/ No-Go Association Task [Nosek & Banaji, 2001] and the Single-Category Implicit Association Task [Karpinski & Steinman, 2006]) and found that although trait anxiety was significantly correlated with attentional biases for angry faces, the implicit measures of threat association with these faces did not correlate significantly with either trait anxiety or attention to threat. Taken together, these results suggest that the nature of the attentional bias to threat is affected by Pavlovian, rather than evaluative, conditioning mechanisms. Another recent study by Raes and colleagues (Raes, De Raedt, Fias, Koster, & Van Damme, 2009) investigated the influence that contingency awareness in associative learning may have on biased attention toward threat. Raes et al. used a combined spatial-cueing task (with 200 ms colored rectangle cue presentations) and an evaluative conditioning paradigm, in which either aversive pictures or neutral pictures were presented following half of the spatial-cueing trials. Participants were either told beforehand that some of the colored cue slides predicted an
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unpleasant image and some predicted a neutral image, or they were told nothing. Participants receiving contingency instructions demonstrated slower disengagement from the cues predicting threat (slower responding on invalid CS+ than CS– trials), but no difference in orientation to threat cues. Perplexingly, the group not receiving contingency instructions did not differ in disengagement from CS+ and CS– cues, but showed faster orientation toward the CS– cues than the CS+ cues. However, the overall index of cue validity between the contingency instruction group and no instruction group was significant, indicating that biased attentional processing of the threat cues occurred only when the participants were aware of this contingency. Because of the small sample size of the study (n = 40), the authors cautioned against weighting the orientation versus disengagement results too heavily, but they stressed the considerable overall difference in cue-validity effects between groups. Trait Anxiety and Conditioning in Attentional Biases
Given that the majority of attentional threat bias research has investigated the relationship of trait anxiety or other clinical symptoms to abnormal attentional processing, surprisingly few studies combining threat biases and conditioning have assessed the phenomena among clinical populations or assessed concurrent levels of trait anxiety or anxiety symptoms in a normal sample. In fact, we were only able to identify one study that specifically sought to assess trait anxiety associated with attentional biases following a conditioning procedure. In this experiment (Lee, Lim, Lee, Kim, & Choi, 2009), trait anxiety was measured by self-report among normal participants, who were then administered a discriminative fear-conditioning task using face stimuli as CSs and a shock US followed by an emotional Stroop task. As found in other studies, an emotional Stroop bias for CS+ versus CS– was found across all participants. Importantly, however, the acquired attentional bias was significantly correlated with both the self-reported trait anxiety as well as the degree of self-reported aversiveness of the US.
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Summary
In our review of the literature specifically combining conditioning and attentional methodologies, we found that researchers have focused intently on the question of whether conditioned fear associations may be detected with attentional bias measures. The answer to this question appears from the majority of these studies to be that yes, conditioned fear associations may be detected with a variety of attentional bias paradigms. Exceptions to this appeared most consistently in studies employing evaluative procedures, in which the US is an aversive pictorial stimulus rather than an aversive noise or shock. This finding is particularly interesting given the failure of implicit association measures to detect associations conditioned through classical, but not evaluative, means. Specific questions that researchers have sought to explore using conditioning paradigms have varied from determining the specific components of attention that may be affected by newly conditioned threat stimuli to testing theories of the automaticity of attention to threat. However, we were surprised to find so few studies explicitly incorporating trait-anxious or clinical populations, given that the majority of threat-relevant attentional bias research has focused on the relationship between subjective anxiety and attentional biases. We hope that future studies will move toward more rigorous testing of possible mediating relationships between acquisition of conditioned fear and biased attentional processing of threat. Also notable is the lack of studies employing contextual conditioning, which, according to the most recent conditioning research reviewed earlier, is more akin to states of anxiety rather than fear. This suggests another avenue that may be especially fruitful to pursue. Finally, we would like to underscore the fact that all of the studies identified tested the ability of conditioned threat stimuli to affect attentional biases. The possibility of the converse—that attentional biases for threat associated with trait anxiety may influence the nature of fear and anxiety conditioning—has not yet been explored, to our knowledge. Given that abnormal attentional processing of threat has been fairly consistently
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associated with trait anxiety, and that attention plays an important role in modern conditioning theories, it seems likely that such a relationship may exist.
CONCLUSION The study of fear and anxiety in humans has benefitted considerably from diverse experimental perspectives and methodologies in the past century, including behavioral, cognitive, physiological, neuroanatomical, and genetic approaches. In this chapter, we have described some of the highlights and main findings in research on two phenomena closely linked with both trait anxiety and clinical anxiety disorders: biased attentional processing of threat, and conditioning of fear and anxiety. Although the experiments published to date specifically combining fear/anxiety conditioning and attentional bias paradigms are limited, this is an emerging literature and we hope this review will encourage researchers to explore further some of the ways in which attentional biases for threat may interact with conditioned fear and anxiety. In threat-relevant attentional bias research, a consensus appears to have been reached that anxious, relative to nonanxious, individuals (1) do not orient toward threat stimuli more quickly but are more likely to select them as objects of attention than neutral stimuli, (2) tend to dwell upon threat stimuli for longer than neutral stimuli, and (3) are more likely to avoid reengaging with threat stimuli after disengagement than neutral stimuli. Moreover, research suggests that attention to threat is associated with similar neuroanatomical areas and genetic polymorphisms implicated in trait anxiety. These genetic and neuroimaging findings are especially interesting to consider in light of the recent conditioning experiment described earlier (Lonsdorf et al., 2009), which found greater fear acquisition among carriers of one or two short 5-HTT alleles, and slower extinction among carriers of two COMT met alleles. We hope that juxtaposing these findings in this review will encourage future research that attempts to link attentional biases to conditioning through several such levels of analysis.
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In our review of studies linking conditioning and attentional biases, we found a growing body of evidence suggesting that a range of attentional bias measures are sensitive to conditioned fear associations. However, the majority of these studies were not directly aimed at investigating whether such conditioning processes may mediate the trait anxiety and attentional bias link, which we suggest is a promising area for future research. Moreover, despite the fact that conditioning abnormalities and attentional biases for threat are associated with trait anxiety, we were unable to find any studies assessing whether individual differences in attention to threat may account for conditioning abnormalities. The relative inattention to personality variables such as trait anxiety reflects, perhaps, the common focus among researchers in both fear learning and cognitive processing biases upon experimental manipulation and causal mechanisms rather than sources of individual differences and personality traits (Revelle & Oehlberg, 2008). However, our review of current findings in fear conditioning, contextual conditioning, and attentional biases for threat suggest several areas of potential overlap. It is possible that the preferential attentional processing of threat stimuli by anxious individuals may help to account for these conditioning abnormalities; however, as of yet, no coherent theoretical framework or experimental data exist to describe the possible nature of this relationship. In the future, we hope there may be an attempt to integrate further the current findings of fear and anxiety conditioning with our considerable knowledge of threat-relevant attentional biases.
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Smolka, M. N., Schumann, G., Wrase, J., Grusser, S. M., Flor, H., Mann, K., . . . Heinz, A. (2005). Catechol-O-methyltransferase val158met genotype affects processing of emotional stimuli in the amygdala and prefrontal cortex. Journal of Neuroscience, 25(4), 836–842. Stein, M. B., Fallin, M. D., Schork, N. J., & Gelernter, J. (2005). COMT polymorphisms and anxietyrelated personality traits. Neuropsychopharmacology, 30(11), 2092–2102. Stormark, K. M., & Hugdahl, K. (1996). Peripheral cuing of covert spatial attention before and after emotional conditioning of the cue. International Journal of Neuroscience, 86(3), 225–240. Stormark, K. M., Hugdahl, K., & Posner, M. I. (1999). Emotional modulation of attention orienting: A classical conditioning study. Scandinavian Journal of Psychology, 40(2), 91–99. Tipples, J., Young, A. W., Quinlan, P., Broks, P., & Ellis, A. W. (2002). Searching for threat. The Quarterly Journal of Experimental Psychology: Section A, 55(3), 1007–1026. Treit, D., Pesold, C., & Rotzinger, S. (1993). Dissociating the anti-fear effects of septal and amygdaloid lesions using two pharmacologically validated models of rat anxiety. Behavioral Neuroscience, 107(5), 770–785. Tunbridge, E. M., Bannerman, D. M., Sharp, T., & Harrison, P. J. (2004). Catechol-omethyltransferase inhibition improves setshifting performance and elevates stimulated dopamine release in the rat prefrontal cortex. Journal of Neuroscience, 24(23), 5331–5335. Uhrich, J. (1938). The social hierarchy in albino mice. Journal of Comparative Psychology, 25, 373–413. Van Damme, S., Crombez, G., Hermans, D., Koster, E. H. W., & Eccleston, C. (2006). The role of extinction and reinstatement in attentional bias to threat: A conditioning approach. Behaviour Research and Therapy, 44(11), 1555–1563. Van Damme, S., Lorenz, J., Eccleston, C., Koster, E. H. W., De Clercq, A., & Crombez, G. (2004). Fear-conditioned cues of impending pain facilitate attentional engagement. Neurophysiologie Clinique/Clinical Neurophysiology, 34(1), 33–39. Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition 1. Perspectives on Psychological Science, 4(3), 274–290.
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CHAPTER 4 Behavioral Techniques to Reduce Relapse After Exposure Therapy Applications of Studies of Experimental Extinction Mario A. Laborda, Bridget L. McConnell, and Ralph R. Miller
Pavlovian phenomena have long served as models for the etiology, treatment, and relapse from treatment of diverse disorders (e.g., phobias, addictions). Here we briefly review Pavlovian conditioning models of anxiety disorders, experimental extinction models of exposure therapy, and recovery from extinction models of relapse following exposure therapy. We then focus on how research on experimental extinction has led to the development of specific behavioral techniques to reduce recovery from extinction and hence relapse from exposure therapy. These techniques include conducting extinction treatment in multiple contexts, giving a massive amount of extinction, increasing the time between extinction trials and between extinction sessions, administering extinction in the presence of a second excitor, and testing in the presence of a retrieval cue from extinction. It is concluded that these behavioral techniques, all of which were discovered in the experimental laboratory, are potent and important tools to be considered by psychotherapists trying to make their patients less susceptible to relapse.
The pioneering work of Pavlov (1927) laid the groundwork for the development of many successful models of select psychopathologies. Pavlovian phenomena have subsequently served as models of the etiology, treatment, and relapse of numerous disorders. In this chapter we briefly review some of these roles of Pavlovian conditioning in modeling behavioral/mental disorders, and then focus on how the empirical study of extinction has resulted in the development of behavioral techniques that are applicable by clinicians who are trying to decrease relapse after exposure therapy. We will recurrently use fear and anxiety disorders as examples of how Pavlovian conditioning has been used as a model; however, these are only a few of the disorders for which these Pavlovian techniques are useful in preventing relapse after exposure therapy.
EARLY ASSOCIATIVE ACCOUNTS OF THE ETIOLOGY OF PSYCHOPATHOLOGY Pavlov’s (1927) discovery of so-called experimental neurosis represents one of the first known experimental demonstrations of learned emotional responses, and it constitutes the first associative model for the etiology of anxiety disorders. Pavlov and his colleagues showed how different environmental manipulations provoked learned emotional responses in dogs. In one exemplar study (Shenger-Krestinikova, 1921, cited in Pavlov, 1927), they showed that, after their subjects mastered a two-stimulus visual discrimination (i.e., training the subjects to respond to one of two visual stimuli for reinforcement), making this discrimination gradually more difficult
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(i.e., making the stimuli more similar) caused their subjects to display behaviors characteristic of human neurosis (e.g., erratic and aggressive behaviors). These results were soon extended using other manipulations (for a review of these different manipulations and their commonalities, see Mineka & Kihlstrom, 1978), and they were also replicated with children as experimental subjects (Krasnogorsky, 1925). Consequently, select psychopathologies were viewed, for the first time, as conditions highly influenced by external stimuli and learning processes, discouraging the view that all psychopathology was based on internal states, structures, and hypothetical conflicts (e.g., the psychoanalytical point of view). As a testament of Pavlov’s influence, later models of the etiology and treatment of anxiety disorders were developed based on his early research (e.g., Wolpe, 1958). In a Pavlovian learning paradigm, two stimuli are paired; one of them elicits responding prior to any specific treatment (i.e., an unconditioned stimulus [US] that elicits an unconditioned response [UR]), and the other does not initially evoke the response in question (i.e., a neutral stimulus). However, when the stimuli are presented contiguously, the once neutral stimulus begins evoking a response [usually] similar to those evoked by the US. In other words, the neutral stimulus becomes a conditioned stimulus (CS) and begins eliciting a conditioned response (CR). This simple associative account of the acquisition of fear and phobias is exemplified by Watson and Rayner’s (1920) demonstration of an infant’s learned fear response to a rat after the rat was paired with a loud noise. Here the rat served as a neutral stimulus that did not initially evoke a fear response by the infant. However, when the presentation of the rat (i.e., the CS) was followed by a stimulus that initially evoked a fear response (i.e., the loud noise, which served as the US), the infant seemingly associated them and began emitting conditioned fear responses upon presentation of the rat, similar to those evoked by the US. Generalizing from this demonstration, Watson and Rayner suggested that “It is probable that many of the phobias in psychopathology are true conditioned emotional reactions. . .” (p. 14). This simple
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conditioning model served as the foundation for more modern associative accounts of the etiology of fear and anxiety disorders, which has been expanded to include factors that can account for individual differences in associative learning, including personality/temperament factors, experiential factors, and evolutionary variables (e.g., Mineka & Oehlberg, 2008; Mineka & Sutton, 2006; Mineka & Zinbarg, 2006).
EXPERIMENTAL EXTINCTION AS A MODEL OF EXPOSURE THERAPY After an association between a CS and a US has been formed through contiguous activation of the mental representation of these stimuli, repeated presentations of the CS in the absence of the US decreases the conditioned responding it elicits. This manipulation is one of the most widely studied phenomena of associative learning and is referred to as experimental extinction (Pavlov, 1927; for reviews, see Delamater, 2004; Rescorla, 2001). In addition to the importance of the empirical study of extinction for assessment of general theories of associative learning (e.g., Gallistel & Gibbon, 2000; Rescorla & Wagner, 1972; Stout & Miller, 2007; Wagner, 1981), extinction has also been pivotal as an associative model of exposure therapies (e.g., Bouton, 2000; Bouton & Nelson, 1998; Hofmann, 2008). In exposure therapy for a specific phobia, the phobic object is considered a CS, which, if repeatedly presented without the aversive stimulus (i.e., the US) loses its potential to elicit a fear response. This associative model has received extensive attention from researchers and, as a result, today exposure therapy is one of the best empirically supported treatments for specific phobias and other anxiety disorders (Chambless et al., 1996; Chambless & Ollendick, 2001). Despite the success of extinction in decreasing conditioned responding, evidence indicates that extinction does not erase the original association between a CS and a US as some associative models have proposed (e.g., Rescorla & Wagner, 1972). Instead, experimental extinction is assumed to result in the formation of an inhibitory-like association (for reviews, see Bouton, 1993, 2000,
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2004), which has led researchers to return to Pavlov’s (1927) view of extinction as an inhibitory learning experience that creates a memory which competes with the original excitatory association for behavioral control. This view of extinction building new associations instead of erasing them has been incorporated into some associative models (e.g., Pearce & Hall, 1980). In the same manner that extinction does not erase the original learned associations, exposure therapy for anxiety disorders does not fully destroy the potential of the phobic object to elicit responding, and consequently relapse after treatment is common (Craske, 1999). This, of course, is problematic, given the long-term goals of therapy.
SOME ASSOCIATIVE MODELS OF RELAPSE AFTER EXPOSURE TREATMENT Several associative phenomena have been widely cited to support the new-learning (as opposite to erasure) account of extinction, and most of them also serve as evidence of the susceptibility of extinction to recovery. For example, postextinction presentations of the US often induce partial recovery of the extinguished CR (reinstatement; e.g., Bouton & Bolles, 1979b; Rescorla & Heth, 1975), and retraining of an extinguished cue is usually faster than training a novel cue (rapid reacquisition; e.g., Napier, Macrae, & Kehoe, 1992; Pavlov, 1927; Ricker & Bouton, 1996). More widely studied, a long delay between extinction treatment and testing has been seen to provoke partial recovery of the extinguished CR (spontaneous recovery; e.g., Brooks & Bouton, 1993; Pavlov, 1927), and partial recovery of the extinguished CR also occurs when testing takes place outside the extinction context (renewal; e.g., Bouton & Bolles, 1979a; Bouton & King, 1983; Bouton & Ricker, 1994). Given their similarities with relapse situations outside the laboratory (where relapse often occurs in a context different from the one used during treatment and often after a period of time has elapsed since the end of treatment), spontaneous recovery and renewal will be reviewed in more detail.
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There are three types of renewal that differ based on the contexts in which acquisition, extinction, and testing take place. ABA renewal is the recovery of an extinguished CR when subjects are tested in the acquisition context (A) after extinction treatment in a different context (B; e.g., Bouton & King, 1983). ABC renewal is the recovery of an extinguished CR that occurs when acquisition (Context A), extinction (Context B), and testing (Context C) all take place in different contexts (e.g., Bouton & Bolles, 1979a). AAC renewal is the recovery of an extinguished CR when acquisition and extinction occurs in the same context but testing occurs in a different context, which is associatively neutral (C; Bouton & Ricker, 1994). Most evidence suggests that ABA and ABC renewal result in more recovery from extinction than AAC renewal, which is sometimes completely ineffectual (e.g., Laborda, Witnauer, & Miller, in press; Rescorla, 2008; Tamai & Nakajima, 2000; Thomas, Larsen, & Ayres, 2003; Üngör & Lachnit, 2008). There are several theoretical accounts to explain why renewal of extinguished responses occurs when testing takes place outside the context of extinction. For example, according to Pearce’s (1987) configural model, subjects do not process stimuli elementally; rather, they process the entire perceptual field as a single configural stimulus. Generalization occurs between configured stimuli to the extent that they are similar. In this framework, ABA renewal occurs because the target cue and the acquisition context are processed as a single configured cue that acquires an excitatory association with the US during acquisition treatment. Then, because the target cue provides similarity between configural units representing the acquisition and extinction trials, some of the strength of the excitatory configured cue generalizes to the extinction configured cue (i.e., the target and extinction contexts), creating a situation in which this second configuration develops an inhibitory association with the US (because of the unfulfilled expectancy of US occurrence). When testing occurs in the acquisition context, the excitatory strength of the configured cue formed by the target cue and the acquisition context is only partially reduced by the generalization of inhibition from
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the compound formed by the extinction context and the target cue, thereby producing a recovery of responding. Pearce’s model anticipates ABC renewal due to the inhibitory properties of the configured extinction cue (composed of the target cue and extinction context) not fully generalizing to the test situation, due to the absence of the extinction context at test. According to the Rescorla and Wagner (1972) model, during acquisition training in Context A both the target cue and the context acquire elemental excitatory associations with the US. Then, when subjects receive extinction training in a different context, the CS predicts the US and its absence causes the extinction context to develop an inhibitory relationship with the US. Importantly, summation of the excitation from the target cue with inhibition from the extinction context results in little stimulus control of behavior in the extinction context. Moreover, the inhibitory-like status of the extinction context protects the CS for further extinction (e.g., Lovibond, Davis, & O’Flaherty, 2000; McConnell & Miller, 2010; Rescorla, 2003). When subjects are tested back in the acquisition context, its excitatory association summates with the remaining excitatory association of the target cue provoking ABA renewal. Similarly, ABC renewal is anticipated because of the absence of the inhibitory status of Context B when testing in Context C, thereby allowing for more excitation relative to the ABB control group. The ABB group exhibits low responding presumably because at test it is under the influence of the inhibitory Context B. As stated elsewhere (Laborda et al., in press), although the comparator hypothesis (Stout & Miller, 2007) does not account for renewal (i.e., ABB < ABC; ABB < ABA; AAA < AAC) because it lacks of a rule to allow summation between the testing context and the target stimulus, there is no reason to think that such summation does not actually take place. The implementation of such summation rule would permit SOCR to predict renewal in a way similar to that of Rescorla and Wagner. From another perspective, Bouton’s (1993, 1994, 1997) retrieval model suggests that recovery from extinction occurs for two reasons: First, extinction learning is akin to inhibition learning, that is, learning when an outcome will not occur,
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and memories that support inhibitory learning are thought to be more labile than excitatory memories. Second, extinction learning involves the creation of ambiguity (because it contradicts the previously acquired knowledge concerning reinforcement of the cue), and ambiguity presumably fosters context-specific encoding of the extinction information as a means of resolving the ambiguity. That is, when two types of inconsistent information (i.e., reinforcement and nonreinforcement of a cue) are sequentially acquired, retrieval of the second-learned information depends on the spatial (renewal) and temporal (spontaneous recovery) test context matching that of the second-learned treatment (i.e., extinction). Thus, moving outside the context of second learning (i.e., the extinction context) should cause a failure to retrieve the second-learned information because it is inhibitory (in the case of extinction) and because it was coded as context specific due to its ambiguity (in contrast to the unambiguous firstlearned memory; i.e., according to Bouton’s model, ambiguous memories do not transfer well between contexts). These mechanisms conjointly account for ABA and ABC renewal. Rosas and colleagues (Rosas, Callejas-Aguilera, RamosÁlvarez, & Abad, 2006) recently revised the second mechanism of Bouton’s (1993, 1994, 1997) retrieval model. For them it is not ambiguity that makes second-learned information become context specific, but any manipulation that makes organisms to pay attention to the context (e.g., ambiguity, previous experience focusing on the context, instructions to pay attention to the context, informational value of the context, and the salience of the context relative to the target cue). According to Rosas and his colleagues, in the case of ABA and ABC renewal, the ambiguity experienced during extinction treatment (i.e., the CS acquiring a second meaning) makes subjects attend to the context, thereby encoding the extinction information as specific to the extinction context. When testing occurs in the acquisition context or a neutral context, extinction learning does not generalize to test because it was encoded as an “exception” occurring in the extinction context, thereby producing renewal. In general, Bouton’s approach has been successful
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in predicting several recovery phenomena and related data. For example, it accounts for AAC renewal being weaker than ABA and ABC renewal by noting that initial acquisition should interfere with the extinction context becoming a conditioned inhibitor for the AAC condition, but not the ABC or ABA conditions. Like renewal, there are several theoretical accounts to explain why spontaneous recovery occurs. For example, Skinner (1950) proposed that acquisition cues, such as handling of the experimental subjects, were not properly extinguished and those cues provoke the recovery of extinguished CRs at a delayed test. Robbins (1990) proposed another account in which a loss of attention to the CS during extinction wanes over a retention interval, thereby producing spontaneous recovery (but see Bouton & Peck, 1992, for difficulties with this account). More recently, Devenport (1998) used a temporal weighting rule to explain spontaneous recovery. This theory states that events are weighted differently depending on their recency. Recent events are given more weight in determining behavior than remote events. But as time elapses between extinction treatment and testing, the subject weights the acquisition and extinction phases more evenly. Thus, for an experimental group in which a long delay has been imposed between the extinction treatment and testing, subjects give relatively more weight to what was learned before extinction treatment (i.e., a high level of responding) than if no retention interval was imposed. Bouton (1993; 2010) proposed a related account in which spontaneous recovery occurs because changes of the so-called temporal context can produce similar effects to those produced by changes in physical contexts (i.e., renewal). For Bouton, renewal and spontaneous recovery are examples of the same phenomenon. In this framework, like renewal, spontaneous recovery is due to some combination of two factors. First, inhibitory-like information (extinction) does not generalize as well as excitation (from acquisition) to the time of test when testing is appreciably delayed. Also, second-learned information (extinction in this case) is temporally more context dependent than first-learned information (acquisition in this case), making
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second-learned information less likely to transfer to a different temporal context. Rescorla (2005) suggested that the former was the major factor responsible for spontaneous recovery from extinction. But Sissons and Miller (2009) identified a lack of equivalence between Rescorla’s inhibitory information and his excitatory information. With this corrected (i.e., using the same number of trials in both phases of the experiment, using Pavlovian conditioned inhibition treatments in each phase as the nonreinforced treatment, and using a nontarget excitor for inhibition training to decrease the possibility of indirectly diminishing the inhibitory status of the stimulus trained as an inhibitor when nonreinforcing the target excitor in phase 2), Sissons and Miller’s data suggested that extinction being the second-learned information contributes more to spontaneous recovery than does the inhibitorylike nature of extinction. In addition to the vast number of reports of recovery of extinguished CRs in the (nonhuman) animal learning literature, these phenomena have also been observed in humans. For example, Vila and Rosas (2001; see also GarcíaGutiérrez & Rosas, 2003; Rosas & CallejasAguilera, 2006; Rosas, Vila, Lugo, & Lopez, 2001) reported recovery phenomena in a human contingency learning task. Using a fictitious story relating a medicine (cue) with a deleterious side effect (outcome), they demonstrated acquisition of an excitatory relationship between the medicine and the side effect, extinction of this association, and then recovery of it when testing occurred in a context different than the extinction context (i.e., renewal) or when a retention interval was imposed between extinction and testing (i.e., spontaneous recovery). Similarly, Van Gucht, Vansteenwegen, Beckers, and Van den Bergh (2008) showed that chocolate cravings are susceptible to relapse when tested in the acquisition context after extinction in another context (i.e., ABA renewal). In a related line of research, Vansteenwegen et al. (2005; see also Effting & Kindt, 2007; Vansteenwegen et al., 2006; Vansteenwegen et al., 2007) reported recovery phenomena in a human fear-conditioning preparation. Using a differential conditioning paradigm, a picture of a face (i.e., the CS) was
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paired with a loud aversive noise (i.e., the US). After training this association in one context and extinguishing it in another, returning subjects to the acquisition context to test responding to the CS resulted in renewal of the extinguished CRs, compared with a group in which all phases took place in the same context. In more applied situations, these extinction and the recovery phenomena have been replicated when social drinkers (Collins & Brandon, 2002; but see MacKillop & Lisman, 2008; Stasiewicz, Brandon, & Bradizza, 2007) and arachnophobic students (Mineka, Mystkowski, Hladek, & Rodriguez, 1999; Mystkowski, Craske, & Echiverri, 2002; Rodriguez, Craske, Mineka, & Hladek, 1999) were used as participants. For example, Collins and Brandon (2002) reported a recovery of extinguished reactivity of the urge to drink and extinguished salivatory responses to alcohol-related cues in undergraduate social drinkers. In this study all participants received extinction of alcohol-related cues (e.g., a beer can) in one context, decreasing salivation and urge to drink. Participants tested in a different context showed renewal of the extinguished CRs relative to participants tested in the extinction context. As previously mentioned, similar results have been found with subclinical participants. As an illustration, Mystkowski et al. (2002) confirmed previous findings of recovery of extinguished fear responses in arachnophobic undergraduates (Mineka et al., 1999; Rodriguez et al., 1999). In this research, all participants received an exposure session in one context and were tested 1 week later in the same and a different context. An important feature of this study is that both contexts were fully counterbalanced real-world settings. Self-report data showed that fear was much higher when testing occurred in a context different from the one used during the extinction session, thereby extending the renewal and spontaneous recovery literature to phobic participants.
BEHAVIORAL TECHNIQUES TO REDUCE RECOVERY AFTER EXTINCTION The evidence summarized earlier is clear in suggesting that neither experimental extinction nor
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exposure therapy erases memories. Rather, they establish inhibitory-like associations that are more context dependent than the original excitatory association (e.g., Bouton, 1993). Relapse after exposure therapy is one of the greatest problems confronting psychotherapy. However, research concerning experimental extinction has identified some ways to prevent, or at least attenuate, the recovery of extinguished CRs. Recently, Boschen, Neumann, and Waters (2009, p. 97; see also Rachman, 1978) proposed 10 recommendations for preventing relapse after successful exposure treatment of anxiety-related disorders: (1) Extend the duration of exposure sessions past the point of habituation (i.e., past the point in which anxiety is no longer reported in a given exposure session); (2) Increase the overall number of sessions, continuing past successful extinction of the anxiety response across sessions (i.e., past the point in which anxiety is no longer reported at the beginning of the next therapy session); (3) Use massed exposure sessions with short durations between sessions; (4) Encourage patients to relinquish distraction techniques, and do not train patients in using these as part of treatment (i.e., during exposure treatment the anxiety-eliciting stimulus must be attended to; anxiety diminution could be reduced if patients are actively avoiding the situation); (5) Utilize an assortment of fear stimuli during exposure; (6) Conduct exposure in a variety of different environments and contexts; (7) Ensure that exposure tasks are sufficient to elicit anxiety, but do not place excessive demand on the patient; (8) Homework tasks should be used to consolidate treatment and reduce probability of relapse; (9) Where possible, prior to reencountering the phobic stimulus, patients should attempt to recall (mentally reinstate) the treatment context; (10) Cognitive restructuring should be used to assist patients to recognize, reevaluate, and restructure associations between their phobic stimulus and negative outcomes. Most of these recommendations were directly or indirectly derived from experimental extinction research. Although this list is suggestive, most of the procedures have not yet been well researched, thereby raising doubts concerning which ones really work and under what conditions, if any, they are effective.
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Another promising area of research on behavioral techniques to prevent relapse is the study of pharmaceutical cognitive enhancers. For example, the use of d-cycloserine (DCS), a partial agonist of NMDA receptors, has been shown with non-human subjects to enhance extinction of freezing conditioned responses (Ledgerwood, Richardson, & Cranney, 2003), fear-potentiated startle (Walker, Ressler, Lu, & Davis, 2002), conditioned taste aversion (Davenport & Houpt, 2009), and cocaine-induced conditioned place preference (Thanos, Bermeo, Wang, & Volkow, 2009). Also, the use of DCS with humans has been reported to enhance the effects of exposure therapies on social phobia (Guastella et al., 2008; Hofmann et al., 2006), acrophobia (Ressler et al., 2004), and some cases of obsessive-compulsive disorder (Kushner et al., 2007). Adding to this body of data, there is also evidence of DCS attenuating reinstatement of extinguished fear CRs (Ledgerwood, Richardson, & Cranney, 2004) and extinguished cocaine-induced conditioned place preference (Paolone, Botreau, & Stewart, 2009). However, these successes are tempered by reported failures of DCS to enhance extinction of arachnophobia (Guastella, Dadds, Lovibond, Mitchell, & Richardson, 2007), human electrodermal responses (Guastella, Lovibond, Dadds, Mitchell, & Richardson, 2007), and some cases of exposure therapy for obsessivecompulsive disorder (Storch et al., 2007). Of more central interest here is that DCS has failed to prevent renewal of extinguished conditioned fear suppression (Bouton, Vurbic, & Woods, 2008; Woods & Bouton, 2006), and rapid reacquisition of extinguished freezing conditioned responses (Ledgerwood, Richardson, & Cranney, 2005) in rats. There is clearly a need to extend research on the effects of pharmaceutical cognitive enhancers on extinction and recovery situations; however, extended discussion of this area of investigation is beyond the scope of the present review. Next we review some behavioral techniques, discovered and extensively studied in the experimental laboratory, that have been shown to be robust and potentially useful tools to be considered by psychotherapists trying to make their treatments less susceptible to relapse.
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Denniston, Chang, and Miller (2003) compared the effects of moderate and massive extinction training in attenuating ABC and ABA renewal, using rats as subjects in a conditioned lick suppression paradigm. After a white noise (i.e., CS) was paired with a footshock (i.e., US) in one context (A), subjects received moderate (160) or massive (800) amounts of extinction trials in a second context (B). Notably, 160 extinction trials have been shown to be sufficient to eliminate conditioned suppression in their preparation. When subjects were later tested for conditioned fear responses in a neutral context (C; Experiment 1; see Fig. 4.1A) and in the acquisition context (Experiment 2; see Fig. 4.1B), those that received a massive number of extinction trials showed attenuated renewal of the extinguished responses compared with subjects that received only a moderate number of extinction trials. These positive results were recently replicated in a similar experimental situation (Laborda & Miller, 2010). However, Thomas, Vurbic, and Novak (2009), Rauhut, Thomas, and Ayres (2001), and Tamai and Nakajima (2000) failed to see reduction in ABA renewal with increasing number of extinction trials, but their large number of trials (144, 100, and 112, respectively) were decidedly less than in the studies that obtained reduced renewal (800 and 810). This suggests that insufficient extinction might account for the failures to obtain this effect. It should be noted that Tamai and Nakajima successfully reduced AAC renewal with only 112 extinction trials, which is in concordance with the evidence suggesting that this type of renewal is weaker than ABA and ABC renewal. Foa et al. (2005; see also Gillihan and Foa, this volume, chapter 2) observed reduced relapse with humans treated for posttraumatic stress disorder when prolonged exposure was used. Prolonged exposure can be viewed as having a similar effect as many extinction trials because both treatments increase time spent in the presence of the CS. Future research should determine whether massive extinction can prevent other recovery situations and whether these results can be replicated using a variety of experimental paradigms and subjects.
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Panel A
Mean time (log s)
2.3 2.1 1.9 1.7 1.5 1.3 1.1 0.9 0.7 No extinction (A–C)
Moderate extinction (ABC)
Massive extinction (ABC)
Moderate extinction (ABB)
Massive extinction (ABA)
Moderate extinction (AAA)
Groups 2.7 2.5
Panel B
Mean time (log s)
2.3 2.1 1.9 1.7 1.5 1.3 1.1 0.9 0.7 No extinction (A–A)
Moderate extinction (ABA) Groups
Figure 4.1 Panel A: Mean log time to complete 5 cumulative seconds of licking in the presence of the
target conditioned stimulus in a neutral context (C). Brackets represent standard error of the mean. Higher scores indicate more conditioned suppression. No extinction (A-C) = group that received no extinction trials and was tested in a neutral but familiar context (C); Moderate extinction (ABC) = group that received a moderate number of extinction trials (160) and was tested in a neutral but familiar context (C); Massive extinction (ABC) = group that received a massive number of extinction trials (800) and was tested in a neutral but familiar context (C); Moderate extinction (ABB) = group that received a moderate number of extinction trials (160) and was tested in the extinction context (B). See text for further explanation. (From J. C. Denniston, R. C. Chang, & R. R. Miller, 2003 Experiment 1, Learning and Motivation, 34, 68–86.) Panel B: Mean log time to complete 5 cumulative seconds of licking in the presence of the target conditioned stimulus in the acquisition context (A). Brackets represent standard error of the mean. Higher scores indicate more conditioned suppression. No extinction (A-A) = group that received no extinction trials; Moderate extinction (ABA) = group that received a moderate number of extinction trials (160) in a context different from the one used for both acquisition and testing; Massive extinction (ABA) = group that received a massive number of extinction trials (800) in a context different from the one used for both acquisition and testing; Moderate extinction (AAA) = group that received a moderate number of extinction trials (160) in the acquisition context. See text for further explanation. (From J. C. Denniston, R. C. Chang, & R. R. Miller. 2003. Experiment 2. Learning and Motivation, 34, 68–86).
Extinction in Multiple Contexts
Bouton (1991) suggested that another possible manipulation to reduce recovery after extinction could be to conduct extinction treatment in several contexts. He reasoned that if an association is extinguished in multiple contexts, the greater
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number of features present in multiple extinction contexts than in a single extinction context may help to generalize extinction learning to contexts other than those used for extinction treatment. In line with this operational suggestion, Gunther, Denniston, and Miller (1998) evaluated the effect of extinction treatment in
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the number of contexts used during acquisition and extinction were equated. This finding has implications for the treatment of patients who have suffered multiple traumas (e.g., war combatants and victims of multiple abuses) or consume drugs in different places and situations (e.g., smokers and drinkers). For these people, extinction in multiple contexts could prove hard to implement because of the number of extinction contexts that would be necessary to compensate for the multiple acquisition contexts. To date, Gunther et al.’s (1998, Experiment 1) results have been replicated several times and in a number of different situations (e.g., Bandarian Balooch & Neumann, 2011; Chaudhri, Sahuque, & Janak, 2008; Chelonis, Calton, Hart, & Schachtman, 1999; Glautier & Elgueta, 2009; Laborda & Miller, 2010; Neumann, 2006; Pineño & Miller, 2004; Thomas et al., 2009; Vansteenwegen et al., 2007; but see Betancourt et al., 2008; Bouton, García-Gutiérrez, Zilski, & Moody, 2006; Neumann, Lipp, & Cory, 2007,
2.7
2.7
2.5
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2.3 Mean time (log s)
Mean time (log s)
multiple contexts on ABC renewal, using rats as subjects in a conditioned lick suppression paradigm. After a white noise (i.e., CS) and a mild shock (i.e., US) were paired in one context, subjects received extinction trials in one or three different contexts (all different from that of conditioning and that of testing). As depicted in Figure 4.2, rats that received extinction treatment in three different contexts showed less renewal of the extinguished CR when tested in an associatively neutral context (i.e., ABC renewal) than rats that received the same amount of extinction treatment but in only one context. Gunther et al. went a step further and also presented data delineating the situations in which this technique can be effective in attenuating renewal. In their Experiment 2, they found that the number of contexts in which acquisition took place limited the effectiveness of extinction in multiple contexts. As depicted in Figure 4.3, the effect of extinction in multiple contexts decreasing ABC renewal was eliminated when
87
2.1 1.9 1.7 1.5 1.3 1.1
2.1 1.9 1.7 1.5 1.3 1.1 0.9
0.9
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0.7 No extinction (A[---]E)
Extinction-1 (A[BBB]E)
Extinction-3 (A[BCD]E)
Acquisition-1/Extinction-3
Acquisition-3/Extinction-3
Groups
Groups
Figure 4.3 Mean log time to complete 5 cumulaFigure 4.2 Mean log time to complete 5 cumula-
tive seconds of licking in the presence of the target conditioned stimulus in a neutral context (E) for all groups. Brackets represent standard error of the mean. Higher scores indicate more conditioned suppression. No extinction (A[—]E) = group that received no extinction trials; Extinction-1 (A[BBB] E) = group that received a moderate number of extinction trials (162) in only one context (B); Extinction-3 (A[BCD]E) = group that received a moderate number of extinction trials (162) in three different contexts (B, C, and D). See text for further explanation. (From L. M. Gunther, J. C. Denniston, & R. R. Miller. 1998. Experiment 1. Behaviour Research and Therapy, 36, 75–91).
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tive seconds of licking in the presence of the target conditioned stimulus in a neutral context (E) for all groups. Brackets represent standard error of the mean. Higher scores indicate more conditioned suppression. Acquisition-1 / Extinction-3 = group that received acquisition in one context and a moderate number of extinction trials (162) across three different contexts. Acquisition-3 / Extinction-3 = group that received the same amount of acquisition but in three different contexts and a moderate number of extinction trials (162) also across three different contexts. See text for further explanation. (From L. M. Gunther, J. C. Denniston, & R. R. Miller. 1998. Experiment 2. Behaviour Research and Therapy, 36, 75–91).
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for negative results). For example, Chelonis et al. (1999) reported successful prevention of ABA renewal in a conditioned taste aversion preparation with rats as subjects. After a single pairing of sucrose (i.e., CS) with lithium chloride (i.e., US) in one context, subjects received extinction trials in one or three new contexts. The results indicated that extinction in only one context was more prone to renewal than when the same amount of extinction was administered in multiple contexts. Neumann extended the previous results to ABA and ABC renewal situations in a conditioned suppression task with humans as subjects. Moreover, two studies found reduced renewal from similar manipulations in arachnophobic participants. Vansteenwegen et al. (2007) reported successful attenuation of renewal of fear responses after extinction in multiple contexts. They exposed arachnophobic participants to a videotaped spider in one or three different locations within a house. When tested with the videotaped spider in a novel location of the house (i.e., an A[BCD]E-like design), participants who received exposure of a videotaped spider in only one location displayed more renewal of the extinguished CRs than participants who received the same amount of exposure to the videotaped spider in three different locations in the house. Finally, Rowe and Craske (1998b) showed attenuated return of fear using an interesting manipulation analogous to the extinction in multiple contexts procedure. The authors exposed arachnophobic participants to one or three different tarantulas during extinction treatment. When tested 3 weeks later, participants who were exposed to three different tarantulas during treatment showed less return of fear than participants that were exposed to only one type of tarantula. These results further support the view that extinction in the presence of multiple features can facilitate generalization of extinction learning to new situations. Overall, extinction treatment in multiple contexts, at least with some parameters, has been found to be an effective way to reduce relapse of extinguished CRs after context shifts (i.e., renewal) and delayed testing (i.e., spontaneous recovery). Future research should test the effectiveness of this manipulation in reducing
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relapse caused by other factors (e.g., reinstatement, rapid reacquisition), evaluate possible theoretical explanation of these results, and test its effectiveness in more clinical settings. Massive Extinction in Multiple Contexts
Thomas et al. (2009) recently reported that there is a summation of the effects of extinction in multiple contexts and large number of extinction trials in reducing renewal of extinguished CRs in conditioned barpress suppression preparation. After pairing the momentary absence of an otherwise present white noise (i.e., the CS) with a footshock (i.e., the US), rats received 36 or 144 extinction trials in Context A. Orthogonal to this manipulation of number of extinction trials, subjects received extinction in either one or three different contexts with the total number of extinction trials held constant (Experiment 2). The results suggested that extinction in multiple contexts was effective in attenuating renewal when subjects were tested in the acquisition context (i.e., A[BCD]A treatment resulted in less renewal than did A[BBB]A treatment), but only when a large number of extinction trials were administered. Thus, extinction in multiple contexts and massive extinction do appear to summate at least in some circumstances. This may offer an explanation for why some researchers have failed to observe reduced renewal after extinction in multiple contexts. For example, Bouton et al. (2006) gave 12 extinction trials compared to Gunther et al.’s (1998) 160 trials. Extending Thomas et al.’s (2009) results, Laborda and Miller (2010) have reported a similar summative effect of extinction in multiple contexts and massive extinction when a more naturalistic relapse situation was used. In therapeutic situations, relapse often occurs when subjects are exposed to the fearful stimulus in a context different than the one in which treatment took place, and after some time has elapsed since the last exposure session. In other words, relapse often occurs after a delayed context shift, which can be thought of as a situation in which the renewal effect summates with the spontaneous recovery of extinguished responses. Similar additive effects of context change and retention
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interval have previously been reported in latent inhibition (Rosas & Bouton, 1997), in an interference task in a human causal judgment preparation (Rosas et al., 2001), and after extinction of a conditioned taste aversion (Rosas & Bouton, 1998). Laborda and Miller extended these findings to a conditioned lick suppression situation. Their Experiment 2 tested the effectiveness of massive extinction, extinction in multiple contexts, and both manipulations together in reducing relapse after a delayed context shift. As indicated in Figure 4.4, the results suggested that extinction in multiple contexts alone and massive extinction alone decreased this strong recovery of extinguished CRs, but only marginally. However, when massive extinction trials (810) took place in three different contexts, the recovery of extinguished CRs was radically attenuated.
2.7 Moderate extinction Massive extinction
2.5 Mean time (log s)
2.3 2.1 1.9 1.7 1.5 1.3 1.1 0.9 0.7 Extinction-1 (A[BBB]---E)
Extinction-3 (A[BCD]---E) Condition
Figure 4.4 Mean log time to complete 5 cumula-
tive seconds of licking in the presence of the target conditioned stimulus in a neutral but familiar context (E) for all groups. Brackets represent standard error of the mean. Higher scores indicate more conditioned suppression. Extinction-1 (A[BBB]— E) = condition that received extinction trials in only one context (B); Extinction-3 (A[BCD]—E) = condition that received extinction trials in three different contexts (B, C, and D); Moderate extinction = condition that received a moderate number of extinction trials (162); Massive extinction = condition that received a massive number of extinction trials (810). See text for further explanation. (From M. A. Laborda & R. R. Miller. 2010. Experiment 2. Massive extinction in multiple contexts reduces fear recovery after a delayed context shift, Paper presented at the meeting of the Eastern Psychological Association, Brooklyn, NY).
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Although more research would be useful in understanding the underlying mechanisms of these techniques, the data available today strongly suggest that these conjoint behavioral manipulations are effective tools to attenuate relapse after successful extinction treatments. Retrieval Cues from Extinction
Another technique that has been proposed to reduce relapse is through the use of extinction cues (ECs; e.g., Brooks & Bouton, 1993). Typically, extinction cues (also known as retrieval cues for extinction) are stimuli that are presented shortly before the target CS on most CS extinction trials. Then at test, the EC is presented again just prior to the CS, which reduces responding (relative to, for example, a group in which no EC is presented at test) in a situation in which renewal would otherwise be expected (Brooks & Bouton, 1994; see Fig. 4.5). In other words, ECs are presented during most extinction trials and, when subjects are tested for responding to the extinguished CS in the presence of the EC, recovery phenomena are usually reduced. During extinction, the EC is typically not presented on all extinction trials in order to prevent it from becoming a conditioned inhibitor, which would protect the excitatory target CS from extinction (e.g., McConnell & Miller, 2010), or the subject from processing the EC and the target cue as a single configured cue. Beside those demonstrations in appetitive preparations (Brooks, 2000; Brooks & Bouton, 1993, 1994; Brooks & Bowker, 2001), there are reports of the effectiveness of ECs attenuating spontaneous recovery of alcohol tolerance (Brooks, Vaughn, Freeman, & Woods, 2004), conditioned taste aversion (Brooks, Palmatier, García, & Johnson, 1999), and directed swimming in a navigation task (Prados, Manteiga, & Sansa, 2003). Recently, Brooks (unpublished data) found that EC-alone presentations during the retention interval further attenuated spontaneous recovery. He speculated that this effect arises from the EC-alone trials extinguishing the EC’s second-order conditioned excitatory properties acquired by its having been paired with the target CS during extinction treatment.
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Percentage of initial performance
120.0
100.0 No extinction cue (ABA Renewal) 80.0
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60.0
40.0
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First renewal test
Figure 4.5 Performance in the presence of the
target conditioned stimulus expressed as a percentage of responding on the first extinction trial. Brackets represent standard error of the mean. No extinction cue (ABA Renewal) = condition in which the target cue was tested for extinction and renewal in the absence of a cue from extinction treatment; Extinction cue (ABA Renewal) = condition in which the target cue was tested for extinction and renewal in the presence of a cue from extinction treatment; Last extinction trial = mean responding on the last extinction trial; First renewal test = mean responding on the first test trial back in the acquisition context. See text for further explanation. (Adapted from D. C. Brooks & M. E. Bouton. 1994. Experiment 3. Journal of Experimental Psychology: Animal Behavior Processes, 20, 366–379).
In addition to the aforementioned demonstrations of ECs decreasing recovery of extinguished CRs with non-human subjects, similar effects have been reported with humans as participants. Dibbets, Havermans, and Arntz (2008) reported attenuated ABA renewal in a human fear-conditioning preparation. Participants received extinction trials with an EC and were then tested on the extinguished cue with or without the EC. Participants tested in the acquisition context with the EC present showed less recovery of US expectancy ratings than participants who were tested without the EC. Similar results were reported by Vansteenwegen et al. (2006), in which renewal of conditioned electrodermal responses and of expectancy ratings were attenuated when testing occurred in the presence of an
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EC. In a few instances the effectiveness of ECs to reduce recovery of extinguished CRs has been examined with subclinical and clinical samples. Collins and Brandon (2002) observed that a reduction in stimulus-induced alcohol craving in social drinkers produced by exposure therapy was more resistant to relapse when testing occurred in the presence of ECs, thereby extending the effect to clinical situations. In an interesting extension of the EC concept, Mystkowski, Craske, Echiverri, and Labus (2006) presented self-report data in which a verbal instruction to recall the context of treatment (what they called “mental reinstatement”) successfully diminished the return of fear after a context change (i.e., renewal), in arachnophobic participants. Thus, mental reinstatement of the circumstances of extinction may serve in place of or in addition to reinstatement of physical ECs. The evidence strongly supports the effectiveness of ECs in attenuating recovery of extinguished conditioned responses in different preparations and populations. Future research should focus on the mechanisms behind the success of ECs. Brooks (unpublished data) has argued that ECs differ mechanistically from both occasion setting (for properties, see Holland, 1992; Miller & Oberling, 1998) and conditioned inhibition, but the actual mechanism is still unclear. Likely, multiple processes drive effective ECs in clinical settings because the procedure that produces ECs resembles those commonly used to produce conditioned inhibition, occasion setting, and configural learning. Extinction in the Presence of a Second Excitor
Another possible manipulation to prevent relapse that is consistent with almost all theories of learning is to conduct extinction of the fearevoking target cue in the presence of a second fear-evoking cue. This should produce deeper (i.e., a greater degree of) extinction, which should, more arguably, attenuate recovery of extinguished responses. This expectation has been confirmed several times (e.g., Rescorla, 2000, 2006; Thomas & Ayres, 2004; but see Pineño,
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Zilsky, & Schachtman, 2007). For example, Rescorla (2000) separately trained two CSs with an appetitive US (i.e., food). After learning was complete, subjects were extinguished on the target CS alone or in compound with the second excitatory CS. When the target CS was immediately tested for conditioned responding, rats that received extinction trials with both excitors together displayed greater extinction than rats that received extinction of the target CS alone. These results were later replicated in an aversively motivated task (Rescorla, 2006), using a relatively short retention interval of 7 days. Rescorla observed less spontaneous recovery when extinction treatment took place in the presence of a second excitor. However, Rescorla’s (2006) conclusions about spontaneous recovery must be qualified due to the absence of an adequate immediate testing control. Thomas and Ayres (2004) extended Rescorla’s results to an ABA design in a fear-conditioning experiment with rats, but because their design lacked of an appropriate experimental control for renewal (e.g., an ABB condition), their conclusions need to be qualified. In a fear-conditioning paradigm, Thomas and Ayres tested fear to the target cues back in the acquisition context (i.e., Context A) after administrating extinction trials with either the target cue alone or the target cue in compound with a second conditioned excitor in Context B. Their results were interpreted as showing more renewal after elemental than compounded extinction training, but, because of the lack of the appropriate comparisons (i.e., one in which the experimental groups are compared with groups tested in the extinction context), it remains unknown if better extinction could be obtained outside a renewal design (for further discussion, see Urcelay, Lipatova, & Miller, 2009). Despite the positive results just reviewed, there is evidence that extinction in the presence of an additional excitor is limited by generalization decrement. Urcelay, Lipatova, et al. (2009) observed enhanced extinction due to this manipulation, but its effects were almost completely obscured by generalization decrement going from elemental training to compound extinction and from compound extinction to elemental
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testing. That is, despite the deepened extinction effect induced by the added excitor, almost no benefit was observed at test due to repeatedly changing the target by adding elements (going from acquisition to extinction) or subtracting elements (going from extinction to test). Presumably, the subjects configured the two CSs as a single cue during extinction, which limited the amount of generalization of learning that could occur between phases (i.e., perceiving the individual CS of training and testing as a component of the compound CS at extinction). This is consistent with work by Pearce and Wilson (1991) who also found a large generalization decrement effect such that extinction with a concurrent excitor actually resulted in less response decrement than elemental extinction. Moreover, conditioned taste aversion experiments with rats (e.g., Pineño et al., 2007) and fear-conditioning experiments with humans to date have so far failed to find enhanced extinction in the presence of a second excitor (Lovibond et al., 2000; Vervliet, Vansteenwegen, Hermans, & Eelen, 2007). To reduce configuring, Urcelay, Lipatova, et al. (2009) used cues with asynchronous onsets (a 15-s target that onset 5 s before a 10-s concurrent excitor) and of different modalities. These two manipulations collectively did result in a modest but significant reduction of ABC renewal in subjects that were extinguished in the presence of a companion conditioned excitor (see Fig. 4.6). More specifically, the use of cues with asynchronous onsets and different sensory modalities reduced generalization decrement when testing elements following extinction trials with compound stimuli, thus supporting the view that the previous failures to find such an effect were due to generalization decrement. Collectively, we see that extinction in the presence of other conditioned excitors has produced mixed results with respect to attenuating recovery phenomena. The results suggest that concurrent extinction of more than one excitor decreases relapse only slightly (e.g., Urcelay, Lipatova, et al., 2009). Experiments that appear to have yielded a large effect have lacked desirable experimental controls (e.g., Rescorla, 2006; Thomas & Ayres, 2004). In conclusion, the
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0.5 Suppression ratio
ABB (Extinction) ABC Renewal Suppression ratio
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0.4 0.3 0.2 0.1 0.0 No extinction
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Massed extinction trials
Spaced extinction trials
Condition 0.0 Extinction with a second excitor
Gen-Dec control
Elemental extinction
Figure 4.6 Mean suppression ratio in the pres-
ence of the target conditioned stimulus. Brackets represent standard error of the mean. Lower scores indicate more conditioned suppression. Extinction with a second excitor = condition in which the target cue was extinguished in compound with a second excitatory cue; Gen-Dec control = condition in which the target cue was extinguished in compound with a neutral cue; Elemental extinction = condition in which the target cue was extinguished alone; ABB (Extinction) = condition in which extinction trials and testing occurred in a context different from the one used for acquisition; ABC Renewal = condition in which acquisition trials, extinction trials, and testing occurred in three different contexts. See text for further explanation. (From G. P. Urcelay, O. Lipatova, & R. R. Miller. 2009. Experiment 3. Learning and Motivation, 40, 343–363).
available data suggest only modest benefits of this technique in reducing relapse. Spaced Training in Extinction
Urcelay, Wheeler, and Miller (2009) showed that spaced extinction trials produce more enduring extinction than do massed extinction trials. In their study, rats that received extinction with 6-s intertrial intervals showed renewal (Experiment 2, Fig. 4.7) and spontaneous recovery (Experiment 3, Fig. 4.8), whereas this recovery was attenuated in subjects that received extinction with 600-s intertrial intervals (a result recently extended to a situation in which ABC renewal and spontaneous recovery summates; Laborda, Miguez, &
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Figure 4.7 Mean suppression ratio in the pres-
ence of the target conditioned stimulus. Brackets represent standard error of the mean. Lower scores indicate more conditioned suppression. No extinction = condition that received no extinction trials; Massed extinction trials = condition that received extinction trials with a short (6 s) intertrial interval; Spaced extinction trials = condition that received extinction trials with a long (600 s) intertrial interval; ABB (Extinction) = condition that received extinction trials and was tested in a context different from the one used during acquisition; ABA Renewal = condition that received extinction trials in a context different from the one used for both acquisition and testing. See text for further explanation. (From G. P. Urcelay, D. S. Wheeler, & R. R. Miller. 2009. Experiment 2. Learning & Behavior, 37, 60–73).
Miller, 2011). This is consistent with evidence that spaced acquisition trials are more effective than massed acquisition trials (e.g., Barela, 1999; Barnet, Grahame, & Miller, 1995; Humphreys, 1940) and the view that extinction involves learning (i.e., acquisition) of a new association. Urcelay, Wheeler, et al.’s results suggest that spacing exposure trials can be an effective means of attenuating relapse after exposure treatments. In a related study using an appetitive preparation, Moody, Sunsay, and Bouton (2006) found that spacing the extinction trials was effective in decreasing recovery in a reinstatement design (Experiment 5b); however, they found no benefit of spacing the extinction trials in preventing spontaneous recovery. Bjork and Bjork (1992, 2006) suggested that an expanding spaced schedule is more effective in producing robust
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Suppression ratio
0.5
Short delay Long delay
0.4 0.3 0.2 0.1 0.0 No extinction
Massed extinction trials
Spaced extinction trials
Condition
Figure 4.8 Mean suppression ratio in the presence
of the target conditioned stimulus. Brackets represent standard error of the mean. Lower scores indicate more conditioned suppression. No extinction = condition that received no extinction trials; Massed extinction trials = condition that received extinction trials with a short (6 s) intertrial interval; Spaced extinction trials = condition that received extinction trials with a long (600 s) intertrial interval; Short delay = condition tested after a short delay following extinction treatment; Long delay = condition tested after a long delay following extinction treatment. See text for further explanation. (From G. P. Urcelay, D. S. Wheeler, & R. R. Miller. 2009. Experiment 3. Learning & Behavior, 37, 60–73.).
extinction than trials spaced evenly. In this schedule, extinction trials are first massed and then gradually spaced out. This is meant to produce fast extinction with the massed trials and robust extinction with the spaced trials. This idea has received support from human verbal learning studies (e.g., Fritz, Morris, Nolan, & Singleton, 2007; Landauer & Bjork, 1978), but there is also evidence that this expanding extinction procedure is not more effective than uniformspaced extinction when subjects are tested in a renewal or spontaneous recovery situation (e.g., Karpicke & Roediger, 2007; Orinstein, Urcelay, & Miller, 2010). Orinstein et al. observed faster extinction with this procedure, but they did not observe a difference at test between the expanding schedule and a uniform-spaced schedule in an ABA renewal design in a human contingency learning task. However, the fact that extinction proceeded faster with the expanding schedule may still be beneficial for reducing
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drop-out rates in therapy, even if the end result is the same. Additional support for a beneficial effect of spacing extinction trials on behavior during extinction learning has been reported using operant techniques with snails (Sangha, Scheibenstock, Morrow, & Lukowiak, 2003) and using conditioned taste aversion preparations with rats (Westbrook, Smith, & Charnock, 1985). However, there are also reports of massed extinction trials supporting better extinction than spaced extinction trials (Cain, Blouin, & Barad, 2003; Moody et al., 2006; Rescorla & Durlach, 1987). Likely, parametric differences in CS duration as well as trial spacing are responsible for these discrepancies. Notably, data originating from tests administered in the extinction context very soon after extinction treatment (as is often done when researchers assess the consequences of extinction treatment) are neither informative nor particularly relevant to exposure therapy because they only identify the final level of behavioral control, which reflects not only associative extinction but also labile nonassociative endogenous and exogenous states immediately created by the extinction trials (Rescorla, 2004). Such data are not predictive of recovery after context changes or long retention intervals. Together these results suggest that there are likely benefits of spacing extinction trials, but much more research is needed to delimit the scope and mechanisms of this manipulation. Spaced Extinction Sessions
As discussed earlier, spacing extinction trials has been found to be of some benefit in preventing recovery from extinction. This suggests that spacing the extinction sessions might also support better extinction and/or promote less recovery. To test this prediction, Tsao and Craske (2000; also see Rowe & Craske, 1998a) compared massed sessions (i.e., four exposure sessions in a single day), uniform-spaced sessions (i.e., four exposure sessions with one every 5 days), and expanding-spaced sessions (i.e., four exposure sessions over a 16-day period, in an expanding schedule [days 1, 2, 6, and 16]) of exposure
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therapy on reducing return of fear with anxious public speakers. Contrary to the author’s expectations, all three groups of participants showed similar reduction of fear at post treatment, a result consistent with Orinstein et al.’s (2010) results when using expanding and uniformly spaced intertrial intervals in a human contingency learning task. However, as hypothesized by Tsao and Craske, when the participants were tested 1 month later, the massed condition showed the greatest fear recovery. The uniformand expanding-spaced extinction sessions both attenuated return of the fear response relative to the massed session condition, but they did not differ between themselves (for negative results of a similar manipulation, see Lang & Craske, 2000). More recently, Laborda et al. (2011) reported a series of three fear-conditioning experiments in which the benefit of spacing extinction trials and of spacing extinction sessions was evaluated, using rats as experimental subjects in a preparation in which renewal and spontaneous recovery summated. In brief, they found that the recovery of an extinguished association after a delayed context shift was reduced by spacing the extinction trials (600-s intertrial intervals vs. 6-s intertrial intervals; extending the results reported by Urcelay, Wheeler, et al., 2009) and by spacing the extinction sessions (7-d intersession intervals vs. 10-m intersession intervals). Moreover, simultaneously spacing the extinction trials and spacing the extinction sessions were shown to further attenuate strong recovery encouraged by a combination of renewal and spontaneous recovery. According to Bouton’s (1993, 2010) account, it is possible that, when using spaced sessions (or even spaced intertrial intervals), we are actually conducting extinction in multiple “temporal” contexts, which would put any benefit in reducing recovery here on common ground with the reduction in recovery observed following extinction in multiple physical contexts. Extinction in a Context as Similar as Possible to That of the Precipitating Conditioning Event
Laborda et al. (in press) demonstrated that the weaker recovery of responding observed with
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AAC renewal relative to ABC renewal is due to extinction treatment in the acquisition context strengthening the association between the target cue and the excitatory acquisition context. Presumably, extinction in the acquisition context facilitates reduction of conditioned responding by not only reducing the effective CS-outcome association but also by increasing the expected background rate of outcome occurrence, which is mediated by CS-training context and training context-outcome associations (see Laborda et al., in press for details). This manipulation is essentially extinction of the target cue in the presence of an additional excitor, which in this case is the acquisition context. Extinguishing the acquisition context or overshadowing the targetcontext association with another cue presented during extinction both reduced initial extinction and increased AAC renewal. This provides a theoretical basis for the empirically supported view that exposure therapy in a context similar to that of the precipitating conditioning event often is more effective than when it occurs in a neutral context (Massad & Hulsey, 2006). Deeper extinction and less recovery can be expected when the extinction context is more similar to the acquisition context. Unconditioned Stimuli Presentations During Extinction
Considerable research has found that the unpaired presentations of USs during extinction treatment can enhance extinction and its resistance to recovery. As shown in Figure 4.9, Rauhut et al. (2001) attenuated renewal by presenting explicitly unpaired USs interspersed among the CS-alone trials of extinction treatment in contrast to administering only CS-alone trials. This created the conditions for combining extinction to the CS and habituation to the US (details of their data indicate that the treatment did not constitute inhibitory training of the target). Alternatively, Bouton, Rosengard, Achenbach, Peck, and Brooks (1993) have suggested that the unsignaled USs make the context of extinction more similar to that of acquisition, which, as proposed in the previous section, could attenuate relapse. In related research, Bouton, Woods,
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To our knowledge, the applicability of such intervention to clinical settings has not been yet studied.
0.5
Suppression ratio
0.4
0.3
The Acquisition-Extinction Interval
0.2 No USs during extinction 0.1
Unpaired USs during extinction-4 Unpaired USs during extinction-16
0.0 Extinction
ABA Renewal test
Figure 4.9 Mean suppression ratio in the pres-
ence of the target conditioned stimulus. Lower scores indicate more conditioned suppression. Brackets represent standard error of the mean. No USs during extinction = group that received conditioned stimulus-only extinction trials; Unpaired USs during extinction-4 = group that received extinction trials with four interspersed noncontingent unconditioned stimuli presentations; Unpaired USs during extinction-16 = group that received extinction trials with 16 interspersed noncontingent unconditioned stimuli presentations; Extinction = mean responding in the last five extinction trials; ABA renewal test = mean responding in the first test trial back in the acquisition context. See text for further explanation. (Adapted from A. S. Rauhut, B. L. Thomas, & J. J. B. Ayres. 2001. Journal of Experimental Psychology: Animal Behavior Processes, 27, 99–114).
and Pineño (2004) observed slower reacquisition when occasional CS-US pairings occurred during extinction. They suggested that these occasional CS-US pairings re-created the context of acquisition, thereby constituting a condition more akin to an AAC renewal situation relative to the ABC situation created by continuous nonreinforcement, with AAC renewal being weaker than ABC renewal. However, a major challenge to translate this manipulation into practice is ethical problems in administering full-strength aversive USs in a therapeutic situation. An alternative approach is distantly suggested by McDonald and Siegel (2004), who determined that weak pharmacological USs can signal stronger versions of the same US (and might encourage habituation to the stronger US).
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A somewhat controversial variable of interest is the acquisition-to-extinction interval. Myers, Ressler, and Davis (2006) reported less reinstatement, renewal, and spontaneous recovery when this interval was very short relative to if it was long, a set of results that Myers et al. discussed in terms of the possible neural mechanisms underling extinction at various intervals. These data can also be interpreted within a behavioral framework based in Bouton’s (1997) context theory. As previously discussed, if the context is defined by spatial and temporal cues, then one can think of a long retention interval as inducing a new temporal context to create a renewal-like situation. That is, imposing a long retention interval between acquisition and extinction and between extinction and testing causes the temporal context to be equivalent to an ABC-like renewal design. A short acquisitionto-extinction interval, however, is analogous to an AAC-like renewal design. As previously mentioned, AAC renewal tends to be much weaker than ABA or ABC renewal. Thus, Myers et al.’s data can be accounted for as an instance of AAC treatment producing poor renewal compared to a strong ABC renewal effect. However, this finding is controversial, because there are many reported failures to confirm this effect (e.g., Alvarez, Johnson, & Grillon, 2007; Huff, Hernandez, Blanding, & LaBar, 2009; Kim & Richardson, 2009; Maren & Chang, 2006; Schiller et al., 2008; Woods & Bouton, 2008). Although there is no agreed-upon reason for the discrepant findings, it is likely related to how each researcher defines “long” and “short” when deciding upon parameters for the retention interval and is a function of the acquisition-to-test interval relative to other intervals rather than an absolute time. In brief, decreased recovery given short acquisition-to-extinction intervals is highly constrained by the temporal parameters used. A series of experiments recently reported by Johnson, Escobar, and Kimble (2010) began
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clarifying the scope of the benefit of immediate extinction. They found that a short acquisitionto-extinction interval decreased spontaneous recovery of extinguished fear responses in rats when the extinction-to-test interval was relatively long (a 3-d interval in Experiment 1 and a 7-d interval in Experiment 2), and increased recovery when the extinction-to-test interval was relatively short (a 2-d interval in Experiment 2), suggesting that it is an interaction between the acquisition-to-extinction and the extinction-totest intervals which determines the long-term effects of extinction.
CONCLUSIONS AND FINAL REMARKS: DEEPENING EXTINCTION AND/OR LINKING EXTINCTION AND TEST CONTEXTS Pavlov’s early studies (1927) and subsequent findings have been used to both understand acquired behavior and model human psychopathology. Research in the animal and human laboratory has found that Pavlovian conditioning provides a useful model of select mental disorders (e.g., anxiety, addictions) and some forms of cognitive-behavioral therapy. The study of experimental extinction has been fundamental for the development and success of exposure therapy. Unfortunately, despite extinction treatments and exposure therapies often immediately decreasing undesired behaviors, recovery and relapse commonly occur (e.g., Bouton, 2000). To decrease the possibility of recovery after successful extinction treatments, behavioral techniques have been developed based on associative models that speak to relapse prevention after exposure therapy. In the present chapter, we have reviewed these contemporary behavioral techniques to decrease recovery from extinction. Bouton, Woods, Moody, Sunsay, and García-Gutiérrez (2006) suggested that these techniques could be divided in two categories: techniques designed to deepen extinction learning, and techniques designed to enhance similarity between the context of extinction and the context of testing (i.e., linking or ‘bridging’ the extinction and test contexts). Dividing these
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strategies into these two categories appears inviting. However, upon careful inspection it becomes clear that almost none of them fit exclusively into just one of these two categories. We instead suggest that these techniques can be categorized as deepening extinction, linking the extinction and test contexts, both, or none of the above, with most of the techniques likely belonging in part to both categories. Using a massive number of extinction trials and extinguishing the target cue with a second conditioned excitor can be considered exemplars of techniques that deepen extinction learning and clearly do not “bridge” the extinction and test contexts. The use of retrieval cues from extinction is the only technique that can be categorized as purely “bridging” the extinction and test contexts. In this technique a component of the extinction context is explicitly presented at test. Extinction in multiple contexts can be thought of as increasing the total number of contextual features associated with extinction, thereby increasing the possibility of generalizing extinction learning to other contexts. But an alternative account of any reduction in recovery from extinction following extinction in multiple contexts is that renewal in each new extinction context may increase fear during extinction, thereby facilitating deeper extinction (Rescorla, 2001; but see Bouton, García-Gutiérrez, et al., 2006). Spaced extinction trials and sessions might reduce recovery of extinguished CRs because each trial and session can be considered as occurring in different “temporal” contexts, and in this way working as extinction in multiple “temporal” contexts. But spaced training has been showed to improve learning during acquisition (e.g., Barela, 1999) and extinction (e.g., Westbrook et al., 1985); consequently, we could think of these techniques as deepening extinction by enhancing the inhibitory-like learning that occurs during extinction. US-alone presentations interspersed during extinction treatment can be characterized as improving extinction learning because these US presentations either make the extinction context excitatory, thereby creating the conditions for explicitly unpaired conditioned inhibition training of the target cue (which is usually
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more effective in reducing responding than extinction) or fostering habituation of the US. Alternatively, unsignaled USs could make the context of extinction more similar to that of acquisition, thereby creating an AAC renewal situation, which is known to support less recovery from extinction than ABA or ABC renewal treatments. Bouton, Woods, et al. (2006) contend that efforts to produce deeper extinction are ineffectual; we respectfully disagree. To date the evidence is still unclear in several cases, but in some of them (e.g., massive extinction), deepening extinction is clearly effective in attenuating relapse. In our view, a more useful distinction is to think of behavioral interventions occurring at the time of extinction or at the time of testing. Those interventions in which the critical manipulation takes place during the extinction trials (e.g., massive extinction in multiple contexts) are potentially more useful to clinicians than manipulations that depend on interventions at the time of testing (e.g., extinction cues) because the clinician can more readily control what occurs during therapy than what occurs at the moment of potential relapse. Despite our tentative conclusions, the results reviewed in this chapter strongly suggest that more research is needed concerning the parameters that optimize these techniques and their underlying mechanisms. Lastly, additional research evaluating the translation of these laboratory techniques to clinical settings is essential so that they can be used to enhance psychotherapy.
ACKNOWLEDGMENTS National Institute of Mental Health grant 33881 supported preparation of this chapter. The authors thank Shannon Gaynor, Jason King, Gonzalo Miguez, Jun Park, Cody Polack, Lee Villinsky, and James Witnauer for their comments on an earlier version. Mario Laborda was supported by the Comisión Nacional de Investigación Científica y Tecnológica (CONICYT-Chile) and the Department of Psychology of the Universidad de Chile. Inquiries concerning this research should be addressed to Ralph R. Miller, Department of Psychology,
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SUNY- Binghamton, Binghamton, NY 139026000; e-mail:
[email protected].
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Tamai, N., & Nakajima, S. (2000). Renewal of formerly conditioned fear in rats after extensive extinction training. International Journal of Comparative Psychology, 13, 137–147. Thanos, P. K., Bermeo, C., Wang, G., & Volkow, N. D. (2009). D-cycloserine accelerates the extinction of cocaine-induced conditioned place preference in C57bL/c mice. Behavioural Brain Research, 199, 345–349. Thomas, B. L., & Ayres, J. J. B. (2004). Use of the ABA fear renewal paradigm to assess the effects of extinction with co-present fear inhibitors or excitors: Implications for theories of extinction and for treating human fears and phobias. Learning and Motivation, 35, 22–52. Thomas, B. L., Larsen, N., & Ayres, J. J. B. (2003). Role of context similarity in ABA, ABC, and AAB renewal paradigms: Implications for theories of renewal and for treating human phobias. Learning and Motivation, 34, 410–436. Thomas, B. L., Vurbic, D., & Novak, C. (2009). Extensive extinction in multiple contexts eliminates the renewal of conditioned fear in rats. Learning and Motivation, 40, 147–159. Tsao, J. C. I., & Craske, M. G. (2000). Timing of treatment and return of fear: Effects of massed, uniform-, and expanding-spaced exposure schedules. Behavior Therapy, 31, 479–497. Üngör, M., & Lachnit, H. (2008). Dissociations among ABA, ABC, and AAB recovery effects. Learning and Motivation, 39, 181–195. Urcelay, G. P., Lipatova, O., & Miller, R. R. (2009). Constraints on enhanced extinction resulting from extinction treatment in the presence of an added excitor. Learning and Motivation, 40, 343–363. Urcelay, G. P., Wheeler, D. S., & Miller, R. R. (2009). Spacing extinction trials alleviates renewal and spontaneous recovery. Learning and Behavior, 37, 60–73. Van Gucht, D., Vansteenwegen, D., Beckers, T., & Van den Bergh, O. (2008). Return of experimentally induce chocolate craving after extinction in a different context: Divergence between craving for and expecting to eat chocolate. Behavior Research and Therapy, 46, 375–391. Vansteenwegen, D., Hermans, D., Vervliet, B., Francken, G., Beckers, T., Baeyens, F., & Eelen, P. (2005). Return of fear in a human differential conditioning paradigm caused by a return to the original acquisition context. Behaviour Research and Therapy, 43, 323–336.
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CHAPTER 5 Learning and Anxiety A Cognitive Perspective Peter F. Lovibond
Traditionally, it has been assumed that associative learning is carried out by a low-level, reflexive, unconscious system, and accordingly associative explanations of clinical disorders have focused on irrational aspects of those disorders. However, research on human associative learning suggests that it depends critically on high-level, propositional, conscious processes. This perspective opens the door for an associative account of cognitive features of disorders, such as beliefs (rational and irrational). In this chapter I explore such an account, with a focus on anxiety. I argue that learned anxiety involves the development of threat beliefs regarding associations between antecedent stimuli and harmful outcomes. Performance is an automatic expectancy-based process. Threat expectancy and anxiety are also modulated by instrumental actions such as avoidance and safety behaviors. Threat beliefs may be established by direct experience, observation, language, and inference. Effective treatment can best be achieved by a coordinated combination of these same pathways.
In the mid 1980s I started working in human conditioning after 10 years of research in nonhuman animal conditioning. By that time I was already persuaded of the centrality of the role of expectancy in both learning and performance, and I was an enthusiast of informational, S-S models of conditioning (e.g., Kamin, 1969; Rescorla & Wagner, 1972). Therefore, I could be said to have had a cognitive orientation to nonhuman animal learning. Nonetheless, I was quite unprepared for what I observed in the human conditioning laboratory. I had assumed that associative learning was a fundamental ability that conferred so much adaptive advantage that it would be robust and ubiquitous. Instead, my human research participants found associative learning quite difficult, and they would regularly fail to notice what seemed to me to be simple and obvious contingencies such as differential conditioning. Even more surprising was that there was a clear positive relationship between their verbally
expressed knowledge and their conditioned responses (CRs). I had fully expected that their CRs would serve as an index of associative links formed by a conditioning system quite separate from language and conscious belief. I soon discovered that my observations were far from atypical. It all started to make sense when I read a chapter by Michael Dawson and Anne Schell (1985), which carefully reviewed the human learning literature and concluded that associative learning in humans met the criteria for a controlled cognitive process. Before long I realized that the connection between associative learning in non-human animals and high-level cognitive processes in humans opened up far greater applications for associative models than the more traditional view I had grown up with. My subsequent research on human conditioning and anxiety over the following 20 years has explored these connections and applications. In this chapter I will briefly summarize laboratory
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research on human associative learning, outline a propositional model of learning my colleagues and I have recently developed, and then consider the applications of this model to anxiety, anxiety disorders, and treatment.
THE ROLE OF COGNITIVE PROCESSES IN HUMAN ASSOCIATIVE LEARNING There are three main empirical findings that demonstrate the critical role of cognitive processes in human associative learning. First, contrary to popular belief, the development of Pavlovian conditioned responses (CRs) is closely associated with the development of explicit contingency knowledge—that is, consciously available and verbally expressible knowledge about the relationships between the conditioned stimulus (CS) and the unconditioned stimulus (US). Every systematic review of the human Pavlovian conditioning literature has reached this conclusion (e.g., Boakes, 1989; Brewer, 1974; Dawson & Schell, 1985; De Houwer, 2009; Lovibond & Shanks, 2002; Shanks, 2010). A similar pattern is observed for instrumental learning (Dawson & Schell, 1987; Shanks, Green, & Kolodny, 1994). Second, associative learning is strongly influenced by verbal instruction, for example, regarding acquisition and extinction (e.g., Grings, Schell, & Carey, 1973). Third, associative learning is subject to principles of reasoning. For example, Kamin’s (1969) blocking effect is only strongly observed when blocking is a permissible inference, both in the case of causal judgment (De Houwer, Beckers, & Glautier, 2002; Lovibond, Been, Mitchell, Bouton, & Frohart, 2003) and Pavlovian fear conditioning (Mitchell & Lovibond, 2002). The Dual-System Model of Associative Learning
Of course, nobody would deny that humans possess high-level cognitive abilities, nor that such abilities could be applied to the problem of learning associations. Therefore, the most common reaction to the extensive body of empirical evidence outlined in the previous section has been to propose that humans can
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achieve associative learning through two independent systems, one high level and one low level. The high-level system is generally considered to be symbolic and representational in nature, to depend on a limited capacity working memory mechanism, to be closely associated with language and consciousness, and to have emerged late in evolution. The low-level system is generally considered to involve excitatory and inhibitory links, to operate automatically and unconsciously, and to have emerged early in evolution. This proposed “dual-system” architecture is pervasive within the fields of learning, cognition, and neuroscience, and it has parallels in virtually every field of psychology (e.g., Ashby & Maddox, 2005; Dienes & Perner, 1999; Evans, 2008; Kahneman & Frederick, 2002; Olson & Fazio, 2001; Reber, 1989; Sloman, 1996; Squire, 1994; Wilson, Lindsey, & Schooler, 2000). However, there are serious limitations to the ability of dual-system models to account for the phenomena of human associative learning. Empirical Evidence Against the DualSystem Model of Associative Learning
The first difficulty faced by dual-system models is empirical. The clearest prediction from such models is that manipulations involving distraction and cognitive load should impact preferentially on the limited-capacity highlevel system, thus allowing the operation of the proposed low-level unconscious system to be observed. It should be common, for example, to observe development of CRs despite a lack of conscious awareness. In fact, however, the consistent outcome is that distraction and cognitive load impair both conscious contingency knowledge and CRs (e.g., Dawson & Biferno, 1973; Ross & Nelson, 1973). Similarly, verbal instruction should impact directly on the highlevel cognitive system and not on CRs, yet it affects both. A second difficulty for dual-system models concerns testability. Most dual-system models are implicitly endorsed rather than explicitly formulated, and hence they are not specified with sufficient detail as to make clear testable predictions beyond the basic ones noted earlier
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(see Mitchell, De Houwer, & Lovibond, 2009b). In particular, there are no published dual-system models of learning that provide a complete specification of how the two systems interact. Does the high-level system suppress output from the low-level system if their outcomes are in conflict? If both systems reach the same conclusion, is the behavioral output increased? Some dualsystem models, such as Squire’s (1994) declarative/procedural theory, allow that both systems can produce CRs. Such models could in principle account for the ability of verbal instruction to produce CRs, but they cannot account for the ability of verbal instruction to inhibit CRs generated by the low-level procedural system without additionally specifying a top-down inhibitory link. But if the high-level system is given too much control over the low-level system, the dual-system model effectively reduces to a single-system model because the two systems are no longer independent. A final and related problem for dual-system models concerns parsimony. It will not have escaped the reader’s attention that all of the empirical evidence reviewed to this point could be explained by postulating a single system, namely the cognitive system. The clear prediction of dual-system models that unconscious learning should be commonplace is not supported by the evidence. These models are forced to suggest that the strong relationship between contingency awareness and CRs is simply a coincidence, and one that is all the more astonishing given the radically different architectures attributed to the cognitive and reflexive systems. Many examples of empirical dissociations in the literature that have been classically interpreted in dual-system terms are equally amenable to a single-system account (Chater, 2003; Kinder & Shanks, 2001; Newell & Dunn, 2008). If dualsystem models do not provide a substantial explanatory benefit to justify their greater complexity, then a single-system model is to be preferred on the grounds of parsimony. I do not expect that all readers will be persuaded at this point. I have recently experienced the strength of commitment to dualsystem thinking in the reaction of commentators to a paper in which my colleagues and I have put
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the case for a single-system propositional model (Mitchell, De Houwer, & Lovibond, 2009a). It is likely that many readers will have their own favorite example of apparent unconscious learning. And there are certainly some data that are open to alternative interpretations, such as Perruchet’s (1985) gambler’s fallacy effect in eyeblink conditioning (see Weidemann, Tangen, Lovibond, & Mitchell, 2009). Nonetheless, I hope that readers will find it valuable to explore the novel implications of a single-system approach to learning for anxiety and anxiety disorders, a field that has been dominated by dual-system theorizing. At the very least, they may be persuaded that relatively more of the interesting phenomena of learning and anxiety can be understood in cognitive terms, and fewer by appealing to an unconscious link-based system. Accordingly, I will briefly outline a single-system cognitive model of associative learning that my colleagues and I have recently developed (Mitchell et al., 2009a), before moving on to the applications to anxiety.
A SINGLE-SYSTEM PROPOSITIONAL MODEL OF ASSOCIATIVE LEARNING The essential features of our single-system model of associative learning are as follows. First, learning is a form of reasoning that is effortful and attention demanding. Second, learning gives rise to associative knowledge that is represented in a symbolic, propositional form, such as a causal belief, that is accessible to consciousness and to language. Finally, performance involves the utilization of acquired associative knowledge in flexible ways. Knowledge of action-outcome contingencies can guide voluntary (instrumental) behavior. Knowledge of stimulus-outcome contingencies can guide expectancies (beliefs about future states of affairs), which in turn elicit speciesspecific anticipatory behaviors (CRs). Associative knowledge can also be combined with other knowledge to guide inference and further action. For a more complete presentation of this model, see Mitchell et al. (2009a). At this stage, however, it is important to dispense with some mischaracterizations of single-system models, and our model in particular.
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First, our single-system model does not deny the role of lower level, unconscious processes. Indeed, it depends critically on processes such as perception, object recognition, and episodic memory. The critical difference between our model and dual-system models is that we deny that low-level unconscious processes constitute an independent associative system capable of directly driving behavior. Rather, we argue that low-level processes work cooperatively with higher level symbolic processes to achieve learning and performance, and that the output of this coordinated system is available to consciousness. Second, our model does not assume that all behavioral output is voluntary. There is good evidence that the performance of Pavlovian CRs is automatic, as shown for example by research on omission schedules in appetitive conditioning (Williams & Williams, 1969). However, automaticity is not in itself a reason to retreat to a reflexive, link-based model of learning. There are many examples of high-level cognitive processes that are automatic (for example, reading, as in the color Stroop task). Furthermore, the automatic nature of anticipatory behavior is not restricted to learning based on direct exposure to stimulus contingencies; it applies equally to knowledge gained symbolically—witness, for example, the emotional reaction of a person who receives bad news by verbal means. Similarly, our propositional model does not imply that all learning is logical, normative, and adaptive. There may well be biases, errors, and confusions that lead to faulty attributions and false beliefs. Finally, our model does not imply that nonhuman animals learn through a different system from humans, nor does it imply conversely that animals must have conscious beliefs of a human type. The similarity in behavioral response of humans and laboratory animals leads us to suppose that there is a great deal of overlap in mechanism. Accordingly we propose that laboratory animals such as rats have a precursor to the human cognitive system. Across the evolution of humans, we assume an increase in the sophistication of the cognitive system, leading to a progressively richer capacity to represent the environment, eventually allowing an artificial symbolic system (language) to emerge. Such an
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evolutionary pathway is far more plausible biologically than the traditional dual-system model, which assumes that humans inherited almost unchanged a primordial associative link system, and at the same time suddenly acquired a unique cognitive system that had little or no precedent in our immediate ancestors (for further discussion, see Mitchell, De Houwer, & Lovibond, 2009b).
APPLICATIONS OF THE SINGLE-SYSTEM PROPOSITIONAL MODEL TO ANXIETY AND ANXIETY DISORDERS Expectancy, Threat Beliefs, and Anxiety
The first and most compelling advantage of a single-process, propositional model of associative learning is its compatibility with the “cognitive content” of learned anxiety. In the laboratory, participants who learn positive or negative contingencies between CSs and aversive USs such as electric shock are able to describe these relationships verbally, and when presented with CSs they can rate their expectancy of the US (e.g., Dawson & Biferno, 1973). These contingency beliefs and expectancy ratings are highly correlated with anxiety-related CRs such as skin conductance, heart rate, and startle modulation (e.g., Purkis & Lipp, 2001). We have recently extended these procedures to provide a laboratory model of instrumental avoidance learning (Lovibond, 2006; Lovibond, Saunders, Weidemann, & Mitchell, 2008). In this model, participants first learn that a Pavlovian CS is a predictor of shock; that is, they receive CS-shock pairings. They then learn that an instrumental response (pressing a particular button) is able to cancel the shock predicted by the Pavlovian CS. As their belief in the efficacy of the avoidance response increases, both their rated expectancy of shock and their skin conductance responses decline. But if response availability is suddenly withdrawn, there is an immediate return of shock expectancy and skin conductance responses. These laboratory data are consistent with the single-system view that expectancy of a harmful outcome is the critical cognitive precursor
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to anxiety. According to this view, Pavlovian conditioning involves a progressive increase in the strength of a causal belief regarding the contingency between a stimulus and a harmful outcome. Such beliefs are stored in long-term memory. Presentation of the stimulus activates the belief, leading to an expectation of the harmful outcome, which in turn activates the anxiety system. The opportunity to perform an instrumental avoidance response can attenuate the expectation of harm and hence reduce anxiety. This view is highly consistent with cognitive appraisal models of anxiety that have been developed entirely independently by researchers working with anxiety and stress in naturalistic settings. For example, the classic cognitive model of Lazarus and Folkman (1984) proposes an initial “threat appraisal” component that involves the rapid detection of threat in the environment. Threat appraisal may, however, be moderated by “coping appraisal,” the perception that one has a response available that can avoid or mitigate the impact of the threatened event. The overall level of anxiety or stress that is elicited will be determined by the combination of these two forms of appraisal. The notion of coping appraisal also has parallels with Bandura’s (1977) concept of self-efficacy, and (inversely) with Seligman’s (1975) concept of learned helplessness. The phenomenology of clinical anxiety disorders is likewise dominated by threat beliefs and expectancy of harm. In addition to the defining diagnostic feature of anxious emotional arousal, patients with anxiety disorders report a wide variety of specific threat beliefs and expectancies that are consistent with the stimuli they fear and the behaviors in which they engage. For example, height phobics report exaggerated perceptions of the risk of falling and being injured (Menzies & Clark, 1995a); social phobics report a preoccupation with rejection and negative appraisal (Rapee, 1995), and patients with panic disorder report believing they they are going to collapse, go crazy, suffocate, or have a heart attack (Clark, 1986). According to a cognitive, propositional model of learning, the symptoms of anxiety and panic follow directly from these exaggerated threat beliefs. According to dual-system models,
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however, threat beliefs are causally irrelevant to anxiety—they either arise coincidentally from the operation of an independent cognitive system, or secondarily from interpretation of anxiety symptoms that are driven by an unconscious conditioning mechanism (Bouton, Mineka, & Barlow, 2001; Öhman & Mineka, 2001). The idea that a person’s conviction of his or her own imminent death has no casual role in anxiety whatsoever is hard to accept at face value. But if the concession is made that cognitive appraisal can contribute to anxiety, then the value of the dual-system model evaporates. Why postulate two systems to explain a phenomenon when one will do, especially when there is so little evidence for a separate unconscious system in the laboratory? More direct evidence for the causal role of threat beliefs comes from studies of the interaction between anxiety and coping behavior. Clinicians have noted that patients with anxiety disorders will often engage in “safety behaviors” in an attempt to forestall or minimize the catastrophic outcomes they fear. The behaviors that patients choose align closely with the outcomes they fear (Salkovskis, Clark, & Gelder, 1996). For example, social phobics who are concerned about blushing will wear high-necked clothing, and panic patients who fear collapsing will seek physical support. Furthermore, it has been shown that allowing patients to engage in safety behaviors interferes with exposure therapy. This type of therapy involves exposing patients to antecedent stimuli they fear, and it has been shown to be a powerful method for reducing both anxiety symptoms and threat beliefs. According to the single-system model, exposure works by the same mechanism as extinction in the laboratory: Experience with the antecedent stimulus (CS) in the absence of harm (US) contradicts and thereby weakens the threat belief, which in turn generates less anxiety on subsequent encounters. The reason that safety behaviors interfere with the extinction process is that they provide an alternative causal explanation for the absence of the harmful outcome, thus protecting the initial threat belief. Indeed, patients often report thinking that if they hadn’t engaged in a particular protective strategy, the
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harmful event they fear would surely have eventuated. Clinical research has shown that asking patients to refrain from safety behaviors during exposure can increase the benefits they obtain from this therapy (e.g., Salkovskis, Clark, Hackmann, Wells, & Gelder, 1999). A similar pattern emerges from an older literature on obsessive-compulsive disorder, in which patients engage in a variety of “neutralizing behaviors” that are closely tied to their obsessions—for example, excessive washing in the case of patients concerned with contamination, or cognitive rituals such as thinking “good thoughts” in the case of patients who believe their “bad thoughts” will cause harm to themselves or others. Clinical research with this disorder has shown that exposure therapy is most effective when combined with “response prevention”; in other words, asking the patient to experience the antecedent stimuli he or she fears (e.g., touching a door handle) while refraining from neutralizing behavior (Abramowitz, 1997). The single-system propositional model provides a coherent account of the strong interrelationships observed between threat beliefs, safety behavior, and anxiety. Traditional dualsystem models, by contrast, attribute anxiety to an unconscious Pavlovian conditioning system, safety behavior to an unconscious instrumental response-shaping system, and threat beliefs to a causally irrelevant cognitive system. Any coherence among these phenomena is essentially coincidental. To examine more directly the role of safety and threat beliefs in the extinction of Pavlovian conditioned fear, we have carried out a number of studies on “protection from extinction” in the laboratory. This phenomenon refers to the ability of an inhibitory CS to interfere with extinction of an excitatory CS when the inhibitory CS is presented at the same time as the excitatory CS during nonreinforced exposure (Rescorla, 2003; Soltysik, Wolfe, Nicholas, Wilson, & Garcia-Sanchez, 1983). We have shown this effect both with a Pavlovian CS (Lovibond, Davis, & O’Flaherty, 2000) and more recently with an instrumental avoidance response (Lovibond, Mitchell, Minard, Brady, & Menzies, 2009). In each case the effect was observed on
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both self-report US expectancy and on skin conductance responding. The parallel with the ability of safety stimuli and safety behaviors to interfere with exposure therapy in patients is striking. In both of these situations, a source of safety (in the environment, or arising from the person’s own actions) prevents the reduction in threat value of a feared stimulus when it is presented without any adverse consequence. Furthermore, the explanations given by learning theorists and clinicians are highly similar. For example, the well-established Rescorla-Wagner (1972) model of associative learning explains protection from extinction in terms of the inhibitory CS canceling the expectation of the US that is elicited by the excitatory CS, thus eliminating the “surprisingness” of the nonoccurrence of the US, and leaving the excitatory strength of the excitatory CS unchanged. This explanatory convergence, coupled with the congruence that is observed between anxiety and threat beliefs in both the laboratory and the clinic, strongly suggests that the Rescorla-Wagner model is capturing critical functional characteristics of a propositional learning system. In other words, there is good reason to consider that associative theories such as the Rescorla-Wagner model, and the non-human animal conditioning data they were designed to account for, have relevance not for supposedly unconscious processes in humans but for mainstream conscious cognition—reasoning and belief formation (Mitchell et al., 2009a). Irrational Anxiety
If learning in humans is a form of reasoning, as proposed by a single-system propositional model, an important question that arises is how learning processes could explain irrational behavior observed in patients. One answer to this question is the same as that provided by traditional dual-system models—namely, unfortunate learning experiences. An obvious example is exposure to a traumatic event or a sustained period of abuse or mistreatment. But there is opportunity for maladaptive learning even in more common situations. For example, a child whose first experience with public speaking leads to embarrassment and humiliation may
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represent social situations and their potential threat in a way that is resistant to subsequent corrective information. Processes such as situational avoidance and within-situation safety behaviors may further protect these initial threat beliefs from disconfirmation. A second way in which irrational behavior can arise in patients is through faulty reasoning. As noted earlier, the acquisition and modification of beliefs is not necessarily a cold and logical process. A popular example in the reasoning literature is the Wason (1966) selection task, which is answered incorrectly by more than 90% of university students (Johnson-Laird & Wason, 1970). Reasoning errors often arise from selection of the wrong strategy, for example, a shortcut heuristic that has worked in similar situations in the past but is not applicable to a target situation (Tversky & Kahneman, 1974). Social psychologists have identified a wide range of shortcut strategies (for example, conformity, stereotyping) that are self-serving or save mental work but lead to unfortunate consequences such as intolerance (e.g., Hilton & von Hippel, 1996). In clinical populations, examples of faulty reasoning are similarly abundant. The majority of contemporary models of psychopathology identify faulty reasoning and maladaptive beliefs as the core locus of disturbance—for example, threat beliefs in anxious patients, beliefs about body shape in patients with eating disorders, and beliefs in one’s own inadequacy in depression (e.g., Beck, 1967; Clark, 1986; Waller, Ohanian, Meyer, & Osman, 2000). As discussed further in the sections that follow, clinical interventions similarly focus on the correction of maladaptive beliefs and the reasoning errors that serve to maintain them. Although it is certainly the case that cognitive psychologists themselves often appeal to unconscious processes with an associative flavor to explain errors in reasoning (e.g., Sloman, 1996), a viable alternative is that human reasoning itself is an imperfect process (see Evans, 2008). Routes to Anxiety
It is well accepted in the clinical literature that anxiety reactions may arise through at least three routes: associative learning, observation, and
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language (e.g., Rachman, 1977). The singlesystem propositional model of learning readily accommodates these three etiological pathways, because they all provide information that is relevant to the development of a causal representation of the environment in general, and potential threat in particular. Dual-system theories, by contrast, typically assert that anxiety arising from associative learning and observational learning is mediated by a low-level unconscious system, whereas anxiety arising from language is mediated by the high-level cognitive system (e.g., Le Doux, 1996; Öhman & Mineka, 2001). Thus, evidence for integration between sources of information, particularly between associative learning and language, is problematic for the dual-system model. Lovibond (2003) used a retrospective revaluation design in autonomic fear conditioning to test for integration of this type in the laboratory. The design required two pieces of information (AB+ trials and A– trials), given in successive phases, to be combined in order to “solve” the task, namely to revalue upward the causal status of stimulus B. Participants were able to perform this revaluation when the two pieces of information were both provided by direct exposure to the contingencies, when they were both provided by verbal instruction, and critically, when one piece of information was provided by direct exposure and the other by instruction. The latter result demonstrates successful integration of causal knowledge about threat acquired through direct experience and through language, implying that associative learning gives rise to information at a symbolic level similar to that conveyed by language. The single-system propositional model suggests a further route to anxiety that has similarities with an associative learning episode but does not involve direct experience with harm, namely the “near-miss” experience. The most harmful event that can befall any organism is its own death, but by definition any organism that experiences this outcome is not going to learn about it! However a close brush with death is a different matter. A subset of traumatic events that lead to posttraumatic stress syndrome (PTSD) involve no direct physical harm, but
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LEARNING AND ANXIETY
they do involve the perception on the part of individuals that they were going to die or could easily have died. A traditional reflexive theory (e.g., Hull, 1943; Levis, 1989) would attribute learning in such a case to the formation of a stimulus-response (S-R) association between stimuli present at the time and the fear response. However, there is little direct evidence for S-R learning in the laboratory in either non-human animals or humans, and good evidence for Pavlovian S-S learning, that is, learning about the actual stimulus relationships in the environment (e.g., Dickinson, 1980). The propositional model offers an alternative account of near-miss learning, based on reasoning on the part of the individual that he or she nearly experienced a catastrophic outcome and could actually experience that outcome if exposed to the same situation again. This account offers the ability to account for the cognitive content of near-miss learning (e.g., rumination), as well as circumstances in which fear learning does not occur, such as when the person attributes the nonoccurrence of harm to a stable source of safety (Lovibond, 2001). A final route to anxiety that needs to be considered is the genetic route. Disentangling the roles of genetic and learning processes in fear and anxiety has proven to be surprisingly difficult. One relatively clear finding is that trait anxiety, the general tendency to be chronically anxious or be easily provoked to anxiety, has a strong genetic basis. This trait is associated with anxiety disorders, as well as depressive disorders (Hettema, Neale, & Kendler, 2001). It is generally considered that environmental triggers work additively or multiplicatively with trait anxiety to determine the level of state anxiety at any time (e.g., Barlow, 2002). One environmental factor that has received a great deal of attention, of course, is associative learning. Since Watson and Rayner (1920), learning theorists have argued that fear reactions, especially specific phobias that are tied to particular stimuli such as insects, heights, or water, have an associative basis. The primary support for this idea is that conditioning with an aversive US is sufficient to establish a fear reaction to any arbitrary stimulus. Therefore, it is a plausible hypothesis that an observed fear
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reaction to a specific stimulus may have had its origin in conditioning. However, it became apparent that the range of stimuli that supported phobic reactions is very restricted, and not in a way that would be predicted by conditioning theory. For example, the majority of phobic stimuli are naturally occurring rather than human made, yet many human-made stimuli are associated with pain and trauma. Thus, genetic factors may again play a role, but this time in relation to specific stimuli rather than general trait anxiety. The first person to articulate this possibility in a formal model was Seligman (1971) in his classic “preparedness” theory of phobias. Seligman did not abandon associative learning in favor of genetic influences; rather, he supplemented associative learning with the idea of biological belongingness. According to this idea, potentially phobic stimuli such as insects are initially neutral, like other stimuli, but are particularly predisposed or prepared to enter into fear learning if paired with an aversive event in a particular individual. If the aversive event is sufficiently traumatic, a single pairing with a prepared stimulus was thought to be able to establish a long-lasting phobia. Seligman’s (1971) model is an important one to consider in the context of unconscious conditioning and the single-/dual-system debate, because Seligman suggested that prepared associations may be formed by an especially primitive and cognitively inpenetrable conditioning mechanism. Furthermore, specific phobia is often presented as a clear example of irrational learning (e.g., Öhman & Mineka, 2001). One immediate problem with the direct conditioning model of specific phobia, including Seligman’s (1971) preparedness model, is that patients with specific phobia often cannot recall a conditioning episode in their past (e.g., Menzies & Clarke, 1995b; Öst, 1991). At face value, it seems extremely unlikely that an event sufficiently traumatic to condition a long-lasting phobia would be forgotten by the patient. Furthermore, people who are exposed to a documented traumatic event, if they develop any long-lasting symptoms at all, tend to show a distinct pattern of symptoms known as posttraumatic stress disorder (PTSD) rather than
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specific phobia (Bryant, 2003). Far from being unable to recall their traumatic event, patients with PTSD experience intrusive thoughts and flashbacks of the
