From DNA to Social Cognition

FROM DNA TO SOCIAL COGNITION

ffirs01.indd i

7/25/2011 10:10:27 AM

FROM DNA TO SOCIAL COGNITION

Edited by

RICHARD EBSTEIN SIMONE SHAMAY-TSOORY SOO HONG CHEW

A JOHN WILEY & SONS, INC., PUBLICATION

ffirs02.indd iii

7/25/2011 10:10:28 AM

Copyright © 2011 by Wiley-Blackwell. All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada 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, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com. Library of Congress Cataloging-in-Publication Data: Ebstein, Richard P., 1943From DNA to social cognition / Richard Ebstein, Simone Shamay-Tsoory, Soo Hong Chew. p. cm. Includes index. ISBN 978-0-470-54396-2 (cloth) 1. Cognitive neuroscience. 2. Affective neuroscience. 3. Social perception. 4. Decision making. 5. Genetic psychology. I. Shamay-Tsoory, Simone. II. Chew, Soo Hong, 1954- III. Title. QP360.5.E27 2012 612.8′233–dc22 2011008254 Printed in the United States of America oBook ISBN: 978-1-118-10180-3 ePDF ISBN: 978-1-118-10178-0 ePub ISBN: 978-1-118-10179-7 10

ffirs03.indd iv

9

8

7

6

5

4

3

2

1

7/25/2011 10:10:28 AM

CONTENTS

Contributors

vii

Introduction

1

Richard P. Ebstein, Mikhail Monakhov, Poh San Lai, and Simone G. Shamay-Tsoory

PART 1 EMPATHY: NEURAL BASES AND GENETIC CORRELATES

19

1.1 Genes Related to Autistic Traits and Empathy

21

Bhismadev Chakrabarti and Simon Baron-Cohen

1.2 The Behavioral Genetics of Human Pair Bonding

37

Hasse Walum and Lars Westberg

1.3 Brain Networks Supporting Empathy

47

Martin Schulte-Rüther and Ellen Greimel

1.4 The Human Mirror Neuron System and Social Cognition

63

Sook-Lei Liew and Lisa Aziz-Zadeh

1.5 Motivational Aspects of Future Thinking in the Ventromedial Prefrontal Cortex

81

Arnaud D’Argembeau

PART 2 MORAL NEUROSCIENCE AND EMOTION

91

2.1 Contributions of the Prefrontal Cortex to Social Cognition and Moral Judgment Processes

93

Chad E. Forbes, Joshua C. Poore, and Jordan Grafman v

ftoc.indd v

7/25/2011 10:10:29 AM

vi

CONTENTS

2.2 Emotion and Moral Cognition

111

Michael Koenigs

2.3 The Neuroanatomical Basis of Moral Cognition and Emotion

123

Roland Zahn, Ricardo de Oliveira-Souza, and Jorge Moll

2.4 Envy and Schadenfreude: The Neural Correlates of Competitive Emotions

139

Jonathan Dvash and Simone G. Shamay-Tsoory

PART 3 Genes and Decision Making

157

3.1 The Somatic Marker Framework and the Neurological Basis of Decision Making

159

Antoine Bechara

3.2 A Model of the Initial Stages of Drug Abuse: From Reinforcement Learning to Social Contagion

185

Gilly Koritzky, Adi Luria, and Eldad Yechiam

3.3 Extrinsic Effects and Models of Dominance Hierarchy Formation

203

Matthew Druen and Lee Alan Dugatkin

3.4 Complex Social Cognition and the Appreciation of Social Norms in Psychiatric Disorders: Insights from Evolutionary Game Theory

215

Martin Brüne and Julia Wischniewski

3.5 From Neuroeconomics to Genetics: The Intertemporal Choices Case as an Example

233

Itzhak Aharon and Sacha Bourgeois-Gironde

Index

ftoc.indd vi

245

7/25/2011 10:10:29 AM

CONTRIBUTORS

Itzhak Aharon, Interdisciplinary Centre, Herzliya, Israel Liza Aziz-Zadeh, The Brain and Creativity Institute, University of Southern California, Los Angeles, California Simon Baron-Cohen, Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, U.K. Antoine Bechara, Department of Psychiatry, Faculty of Medicine, and Desautels Faculty of Management, McGill University, Montreal, Quebec, Canada, and Department of Psychology, and Brain and Creativity Institute, University of Southern California, Los Angeles, California Sacha Bourgeois-Gironde, Institut Jean-Nicod (ENS-EHESS), Paris, France Martin Brüne, Research Department of Cognitive Neuropsychiatry and Psychiatric Preventive Medicine, LWL University Hospital, Ruhr-University Bochum, Germany Bhismadev Chakrabarti, Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading, Reading, U.K., and Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, U.K. Arnaud D’Argembeau, Department of Psychology, University of Liège, Belgium Matthew Druen, Department of Biology, University of Louisville, Louisville, Kentucky Lee Alan Dugatkin, Department of Biology, University of Louisville, Louisville, Kentucky Jonathan Dvash, University of Haifa, Haifa, Israel Chad E. Forbes, Imaging Sciences Training Program, Radiology and Imaging Sciences, Clinical Center and National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD vii

flast.indd vii

7/25/2011 10:10:28 AM

viii

CONTRIBUTORS

Jordan Grafman, Traumatic Brain Injury Research Laboratory, Kessler Foundation Research Center, West Orange, NJ Ellen Greimel, Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of the RWTH, Aachen, Germany, and Cognitive Neurology Section, Institute of Neuroscience and Medicine (INM-3), Research Center, Jülich, Germany, and Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of the RWTH, Aachen, Germany Michael Koenigs, Department of Psychiatry, University of Wisconsin—Madison, Madison, Wisconsin Gilly Koritzky, Technion—Israel Institute of Technology, Haifa, Israel Sook-Lei Liew, The Brain and Creativity Institute, University of Southern California, Los Angeles, California, and The Division of Occupational Science & Occupational Therapy, University of Southern California, Los Angeles, California Adi Luria, Technion—Israel Institute of Technology, Haifa, Israel Jorge Moll, Cognitive and Behavioral Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil Ricardo de Oliveira-Souza, Cognitive and Behavioral Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, RJ, Brazil, and Gaffrée e Guinle University Hospital, Rio de Janeiro, RJ, Brazil Joshua C. Poore, Cognitive Neuroscience Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD Martin Schulte-Rüther, Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of the RWTH Aachen, Germany, and Cognitive Neurology Section, Institute of Neuroscience and Medicine (INM-3), Research Center, Jülich, Germany Simone G. Shamay-Tsoory, University of Haifa, Haifa, Israel Hasse Walum, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden Lars Westberg, Department of Pharmacology, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden Julia Wischniewski, Research Department of Cognitive Neuropsychiatry and Psychiatric Preventive Medicine, LWL University Hospital, Ruhr-University Bochum, Germany Eldad Yechiam, Technion—Israel Institute of Technology, Haifa, Israel Roland Zahn, The University of Manchester, School of Psychological Sciences, Neuroscience and Aphasia Research Unit, Manchester, U.K., and Cognitive and Behavioral Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil

flast.indd viii

7/25/2011 10:10:28 AM

Log of CD38 expression

1.50 1.00 0.50 0.00 −0.50 −1.00 −1.50 Unaffected parents

ASD

Figure 1. Distribution of the expression (log transformed) of the CD38 gene. Lower expression in the ASD group is significant (p = 0.003). See Lerer et al. (2010), Ebstein et al. (2011), and Riebold et al. (2011) for more details.

bins.indd 1

7/25/2011 10:10:04 AM

(a)

OXT

PLACEBO

−0.21

−0.07

0.00

Suppression index

−0.05 −0.10 −0.15 −0.20 −0.25 ∗

−0.30 −0.35

Hormone

(b) 1.2

Log of power

1.15 1.1 1.05 1 0.95 0.9 0.85 0.8 placebo

Oxytocin Hormone

Biological Motion

Non-biological Motion

Figure 2. (a) Suppression in the 8–10-Hz range, OT versus placebo. Both bars show suppression for the biological motion conditions compared with the nonbiological condition, but this suppression is enhanced significantly by OT. Error bars represent standard error (SE). (b) An 8–10-Hz interaction between Treatment × Motion. OT had an opposite effect on EEG for perception of biological versus nonbiological stimuli.

bins.indd 2

7/25/2011 10:10:05 AM

HSD11B1 LHCGR

CYP11B1∗

CYP17A1

ESR1

CYP19A1

ESR2

SCP2

ARNT2

IGF1 NTF3 OXT

NTRK1∗ NTRK1∗

HSD17B4 HSD17B2∗ NLGN4X EN-2∗ HOXA1 OXTR GABRB3∗ AVPR1B∗ CNR1∗ GABRA6 MAOB VIPR WFS1∗

Experiment 2:

Experiment 2:

AS case-control nominal association (uncorrected p 3). If all possible relationships in a group exhibit transitivity, the hierarchy is said to be strictly linear; if intransitive relationships exist (e.g., 1 beats 2, 2 beats 3, but 3 beats 1), the hierarchy is considered less linear or nonlinear (Chase, 1980). In animal populations, individuals at the top of a hierarchy often typically have access to more resources, better mates, or larger territories than others in a group. For example, in groups of male green swordtail fish (Xiphophorus helleri),

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

203

c12.indd 203

7/25/2011 10:10:24 AM

204

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

top-ranked individuals are responsible for up to 75% of paternity in the next generation (Luo, Sanetra, Scharti, & Meyer, 2005). Variables that determine fighting ability—and ultimately the structure of a dominance hierarchy—fall into two categories: intrinsic and extrinsic factors. Intrinsic factors describe an animal’s physical fighting prowess or resource holding power (RHP) (Parker, 1974). The most frequently studied intrinsic traits are those related to body size or weaponry. Generally, such traits reliably predict the outcome of fights when large differences exist between opponents at the start of a contest. Extrinsic effects capture how types of antecedent social experiences—winning a fight, losing a fight, watching a fight—alter an individual’s performance in subsequent fights (Dugatkin & Dugatkin, 2007; Fawcettt & Johnstone, 2010; Fuxjager & Marler, 2010; Hock & Huber, 2009; Hsu et al., 2005, 2009; Landau, 1951b). Winner and loser effects are defined as an increased probability of winning at time T, based on victories at times T − 1, T − 2, and so on, and an increased probability of losing at time T, based on losing at time T − 1, T − 2, and so on, respectively. Although winner effects seem to be less common than loser effects, both have been documented in a variety of animal types including freshwater fish, crustaceans, and mammals (Chase, Bartolomeo, & Dugatkin, 1994; Dugatkin & Druen, 2004; Dugatkin, 2009; Fawcettt & Johnstone, 2010; Fuxjager & Marler, 2010; Hsu et al., 2005, 2009). Winner and loser effects occur when an individual’s direct involvement in aggressive contests increases or decreases that individual’s assessment of its own RHP. It is important to note that winner and loser effects are not necessarily flip sides of a coin, as one effect can exist in the absence of the other. For example, animal A may be more likely to defeat animal B if it has just won a fight with animal C (winner effect), but this does not necessarily mean that animal C is more likely to be defeated during a subsequent fight when there is an absence of loser effects. A related type of extrinsic factor, the bystander effect—also known as the eavesdropper effect—occurs when the observer of an aggressive interaction between two other individuals changes its estimate of the RHP of the individuals it has observed (Earley & Dugatkin, 2002; McGregor, 2005). That is, bystanders acquire information about future adversaries before having to fight them directly (Coultier, Beaugrand, & Lague, 1996; Johnsson & Akerman, 1998; Oliveira, McGregor, & Latruffe, 1998). Bystander effects come in two flavors. Bystander-winner effects occur when a bystander increases its appraisal of the RHP of an individual it observed defeating a rival. Bystander-loser effects occur if a bystander devalues its appraisal of an individual’s RHP it has observed lose a fight. Experimental studies have identified the presence of bystander effects in birds, mammals, and fish (Dugatkin, 2009). To date, however, most researchers have focused more on intrinsic traits and less on bystander, winner, and loser effects. There is, though, mounting evidence that extrinsic effects change behaviors in important ways that strongly determine the final structure of dominance hierarchies (Chase, 1982b, 1985; Dugatkin, 2001; Fawcettt & Johnstone, 2010; Fuxjager & Marler, 2010; Hock & Huber, 2009; Hsu et al., 2005, 2009). Pioneering work on the impact of winner and loser effects on hierarchy formation was initiated by H. G. Landau in the early 1950s (Landau, 1951a, 1951b). Landau was puzzled that the linear hierarchies often found in nature did not at first emerge from his mathematical models. Initially, his models were driven by individual differ-

c12.indd 204

7/25/2011 10:10:24 AM

MODELS OF EXTRINSIC EFFECTS

205

ences in inherent fighting abilities—conceptually analogous with intrinsic traits. However, after Landau added winner and loser effects—extrinsic effects—hierarchy structures became more linear and were thus more similar to hierarchies observed in animals. One of the earliest efforts to incorporate bystander effects into a theoretical framework was Ivan Chase’s “jigsaw model” (Chase, 1980, 1982a). This model begins with three players and in this group, a bystander (3) observes an aggressive exchange between the other two players (1 and 2). For the purposes of illustration, now suppose that 1 defeats 2. Next, the bystander is allowed to fight either 1 or 2. Chase described the four possible outcomes of dyadic fights: (1) double dominance in which 1 defeats 3, (2) double subordinance in which 3 defeats 2, (3) bystander dominance in which 3 defeats 1, and (4) the case in which 2 defeats 3 (initial subordinate defeats the bystander). Chase found that in his model, double dominance and double subordinance always led to linear hierarchies, whereas the other two outcomes always led to substantially nonlinear structures. Results from experiments with domesticated chickens supported his model’s finding that double dominance and double subordinance were indeed associated with the linear hierarchies found in chickens (Chase, 1982a, 1982b). Work by Chase and Landau has provided new ways to understand how experience through repeated aggressive encounters influences the structure of dominance hierarchies. Yet, despite the importance of their papers on dominance hierarchy research, several fundamental issues concerning extrinsic effects were not addressed until several decades later. For example, although animals are known to be able to “size up” their opponents, neither researcher included this ability in their models. Is it possible that when RHP is assessed, different kinds or combinations of extrinsic effects have different implications for hierarchy formation? In addition, Landau did not examine winner and loser effects independently but only hierarchy formation when both were at work. Similarly, Chase did not isolate the consequences of different kinds of bystander effects. For example, it could easily be the case that bystander effects might act only via losers or winners of fights but not through both fighters. In the last decade or so, the study of extrinsic effects on dominance hierarchy formation has received renewed interest from theoreticians and empiricists alike. Because each research domain has witnessed significant developments, we will highlight mathematical models as well as experimental work. We will first examine a set of related models developed by one of the authors and his colleagues that simulates the formation of dominance hierarchies when pure winner and loser effects, bystander effects, and several other salient factors are evaluated independently and in combination (Dugatkin, 1997, 2001; Dugatkin & Earley, 2003, 2004). Next, we will review a body of empirical work on extrinsic effects in green swordtail fish. We conclude with a discussion and recommendations for future work.

3.3.1

MODELS OF EXTRINSIC EFFECTS

The basic model begins with randomly chosen individuals paired in potentially aggressive contests (Dugatkin, 1997). Before the start of a simulation, each player is assigned a score that denotes the individual’s assessment of its own fighting ability.

c12.indd 205

7/25/2011 10:10:24 AM

206

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

This is analogous to a player’s estimate of its own RHP. In encounters with other group members, players can opt either to be aggressive and initiate a fight or to retreat from the contest. To provide a rule for deciding whether to fight or to flee, an “aggression threshold” variable was incorporated into the model. Lowering the aggression threshold creates conditions in which individuals are more likely to choose to fight individuals whose RHP is significantly greater than their own. Conversely, higher aggression thresholds produce conditions in which individuals are more likely to submit to an individual whose RHP is only slightly greater than their own. As a result, there are three potential outcomes when two players meet: (1) both players meet the aggression threshold and both decide to fight, (2) one player meets the threshold and fights while the other player does not meet the threshold and retreats, and (3) neither player meets the aggression threshold and so neither individual initiates a fight (a double kowtow).

3.3.2 WINNER/LOSER EFFECTS In simulations in which winner effects alone were examined, linear hierarchies emerged in which it was possible to unambiguously determine the relative rank of each individual. A different pattern emerged when loser effects were examined in the absence of winner effects. Under these conditions, a clear α (top-ranked) individual always emerged in the group, but the relationships of all other individuals were indeterminate. When both winner and loser effects were considered, increasing winner effects for a given value of the loser effect raised the number of individuals with clearly delineated positions in a hierarchy, and the converse was also true. These results suggest that the type of hierarchy predicted depends fundamentally on whether winner or loser effects operate in isolation or in combination. Winner effects alone produce structured hierarchies in which group members fall into unambiguous positions within the hierarchy. This is because when only winner effects are in play, pairs of individuals are more likely to interact by fighting, rendering it relatively easy to determine each individual’s rank. Loser effects alone produce hierarchies in which a clear α individual is detectable, but the rank-order among other group members remains unclear. This occurs because loser effects quickly produce individuals that refrain from aggression because of their low estimate of their own RHP after a few losses, leading to an abundance of double kowtows and to a limited number of actual fights or attack/retreat interactions.

3.3.3 WINNER AND LOSER EFFECTS AND INDIVIDUAL RECOGNITION Individual recognition is generally assumed to stabilize hierarchies. Yet so far we have only discussed models in which individuals do not recognize the identity of others in their group. To mimic individual recognition, the basic model described above was modified so that at the start of a contest, individuals were aware of their own starting RHP value as well as of the starting RHP score of all other group members (Dugatkin & Earley, 2004). Individuals, however, were not privy to changes in a group member’s RHP that occurred because of winner and loser effects

c12.indd 206

7/25/2011 10:10:24 AM

BYSTANDER EFFECTS

207

experience by other group members. This simulation always generated nonlinear, nontransitive hierarchies. These results are strikingly different from the results described earlier in which winner and loser effects operated in the absence of individual recognition. From an evolutionary perspective, the inclusion of individual recognition results in a somewhat perplexing set of predictions. Individual recognition is believed to have evolved to enable the detection of cheaters—individuals that are likely to bluff in aggressive interactions—and to increase the stability of dominance hierarchies (Barnard & Burk, 1979; Pagel & Dawkins, 1997). As such, the absence of individual recognition might be regarded as the primitive state in animal social groups that may already have dominance hierarchies and winner and loser effects. However, individual recognition nullifies the impact that winner and loser effects have on the probability of linear, transitive hierarchies forming when it is introduced. Much more work needs to be done to understand why this is so.

3.3.4

BYSTANDER EFFECTS

To model bystander effects, the simulation was altered so that all fights in a group were observed by all other group members (Dugatkin, 2001). At the conclusion of a particular round of fighting, all individuals in the group altered their estimates of each group member’s RHP. When bystander-winner effects were in operation, a bystander raised its estimation of the fighting ability of another in the group when it observed that individual win a fight. Conversely, when bystander-loser effects were in play, if a bystander witnessed another individual lose a fight or retreat, that individual’s RHP was devalued. Hierarchies were first simulated when bystander effects alone were in operation— that is, in the absence of pure winner and loser effects. When only bystander-winner effects are at work, each group has a clear bottom-ranking (omega) individual, but the ranking of other group members is difficult to determine. Why? Recall that all individuals begin with the same RHP values. During early rounds, one individual within the group will, by chance, lose a majority of its initial fights, but because only bystander-winner effects are in play, other group members react to this individual— let’s call it X, as if its initial RHP is unchanged. However, because most group members will have emerged victorious in some of their early contests, and such victories would have been observed by others, group members view everyone except X as having a high RHP, and so there are few attacks on anyone except X. As a result, most aggressive interactions are of the “attack” (the omega individual) rather than of the “both individuals opt to fight” variety. Hierarchy structures are dramatically different when only bystander-loser effects are in operation. Now, victories and defeats are randomly distributed throughout the group. In contrast to the bystander-winner case in which most aggressive interactions are attack-retreat, now individuals always fight when they meet. The lack of a clear hierarchy in such groups is because individuals change the RHP value they assign to others, but only by devaluing it while self-estimates of RHP do not change. When two players confront one another, each determines the other to have an RHP lower than their own and they both initiate a fight, but who wins such fights is random as the real RHP of the fighters has not changed.

c12.indd 207

7/25/2011 10:10:24 AM

208

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

3.3.5

JOINT EFFECTS

When pure winner and bystander-winner effects are in play simultaneously, a linear hierarchy—in which the rank order of individuals is clearly defined emerges—with most meetings resulting in attack–retreat exchanges. The same result is found when pure loser and bystander-loser effects are combined and when all four effects are in play simultaneously. In these scenarios, all individuals have the same information regarding RHPs. That is, if individual A assesses its own RHP as R, and any of the situations just described are in play, all other individuals also assess A’s RHP as R, so that there is agreement about the value of each and every individual’s RHP. This in turn creates a social environment in which individuals respond to asymmetries in RHPs, making most interactions take the form of attack–retreat.

3.3.6

EXTRINSIC EFFECTS AND MERGING HIERARCHIES

We have discussed how winner/loser effects and bystander effects might alter the ultimate structure of dominance hierarchies in single groups. But what happens when two smaller hierarchies merge to form a single larger one? In other words, would an individual’s social rank in an initial hierarchy have any predictive power with respect to the social rank that is attained when that hierarchy is merged with another—when hierarchies are fused (Dugatkin & Earley, 2003)? Imagine the case where hierarchies are formed in two groups of four, initially identical, individuals. In these groups, as in previous models, winner, loser, bystanderwinner, and bystander-loser effects then influence RHP, and the eventual rank-order of the group members. These smaller groups are then merged into one larger assemblage. Hierarchy formation redevelops in this larger group and can be influenced by the same suite of extrinsic factors, as well as by any preexisting status asymmetries generated by interactions in the small groups. When only pure winner effects were at play, a clear linear hierarchy with all positions delineated occurred both in the four-member groups and in the fused eightmember hierarchy. Rank position within the large hierarchy is strongly correlated to rank position in an individual’s former hierarchy. That is, the two top-ranked individuals in small groups were also the two top-ranked members in the large group, the two β individuals from the smaller groups became the third- and fourthranked members of the larger group, and so on. Moreover, individuals in the fused group were equally likely to engage in aggressive interactions with the three individuals that were in their initial hierarchy and the four individuals (newly encountered) from the other small group. When loser effects alone were at play, only the top-ranked member in each of the four-member groups was delineated. When groups were merged, only a single α individual could be identified. That is, for each fusion, one of the α individuals from the four-member groups retained its alpha status, and one of the alphas from the four-member group dropped in rank and was part of a group of seven others with ambiguous ranks. In both pre- and post fusion groups, the majority of interactions were either attack–retreats or double kowtows (rather than fights). When only bystander-loser effects were at play, in both prefusion and postfusion groups, the majority of interactions were fights, and wins and losses were randomly distributed among group members, producing no discernable hierarchy. When

c12.indd 208

7/25/2011 10:10:24 AM

EMPIRICAL STUDIES: THE CASE OF THE GREEN SWORDTAIL

209

bystander-winner effects alone were in operation, a clear bottom-ranking (omega) individual emerged but the rank of other individuals was ambiguous in the fourmember groups. In the fused group, most interactions were attack–retreats and double kowtows, but it was difficult to rank any of the eight individuals. Two intriguing patterns, however, did emerge. First, individuals in the eight-member groups interacted almost strictly with those that were not in their original four-member group. Second, within each of the four-member groups (now merged into a group of eight), one individual clearly ranked below the other three, and yet there was no correlation between this individual’s identity and the identity of the bottom-ranked individual from the prefused groups. An interesting pattern also emerged when loser and bystander-loser effects acted together. In the prefusion, four-member hierarchies, results were similar to the case of loser/bystander-loser simulations described earlier. That is, a clear linear hierarchy emerged with all positions being definitive and most interactions taking the form of attack–retreat. When smaller groups were merged, two top-ranking individuals were detected, but the relative ranks of the other players were difficult to assign. As before, most interactions remained attacks–retreats. The reason is that only the two top-ranked individuals interacted with all other group members. The remaining six individuals interacted, but only with those in their former, prefusion group. As such, their rank was only clear with respect to their former groupmates, and it was consistent with previous ranks in the four-member hierarchies. When winner and bystander-winner effects operated simultaneously, the results were similar to the case of pure winner effects—a clear linear hierarchy with all positions occupied existed in the both the small and large hierarchies. Moreover, an individual’s rank in the eight-member hierarchy was strongly related to its rank position in the original four-member group. Again, as in the case when just winner effects were operating, individuals in the fused group were equally likely to undertake aggressive interactions with all postfusion group members.

3.3.7

EMPIRICAL STUDIES: THE CASE OF THE GREEN SWORDTAIL

One of the most frequently used species for experiments on aggression and dominance hierarchies is the green swordtail fish (Xiphophorus helleri). Green swordtails are a member of the family Poeciliidae (e.g., livebearers), and they live primarily in small-order freshwater streams and rivers in Honduras and Mexico. Fights between nonterritorial males often progress through four stages: initiation, escalation, resolution, and reinforcement. During the initiation stage, individuals signal hostile intentions to one another using relatively low-cost, noncontact, frontal and lateral sigmoid displays in which the fins are fully spread and their opercula (gill coverings) are flared. Escalated fighting includes numerous bouts of mouth-wrestling (gripping the upper or lower jaw), circling, chasing, and repeated bites to the body, including the opercula and gonopodium (reproductive organ) (Franck & Ribowski, 1989). Swordtails readily form linear hierarchies in field and laboratory settings making them ideal for work on aggression and dominance. In an empirical test of models that describe how winner and loser affects determine the rank orders of individuals within hierarchies, Dugatkin and Druen (2004) used a random-selection procedure in which individuals within triads of male green swordtails (matched for size to control for intrinsic attributes) were

c12.indd 209

7/25/2011 10:10:25 AM

210

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

provided an experience of winning or losing a fight, or else a “neutral” experience. These initial contests took place out of view of other triad members. Subsequently, individuals were placed within the same arena and allowed to establish dominance relationships. Consistent with the notion that winner and loser effects can alter the structure of hierarchies, it was found that previous winners most often obtained the α rank, previous losers fell to the γ position, and neutral individuals occupied the β position. In addition, rank orders tended to be stable over relatively long periods. Within a group of male swordtails, an individual may alternately assume one of several roles in an emerging hierarchy. It may be actively fighting with another, acting as a bystander within sensory range of a fight between two other group members, or remaining solitary. Several intriguing questions related to bystander effects can be examined in such a system. For example, will bystanders respond differently to individuals that were observed winning versus individuals that were observed losing a fight? Are bystanders sensitive to the dynamics of an observed fight? Does watching fights affect aggressive tendencies in a general way or is behavior altered toward specific individuals? In a series of experiments using groups of swordtails, Earley and his colleagues tested for the presence of bystander effects by using experimental arenas that allowed for the control of the flow of information in staged fights between pairs of males and a bystander (Earley, Tinsley, & Dugatkin, 2003; Earley, Druen, & Dugatkin, 2005; Earley & Dugatkin, 2002). Subsequently, bystanders could be pitted against observed winners or observed losers to test whether information acquired in this manner affected the outcomes of fights. Observing fights had a significant impact on swordtail aggressive behavior. These effects were most evident when bystanders faced individuals that were observed winning an initial fight. In such cases, bystanders exhibited a reduced probability of initiating fights compared with individuals that had not viewed a fight. A bystander’s assessment of a winner’s fighting ability did not depend on the particular progression of an observed fight (e.g., whether it had witnessed an escalated or a nonescalated match). Fight intensity, however, altered bystander behavior when individuals were pitted against individuals who they saw defeated in a contest. Here, bystanders were less likely to initiate aggression or win against previous losers that had persisted for long periods. These findings suggest that bystanders avoid engaging in potentially costly fights by making decisions about fighting particular types of individuals. Earley and his colleagues next examined “aggressive priming” as a possible alternative explanation for changes in bystander aggression brought about by watching fights. Priming occurs when bystanders exhibit a generalized agonistic response that is insensitive to a group member’s identity (Hollis, Dumas, Singh, & Fackelman, 1995). To test for priming effects, fights were first staged in view of a bystander. Bystanders then faced naïve, unfamiliar individuals. Watching and then confronting such individuals increased a bystander’s probability of initiating aggression and winning the fight only when their opponents were slightly larger in body size. This result was opposite to the diminished aggression response detected in the first experiment (wherein eavesdroppers fought previous winners). In other words, bystander responses seemed to rest on familiarity rather than on the observation of aggression per se. Overall, these findings suggest that male swordtails acquire

c12.indd 210

7/25/2011 10:10:25 AM

REFERENCES

211

specific information that enables them to make distinctions between winners and losers of observed fights—a finding that is consistent with bystander effects.

3.3.8

DISCUSSION AND FUTURE DIRECTIONS

Given the preponderance of dominance hierarchies in nature, the growing evidence that fighting experience affects subsequent aggressive interactions, and the strong support for individual recognition in many social systems, models such as those described in this chapter can be used to generate explicit predictions about hierarchy formation. Depending on which effects described in this chapter are in play, a variety of hierarchy forms are possible. For example, we have observed that one might expect only a clear bottom-ranking individual, a clearly defined hierarchy, or no hierarchy at all (where wins and losses are randomly distributed among individuals) based on different combinations of extrinsic effects. Winner, loser, and bystander effects also have implications for hierarchy formation when two groups merge. Again, the precise nature of these implications depends on which effects are operating. Nevertheless, it is important to mention two limitations of the models discussed earlier. First, individuals randomly interact with others in their group. Although this may be the case for some animal social groups, in others, subordinates may actively avoid confrontations with dominant individuals, thus rendering interactions in such systems nonrandom. Second, although such models can examine different strengths of winner, loser, and bystander effects, they do not manipulate how long such effects last. It is certainly possible that the duration of different extrinsic effects may be unequal (i.e., in some systems, loser effects may decay more slowly than winner effects; Drummond & Cannales, 1998). Currently, theoretical work outpaces empirical efforts, and both laboratory and field studies that explicitly test predictions made by mathematical models are needed. Although bystander-winner, bystander-loser, pure winner, and pure loser effects are evident in a wide variety of taxa including insects (Alexander, 1961), mollusks (Zack, 1975), fish (Beaugrand & Zyan, 1985), birds (Drummond & Osorno, 1992), reptiles (Schuett, 1996), and rodents (van de Poll & Smets, 1982), no work has yet definitively documented winner, loser, and bystander effects, and the detailed aggressive interactions when individuals are in groups, in any single species. In an ambitious research program, hierarchy formation could be examined in different species that had documented combinations of bystander-winner, bystander-loser, pure winner, and pure loser effects. The types of hierarchies that emerged could then be compared with the various hierarchy structures from the models discussed earlier.

REFERENCES Alexander, R. D. (1961). Aggressiveness, territoriality and sexual behavior in field crickets. Behaviour 17, 131–223. Barnard, C., & Burk, T. (1979). Dominance hierarchies and the evolution of individual recognition. J Theor Biol 81, 65–73.

c12.indd 211

7/25/2011 10:10:25 AM

212

EXTRINSIC EFFECTS AND MODELS OF DOMINANCE HIERARCHY FORMATION

Beaugrand, J. P., & Zyan, R. (1985). An experimental model of aggressive dominance in Xiphophorus helleri. Behav Process 10, 1–52. Chase, I. 1980. Social process and hierarchy formation in small groups: A comparative perspective. Am Soc Rev 45, 905–924. Chase, I. (1982a). Behavioral sequences during dominance hierarchy formation in chickens. Science 216, 439–440. Chase, I. D. (1982b). Dynamics of hierarchy formation: The sequential development of dominance relationships. Behaviour 80, 218–240. Chase, I. D. (1985). The sequential analysis of aggressive acts during hierarchy formation: An application of the “jigsaw puzzle” approach. J An Behav 33, 86–100. Chase, I. D., Bartolomeo, C., & Dugatkin, L. A. (1994). Aggressive interactions and intercontest interval: How long do winners keep winning? J An Behav 48, 393–400. Coultier, S., Beaugrand, J. P., & Lague, P. C. (1996). The role of individual differences and patterns of resolution in the formation of dominance orders in domestic hen triads. Behav Process 38, 227–239. Drummond, H., & Canales, C. (1998). Dominance between booby nestlings involves winner and loser effects. Anim Behav 55, 1669–1676. Drummond, H., & Osorno, J. L. (1992). Training siblings to be submissive losers: Dominance between booby nestlings. J An Behav 44, 881–893. Dugatkin, L. A. (1997). Winner effects, loser effects and the structure of dominance hierarchies. Behav Ecol 8, 583–587. Dugatkin, L. A. (2001). Bystander effects and the structure of dominance hierarchies. Behav Ecol 12, 348–352. Dugatkin, L. A. (2009). Principles of Animal Behavior, 2nd ed. New York: W.W. Norton. Dugatkin, L. A., & Druen, M. (2004). The social implications of winner and loser effects. Proc Roy Soc Lond Biol Lett 271, S488–S489. Dugatkin, L. A., & Dugatkin, A. D. (2007). Extrinsic effects, estimating opponents’ RHP, and the structure of dominance hierarchies. Biol Lett 3, 614–616. Dugatkin, L. A., & Earley, R. L. (2003). Group fusion: The impact of winner, loser, and bystander effects on hierarchy formation in large groups. Behav Ecol 14, 367–373. Dugatkin, L. A., & Earley, R. L. (2004). Individual recognition, dominance hierarchies and winner and loser effects. Proc Roy Soc Lond 271, 1537–1540. Earley, R. L., & Dugatkin, L. A. (2002). Eavesdropping on visual cues in green swordtails (Xiphophorus helleri): A case for networking. Proc Roy Soc Lond 269, 943–952. Earley, R. L., Tinsley, M., & Dugatkin, L. A. (2003). To see or not to see: Does previewing a future opponent affect the contest behavior of green swordtail males (Xiphophorus helleri)? Naturwissenschaften 90, 226–230. Earley, R. L., Druen, M., & Dugatkin, L. A. (2005). Watching fights does not alter a bystander’s response towards naïve conspecifics in male green swordtail fish (Xiphophorus helleri). J An Behav 69, 1139–1145. Fawcett, T. W., & Johnstone, R. A. (2010). Learning your own strength: Winner and loser effects should change with age and experience. Proc Roy Soc Lond 277, 1427–1434. Fuxjager, M. J., & Marler, C. A. (2010). How and why the winner effect forms: Influences of contest environment and species differences. Behav Ecol 21, 37–45. Franck, D., & Ribowski, A. (1989). Escalating fights for rank-order position between male swordtails (Xiphophorus helleri): Effects of prior rank-order experience and information transfer. Behavioral Ecology and Sociobiology 24, 133–143.

c12.indd 212

7/25/2011 10:10:25 AM

REFERENCES

213

Hock, K., & Huber, R. (2009). Models of winner and loser effects: a cost-benefit analysis. Behaviour 146, 69–87. Hollis, K. L., Dumas, M., Singh, P., & Fackelman, P. (1995). Pavlovian conditioning of aggressive behavior in blue gourami fish (Trichogaster trichopterus): Winners become winners and losers stay losers. J Comp Psychol 109, 123–133. Hsu, Y., Earley, R. L., & Wolf, L. (2005). Modulation of aggressive behavior by fighting experience: Mechanisms and contest outcomes. Biol Rev 80, 1–42. Hsu, Y. Y., Lee, I. H., & Lu, C. K. (2009). Prior contest information: Mechanisms underlying winner and loser effects. Behav Ecol Sociobiol 63, 1247–1257. Johnsson, J., & Akerman, A. (1998). Watch and learn: Preview of the fighting ability of opponents alters contest behaviour in rainbow trout. J An Behav 56, 771–776. Landau, H. G. (1951a). On dominance relations and the structure of animal societies: I. Effects of inherent characteristics. Bull Math Biophys 13, 1–19. Landau, H. G. (1951b). On dominance relations and the structure of animal societies: II. Some effects of possible social causes. Bull Math Biophys 13, 245–262. Luo, J., Sanetra, M., Schartl, M., & Meyer, A. (2005). Strong reproductive skew among males in the multiply mated swordtail Xiphophorus multilineatus (Teleostei). J Hered 96(4), 346–355. McGregor, P. (2005). Animal Communication Networks. Cambridge: Cambridge University Press. McGregor, P. K., & Peake, T. (2000). Communication networks: Social environments for receiving and signalling behaviour. Acta Ethologica 2, 71–81. Oliveira, R. F., McGregor, P. K., & Latruffe, C. (1998). Know thine enemy: Fighting fish gather information from observing conspecific interactions. Proc Roy Soc Lond 265, 1045–1049. Pagel, M., & Dawkins, M. S. (1997). Peck orders and group size in laying hens: “Futures contracts” for non-aggression. Behav Process 40, 13–25. Parker, G. A. (1974). Assessment strategy and the evolution of fighting behaviour. J Theor Biol 47, 223–243. Schuett, G. W. (1996). Fighting dynamics of male copperheads, Agkistrodon contortrix (Serpentes, Viperidae): Stress-induced inhibition of sexual behavior in losers. Zoo Biol 15, 209–221. van de Poll, N. E., & Smeets, J. (1982). Behavioral consequences of agonistic experiences in rats: Sex differences and the effect of testosterone. J Comp Physiol Psychol 96, 893–903. Zack, S. (1975). A description and analysis of agonistic patterns in an opisthobranch mollusc, Hermissenda crassicornis. Behaviour 53, 238–267.

c12.indd 213

7/25/2011 10:10:25 AM

3.4 COMPLEX SOCIAL COGNITION AND THE APPRECIATION OF SOCIAL NORMS IN PSYCHIATRIC DISORDERS: INSIGHTS FROM EVOLUTIONARY GAME THEORY MARTIN BRÜNE AND JULIA WISCHNIEWSKI Research Department of Cognitive Neuropsychiatry and Psychiatric Preventive Medicine, LWL University Hospital, Ruhr-University Bochum, Germany

3.4.1

INTRODUCTION

If there is anything like a common denominator of psychiatric disorders and abnormal psychological conditions, it is perhaps the maladaptive way affected individuals get along with other people. For example, withdrawal from the social environment is typical of depression; avoidance of encounters with strangers characterizes social anxiety disorder; mistrust to the degree of paranoia and uncooperative behavior is part of the schizophrenia phenotype but may also occur in borderline personality disorder; and opportunistic exploitation of others is at the core of psychopathy. This listing is certainly not exhaustive. It may, however, illustrate that across diagnostic categories, social interaction is compromised in psychiatric disorders, sometimes to the extent that basic rules of social conduct are violated and moral values disregarded, sometimes in more subtle ways in that suspicion or anxiety is only elicited in situations that are experienced as extremely stressful—with vague boundaries to normal psychological functioning. In any event, except for the case of psychopathy, the above-mentioned examples have in common that patients assume such interpersonal attitudes in defense of perceived threat, however improbable the actual danger may be (Gilbert, 2001). More specifically, cognitive defense mechanisms are

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

215

c13.indd 215

7/25/2011 10:10:25 AM

216

INSIGHTS FROM EVOLUTIONARY GAME THEORY

often abnormally activated in psychiatric disorders to protect the self from or anticipated violations of rules of cooperation, reciprocity, and trust by significant others (conversely, in the case of psychopathy, the affected individual is the perpetrator of rule violations; Mealey, 1995; Troisi, 2005). To date, however, there has been little empirical research on that matter partly because reliable tools for the assessment of how psychiatric patients appreciate social rules and values such as cooperation, reciprocity, and trust were unavailable. In the last 20 years or so, research into the underlying mechanisms of maladaptive social interaction has shown that impaired social cognition is critical in this regard. The term “social cognition” concerns the perception and interpretation of social signals to construct representations of the relation between oneself and others and to use those representations flexibly to guide social behavior (Lysaker et al., 2005). The concept has somewhat blurry boundaries to the domains of motivation, emotion, attention, memory, and decision making (Adolphs, 2001). It is nevertheless useful in that social cognition emphasizes those cognitive domains that have a direct link to social interaction such as emotion recognition from facial expressions, prosody and body posture, as well as more sophisticated cognitive capacities like conscious reflection on one’s own and others’ mental states in terms of desires, feelings, intentions, knowledge, and so forth. The latter aspect of social cognition has been termed “theory of mind” or “mentalizing,” which broadly overlaps with the concept of “empathy” in terms of both its cognitive and affective aspects (Singer et al., 2006; Shamay-Tsoory, Aharon-Peretz, & Perry, 2009). A wealth of studies have demonstrated that social cognition is compromised in many—if not all—psychiatric conditions, ranging from childhood autism to dementia (Brüne and Brüne-Cohrs, 2006). However, the exact patterns of manifestation of social cognitive deficits differ between psychiatric disorders—some patients may particularly have difficulties in deciphering the emotional content of facial expressions, whereas others may be lost when asked to reason about the mental life of others or may be unable to empathize with others. Comparable variation also exists with regard to the underlying proximate mechanisms (i.e., gene–environment interaction) that cause social cognitive impairment (Ebstein, Israel, Chew, Zhong, & Knafo, 2010). In any case, it has now become increasingly clear that impoverished social cognitive capacities have consequences in many areas of social functioning, as has been demonstrated, for example, in schizophrenia patients (Brüne, Schaub, Juckel, & Langdon, in press). From an evolutionary point of view, this association is logical simply because the core functions of social cognition—mentalizing and empathy—evolved in humans in the first place to maintain complex relationships and to reinforce cooperation among individuals within a social group—which critically entails the ability to detect violations of the rules of cooperative interaction (Trivers, 1971), but also to build and sustain reliable and enduring relationships based on reciprocality and trust. Accordingly, it can be predicted that dysfunctional social cognition, in turn, has behavioral consequences for social interaction as, for instance, expressed in the form of mistrust, and heightened vigilance toward cooperativeness and reciprocity or conversely as violations of social norms and values. The question why cooperation between genetically unrelated individuals exists at all has long puzzled evolutionary theorists. It has been argued that altruistic behavior and reciprocity would not be able to spread in a population because

c13.indd 216

7/25/2011 10:10:25 AM

EVOLUTIONARY ASPECTS OF COOPERATION

217

they easily fell prey to exploitative counter-strategies of free-riders, thus producing a group of egotists who had greater reproductive success than those prosocial individuals who freely shared resources with others—akin to the concept of homo oeconomicus, who exploits others to his own benefit while disregarding the needs of others (Axelrod & Hamilton, 1981). However, humans seem to have evolved a set of rules of social norms that reinforce cooperation and help to keep rule violations at bay. These social norms form the basis of what we call moral values, which are sensitive to what is fair or unfair, and whom to trust or distrust. They constitute human universals, such that unfairness is rejected or even punished at one’s own expense in very similar ways across cultures (Henrich & Boyd, 2001). The conditions under which people choose, or not choose, cooperative strategies have experimentally been modeled by game theorists. A substantial literature exists on how psychologically healthy humans behave in game-theoretical scenarios, in which they assume the role of a proposer or a recipient in the distribution of (virtual) goods (Camerer, 2003a). This approach has only recently begun to be applied to research into psychiatric disorders. Central to this chapter is the hypothesis that the study of patients with psychiatric conditions using game-theoretical approaches can greatly improve our understanding of the nature to psychopathological signs and symptoms because the perceived—or factual—threat of basic human needs for cooperation and trust in others is at the core of many cognitive distortions and abnormal behaviors in patients with psychiatric disorders. The chapter, therefore, aims to summarize evolutionary and ontogenetic aspects of cooperation, game-theoretical approaches to study cooperation, brain mechanisms involved in the ability to appreciate social rules and norms, and research into psychiatric conditions based on game-theoretical models.

3.4.2

EVOLUTIONARY ASPECTS OF COOPERATION

In a seminal paper published in 1971, Robert Trivers highlighted the evolutionary problem of cooperative behavior between genetically unrelated organisms, as well as the evolutionary contingencies under which cooperation to different degrees of altruism could evolve. Accordingly, the simplest form of cooperation involves direct reciprocity, which implies repeated interactions between the same two individuals (or groups of individuals) and resources that are attractive to one another (Trivers, 1971). This primitive form of exchange, termed “conditional cooperation,” usually leads to mutual cooperation; if, however, one of the parties defects, the other one will turn to defection as well. The efficacy of such reciprocity can be experienced by both parties within relatively short periods of time and can frequently be observed in nonhuman animals. Indirect reciprocity is more complex because the benefit to the cooperating or reciprocating individual may be associated with improved reputation (Nowak, 2006) rather than a net increase in resources—a potential pay-off that may lie in the more distant future and may not be experienced instantaneously. Indirect reciprocity can be frequently observed in human societies (and perhaps other primate species) because helpful deeds are usually approved by significant others or the community as a whole, and therefore, they may raise one’s social status (Nowak & Sigmund,

c13.indd 217

7/25/2011 10:10:25 AM

218

INSIGHTS FROM EVOLUTIONARY GAME THEORY

1998). In evolutionary terms, indirect reciprocity may also serve as an “honest signal”: Individuals who are willing to take costs without the (direct) prospect of getting anything in return may in fact be showing that they can afford giving away “surplus” resources. Beyond direct and indirect reciprocity, genuinely altruistic behavior is characterized by the lack of reliable predictors that anything will ever be gained in return of a cooperative act. This so-called “strong” altruism has caused problems for evolutionary theorists because strong altruism may benefit the social group rather than the individual expressing strong altruism; moreover, selfish individuals can easily exploit the altruism of others and increase their own reproductive fitness at the expense of the altruist or the entire group, which eventually would lead to the extinction of strong altruism. To counteract such “free-riding” strategies and keep the level of altruism within social groups high, humans evolved cognitive mechanisms that evaluate the behavior of others in terms of their willingness to cooperate or intentions to defect, including mechanisms of cheating detection, vigilance toward defection, and emotional mechanisms such as friendship, sympathy, trust, but also mistrust and moralistic aggression (Trivers, 1971). In other words, sanctioning noncooperative behavior became critical at some point in human evolution. As far as is known, all human societies have established social rules and standards that enforce cooperative behavior within the group, including obedience to norms of fairness and equity. In fact, most people even feel uncomfortable when witnessing somebody being cheated on by another person, and most experience satisfaction when observing punishment of norm-violators or impute punishment at their own expense (de Quervain et al., 2004; Singer et al., 2006). Thus, groups of highly cooperative individuals can be assumed to have the highest average individual fitness, which declines with the number of defectors in a particular population (Nowak, 2006). Sustaining mutual cooperation at a high level may also have promoted new levels of societal organization with increasing specialization and diversity both biologically and culturally (Tooby & Devore, 1987), and it may have substantially contributed to populating the whole planet by homo sapiens. Outside our species, strong altruism seems to be rare. However, a look at our closest extant relatives, the great apes, may be enlightening with regard to the evolutionary roots of cooperation. Observations in wild populations suggest that chimpanzees share food and even care, to some extent, for wounded or frail conspecifics (Goodall, 1986). In experimental conditions, it was demonstrated that chimpanzees helped others in altruistic ways that were similar to the degree of cooperation of 18-month-old human infants. That is, they aided an unfamiliar individual by handing an object to the recipient that was out of reach for the recipient but within reach of the test subject. This was even observable when the cooperation was “costly” (i.e., required some physical effort on the side of the test subject) (Warneken, Hare, Melis, Hanus, & Tomasello, 2007). However, in an experiment that examined chimpanzees’ sensitivity to unfairness, in that a chimpanzee proposer suggested how to share food items between him and a chimpanzee recipient (where, in case the recipient rejected the offer, neither of the two received any food), the recipient actually accepted any offer, irrespective of the degree of fairness of the proposer. Moreover, when given the choice, chimpanzee proposers did not make fair offers more often than unfair offers (Jensen, Call, & Tomasello, 2007). This suggests that both proposer and recipient had no sense of fairness or aversion of inequity, as is typical for

c13.indd 218

7/25/2011 10:10:25 AM

ONTOGENETIC ISSUES OF COOPERATION

219

humans. Thus, it seems that chimpanzees are more “rational” in their choices, rather than altruistic.

3.4.3

ONTOGENETIC ISSUES OF COOPERATION

In general, social cognitive abilities such as empathy and mentalizing emerge during the first years of life. They critically depend on brain maturation (Singer et al., 2006) as well as on environmental input such as the quality of attachment to caregivers, family relations, and proximity to significant others. Under normal conditions, the ability to understand that others can have beliefs, knowledge, and intentions that differ from one’s own emerges around the age of 3 to 4 years. At that time, children can discriminate between their own and others’ mental states, especially with regard to the possibility that others can have false beliefs about facts such as the location of an object. There is an extensive literature demonstrating that a continuous refinement of the “mentalizing” abilities occurs throughout childhood and adolescence, such that complex mental states associated with the expression of irony, sarcasm, or “faux-pas” can reliably be understood when children have achieved higher levels of mental state representations (“he thinks that she believes that I know . . . ”). Empathetic abilities emerge even earlier, with emotional contagion being already present in young infants. However, higher levels of empathy that require the ability to put oneself into the shoes of others appear later and parallel, to some degree, the development of mentalizing. Arguably, both empathy and mentalizing are prerequisites for the evaluation of more complex social interactions involved in mutual cooperation. Although infants between 14 and 18 months of age already make use of “instrumental helping,” as described, other-regarding preferences to the degree of active avoidance of inequality take several years to develop and may not be observable until the age of 7 to 8 years. In an ingenious series of experiments, children aged 3 to 4, 5 to 6, and 7 to 8, respectively, were given the choice to share an amount of jelly beans between themselves and another child with different degrees of costs to the sharer, and inequality between the two parties. It turned out that 3- to 4-year-olds behaved selfishly in all conditions suggesting little other-regarding preferences. This changed in 5- to 6-yearolds, with the strongest effects for 7- to 8-year-olds, who showed a strong aversion of inequality and a high preference for egalitarian outcomes. This effect was pronounced when children’s choices were analyzed with regard to in-group versus out-group membership of the recipient, with higher preferences for egalitarian allocations for in-group members as compared with out-group members (Fehr, Bernhard, & Rockenbach, 2008). In support of the assumption that social cognitive abilities play a role in the comprehension of cooperation scenarios and fairness, children who assumed the role of a proposer in a game, in which several candies had to be shared with another child, tended to make more fair-split offers, if they had acquired mentalizing abilities such as false belief understanding. In contrast, children with less mentalizing abilities made more unfair offers and, hence, ran the risk to get nothing because, in case of an unfair offer, the recipient had the opportunity to reject the offer such that neither received anything. Interestingly, mentalizing abilities had no influence on the rejection rate on the side of the recipient, although good mentalizers anticipated anger

c13.indd 219

7/25/2011 10:10:25 AM

220

INSIGHTS FROM EVOLUTIONARY GAME THEORY

in the recipient of unfair offers better than poor mentalizers (Takagishi, Kameshima, Schug, Koizumi, & Yamagishi, 2010). The link of social cognitive abilities with people’s behavior in cooperative scenarios is further buttressed by studies in adults that revealed a link between emotional intelligence (a concept similar to empathy) and social exchange reasoning that was designed to detect violations of social rules and norms (Reis et al., 2007), as well as by imaging and neurophysiological studies showing an overlap of brain activation during mentalizing, cooperation, and contemplation of the degree of unfairness in the allocation of resources (Elliott et al., 2006; Polezzi et al., 2008).

3.4.4 ASSESSMENT OF COOPERATION USING GAME-THEORETICAL APPROACHES To understand the environmental contingencies in which individuals cooperate or not cooperate, it is vital to manipulate conditions experimentally that may prompt cooperation, rejection of cooperation, and punishment of noncooperative behavior. Although transferring the evolutionary scenarios of social exchange into empirical settings is difficult in light of the complexity of real-life interactions between individuals, groups, or larger societal organizations, various evolutionary gametheoretical scenarios have been developed to examine behavior in (virtual) social interactions (Axelrod & Hamilton, 1981). Generally, these scenarios deal with the distribution of resources between two or more parties (Nowak, 2006). They differ in complexity according to the number of participants and repetitions of social exchange. The Prisoner’s Dilemma (PD), for example, involves the decision to cooperate or to defect in a scenario played by two parties (Axelrod, 1984). The idea is that each player gains when both cooperate, but if only one of them cooperates, the other one, who defects, will gain more. If both defect, both lose (or gain very little), but not as much as the “cheated” cooperator whose cooperation is not returned. The game is named after a hypothetical situation of two criminals who are under the suspicion of having committed a crime together. The two are interrogated separately and offered a deal, according to which the one who provides evidence against the other one will be freed. If both reject the offer, and instead, cooperate against the police, either one will get only a small punishment. However, if one of them is disloyal to the other, the defector will gain more, whereas the other will receive the full punishment. If both betray, both will be punished but less severely than if they had refused to talk. Theoretically, cooperation is not the best strategy in this scenario because without knowing the strategy chosen by the other player, noncooperation is associated with a higher expectancy value. Interestingly, most people nevertheless cooperate in the PD at a considerable rate (Rilling, Sanfey, Aronson, Nystrom, & Cohen, 2004). The Trust Game (TG) is similar to the PD in that it involves reciprocity. One player, called the investor, is endowed with a sum of money, of which he/she can pass some amount to another player (the trustee). The investment is multiplied by the experimenter. The trustee decides how much of the multiplied money he/she wants to return to the investor. Selfish trustees would not reciprocate, particularly if there is no sanction in place for noncooperation; moreover, the investor, if suspi-

c13.indd 220

7/25/2011 10:10:25 AM

ASSESSMENT OF COOPERATION USING GAME-THEORETICAL APPROACHES

221

cious, would not invest a significant amount of money. However, in the TG, the investor usually sends a significant proportion of his/her endowment to the trustee, who in most instances reciprocates. One reason for trustful behavior in both the TG and the PD resides in implicitly accepted social norms and moral values. People tend to cooperate with the expectation that their counterpart is willing to cooperate in return. In contrast, most people would consider it appropriate to defect when the other player defects (a shorthand for this kind of interaction is “tit-for-tat”; Axelrod & Hamilton, 1981). Indeed, if repetitively played, the willingness to cooperate can change over time. The Public Goods (PG) game illustrates this. It is played by an optional number of players who initially receive a defined amount of money or tokens, which participants are asked to invest simultaneously in a common pool (the public good) without knowing the contribution of the other players. The experimenter multiplies the whole sum by a factor that is larger than one and smaller than the number of players, and experimenter returns an equal share of that money to each player. This suggests that all players benefit from the public goods, irrespective of how much they have invested before. Accordingly, the PG game examines the extent to which players are tempted to choose an exploitative or “free-riding” strategy because players who refuse to invest will gain more than the other players who contributed (Fehr & Fischbacher, 2004a). If played repetitively, players of the PG game tend to reduce their investment over successive rounds, unless non-cooperation is sanctioned. Punishment of noncooperators, in turn, leads to a steep increase of investment (Fehr and Gächter, 2002). In one-shot PG games the degree of punishment is largely determined by the magnitude by which a noncooperator’s contribution deviates from the average investment. This suggests that the recognition of “free-riding” induces negative emotions in cooperators, including a sense of unfairness, which increases the likelihood of sanctioning noncooperative behavior, even in singular events (Fehr & Gächter, 2002). The PG game imitates “real-life” situations associated with the distribution of goods within social groups such as taxes, donations, fees, and so on, with relatively little differences in performance across cultures (Okada & Riedl, 1999). However, PG scenarios have also revealed the dark side of human cooperation that may surface, if people need not be afraid of sanctions of noncooperators. In such situations, “antisocial” punishment may occur; that is, altruistic behavior is punished, particularly if the punishment can be pursued anonymously (Herrmann, Thöni, & Gächter, 2008). In games such as the PD, TG, and PG, players do not have the option to reject unfair offers straightaway. This option is modeled in a scenario, called the Ultimatum Game (UG), where two players have to split up a sum of money (e.g., 10 MU). One player (“A”) is asked to propose how to distribute the money. In contrast to the PD, Player “B” has the option to either accept or decline the offer. If B agrees, the sum will be split according to A’s proposal. If, however, B rejects, both receive nothing (Falk & Fischbacher, 2000). Rejecting an offer, however unfair it may be, is therefore costly not only for A but also for B. Therefore, the UG involves a mild form of costly punishment (Camerer, 2003b; Falk & Fischbacher, 2000; Güth, Schmittberger, & Schwarze, 1982; Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003). From a strictly rational point of view, B should to be happy with every offer and should not reject offers larger than zero. Practically, however, offers smaller than 50% of the total amount are perceived as unfair and are usually rejected (Sanfey et al., 2003).

c13.indd 221

7/25/2011 10:10:25 AM

222

INSIGHTS FROM EVOLUTIONARY GAME THEORY

As player A in the UG knows in advance that his or her offer can be rejected by B, an interesting question is how people would behave if they did not have to fear any sanctions. The Dictator Game (DG) represents such a scenario, in which the recipient is forced to accept all offers (Falk & Fischbacher, 2000). Accordingly, from the perspective of A, the DG is a more direct measure of altruism (Camerer, 2003a). Not unexpectedly, in the DG, player A usually makes offers of around 15% of the whole amount (Charness & Gneezy, 2003). Altruistic behavior, therefore, seems to be highly context dependent, which includes predictions of possible sanctions. Interestingly, if the DG is modeled such that a third party (player “C”) has the option to reinforce reciprocity and fairness by investing his or her own money—a selfless cost to the punisher, hence, a case of strong altruism (Seymour, Singer, & Dolan, 2007)—the common observation is that people are willing to invest in punishing uncooperative behavior, even when they just witness an unfair interaction between others (de Quervain et al., 2004; Fehr & Fischbacher, 2004b). Normally, about 60% of nonparticipating observers of a DG sanction dictators who are proposing less than 50% of the whole sum. Taken together, these game-theoretical approaches suggest that a delicate balance exists between cooperative and noncooperative strategies in social exchange situations. Specifically, individuals seem to have a clear motivation to punish noncooperative behavior within their social in-group—exactly what Trivers (1971) proposed would be expected for the establishment of reciprocal altruism and cooperation between genetically unrelated individuals of long-lived social animals like humans. This complexity of social behavior almost certainly drove human brain evolution because large computational resources for dealing with the manifold behavioral options of coalition formation, reciprocity, or detection of exploitative strategies were likely associated with the odds of individual reproductive success (Brothers, 1990; Dunbar, 2003).

3.4.5 NEURONAL CORRELATES OF SOCIAL COGNITION AND COOPERATION Social cognitive processes—especially mentalizing and empathy—are associated with activity in a neural network that includes cortical midline structures such as the medial prefrontal cortex (mPFC), the anterior cingulate cortex (ACC) and paracingulate gyrus, the medial part of the orbitofrontal cortex (mOFC), the precuneus, as well as lateral portions of the middle temporal lobes (MTLs), the temporoparietal junction (TPJ), the superior temporal sulcus (STS), and the temporal poles (reviewed in Brüne & Brüne-Cohrs, 2006; Hynes, Baird, & Grafton, 2006; Saxe, 2006; Saxe, Carey, & Kanwisher, 2004; Völlm et al., 2007). The area extending from the ACC to the anterior frontal poles, particularly the paracingulate cortex, is supposed to be engaged in self-reflection, person perception, and inferring others’ thoughts, feelings, and intentions (Amodio & Frith, 2006; Völlm et al., 2006). Furthermore, the TPJ is involved in reasoning about the contents of another person’s mind (Saxe & Kanwisher, 2003; Saxe & Wexler, 2005), the attribution of a character’s true and false beliefs, or deceptive intentions (Lissek et al., 2008; Saxe, 2006; Sommer et al., 2007). Moreover, the TPJ comprises an area that contributes to the discrimination of self and other (Gallagher et al., 2000). The role of the

c13.indd 222

7/25/2011 10:10:25 AM

COOPERATION IN PSYCHOPATHOLOGICAL CONDITIONS

223

precuneus is less well known, but this brain area seems to be important for the experience of agency and self-consciousness (Cavanna & Trimble, 2006; Schilbach et al., 2006). Amygdalar and orbitofrontal activity contribute the affective “tone” to the evaluation of thoughts and intentions (Baron-Cohen et al., 1999), and the mOFC is selectively active when subjects engage in emotional perspective-taking as compared with cognitive perspective-taking (Hynes et al., 2006). This neural network broadly overlaps with that found active during performance of economic games (Elliott et al., 2006). Functional brain imaging studies suggest that the dorsolateral prefrontal cortex (dlPFC) is active during processing of unfair offers, irrespective of the degree of unfairness, which is consistent with its role in goal-maintenance, working-memory, and executive control (Bechara, Damasio, Tranel, & Anderson, 1998; Miller & Cohen, 2001; Sanfey et al., 2003). Similarly, the mPFC, temporal pole, and TPJ were activated in a guessing-game in which cooperation with another player was simulated (Elliott et al., 2006). Moreover, a functional magnetic resonance imaging (fMRI) study using versions of the UG and PD revealed activations in the paracingulate gyrus and the posterior STS during cooperation relative to defection (Rilling et al., 2004). The perception of unfairness seems to be specifically associated with bilateral activation of the anterior insula, and the ACC that seems to be “dose dependent.” The strength of activation in the anterior insula and ACC correlated with the degree of unfairness of offers and in the insula alone with the number of rejected offers in the UG (Sanfey et al., 2003). Interestingly, other brain structures such as the striatum including the caudate nucleus, the nucleus accumbens, and the thalamus are active when people are engaged in costly punishment (de Quervain et al., 2004). In particular, the activation of the caudate nucleus, which is known to be involved in reward processing (Knutson, Adams, Fong, & Hommer, 2001), was positively correlated with the investment in punishment. Conversely, if the desire to punish could not be satisfied, caudate activation was below average. In addition, increased activation in the thalamus, the mPFC, and the mOFC was observed in conditions in which subjects verbalized a strong desire to punish (de Quervain et al., 2004). This study illustrates, therefore, that people may experience punishment of unfair behavior as rewarding, even if costly to oneself. In summary, it can be pointed out that an extended neural network is involved in the evaluation of costs and benefits of social exchange and socioeconomic decision making, including cooperation and altruistic punishment. Importantly, this network overlaps with that involved in more basic social cognitive processes such as mentalizing and empathy, which suggests that intact social cognition is essential for the understanding of the more complex rules of cooperation and exchange.

3.4.6

COOPERATION IN PSYCHOPATHOLOGICAL CONDITIONS

Abundant research has shown that social cognition (i.e., empathy and mentalizing) is compromised in many psychiatric disorders. The broadest empirical evidence for this contention exists for autism spectrum disorders and schizophrenia (reviewed in Brüne and Brüne-Cohrs, 2006), with growing support for abnormal empathy and mentalizing in bipolar affective disorder (Shamay-Tsoori et al., 2009), neurodegenerative disorders (reviewed in Brüne and Brüne-Cohrs, 2006), borderline

c13.indd 223

7/25/2011 10:10:25 AM

224

INSIGHTS FROM EVOLUTIONARY GAME THEORY

personality disorder (Shamay-Tsoori et al., 2009), and psychopathy (Blair, 2005). Certainly, alterations of empathetic responses and mentalizing are not uniform across disorders. In autism, for example, deficits in these domains seem to be associated with a developmental delay, whereas in schizophrenia and, partly, in borderline personality disorder, mentalizing seems to be overactive to the degree of paranoid ideation, at least in some patients. Whether impaired empathy and mentalizing impact on more complex social cognitive processes as expressed in game-theoretical scenarios has only recently begun to be explored. The rationale behind the hypothesis that this might be the case relates to the observation of patients’ behavior, as outlined in the introductory paragraphs. In addition, several studies have revealed abnormal activations of the neural network involved in social cognition in autism, schizophrenia, bipolar disorder and so on, which in turn could lend support to the assumption that economic decision making in psychiatric disorders differs from that of psychologically healthy subjects. 3.4.6.1 Autism Autism is characterized by an impoverishment of social interaction, stereotyped behaviors, and social insecurity. Accordingly, one would expect that violations of cooperative rules occur more often in autism compared with healthy children. In fact, children with autism seem to have a diminished sense of fairness compared with normally developing children. In an UG, autistic children accepted unfair offers more often but also rejected fair offers more frequently than healthy children of the same developmental age (Sally & Hill, 2006). Moreover, in the initial round of the game, autistic children who acted as proposers displayed a balanced preference for even offers or extremely unfair offers. In addition, false-belief understanding predicted, to some degree, cooperation in a PD and whether children made fair offers in the UG (Sally & Hill, 2006). These findings are in line with the literature on the ontogenetic development of economic decision making suggesting that basic social cognitive skills such as false-belief understanding predict children’s behavior in more complex interactions involving the recognition of fairness and rules of cooperation. 3.4.6.2

Schizophrenia

The term “schizophrenia” embraces a group of psychotic disorders that are characterized by delusions, hallucinations, thought disorder, affective flattening, and disorganised behavior. Paranoid patients are highly mistrustful and suspicious, whereas patients with negative symptoms often withdraw form their social environment. Both may be associated with a lack of understanding of rules of social exchange. Indeed, in schizophrenia research, the question of whether patients have difficulties in appreciating social rules and moral values, including those associated with reciprocity and cooperation, has been debated since the 19th century. Kahlbaum, for instance, coined the term “heboidophrenia” for a disorder for which violations of social rules were pathognomonic (Kahlbaum, 1885). A few empirical studies of schizophrenia patients’ ability to appreciate moral values carried out some 50 years ago revealed that schizophrenia patients chose humanitarian responses to moral

c13.indd 224

7/25/2011 10:10:25 AM

COOPERATION IN PSYCHOPATHOLOGICAL CONDITIONS

225

problems less often than controls, indicating that patients disregarded the needs of others (Baruk, 1968; Johnson, 1960). However, this research was conducted in longterm hospitalized patients with chronic schizophrenia. Thus, in cannot be ruled out that artiefacts of chronic hospitalization influenced the outcome of studies. A more recent study revealed that schizophrenic patients made overly fair offers (as proposers) in the UG compared with unaffected control subjects (Agay, Kron, Carmel, Mendlovic, & Levkovitz, 2008). However, schizophrenia patients failed to adjust their proposals to the response they received from the recipient in the previous trial. For example, they raised their offer after trials in which recipients had accepted the proposal. Interestingly, when schizophrenic patients took the role of the recipient in the UG, their behavior did not differ from that of control participants (Agay et al., 2007). In our own study of schizophrenia patients’ performance in the UG and in a DG with the option to punish observed unfair behavior, patients accepted more often unfair offers in the role of the recipient in the UG. When assigned the role of a third-party punisher in the DG, however, patients with schizophrenia performed similarly to healthy controls. Interestingly, excitement and disorganization scores on the Positive and Negative Syndrome Scale (Kay, Opler, & Lindenmayer, 1989) correlated with the acceptance rates of unfair offers. Moreover, we found correlations between the amount of negative symptoms and the rejection of fair offers, as well as with the punishment investment in the most unfair DG condition. These findings suggest that the severity of psychopathology influences patients’ economic decision making in ways that seem inconsistent or incongruent. No correlation emerged between patients’ empathetic ability as measured using the Reading the Mind in the Eyes Task (Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001) and performance in the economic games, which may reside in the fact that the patient group was exceptionally good at this and did not differ from controls (Wischniewski & Brüne, submitted). In summary, the studies available so far show that patients with schizophrenia are able to appreciate rules of cooperation, but they deviate from normals in their appreciation of fairness norms. 3.4.6.3

Borderline Personality Disorder

Borderline personality disorder (BPD) is characterized by unstable interpersonal relationships, emotional dysregulation, and rapid changes of over-idealization and devaluation of close others. Thus, it is conceivable that patients with BPD differ from controls in their ability to appreciate social rules and norms. In support of this hypothesis, a recent study demonstrated that in a TG that was played repetitively, patients with BPD were unable to maintain high levels of investment by an investor, when assuming the role of the recipient. In fact, patients’ behavior induced a downward shift in investment by healthy investors, which was not observed when the TG was played by two healthy subjects. In addition, whereas healthy trustees (recipients) were able to increase the investment in situations of impending break of cooperation by returning amounts larger than 50% of the tripled money to the investor, thus signaling their trustworthiness, patients with BPD failed to rescue cooperation. In other words, BPD subjects were unable to show generosity to repair a breakdown of reciprocity (King-Casas et al., 2008). Notably, neural activity in the anterior insula during functional brain imaging was similar in BPD and controls upon repayment. Unlike healthy controls, however, patients with BPD did not show

c13.indd 225

7/25/2011 10:10:25 AM

226

INSIGHTS FROM EVOLUTIONARY GAME THEORY

insular activation upon receipt of offers from the investor, which could reflect the difficulty individuals with BPD have in deciphering social signals of cooperation (King-Casas et al., 2008). 3.4.6.4

Psychopathy

In contrast to the aforementioned disorders that reflect distorted defense mechanisms, psychopathy involves exploitation of others associated with an inability to empathize. Accordingly, psychopaths lack feelings of guilt and remorse and tend to manipulate others to their own advantage. In game-theoretical scenarios, psychopaths are therefore expected to behave selfishly and to violate rules of cooperation. Indeed, in a PD scenario, individuals with high psychopathy scores were more likely than low scorers to defect and discontinue cooperation more often. High psychopathy scorers were also more likely to experience defection, probably as retaliation of their former defection. However, unlike low psychopathy scorers, high scorers showed less amygdala activity upon being cheated. In contrast, when choosing to defect, psychopaths displayed less activation in the dlPFC and ACC, which could indicate that they experienced less cognitive conflict when cheating the other player. Conversely, psychopathic individuals activated the dlPFC more strongly when choosing to cooperate, suggesting that they had to override cognitively a tendency to defect (Rilling et al., 2007). These findings are in line with another fMRI study that examined the relationship of Machiavellianism to economic decision making. Machiavellianism is often considered part of the psychopathy phenotype because it involves a tendency to deceive and manipulate others for personal benefit. In a DG with the option of being punished for uncooperativeness, subjects scoring high on Machiavellianism activated the left anterior OFC, which has been shown activated during evaluation of punishment threats, more strongly than low Machs. Impending punishment led to an increase of transferred money, whereas in a nonpunishment condition, the actual transfer level was negatively correlated with Machiavellianism (Spitzer, Fischbacher, Herrnberger, Grön, & Fehr, 2007). These findings suggest that high Machs seem to pretend increased cooperation when threatened with punishment, whereas they behaved selfishly when no punishment was expected.

3.4.7

SUMMARY AND FUTURE DIRECTIONS

Clinical observation suggests that social cognitive deficits are core features of many, if not all, psychiatric disorders. Patients with psychopathological conditions seem to assume defensive attitudes in the fear of being victimized by others who refuse cooperation and reciprocity, however unrealistic this perception may be. This misperception may induce mistrust, paranoid ideation, or social withdrawal on the side of the patients. As empathetic abilities and mentalizing skills are frequently distorted in psychiatric disorders, a vicious circle may emerge from continuing misinterpretation of social signals as malevolent. Although to date sparse, data on economic decision making in psychiatric disorders support this view. Patients with psychiatric disorders seem to have difficulties in appreciating social rules of exchange and reciprocality, partly, as may be the case

c13.indd 226

7/25/2011 10:10:25 AM

REFERENCES

227

in autism and schizophrenia, because of problems in empathizing and mental state attribution. Moreover, a failure to establish and maintain trust and enduring relationships based on reciprocity is at the core of the psychopathology of personality disorders. For many other psychiatric disorders such as depression, drug addiction, or anxiety disorders, no data are available. It is, however, conceivable, that patients with any one of these disorders also differ from unaffected controls in performance on economic games. Little is known about the underlying mechanisms that may cause these differences. It is likely that gene–environment interactions play a role. For example, the quality of early attachment determines whether an individual can accept the (social) world as a safe place or, conversely, whether interpersonal relationships are perceived as unreliable and untrustworthy. The hormone oxytocin may be a decisive proxy that mediates social attachment. For example, it has been shown that the administration of oxytocin increases trust in healthy individuals (Kosfeld et al., 2005). Conversely, individuals who as children had experienced emotional abuse have smaller concentrations of oxytocin in their cerebrospinal fluid (Heim et al., 2009). In a similar vein, an oxytocin receptor polymorphism may be associated with an increased risk for depression that is possibly mediated by the quality of adult attachment (Costa et al., 2009). With regard to economic decision making, individual differences in the length of the vasopressin 1a receptor RS3 (AVRP1) as well as an oxytocin receptor polymorphism have been shown to predict behavior in a DG. Like oxytocin, AVRP1a plays an important role in affiliative behaviors in mammals (Hammock & Young, 2006). Individuals with the short AVRP1a allele offered significantly less money to recipients than participants with a longer version of the allele (Knafo et al., 2008). Similarly, a single nucleotide polymorphism at the oxytocin receptor was found to predict higher allocations in the DG (Israel et al., 2009). Other candidates may be variations of the dopamine and serotonin transporter coding genes that have been shown to mediate the level of risk aversion and risk tolerance (Zhong et al., 2009). Similarly, antisocial behavior has been found linked to genetic variations of the MAOA and serotonin transporter coding genes. However, this tendency toward antisocial behavior only manifests if associated with adverse childhood experiences (Caspi et al., 2002). These examples clearly suggest that differences in genetic endowment and gene– environment interaction not only convey individual differences in cooperation and trust but also differences in vulnerability to psychiatric disorders (Israel et al., 2008). Future studies into the nature of psychiatric disorders may make use of these insights from evolutionary game theory.

REFERENCES Adolphs, R. (2001). The neurobiology of social cognition. Curr Opin in Neurobiol 11(2), 231–239. Agay, N., Kron, S., Carmel, Z., Mendlovic, S., & Levkovitz, Y. (2008). Ultimatum bargaining behavior of people affected by schizophrenia. Psychiatr Res 157(1–3), 39–46. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nat Rev Neurosci 7(4), 268–277.

c13.indd 227

7/25/2011 10:10:25 AM

228

INSIGHTS FROM EVOLUTIONARY GAME THEORY

Axelrod, R. (1984). The Evolution of Cooperation. New York: Basic Books. Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science 211(4489), 1390–1396. Baron-Cohen, S., Ring, H. A., Wheelwright, S., Bullmore, E. T., Brammer, M. J., et al. (1999). Social intelligence in the normal and autistic brain: An fMRI study. Eur J Neurosci 11(6), 1891–1898. Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “Reading the Mind in the Eyes” test revised version: A study with normal adults, and adults with Asperger syndrome or high-functioning autism. J Child Psychol and Psychiatr 42(2), 241–251. Baruk, H. (1968). La schizophrénie morale. Annales Medico-Psychologique 2, 221–227. Bechara, A., Damasio, H., Tranel, D., & Anderson, S. W. (1998). Dissociation of working memory from decision making within the human prefrontal cortex. J Neurosci 18(1), 428–437. Blair, R. (2005). Responding to the emotions of others: Dissociating forms of empathy through the study of typical and psychiatric populations. Conscious Cognit 14, 698–718. Brothers, L. (1990). The neural basis of primate social communication. Motiv Emot 14(2), 81–91. Brüne, M., & Brüne-Cohrs, U. (2006). Theory of mind-evolution, ontogeny, brain mechanisms and psychopathology. Neurosci Biobehav Rev 30(4), 437–455. Brüne, M., Schaub, D., Juckel, G., & Langdon, R. (in press). Social skills and behavioral problems in schizophrenia: The role of mental state attribution, neurocognition and clinical symptomatology. Psychiatr Res. Camerer, C. F. (2003a). Behavioral studies of strategic thinking in games. Trends Cognit Sci 7(5), 225–231. Camerer, C. F. (2003b). Psychology and economics. Strategizing in the brain. Science 300, 1673–1675. Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., et al. (2002). Role of genotype in the cycle of violence in maltreated children. Science 297(5582), 851–854. Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: A review of its functional anatomy and behavioural correlates. Brain 129(3), 564–583. Charness, G., & Gneezy, U. (2003). Whats in a Name? Anonymity and Social Distance in Dictator and Ultimatum Games. Technical report, University of California, Santa Barbara, Department of Economics working paper series. Costa, B., Pini, S., Gabelloni, P., Abelli, M., Lari, L., et al. (2009). Oxytocin receptor polymorphisms and adult attachment style in patients with depression. Psychoneuroendocrinology 34(10), 1506–1514. Dunbar, R. (2003). The social brain: Mind, language, and society in evolutionary perspective. Ann Rev Anthropol 32, 163–181. Ebstein, R., Israel, S., Chew, S., Zhong S., & Knafo, A. (2010). Genetics of human social behavior. Neuron 65(6), 831–844. Elliott, R., Völlm, B., Drury, A., McKie, S., Richardson, P., et al. (2006). Co-operaton with another player in a financially rewarded guessing game activates regions implicated in theory of mind. Soc Neurosci 1, 385–395. Falk, A., & Fischbacher, U. (2000). A Theory of Reciprocity, Technical Report, Institute for Empirical Economic Research. Fehr, E., Bernhard, H., & Rockenbach, B. (2008). Egalitarianism in young children. Nature 454(7208), 1079–1083.

c13.indd 228

7/25/2011 10:10:25 AM

REFERENCES

229

Fehr, E., & Fischbacher, U. (2004a). Social norms and human cooperation. Trends Cognit Sci 8(4), 185–190. Fehr, E., & Fischbacher, U. (2004b). Third-party punishment and social norms. Evol Hum Behav 25, 63–87. Fehr, E., & Gächter, S. (2002). Altruistic punishment in humans. Nature 415(6868), 137–140. Gallagher, H. L., Happé, F., Brunswick, N., Fletcher, P. C., Frith, U. et al. (2000). Reading the mind in cartoons and stories: An fMRI study of theory of mind in verbal and nonverbal tasks. Neuropsychologia 38(1), 11–21. Gilbert, P. (2001). Evolutionary approaches to psychopathology: The role of natural defences. Austr New Zeal J Psychiatr 35(1), 17–27. Goodall, J. (1986). The Chimpanzees of Gombe: Patterns of Behavior. Cambridge, MA: Harvard University Press. Güth, W., Schmittberger, R., & Schwarze, B. (1982). An experimental analysis of ultimatum bargaining. J Econo Behav Organ 3(4), 367–388. Hammock, E. A. D., & Young, L. J. (2006). Oxytocin, vasopressin and pair bonding: Implications for autism. Phil Trans Roy Soc B Biol Sci 361(1476), 2187–2198. Heim, C., Young, L. J., Newport, D. J., Mletzko, T., Miller, A. H., et al. (2009). Lower CSF oxytocin concentrations in women with a history of childhood abuse. Mol Psychiatr 14(10), 954–958. Henrich, J., & Boyd, R. (2001). Why people punish defectors. Weak conformist transmission can stabilize costly enforcement of norms in cooperative dilemmas. J Theor Biol 208(1), 79–89. Herrmann, B., Thöni, C., & Gächter, S. (2008). Antisocial punishment across societies. Science 319, 1362–1367. Hynes, C. A., Baird, A. A., & Grafton, S. T. (2006). Differential role of the orbital frontal lobe in emotional versus cognitive perspective-taking. Neuropsychologia 44(3), 374–383. Israel, S., Lerer, E., Shalev, I., Uzefovsky, F., Reibold, M., et al. (2008). Molecular genetic studies of the arginine vasopressin 1a receptor (AVPR1a) and the oxytocin receptor (OXTR) in human behaviour: From autism to altruism with some notes in between. Progr Brain Res 170, 435–449. Israel, S., Lerer, E., Shalev, I., Uzefovsky, F., Riebold, M., et al. (2009). The oxytocin receptor (OXTR) contributes to prosocial fund allocations in the dictator game and the social value orientations task. PLoS One 4(5), e5535. Jensen, K., Call, J., & Tomasello, M. (2007). Chimpanzees are rational Maximizers in an ultimatum game. Science 318, 107–109. Johnson, D. L. (1960). The moral judgment of schizophrenics. J Nerv Ment Dis 130, 278–285. Kahlbaum, K. (1885). Über eine klinische Form des moralischen Irreseins. Allgemeine Zeitschrift für Psychiatrie 41, 711. Kay, S. R., Opler, L. A., & Lindenmayer, J. P. (1989). The positive and negative syndrome scale (PANSS): Rationale and standardisation. Br J Psychiatr Suppl 155(7), 59–67. King-Casas, B., Sharp, C., Lomax-Bream, L., Lohrenz, T., Fonagy, P., et al. (2008). The rupture and repair of cooperation in borderline personality disorder. Science 321(5890), 806–810. Knafo, A., Israel, S., Darvasi, A., Bachner-Melman, R., Uzefovsky, F., et al. (2008). Individual differences in allocation of funds in the dictator game associated with length of the arginine vasopressin 1a receptor RS3 promoter region and correlation between RS3 length and hippocampal mRNA. Gene Brain Behav 7(3), 266–275.

c13.indd 229

7/25/2011 10:10:25 AM

230

INSIGHTS FROM EVOLUTIONARY GAME THEORY

Knutson, B., Adams, C. M., Fong, G. W., & Hommer, D. (2001). Anticipation of increasing monetary reward selectively recruits nucleus accumbens. J Neurosci 21(16), RC159. Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature 435, 673–676. Lissek, S., Peters, S., Fuchs, N., Witthaus, H., Nicolas, V., et al. (2008). Cooperation and deception recruit different subsets of the theory-of-mind network. PLoS ONE 3(4), e2023. Lysaker, P. H., Carcione, A., Dimaggio, G., Johannesen, J. K., Nicolò, G., et al. (2005). Metacognition amidst narratives of self and illness in schizophrenia: Associations with neurocognition, symptoms, insight and quality of life. Acta Psychiatr Scandi 112(1), 64–71. Mealey, L. (1995). The sociobiology of sociopathy: An integrated evolutionary model. Behavior and Brain Sci 18(3), 523–599. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24, 167–202. Nowak, M. A. (2006). Five rules for the evolution of cooperation. Science 314(5805), 1560–1563. Nowak, M. A., & Sigmund, K. (1998). Evolution of indirect reciprocity by image scoring. Nature 393(6685), 573–577. Okada, A., & Riedl, A. (1999). When culture does not matter: Experimental evidence from coalition formation. Ultimatum Games in Austria and Japan, University of Amsterdam (CREED), Working paper. Polezzi, D., Daum, I., Rubaltelli, E., Lotto, L., Civai, C., et al. (2008). Mentalizing in economic decision-making. Behav Brain Res 190(2), 218–223. de Quervain, D. J.-F., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., et al. (2004). The neural basis of altruistic punishment. Science 305(5688), 1254–1258. Reis, D. L., Brackett, M. A., Shamosh, N. A., Kiehl, K. A., Salovey, P. et al. (2007). Emotional intelligence predicts individual differences in social exchange reasoning. Neuroimage 35(3), 1385–1391. Rilling, J. K., Glenn, A. L., Jairam, M. R., Pagnoni, G., Goldsmith, D. R., et al. (2007). Neural correlates of social cooperation and non-cooperation as a function of psychopathy. Biol Psychiatr 61(11), 1260–1271. Rilling, J. K., Sanfey, A. G., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2004). The neural correlates of theory of mind within interpersonal interactions. Neuroimage 22(4), 1694–1703. Sally, D., & Hill, E. (2006). The development of interpersonal strategy: autism, theory-of-mind, cooperation and fairness. J Econ Psychol 27(1), 73–97. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the ultimatum game. Science 300(5626), 1755–1758. Saxe, R. (2006). Uniquely human social cognition. Curr Opin Neurobiol 16(2), 235–239. Saxe, R., Carey, S., & Kanwisher, N. (2004). Understanding other minds: Linking developmental psychology and functional neuroimaging. Annu Rev Psychol 55, 87–124. Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people. The role of the temporo-parietal junction in “theory of mind”. Neuroimage 19(4), 1835–1842. Saxe, R., & Wexler, A. (2005). Making sense of another mind: The role of the right temporoparietal junction. Neuropsychologia 43(10), 1391–1399. Schilbach, L., Wohlschlaeger, A. M., Kraemer, N. C., Newen, A., Shah, N. J., et al. (2006). Being with virtual others: Neural correlates of social interaction. Neuropsychologia 44(5), 718–730.

c13.indd 230

7/25/2011 10:10:25 AM

REFERENCES

231

Seymour, B., Singer, T., & Dolan, R. (2007). The neurobiology of punishment. Nat Rev Neurosci 8(4), 300–311. Shamay-Tsoory, S. G., Aharon-Peretz, J., & Perry, D. (2009). Two systems for empathy: A double dissociation between emotional and cognitive empathy in inferior frontal gyrus versus ventromedial prefrontal lesions. Brain 132(Pt 3), 617–627. Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Dolan, R. J., et al. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature 439(7075), 466–469. Sommer, M., Döhnel, K., Sodian, B., Meinhardt, J., Thoermer, C., et al. (2007). Neural correlates of true and false belief reasoning. Neuroimage 35(3), 1378–1384. Spitzer, M., Fischbacher, U., Herrnberger, B., Grön, G., & Fehr, E. (2007). The neural signature of social norm compliance. Neuron 56(1), 185–196. Takagishi, H., Kameshima, S., Schug, J., Koizumi, M., & Yamagishi, T. (2010). Theory of mind enhances preference for fairness. J Exp Child Psychol 105(1–2), 130–137. Tooby, J., & Devore, I. (1987). The reconstruction of hominid behavioral evolution through strategic modeling, The Evolution of Human Behavior: Primate Models, edited by W.G. Kinzey. Albany: SUNY Press. Trivers, R. L. (1971). The evolution of reciprocal altruism. Q Rev Biol 46, 35–57. Troisi, A. (2005). The concept of alternative strategies and its relevance to psychiatry and clinical psychology. Neurosci Biobehav Rev 29(1), 159–168. Völlm, B., Richardson, P., McKie, S., Elliott, R., Dolan, M. et al. (2007). Neuronal correlates of reward and loss in Cluster B personality disorders: A functional magnetic resonance imaging study. Psychiatr Res 156(2), 151–167. Warneken, F., Hare, B., Melis, A. P., Hanus, D., & Tomasello, M. (2007). Spontaneous altruism by chimpanzees and young children. PLoS Biol 5(7), e184. Wischniewski, J., & Brüne, M. (submitted). Moral reasoning in schizophrenia: An explorativestudy into economic decision-making. Zhong, S., Israel, S., Xue, H., Sham, P. C., Ebstein, R. P., et al. (2009). A neurochemical approach to valuation sensitivity over gains and losses. Proc Biol Sci 276(1676), 4181–4188.

c13.indd 231

7/25/2011 10:10:25 AM

3.5 FROM NEUROECONOMICS TO GENETICS: THE INTERTEMPORAL CHOICES CASE AS AN EXAMPLE ITZHAK AHARON1 and SACHA BOURGEOIS-GIRONDE2 1

Interdisciplinary Centre, Herzliya, Israel Institut Jean-Nicod (ENS-EHESS), Paris, France

2

Despite substantial advances, the question of how we make decisions and judgments continues to pose important challenges for scientific research. Historically, different disciplines have approached this problem using different techniques and assumptions, with few unifying efforts made (Sanfey, Loewenstein, McClure, & Cohen, 2006). Previous behavioral and economic research has focused on stimulus input and behavioral output, often ignoring the intermediary steps by which information is processed and decisions are made. There is now an extensive literature describing the many ways in which human decision making violates the principles of rationality as defined by the expected utility (EU) (Starmer, 2000) and discounted utility (DU) (Frederick et al., 2002) models (Sanfey et al., 2006). Early work in economics revealed situations (e.g., Ellsberg & Allais paradoxes) whereby behavior violated key axioms of the EU model. More recently, the “heuristics and biases” approach in psychology has documented many instances of deviations from economic rationality (Kahneman et al., 1982). For example, most people are reluctant to take a gamble with 50% chance of winning $25 and 50% chance of losing $20, despite the gamble’s overall positive expected value. This illustrates the phenomena of “loss aversion,” whereby people often place disproportionate weight on losses relative to gains of similar absolute value (Kahneman et al., 1991).

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

233

c14.indd 233

7/25/2011 10:10:26 AM

234

FROM NEUROECONOMICS TO GENETICS

3.5.1 WHAT IS NEUROECONOMICS A very recent approach, popularly known as neuroeconomics, has sought to integrate ideas from the fields of psychology, neuroscience, and economics in an effort to specify more accurate models. Brain imaging techniques such as functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) can be used to determine areas of relative brain activation and to deduce the specific portions of the brain used, during decision-making tasks. Functional neuroimaging doing the past decade has provided a new way to examine brain behavior relationships (Mandzia & Black, 2001). As human beings, we live in an unpredictable and complex world. Yet, we have learned to adapt and to make choices that ensure our well-being. Evolution has endowed organisms with various mechanisms that ensure its behaviors maintain Darwinian fitness. Specifically, the brain’s reward system encodes information about the value of the potential outcomes of our actions. This information is highly relevant to other cognitive brain systems because it motivates our actions and, in an abstract sense, adds purpose to our behavior. Neuroeconomics research has identified a biological mechanism mediating behavior motivated by events commonly associated with pleasure in humans. These events are termed “rewards” and are viewed as primary factors governing normal behavior. The subjective impact of rewards (e.g., pleasure) can be considered essential (e.g., Young, 1959) or irrelevant (e.g., Skinner, 1953) to their effect on behavior, but the motivational effect of rewards on behavior is universally acknowledged by experimental psychologists. Neuroeconomics provides an important scientific approach to the study of behavior because of the ability to improve understanding of the role of the human brain in decision making (Clithero, Tankersley, & Huettel, 2008). Research in the field of neuroeconomics suggests a common neural currency (i.e., utility) in the judgment of various reward types as well as other factors that affect value (e.g., reward size,

BOX 3.5-1

Neuroimaging

Neuroimaging refers to the methodology that provides a visualization of the structure or function of elements of the nervous system. It includes the use of various techniques to image either directly or indirectly the structure and/or function/pharmacology of the nervous system. In addition to being used to diagnose disease and assess brain health, neuroimaging is also valuable in the study of brain dynamics, aiming to understand how the brain works and how various activities impact the brain. In a classic example of functional neuroimaging, fMRI can generate images in which different sections of the brain light up as they become active. Structural neuroimaging is static and concerned with the physical structure of the brain. Other neuroimaging modalities include electroencephalography (EEG), magnetoencephalography (MEG), PET, or optical imaging. For review, please see R.J. Dolan (2008).

c14.indd 234

7/25/2011 10:10:26 AM

WHAT IS NEUROECONOMICS

235

BOX 3.5-2 The Brain Reward System The brain reward system is a collection of brain structures that attempts to regulate and control behavior by inducing pleasurable effects. A psychological reward is a process that reinforces behavior. In neuroeconomics, reward and social interaction are central concepts to understand what motivates human behavior. Both concepts are investigated in humans using neuroimaging and physiological methods. For review, please see Schultz (2002).

BOX 3.5-3

Criticizing Neuroeconomics

Recently the use of neuroimaging techniques in research procedures has come under fire. According to one line of criticism, much of this use involves fundamental errors in data handling and statistics (Vul, Harris, Winkielman, & Pashler, 2009). According to another line, much of it involves inappropriate experimental protocols that ignore basic features of the circuitry and functional organization of the brain (Logothetis, 2008).

Breiter et al., 2001; temporal delay to reward, McClure et al., 2004; Montague & Berns, 2002). Neuroeconomics also suggests that decision making is an emergent process that results from the interaction (synergistically or competitively) of independent neuronal subsystems (Sanfey et al., 2006). Among the topics that neuroeconomic methods may be best suited to examine, in combination with imaging techniques, is brain functioning in optimal and suboptimal decision making. Clithero and his coauthors (2008) report that the neuroscience evidences that improves our understanding of economic phenomena (Camerer, Loewenstein, & Prelec, 2005; Camerer 2007; Glimcher, 2003; Sanfey et al., 2006) come from a broad array of novel experimental findings, including demonstrations of brain regions that guide responses to fair (King-Casas et al., 2005; Singer et al., 2006) and unfair (Sanfey et al., 2003) social interactions, that resolve uncertainty during decision making (Yoshida & Ishai, 2006), that track loss aversion (Tom, Fox, Trepel, & Poldrack, 2007) and subjective value (Padoa-Schioppa & Assad, 2006), and that encode willingness to pay (Knutson, Rick, Wirnmer, Prelec, & Loewenstein, 2007; Plassmann, O’Doherty, & Rangel, 2007) and reward error signals (Donchin, 2006). Yet, neuroeconomics has been characterized as a faddish juxtaposition, not integration, of disparate domains. In addition, critics have charged that neuroscience and economics are fundamentally incompatible (Gul & Pesendorfer, 2008), an argument that resonates with many social scientists. Economics thrived for centuries in the absence of neuroscience, and some economists argue that existing neuroeconomics research is not useful to mainstream economics (Harrison, 2008). Neuroeconomics is at a crossroads, poised to demonstrate that neuroscience can provide the same types of benefits it has long received from the social sciences (Clithero et al., 2008). Ideas from game theory and expected utility theory can

c14.indd 235

7/25/2011 10:10:26 AM

236

FROM NEUROECONOMICS TO GENETICS

explain the responses of individual neurons to incoming information (Glimcher, 2003). Similarly, aspects of utility theory can be used to describe the activity of populations of neurons within the brain’s reward system (Schultz, 2006). There is also an opportunity for the axiomatic approach of decision theory to explain decision-making mechanisms, such as building from the response properties of dopaminergic neurons (Caplin & Dean, 2008). Without comparable examples of neuroscience data contributing to economic models, critics could argue that neuroeconomics research is a brain-centric enterprise that incorporates ideas from the social sciences without reciprocation (Harrison, 2008). Neuroeconomics draws attention to motive forces that are ignored in the standard framework of economic theory. For example, impulsivity and inconsistency in intertemporal choice have been attracting attention in economics. Although loss of self-control by substance abusers is strongly related to their inconsistency in intertemporal choice, researchers in economics have usually studied impulsivity in intertemporal choice using a discount rate (e.g., hyperbolic k), with little effort being expended on motive forces. In the rest of the chapter, we will describe and discuss intertemporal research from the neuroeconomics perspective.

3.5.2

MODELING INTERTEMPORAL CHOICES

When it comes to choice over time, there is also ample evidence of violations of the DU model. Perhaps most importantly, there is strong evidence that discounting is much steeper for shorter time delays than for longer delays, a phenomenon known as “hyperbolic time discounting” (Ainslie & Haslam, 1992). For example, offered a choice between $10 today and $11 in a week, many people are likely to choose the immediate $10. However, offered the choice between $10 in a year and $11 in a year and a week, most people would chose the $11, now considering the extra week of wait inconsequential. From the economist’s perspective, however, this implies a reversal of preference (i.e., whether an extra dollar is worth a week’s wait) and, therefore, does not conform to the rational model (Frederick, Loewenstein, O’Donoghue, 2002). The idea that the value of a good depends on the timing of its consumption was already present in the economic thought of the 18th century but discussed in more details by John Rae (1834, 1905) who is considered the “father” of intertemporal choices modeling. According to Rae, someone’s time preferences are explained by his “effective desire of accumulation.” In 1884, Eugen von Böhm-Bawerk (1890) claimed that this systematic tendency to underestimate future pleasures may be attributed to humans lacking the capacity to make a complete picture of their future wants, especially when it comes to remotely distant ones. Fisher (1930)—who announced the basic economic relations in intertemporal choice—continued this approach, suggesting that every person has his own rate of “impatience,” one that depends on objective factors (size and risk of future income) and subjective factors (foresight, strength of will, habit, uncertainty, selfishness, and influence of fashion). Paul Samuelson (1937) was the first economist who suggested the DU model, a mathematical function describing time preferences in general assuming that “the individual behaves so as to maximize the sum of all future utilities” (p. 156).

c14.indd 236

7/25/2011 10:10:26 AM

NEUROECONOMICS AND INTERTEMPORAL CHOICES

237

Many economic decisions involve outcomes that occur at different points in time. To model such decisions, discounted utility models are typically used. These models combine a utility function that reflects attitudes toward outcomes and a discount function that captures the effect of the passage of time. The most widely used discounted utility model in economics is constant discounting in which the discount function is determined by a constant rate of discount. However, much empirical research has underlined “various inadequacies of the DU model as a descriptive model of behavior” which is a phenomenon referred to as decreasing impatience (Frederick et al., 2002; Read, 2001). One of the major anomalies is that discount rates seem not to be invariant over different horizons. Indeed, as noted by Strotz (1956), agents seem to discount the future relative to the present more rapidly than they discount between different dates in the future. According to this hypothesis, “people are impatient at present, but claim to be patient in the future” (Nir, 2004). These findings have led to the development of alternative discounted utility models, which are commonly referred to as hyperbolic discounting. The hyperbolic discounting models are consistent with decreasing impatience and have become quickly popular in economics. Today many applications are based on hyperbolic discounting, in particular on quasi-hyperbolic discounting a model that was first proposed by Phelps and Pollak (1968) and made popular by Laibson (1997). In his work, Laibson (1997) suggested a discrete time discount function, the “quasihyperbolic,” which captures the key property of hyperbolic discounting in a more tractable functional form: preferences at time t are inconsistent with preferences at time t + 1. Thus, a gap occurs between one’s long-run goals and short-run behavior. This gap may elicit some type of behavior that should be invested by policy makers (for example, retirement issues and procrastination).

3.5.3

NEUROECONOMICS AND INTERTEMPORAL CHOICES

The tendency to choose lesser immediate benefits over greater long-term benefits characterizes alcoholism and other addictive disorders. However, despite its medical and socioeconomic importance, little is known about its neurobiological mechanisms. Brain regions that are activated when deciding between immediate or delayed rewards have been identified (McClure et al., 2004; McClure, Ericson, Laibson, Loewenstein, & Cohen, 2007), as have areas in which responses to reward stimuli predict a paper-and-pencil measure of temporal discounting (Hariri et al., 2006). These studies assume “hot” and “cool” response selection systems, with the hot system proposed to generate impulsive choices in the presence of a proximate reward. However, to date, brain regions in which the magnitude of activity during decision making reliably predicts intertemporal choice behavior have not been identified (Boettiger et al., 2007). Like humans, nonhuman animals can be run in experimental paradigms in which they choose between smaller earlier rewards and larger later rewards (although animals need to learn about the rewards through multiple trials, whereas humans can simply be informed of the contingencies). Monterosso and Ainslie (1999) note that “people and less cognitively sophisticated animals do not differ in the hyperbolic form of their discount curves” (p. 343). Some researchers (e.g., Herrnstein, 1997; Rachlin, 2000) hold the view that hyperbolic time discounting is effectively

c14.indd 237

7/25/2011 10:10:26 AM

238

FROM NEUROECONOMICS TO GENETICS

“hardwired” into our evolutionary apparatus. However, there is considerable evidence that the time discounting of humans and other animals relies on qualitatively different mechanisms (e.g., Loewenstein, 1996; Shefrin & Thaler, 1988). In support of this evidence, Rangel, Camerer, & Montague, (2008) arguer that although both humans and animals discount the future at dramatically different rates, both humans and animals display a common pattern of time discounting commonly referred to as “hyperbolic time discounting.” However, they believe that although such findings do not rule out the possibility that humans and animals discount the future similarly, the quantitative discontinuity is indicative of a qualitative discontinuity. There is, in fact, considerable evidence that the time discounting of humans and other animals relies on qualitatively different mechanisms. Specifically, human time discounting reflects the operation of two fundamentally different systems, one that heavily values the present and cares little about the future (which we share with other animals), and another that discounts outcomes more consistently across time (which is uniquely human) (e.g., Loewenstein, 1996; Shefrin & Thaler, 1988). Although (some) animals display far-sighted behaviors (e.g., storing nuts for winter), these are typically preprogrammed and distinct from the type of spontaneous selfcontrol observed in humans (e.g., deciding to go on a diet). The almost uniquely human capacity to take the delayed consequences of our behavior into account seems to be directly attributable to the prefrontal cortex, the part of the brain that was the most recent to expand in the evolutionary process that produced humans (Manuck, Flory, Muldoon, & Ferrell, 2003), and that is also the latest part of the brain to develop with age. Patients with damage to prefrontal regions tend to behave myopically, placing little weight on the delayed consequences of their behavior (Damasio, Grabowski, Frank, Galaburda, & Damasio, 1994). Many different microfoundations have been proposed to explain the preference patterns captured by the hyperbolic and quasi-hyperbolic discount functions. The most prominent examples include temptation models and dual-brain neuroeconomic models (Bernheim & Rangel, 2004; Gul & Pesendorfer, 2001; McClure et al., 2004; Thaler & Shefrin, 1981). However, both the properties and the mechanisms of time preferences remain in dispute. For example, using fMRI, McClure et al. (2004) examined the brain activity of participants while they made a series of intertemporal choices between smaller proximal rewards ($R available at delay d) and larger delayed rewards ($R’ available at delay d’), where $R < $R’ and d < d’. Rewards ranged from $5 to $40 Amazon.com gift certificates, and the delay ranged from the day of the experiment to 6 weeks later. McClure et al. (2004) found that time discounting is associated with the engagement of two neural systems: Limbic and paralimbic cortical structures are preferentially recruited for choices involving immediately available rewards; and frontoparietal regions, which support higher cognitive functions, are recruited for all intertemporal choices. Moreover, the authors find that when choices involved an opportunity for immediate reward, thus engaging both systems, greater activity in frontoparietal regions than in limbic regions is associated with choosing larger delayed rewards. A subsequent fMRI study that replaced gift certificates with primary rewards (juice and water) that could be delivered instantly in the scanner replicated this pattern (McClure et al., 2007). Yet another study by a different set

c14.indd 238

7/25/2011 10:10:26 AM

FUTURE RESEARCH SHOULD USE DISCOUNT RATES AS PHENOTYPES

239

of authors (Hariri et al., 2006) found a similar pattern in a between- rather than within-subject study. These studies seemed to support, at a neurobiological level, the dual-self β−δ model of David Laibson (1997), which was developed to explain the hyperbolic-like (quasi-hyperbolic) discounting behavior mentioned previously. However, contradictory to McClure studies (2004, 2007), Kable and Glimcher (2007) found no evidence of separable neural agents that could account for the multiple-selves theories. Current knowledge regarding the neural basis of temporal discounting derives primarily from lesion studies (Boettiger et al., 2007). For example, damage to the human orbitofrontal cortex (OFC) (Berlin, Rolls, & Kischka, 2004), but not to the ventromedial frontal lobe (Fellows & Farah, 2005), increases immediate reward bias, whereas in rats, lesions of the OFC (Mobini et al., 2002; Rudebeck, Walton, Smyth, Bannerman, & Rushworth, 2006), basolateral amygdala (Winstanley et al., 2004), nucleus accumbens (Cardinal, Pennicott, Sugathapala, Robbins, & Everitt, 2001), or hippocampus (Cheung & Cardinal, 2005) bias selection toward immediate rewards. Such selection bias may be viewed as a form of impulsivity (Evenden, 1999). Correspondingly, polymorphisms (a genetic variant that appears in at least 1% of a population) in several genes in the dopaminergic system (related with the brain reward system and the previously mentioned brain regions) have been identified as likely contributors to impulsivity (Kreek, Nielsen, Butelman, & LaForge, 2005). Following the studies by McClure and his colleagues (McClure et al., 2004, 2007), Boettiger and his group (Boettiger et al., 2007) used fMRI and a modified delay discounting task (Mitchell, Fields, D’Esposito, Boettiger, 2005; Mitchell, Tavares, Fields, D’Esposito, Boettiger, 2007) to identify brain regions associated with immediate reward bias or anti-bias. To maximize the range of individual differences across subjects, participants were either abstinent alcoholics (AA; n = 9) or age-matched controls with no history of substance abuse (CS; n = 10). In each trial of the task, subjects were instructed to choose between two amounts of money, a smaller amount available “Now” (e.g., “$80 TODAY”) or a larger amount available “Later” (e.g., “$100 in 1 month”). The authors report that the tendency of an individual to wait for a larger, delayed reward correlates directly with the blood oxygenation leveldependent (BOLD) signal in the lateral orbitofrontal cortex. In addition, a genotype at the Val158Met polymorphism of the catechol-O-methyltransferase gene (an enzyme playing an important role in prefrontal cortex dopamine metabolism) predicts both impulsive choice behavior and activity levels in the prefrontal cortex during decision making.

3.5.4 FUTURE RESEARCH SHOULD USE DISCOUNT RATES AS PHENOTYPES IN GENETIC STUDIES Different people are likely to have different discount rates (Chabris et al., 2009) as some people are more patient (low discount rate) and others are more impatient (high discount rate). Do individuals’ discount rates help to explain their decisions about behaviors like saving, health, and smoking? The authors use a laboratory task to compute an individual-specific discount rate and then estimate the effect of the discount rate and demographic factors on

c14.indd 239

7/25/2011 10:10:26 AM

240

FROM NEUROECONOMICS TO GENETICS

behaviors such as saving and smoking. Although other studies have demonstrated a relationship between laboratory measures of discounting and various behaviors (Setlow, Mendez, Mitchell, & Simon, 2009), this study is unique for its use of a large, diverse sample to examine a wide range of behaviors (15 in all) and to compare the predictive strength of the discount rate with that of demographic variables in explaining these behaviors. The authors begin by estimating discount rates for more than 500 subjects using a laboratory task. The subjects engaged in three different substudies examining a large set of behaviors and health-related variables associated with making tradeoffs between the present and future. Next the authors present a theoretical framework to explore how much of the variation in behavior we would expect discounting to explain. The paper’s results support two broad conclusions. First, “there exists a domaingeneral behavioral disposition towards impatience/impulsivity,” and second, “a discount rate estimated through a set of intertemporal monetary choice questions constitutes a useful, though noisy, measure of this disposition.” Therefore, the authors of the study (Chabris et al., 2009, p. 17) suggest that future research could use discount rates as phenotypes in genetic studies design to indentify biological intertemporal choices mechanisms.

3.5.5 OTHER FACTORS AFFECTING INDIVIDUAL’S TIME PREFERENCES Independently of subjects’ discount rates, there are many other factors affecting individual’s time preferences. Using data collected by questionnaires, Van der Pol and Cairns (2000, 2001) argue that younger people expect to have more responsibilities in the future. They are thus more likely to have negative discount rates. Older people are more likely to have positive discount rates because of their reduced life expectancy. Another factor impacting on individual’s time preferences is optimism (Berndsen & van der Pligt, 2001). Uncertainty about the future allows for optimism (i.e., the hope that future losses will be avoided), and this optimism can underlie the preferences for present or future care. A study by Berndsen and van der Pligt (2001) found that people are willing to delay gains and to speed up losses. The authors suggest that optimism underlies the strong preference for immediate gains in both the monetary and the health domain. It is argued that optimism has asymmetric effects on time preferences for gains versus losses: One reason why decision makers prefer immediate gains is because they are optimistic that these gains will be followed by additional gains in future. In contrast, decision makers prefer to delay losses because they are optimistic that losses are avoidable in the future. Optimism about outcomes affects time preferences for both gains and losses, such that low optimism reduces the discount rates while increasing optimism. In addition, many other factors affect time preferences, for example, people’s assumptions regarding future technology in the field of health care. However, the evidence for correlation between these factors and the discount rate is weak and their individual effect on time preferences is hard to measure.

c14.indd 240

7/25/2011 10:10:26 AM

REFERENCES

241

REFERENCES Ainslie, G., & Haslam, N. (1992). Hyperbolic discounting. Choice Over Time, edited by G. Loewenstein and J. Elster, New York: Russell Sage. Arnea, A., Cronqvist, H., & Siegel, S. (2010). Nature or nurture: What determines investor behavior? J Financ Econ 98(3), 583–604. Bernheim, D., & Rangel, A. (2004). Addiction and cue-triggered decision processes. Am Econ Rev 94(5), 1558–1590. Berlin, H. A., Rolls, E. T., & Kischka, U. (2004). Impulsivity, time perception, emotion and reinforcement sensitivity in patients with orbitofrontal cortex lesions. Brain 127, 1108–1126. Berndsen, M., & van der Pligt, J. (2001). Time is on my side: Optimism in intertemporal choice. Acta Psychol 108(2), 173–186. Boettiger, C. A., Mitchell, J. M., Tavares, V. C., Robertson, M., Joslyn, G., et al. (2007). Immediate reward bias in humans: Fronto-parietal networks and a role for the catecholO-methyltransferase 158(Val/Val) genotype. J Neurosci 7(52), 14383–14391. Bohm-Bawerk, E.-von. (1890) [1884]. Capital and interest: A critical history of economical Theory. London: Macmillan. Breiter, H. C., Aharon, I., Kahneman, D., Dale, A., & Shizgal, P. (2001). Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron 2001(2), 619–639. Camerer, C., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics. J Econ Lit 43, 9–64. Camerer, C. F. (2007). Neuroeconomics: Using neuroscience to make economic predictions. Econ J 117, C26–C42. Caplin, A., & Dean, M. (2008). Dopamine, reward prediction error, and economics. Quart J Econ 123, 663–701. Cardinal, R. N., Pennicott, D. R., Sugathapala, C. L., Robbins, T. W., & Everitt, B. J. (2001). Impulsive choice induced in rats by lesions of the nucleus accumbens core. Science 292, 2499–2501. Chabris, C. F., Laibson, D., Morris, C. L., Schuldt, J. P., Taubinsky, D. (2009). The allocation of time in decision-making. J Eur Econ Assoc 7(2), 628–637. Cheung, T. H., Cardinal, R. N. (2005). Hippocampal lesions facilitate instrumental learning with delayed reinforcement but induce impulsive choice in rats. BMC Neurosci 6, 36. Clithero, J. A., Tankersley, D., & Huettel, S. A. (2008). Foundations of neuroeconomics: From philosophy to practice. PLoS Biol 6(11), 2348–2353. Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., & Damasio, A. R. (1994). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. Science 264(5162), 1102–1105. Dolan, R. J. (2008). Neuroimaging of cognition: Past, present, and future. Neuron 60(3), 496–502. Donchin, E. (2006). fMRI: Not the only way to look at the human brain in action. Observer 19(11). Evenden, J. L. (1999). Varieties of impulsivity. Psychopharmacology 146, 348–361. Fellows, L. K., Farah, M. J. (2005). Dissociable elements of human foresight: A role for the ventromedial frontal lobes in framing the future, but not in discounting future rewards. Neuropsychologia 43, 1214–1221.

c14.indd 241

7/25/2011 10:10:26 AM

242

FROM NEUROECONOMICS TO GENETICS

Fisher, I. (1930). The Theory of Interest. New York: Macmillan. Frederick, S. et al. (2002). Time discounting and time preference: A critical review. J. Econ Lit 40, 351–401. Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preferences: A critical review. J Econ Lit 40(2), 351–401. Glimcher, P. W. (2003). Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics. Cambridge: MIT Press. Gul, F. & Pesendorfer, W. (2001). Temptation and self-control. Econometrica 69(6), 1403–1435. Gul, F., & Pesendorfer, W. (2008). The case for mindless economics. Foundations of Positive and Normative Economics, Methodologies of Modern Economics, edited by A. Caplin, & A. Schotter. Oxford: Oxford University Press. Hariri, A.R., Brown, S.M., Williamson, D.E., Flory, J.D., de Wit, H., & Manuck, S.B. (2006). Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity. J Neurosci 26(51), 13213–13217. Harrison, G. W. (2008). Neuroeconomics: A critical reconsideration. Econ Philo 24, 303–344. Herrnstein, R. J. (1997). The Matching Law: Papers in Psychology and Economics, edited by H. Rachlin & D. Laibson. New York: Sage Publishers. Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nat Neurosci 10(12), 1625–1633. Kahneman, D., et al. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge, MA: Cambridge University Press. Kahneman, D., et al. (1991). The endowment effect, loss aversion, and the status quo bias: Anomalies. J Econ Perspec 5, 193–206. King-Casas, B., Tomlin, D., Anen, C., Camerer, C. F., Quartz, S. R., et al. (2005). Getting to know you: Reputation and trust in a two-person economic exchange. Science 308, 78–83. Knutson, B., Rick, S., Wirnmer, G. E., Prelec, D., & Loewenstein, G. (2007). Neural predictors of purchases. Neuron 53, 147–156. Kreek, M. J., Nielsen, D. A., Butelman, E. R., & LaForge, K. S. (2005) Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat Neurosci 8, 1450–1457. Laibson, D. (1997). Golden eggs and hyperbolic discounting. Q J Econ 112(2), 443–477 Loewenstein, G. (1996). Out of control: Visceral influences on behaviour. Organ Behav and Hum Decis Process 65, 272–292. Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI? Nature 435, 869–878. Mandzia, J., & Black, S. E. (2001). Neuroimaging and behavior: Probing brain behavior relationships in the 21st century. Curr Neurol Neurosci Rep 1, 553–561. Mandzia, J., & Black, S. E. (2007). Neuroimaging and behavior: Probing brain behavior relationships in the 21st century. Curr Neurol Neurosci Rep 1528–4042, 553–561. Manuck, S. B., Flory, J. D., Muldoon, M. F., & Ferrell, R. E. (2003). Central nervous system serotonergic responsivity and aggressive disposition in men. Physiol Behav 77(4–5), 705–709. McClure, S. M. et al. (2004). Separate neural systems value immediate and delayed monetary rewards. Science 306, 503–507. McClure, S. M., Ericson, K. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2007). Time discounting for primary rewards. J Neurosci 27(21), 5796–5804.

c14.indd 242

7/25/2011 10:10:26 AM

REFERENCES

243

Mitchell, J. M., Fields, H. L., D’Esposito, M., & Boettiger, C. A. (2005). Impulsive responding in alcoholics. Alcohol Clin Exp Res 29, 2158–2169. Mitchell, J. M., Tavares, V. C., Fields, H. L., D’Esposito, M., & Boettiger, C. A. (2007). Endogenous opioid blockade and impulsive responding in alcoholics and healthy controls. Neuropsychopharmacology 32, 439–449. Mobini, S., Body, S., Ho, M. Y., Bradshaw, C. M., Szabadi, E., et al. (2002). Effects of lesions of the orbitofrontal cortex on sensitivity to delayed and probabilistic reinforcement. Psychopharmacology 160, 290–298. Montague, P. R., Berns, G. S. (2002). Neural economics and the biological substrates of valuation. Neuron 36, 265–284. Monterosso, J. R., Ainslie, G. (1999). Beyond discounting: Possible experimental models of impulse control. Psychopharmacology 146, 339–347. Nir, A. (2004), Cognitive Procedures and Hyperbolic Discounting, Tilburg University, Center for Economic Research, Discussion Paper No. 47. Padoa-Schioppa, C., & Assad, J. A. (2006). Neurons in the orbitofrontal cortex encode economic value. Nature 441, 223–226. Phelps, E. S., & Pollak, R. (1968), On second best national saving and game-equilibrium growth. Rev Econ Stud 35 185–199. Plassmann, H., O’Doherty, J., & Rangel, A. (2007). Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. J Neurosci 27, 9984–9988. Rae, J. (1905) [1834]. The Sociological Theory of Capital. London: Macmillan. Rachlin, H. (2000). The Science of Self-Control Cambridge. London: Harvard University Press. Rangel, A., Camerer, C., & Montague, R. (2008). A framework for studying the neurobiology of value-based decision making. Nat Rev Neurosci 9, 545–556. Read, D. (2001). Is time-discounting hyperbolic or subadditive? J Risk Uncertainty 23(1), 5–32. Rudebeck, P. H., Walton, M. E., Smyth, A. N., Bannerman, D. M., & Rushworth, M. F. (2006). Separate neural pathways process different decision costs. Nat Neurosci 9, 1161–1168. Samuelson, P. (1937). A note on measurement of utility. Rev Econ Stud, 4, 155–161. Sanfey, A. G., Loewenstein, G., McClure, S. M., & Cohen, J. D. (2006). Neuroeconomics: Crosscurrents in research on decision-making. Trends Cogn Sci 10, 108–116. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the ultimatum game. Science 300, 1755–1758. Schultz, W. (2002). Getting formal with dopamine and reward. Neuron 36(2), 241–263. Schultz, W. (2006). Behavioral theories and the neurophysiology of reward. Annu Rev Psychol 57, 87–115. Setlow, B., Mendez, I. A., Mitchell, M. R., & Simon, N. W. (2009). Effects of chronic administration of drugs of abuse on impulsive choice (delay discounting) in animal models. Behav Pharmacol 20(5–6), 380–389. Shefrin, H. M., & Thaler, R. H. 1988. The behavioral life-cycle hypothesis. Econ Inq 26(4), 609–643. Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Dolan, R. J., et al. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature 439, 466–469. Skinner, B. F. (1953). Science and Human Behavior. New York: Macmillan. Starmer, C. (2000). Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk. J Econ Lit 38, 332–382.

c14.indd 243

7/25/2011 10:10:26 AM

244

FROM NEUROECONOMICS TO GENETICS

Strotz, R. (1956). Myopia and inconsistency in dynamic utility maximization. Rev Econ Stud 23, 165–180. Thaler, R. H., & Shefrin, H. M. (1981). An economic theory of self-control. J Polit Econ 89 (2), 392–406. Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science 315, 515–518. Van der Pol, M., & Cairns, J. (2000). Negative and zero time preferences for health. Health Econ 9, 171–175. Van der Pol, M., & Cairns, J. (2001). Estimating time preferences for health using discrete choice experiments. Soc Sci Med 52, 1459–1470. Vul, E.., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspect Psychol Sci, 4(3), 274–290. Winstanley, C. A., Theobald, D. E., Cardinal, R. N., & Robbins, T. W. (2004). Contrasting roles of basolateral amygdala and orbitofrontal cortex in impulsive choice. J Neurosci 24, 4718–4722. Yoshida, W., & Ishii, S. (2006). Resolution of uncertainty in prefrontal cortex. Neuron 50, 781–789. Young, P. T. (1959). The role of affective processes in learning and motivation. Psychol Rev 66, 104–125.

c14.indd 244

7/25/2011 10:10:26 AM

INDEX

ABAT gene, 25 Accuracy, empathic, 69 Acetylcholine, 163–164 Action, generally observation, 66–68, 73 planning, 81, 87 sounds, 69–70 Addiction/addictive behavior, 168–169, 237. See also Alcoholism; Drug abuse Adolescents, drug use in, 187–188 Affect, neural mechanisms of, 51, 53 Affective empathy, 56 Affective processing, 112, 119 Affective response, 8, 48, 145 Affiliation, social/social group, 65–66, 70–71, 97 Affiliative behaviors, 148 Agency, 223 Aggression, generally in hiearchy formation. See Dominant hierarchy formation threshold, 206 Aggressive behavior, 38, 142, 148 Agonist drugs, 169 AGRIN gene, 25 AIDS, 185

Alcoholism, 186, 237 Alcohol use, 188 Aldehyde dehydrogenase (ALDH), 186 Altruism, 1, 39, 124–125, 134, 177, 129 Amygdala, functions of, 3, 94, 119, 128, 162–163, 171, 175, 223 Aneurysms, 160 Anger, 127–128 Antagonist drugs, 169 Anterior cingulate cortex (ACC), 51, 57, 73, 94, 128, 141, 145, 151, 222, 226 Anterior fossa, 160 Anterior insular (AI) cortex, 51, 73, 128, 141 Anterior temporal lobe (ATL), 129–131 Antisocial behavior, 114 Antisocial disorders, 120 Anxiety disorder, 215 Approach behavior, 38 AR gene, 26, 30 ARNT gene, 25, 28–29 As-if body loops, 66. See also Body loops Asperger Syndrome (AS), 2, 22–24, 27–31, 147 Aspirations, 85 Association Value, 97

From DNA to Social Cognition, First Edition. Edited by Richard Ebstein, Simone Shamay-Tsoory, and Soo Hong Chew. © 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.

245

bindex.indd 245

7/25/2011 10:10:03 AM

246

INDEX

Association studies, autism spectrum conditions, 22, 25–26 Attachment characteristics of, 127, 134 in pair bonding, 41–42 parent-infant, 42 Attention deficit hyperactivity disorder (ADHD), 7 Attitude, 100 Attributional processes, social, 96, 101–103 Autism, see specific types of autism characteristics of, 7, 11, 224, 227 classic, 22, 24–26 Autism Diagnostic Observation Schedule (ADOS), 22 Autism Quotient (AQ), 23, 28–30 Autism spectrum conditions (ASC) foetal adrogen theory, 2, 24–25 gene mutations, 25–26 measurement of, 23–24 neurocognitive theories of, 2 overview of, 21–22 Autism spectrum disorders (ASD), 40, 51, 56, 151, 224 Autobiographical memory, 54, 132 Autonomic nervous system, 176 AVPR1 gene, 9–10, 25, 28, 30, 39, 227 Balanced Emotional Empathy Scale (BEES), 69, 74 Barbiturates, 188 Basal forebrain, 129–130, 174 Basal ganglia, 94, 128 Behavioral disorders, 7, 11 Beliefs, rational, 127 Benevolence, 124 Bias covert, 170 emotional, 133 explicit processes, 104 nonconscious signals, 169 overt, 170 racial, 100 unconscious, 168 Bilateral temporoparietal junctions, 66 Biomarkers, peripheral, 10–11 Bipolar disorder, 7, 11, 172 Blood oxygenation level-dependent (BOLD) response, 143, 145 Body-brain channel, 163 Body loop, 162–163, 178

bindex.indd 246

Borderline personality disorder (BPD), 225–226 Brain, anatomy of, see specific parts of brain anatomy Brain imaging, 116–119. See also Neuroimaging; specific types of neuroimaging techniques Brain injury, 113, 151 Brain lesions studies, 114–117, 126, 132–133, 146, 159, 170, 177, 192 Brain networks supporting empathy multiple processes, 48–49 overview of, 47 self-referential processing and self-other distinction, 55–56 shared respresentations of emotions, 49–53 theory of mind, 53–58, 66 Brain reward system, 234–235 Brainstem, 162, 16, 176, 179 Broader autism phenotype (BAP), 23 Broca’s area, 65 Brodman area (BA), 160 Bystander effects, hierarchy formation, 4–5, 204–205, 207–209, 211 Callithrix penicillata, pair-bonding studies, 40 Candidate gene association studies, 9 Canine studies, social comparison-based emotions, 141 Cannabinoids, 26 Cardiovascular responses, 167 Case-control studies, autism spectrum conditions, 22, 24, 28 Categorization processes, social, 96 Central nervous system (CNS), 25, 29, 163, 167 Cerebellum, 67 Cerebrospinal fluid, 227 CGA gene, 26 Chameleon Effect, 69 Children autism spectrum conditions, 23 childhood abuse, 8 cooperation studies, 219–220 crying behavior, 48 drug use in, 187–188 empathy development, 49, 51 Cholesterol, 26 Chromatin, 8 Chronic promotion orientation, 85

7/25/2011 10:10:03 AM

INDEX

Cingulate gyrus, 125, 128 CNR gene, 25, 28, 30 Cocaine, 188, 197. See also Drug abuse Coercion, 1 Cognitive neuroscience, 81 Cognitive distortion, 5 Compassion, 128, 133 Competitive emotions characteristics of, 4 neural substrates of, 143–147 social comparison-based, 142–143 Conflict, 103, 132 Congenital Adrenal Hyperplasia (CAH), 28 Congenital aplasia, 68 Conjunction analysis, 135 Connectedness, 87 Connectivity, 94–95 Contempt, 128 Contraception, oral, 40 Cooperation assessment using game-theoretical approaches, 217, 220–223 conditional, 217–218 evolutionary aspects of, 5, 217–219 ontogenetic aspects of, 219–220 in psychopathological conditions, 223–226 Cooperative emotions, 4, 139–140, 151 Cooperativity, 1 Copy number variations (CNVs), 7–8, 22 Coronary heart disease, 185 Corpus callosum, 128 Covariance analysis, 102 Covariation, 102 CpG sites, 9 Creativity, 177 Crying behavior, 48–49 CYP gene, 26–28 Cyproheptadine, 169 DAT gene, 40–41 Deception, 1 Decision making brain injury, impact on, 160–161 brain regions involved in, 162–163 deficits, 161 economic, 227 emotional signals guiding, 166–169 impaired, 161 influential factors, 4, 82, 86–87, 113–114, 132–134 overview, 159–160

bindex.indd 247

247

prosocial, 40 substance abuse and, 186 Decision theory, 236 Deductive reasoning, 102–103 Depression, 215 Dextroamphetamines, 170 DHCR gene, 26 Dictator Game (DG), 114, 222, 225, 227 Dignity, 129 Dinucleotides, pair bonding, 39 Discount rates, 239–240 Discounted utility (DU) model, 236–237 Disgust, 119, 128 Dispositional Envy Scale (DES), 147 Distant selves, 86–87 Dizygotic twin studies, autism spectrum conditions, 22–23 DNA methylation, 8 Dominance hierarchy formation bystander effects, 204–205, 207 empirical studies, green swordtail case, 209–211 extrinsic effects models, 205–206, 208–209 future research directions, 211 joint effects, 208 overview, 203–205 winner/loser effects, 204–211 Donation behavior studies, 134–135 Dopamine impact of, 2, 4, 25, 163–164, 170, 227 pair-bonding behavior, 38, 40–42 Dopaminergic system, 149, 178, 239 Dorsal anterior cingulate cortex (dACC), 145 Dorsal MPFC (dMPFC), 55–56, 94, 98–99 Dorsolateral frontal cortex, 128 Dorsolateral prefrontal cortex (DLPFC) decision making and, 162, 164, 171, 174 empathy support, 67 moral cognition and emotion, 132, 135 moral judgment studies, 95, 100–101 psychiatric disorders, 223, 226 Dorsomedial prefrontal cortex (DMPFC), 94, 127 Double-blind studies, 12 Drug abuse accounting formulas, 193–197 addiction risk factors, 186–188, 197 chronic, 194, 196–197 defined, 186 high-functioning users, 192–194, 197–198 impact of, 4, 185

7/25/2011 10:10:03 AM

248

INDEX

Drug abuse (cont’d) intervention programs, 198 model for joint effect of individual sensitivity and social exposure, 188–193, 197–198 social drugs, 189 social exposure and, 187, 191, 193, 198 social factors leading to, 187–188 social learning theory, 187 Dual-process model, 132–133 Electrical stimulation, 175 Electroencephalograph (EEG) studies, 93, 104, 234 Electrophysiological studies, 11–12 Embarassment, 126 Embedded Figures Test, 30 Emotional cues, types of, 47 Emotional processing, influential factors, 71 Emotional signals, impact on decisionmaking process, 166–169 Emotional state impact on moral judgment, 102 manipulation of, 112 Emotion(s), see also specific emotions competitive, 139–151 moral cognition and, 111–135 perception, 40 regulation, 81, 87, 100 systems, in decision making, 163 Emotions, shared representations of action and perception, 49–50 mirror neuron system and empathy, 50–51, 56 shared affect, neural mechanisms of, 51, 53 Empathic processing, 69 Empathy ASC and, 21, 23 brain networks, supportive, 47–57 characteristics of, 40, 141 competitive emotion studies, 150 defined, 48, 56 future thinking and, 81–87 genetic correlations, 2–3 human mirror neuron system, 63–68 negative, 4, 140, 142 neural basis, 2–3 pair bonding and, 37–41 positive, 4, 139–140 social comparison-based emotion studies, 148

bindex.indd 248

Empathy Quotient (EQ), 23, 27–28, 30 Emulation, 65 EN-2 gene, 25, 28 Endophenotypes, autism spectrum conditions, 22, 30–31 Envy, 3–4, 143–147 Epigenetics, autism spectrum conditions, 23 Epigenome, 8–9 Epilepsy, 22, 175 Episodic memory, 1 Episodic future thinking cognitive thinking and, 3 defined, 81 personal goals and, 83–85 ESR gene, 26, 28 Estrogen, 26 Event-feature-emotion complexes (EFEC), 127 Event-related potentials (EVPs), 143 Expectancy-valence (EV) learning model, drug abuse studies choice consistency parameter, 196–197 motivation parameter, 194–195 overview, 193–197 recency parameter, 195–196 Expected utility theory, 235 Expressive language, 71–72 External effects, 204 Extrinsic effects, dominant hierarchy formation, 205–206, 208–210 Face recognition, 40, 71 Facial attractiveness, 40 Facial expression, 50, 54, 56, 69 Facial trustworthiness, 40 False-belief tasks, 56 Familywise error rate (FWER), 6, 27, 29–30 Fear, 70 Fluvoxamine, 169 Foregone payoffs, 192–193, 197 Free will, 124 Frontal cortex, 113, 125–126, 133 Frontal lobe, 151, 162 Frontal lobe syndrome, 160 Frontal operculum (IFO), 141 Fronto-mesolimbic subregions, 129 Fronto-temporo-mesolimbic integration model, moral reasoning, 133–134 Fronto-tempero-mesolimbic networks, 129 Frontoparietal neural network, 2. See also Human mirror neuron system; Mirror neuron system

7/25/2011 10:10:03 AM

INDEX

Frontopolar cortex (FPC), 99, 102, 126–127, 128, 132 Frontotemporal dementia (FTD), 114, 116 FSHB gene, 26 Functional magnetic resonance imaging (fMRI) studies characteristics of, 234, 238–239 competitive emotions, 143–145 emotional states, 51 episodic future thinking, 81–86 mirror neuron systems, 64, 68, 71, 73 moral judgment processes, 93 moral motivations, 125 moral sentiments, 126, 128–130 support of somatic marker hypothesis, 170–171, 176 values, moral and social, 129 Fundamental attribution error, 102–103 Fusiform face area (FFA), 94 Future selves, 86–87 GABRB gene, 25, 28–29 Gage, Phineas, 113, 160 Gambling behavior, 13 Game theory, 5 Gender studies, brain networks supporting networks, 56 GeneCards, 9 Gene-environment interaction, 5 Gene expression, 7, 11 Genes, decision making and dominance hierarchy formation, 203–211 drug abuse studies, 185–198 neuroeconomics, 233–240 psychiatric disorders and, 215–227 somatic marker framework, 159–179 Gene x Environment Interaction, 8–9 Generosity, 40, 125, 148 Genetic neuroscience, 2 Genetic studies future research directions, 239–240 large-scale, 24 Genome-wide association studies (GWAS) autism spectrum conditions, 22, 24 overview of, 5–7, 10 Genomic dark matter, 7 Genotyping, 6–7, 9 Glucocorticoid receptor (GR), 8 Goal-directed behavior, 176 Gratitude, 129–130 Gray matter, 126

bindex.indd 249

249

Green swordtail fish, hierarchy formation study, 203, 209–211 Grief, 49 Guilt, 125–126, 128–129, 133, 226 Hallucinogens, 191 Haloperidol, 170 Haplotypes, 6 Haploview, 9 HapMap database, 6, 27 Hardy-Weinberg equilibrium, 27 Heboidophrenia, 224 Heritability, 7 Heroin, 191 High-Functioning Autism (HFA), 22–24, 147 Hippocampus, 8, 163 Homeostasis, 176–177, 179 Honesty, 125 Hopes, 85 Hormones anti-diuretic, 38 sex, 26 steroid, 26 testosterone, 26, 28 HOX gene, 25, 28–29 HSD gene, 26, 28 Human mirror neuron system empathy and, 69–70 function of, 11–13, 65 future research directions, 75–76 language and embodied semantics, 71–72 overview of, 63–66 shared representations beyond, 72–74 social experiences and, 66–68 social group affiliations, 66, 70–71 social networks, 74–75 Human disease, 6–7 Human morality, historical perspectives, 111 Human genome, 5–6 Human mating behavior, influences on, 40–41 Hume, David, 129 Hunger, 134 Hutcheson, Francis, 124 Hyperactivity, 22 Hyperbolic time discounting, 236–238 Hypothalamus, 162–163 IGF gene, 25, 28 Imitation, 65, 67, 69 Imitative learning, 67

7/25/2011 10:10:03 AM

250

INDEX

Impatience, 240 Impulsivity, 186, 236, 240 Indignation, 128–129 Indirect reciprocity, 217–218 Inferior frontal gyrus (IFG), 2, 64, 67–71, 141, 151 Inferior parietal cortex, 54–55 lobule (IPL), 64, 67, 70–71, 84 Inferior temporal cortex, 94 Infidelity, sexual, 41 Instrumental helping, 219 Insula, 3 Insular cortex, 3, 162, 175–176 Intelligence, 160–161. See also IQ; Knowledge Intentional harm, 116–118 Intentional states, 50 Intentions, 102 Interoceptive agnosia, 161 Interpersonal Reactivity Index (IRI), 69, 74, 147 Intertemporal choice models, 236–237 Intrinsic factors, 204 Introspective processes, 98 Iowa Gambling Task (IGT), decision making studies, 164–176, 186 drug abuse studies, 191–193, 196 IQ, autism spectrum conditions (ASC), 22, 24 Kant, Immanuel, 111, 124 Knowledge, 162, 168–169, 179 Language delay, 24 Lateral prefrontal cortex (LPFC), 3, 95, 99–101, 103–104 Lateral orbitofrontal cortex (latOFC), 128, 135 Lateral septal nuclei, 12 L-DOPA, 13 6 Learning, 176 Learning influential factors, 67 individual, 4 reward-based, 97 LHB gene, 26 LHCGR gene, 26, 28 LHRHR gene, 26 Life events, stressful, 8 Limbic system, 51, 56, 98–99 Linear hierarchies, 203–205

bindex.indd 250

Linkage disequilibrium, 6 Linkage studies, autism spectrum conditions (ASC), 22, 25–26 Loser effects, hierarchy formation characteristics of, 204–206, 208–209, 211 empirical study, green swordtail case, 209–211 individual recognition and, 206–207 joint effects, 208–209 Loss aversion, 172–173, 233, 235 Love Attitudes Scale, 41 Lung cancer, 185 Machiavellianism, 226 Magnetic resonance imaging (MRI) functional. See Functional magnetic resonance imaging (fMRI) studies transcranial. See Transcranial magnetic resonance (TMS) studies Magnetoencephalography (MEG), 234 Manipulation, 1, 112 MAO gene, 8, 25, 28–29 Marijuana, 187–188, 197–198 Marriage, 41–42 Maternal care, 8, 42 Mathematical models, 205, 236 Medial frontal gyrus, 170 Medial orbitofrontal cortex (mOFC), 162, 170–171, 223 Medial prefrontal cortices (MPFC) competitive emotions, 145–146, 151 empathy support, 2, 53–55 future thinking, 83 human mirrr neuron system, 66, 76 moral judgment, 94, 97–99, 101–104 psychiatric disorders and, 222–223 Medial preoptic area, 126 Mediobasal hypothalamus, 126 Memory, 53, 132, 162–163, 176, 196–197 Mendelian disorders, 7 Meningiomas, 160 Mentalizing, 3–4, 49, 55, 66, 75, 98, 100, 145–147, 151, 216, 219–220 Merging hierarchies, 208–209 Mesolimbic forebrain, 129–130 Metacognitive representations, 55 Methylation, 8 Microarray studies autism spectrum conditions, 23 features of, 6, 11 Microsatellites, pair-bonding behavior, 39–40

7/25/2011 10:10:03 AM

INDEX

Microtus spp., pair bonding studies, 38–40 Midbrain, dorsal tegmentum, 163 Middle temporal lobes (MTLs), 222 Mind reading, 49 Minor allele frequency (MAL), 7 Mirror neuron system (MNS) brain regions in, 49–50 empathy and, 50–51 extended, 57 functions of, 49–50, 5 historical perspective, 63 human, see Human mirror neuron system ontogeny, 51 racial differences in, 71 Mirrored touch synesthesia, 74 Molecular genetics, 9, 13, 21, 30 Monoamine oxidase (MAO), 186 Monogamy, sexual, 40–41 Monozygotic twins, autism spectrum conditions, 21–23 Moral abilities, 123 Moral behavior, 177 Moral cognition brain imaging studies, 116–119 emotion and, 3 fronto-temporo-mesolimbic integration, 3 neurological patient studies, 113–116 psychological studies, 112–113 Moral condemnation, 112 Moral dilemmas, 114–115, 118 Moral disgust, 112 Moral judgment explicit processes, 96–97, 101–104 influential factors, 3 Moral motivations, 125–130 Moral neuroscience, emotion and competitive emotions, 139–151 moral cognition, 111–135 moral judgment systems, 93–104 Moral reasoning, 114, 116, 132–134 Moral sentiments empathic, 128 evolutionary precursors of, 125–126 prosocial, 128–130, 133 Motivation moral, 124–130 social, 38, 103, 148 Motivational thinking, 81, 87 Motor/behavioral systems, 163 Motor cortex, 68 Motor evoked potentials (MEPs), 67 Motor kinematics, 75

bindex.indd 251

251

Motor learning, 65, 67 Motor planning, 71–72 Motor simulation, 65, 69–70 Mouse studies autism spectrum conditions (ASC), 28, 30 social comparison-based emotions, 141 Movement observation, 55, 65–66 Myopic loss aversion, 172 Nature-nuture debate, 8 Negative affective response, 145 Negative emotional events, impact of, 82–83 Negative emotions, moral cognition and, 112 Negatve empathy, 150 Nerve growth factor (NGF), 29 Neural connectivity direct, 102 significance of, 2 theory, 26 Neural networks, 65–66, 76, 95, 99, 170 Neural phenotypes, 31 Neurexins, 29 Neuroanatomical studies decision making, 132–134 moral motivations, 125–130 moral reasoning, 132–134 sociomoral knowledge, 129–132 Neurobiology, 237, 239 Neurochemical pathways, 11–13 Neurodevelopment, 26, 29 Neuroeconomics characteristics of, 5, 30, 177, 234–236 critique of, 235–236 future research directions, 239–240 individual’s time preferences, 240 intertemporal choices, 236–239 Neuroendocrine cells, 25 Neuroimaging studies characteristics of, 30, 234 mirror neuron systems, 64 motivational aspects of future thinking, 81 theory of mind applications, 53 Neurological studies, 113–116 Neuronal network, 141 Neuropeptides, 11, 26 Neurophilosophy, 124 Neuropsychiatric conditions, 168–169 Neuropsychiatric disorders, 11, 135 Neuropsychological tests, 125, 161 Neuroscience, 13, 125, 235–236

7/25/2011 10:10:03 AM

252

INDEX

Neurotransmitters, 163–164, 169–170, 176, 186 Next-generation sequencing (NGS), 10 NGF gene, 25 NGFR gene, 25 NLGN gene, 25, 28–29 Nonapeptides, 38 Noradrenaline, 163–164 NRCAM gene, 25 NTF gene, 25, 28 NTRK genes, 25, 27–29 Nucleus accumbens, 149 Observation-imitation mapping, 51 Occipital lobe, 95 Occipital/parietal cortex, 144 Oligonucleotides, autism spectrum conditions, 23 Opioids, 26 OPRM gene, 25 Optical imaging, 234 Optimism, 240 Orbitofrontal cortex (OFC), 94–95, 98, 102, 128, 160, 163, 226, 239 Other-Blaming, 128–129 Other-critical moral sentiments, 128–129 OXT gene, 25, 28–30 OXTR gene, 10, 25, 28 Oxytocin (OT) characteristics of, 2, 4, 30, 151, 227 human mirror neuron system, 11–12 pair-bonding behavior, 38–40, 42 social comparison-based emotions, 151 Oxytonergic system, 147–149 Pain empathy, 74 matrix, 73–74 Pair bonding, behavioral genetics of human, see Human mating behavior mating behavior, biology and genetics of, 38–41 overview of, 2, 9, 37–38 Parahippocampal cortex, 144 Parietal lobe, 95 Partner Bonding Scale (PBS), 9, 39 Partner loss, 42 Paternal care behavior, 38 Peer behavior, significance of, 187–188 Perception mirror neuron systems, 66–67 shared representations, 49–50 significance of, 176

bindex.indd 252

Perception-action coupling, 49, 53 mapping, 51 Perceptual processes, social, 96 Periacqueductal gray, 162 Peripheral blood cells, 11 Peripheral nervous disorders, 175 Peripheral nervous system, 179 Permutation testing, 27 Personal goals, 83–86 Personality disorders, 227 Personality factors, 65 Perspective first-person, 55, 67 third-person, 67 Perspective-action coupling, 3 Perspective-taking, 48–49, 223 Pharmacogenetics, 12–13 Phenotypes autism spectrum conditions, 31 characteristics of, 6–7, 11 pair bonding, 40 Pity, 4, 128, 130 Polycystic ovary syndrome (PCOS), 28 Polymerase chain reaction (PCR), 26 Polymorphisms autism spectrum conditions, 21–22, 28–30 pair bonding, 39–40 POR gene, 26 Positive affective response, 145 Positive and Negative Syndrome Scale, 225 Positive emotional events, impact of, 83 Positron emission tomography (PET) studies, 116, 170, 175–176, 234 Posterior cingulate cortex (PCC), 75, 84 Posterior cingulate gyrus, 162 Posterior parietal cortex, 2, 56 Postpartum depression, 11 Prairie voles, see Microtus spp. Prefrontal cortex (PFC) explicit processes, 101–104 functional anatomy and connectivity, 94–95 implicit processes, 101–104 overview of, 3, 93–94, 174 social cognitive and moral judgment processes, 96–101 Premotor cortex, 72 Pride, 129–130 Primate studies cooperation, 218 mirror neuron system, 63–65

7/25/2011 10:10:03 AM

INDEX

pair-bonding studies, 39–40 perception-action coupling, 49–50 social comparison-based emotion, 141 somatic markers and, 177 Prisoner’s Dilemma (PD) game, 220, 223–224, 226 Promiscuity, 41 Prosocial behavior, 1, 148 Pseudopsychopathy, 113 Psychiatric disorders complex social cognition and, 215–217 cooperation and, 217–226 deviant empathic responses, 56 future research directions, 226 game-theoretical studies, 217, 220–223 neurodevelopmental, 5, 51 Psychological studies, 112–113 Psychopathy developmental, 126, 134 types of, 5, 151, 169, 215, 226 Psychophysiological studies, 112 Psychosocial development, 114 Psychostimulants, 170 PTSD, 11 Public Goods (PG) game, 221 Punishment networks, 128–129, 133, 143–145 Pyrosequencing, 8–9 Quantitative trait loci (QTL), 7, 11 Questionnaire Measure of Emotional Empathy (QMEE), 23 Questionnaires drug abuse studies, 192 emotional empathy, 23 self-report, 39 Race bias, 100 Rae, John, 236 RAPGEF gene, 25 Rat studies autism spectrum conditions, 26 gene-environment interactions, 8 Rationality, 177, 233 Rationalization, 112 “Reading the Mind in the Eyes” Test (RMET), 23, 30, 53, 225 Reappraisals, 100 Reasoning. See Deductive reasoning; Moral reasoning decision-making process and, 161–162 empathic, 54

bindex.indd 253

253

financial, 177 influential factors on, 112 Reciprocity, 215, 217, 226 Reinforcement learning models, 191–193 Resource holding pwer (RHP), 204–208 Respect, 124 Retrosplenial cortex, 162 Reversal learning, 164, 174 Reverse inference, 117 Reward/punishment networks, 143–145, 150, 162, 166–167, 175–176, 223 Reward system, 4, 97–98, 104, 129, 144, 237 RNA sequencing, 10 Romantic relationships, 41–42 Sadness, 127 Sanger sequencing, 8 Saving behavior, 239–240 Schadenfreude, 3–4, 142–147, 150 Schizophrenia, 11, 215–216, 224–225, 227 SCP gene, 26, 28 Selective seronin reuptake inhibitors, 169–170 Self-concept, 95 Self-consciousness, 223 Self-control, 100,195, 238 Self-interest, 124, 129, 134 Selfishness, 124 Self-love, 124 Self-other comparison, 140 distinction, 2, 48–49, 54–56, 71 Self-reference, 55 Self-referential processing, 55–56 Self-related processes, 98–99 Self-relevance, 84–86, 145 Self-reported empathy, 23 Self-task, 51–52 Semantics, 71–72 Sensation seeking behavior, 186 Sensory information, 94–95 Sensory mapping, 177 Serotonin, 25, 163–164, 227 Serotonin 5HT2A receptor antagonist, 169–170 Sex steroids, 2, 29 Sexual arousal, 127 Sexual intercourse, 39, 41. See also Pair bonding Shame, 126 Shared representations, 66, 72–74 SHBG gene, 26

7/25/2011 10:10:03 AM

254

INDEX

Simulation processes, 151 Simulation studies extrinsic effects, 205–207, 209 mirror neuron system, 65–66 Single nucleotide polymorphisms (SNPs) autism spectrum conditions (ASC), 22, 24, 2 6–27, 29–30 genomic dark matter, 7 linkage disequilibrium, 5–6 tagging, 9 Skin conductance response (SCR), 166–168 SLC gene, 26 Smith, Adam, 124, 129 Smoking behavior, 239–240 Social behavior, defined, 123–124 Social cognition, generally defined, 1, 96 global, 97 influential factors, 63 molecular toolbox for research, 5–13 neural basis of, 1–2, 47 Social comparison, emotional reactions to envy, 140–141 overview of, 139–140 schadefreude, 140–141 Social comparison-based emotions, 141–143 Social cooperation, 140–141. See also Cooperation Social disorder, 215 Social drugs, 189, 197 Social-emotional responsivity theory, 2, 26, 29–30 Social experiences, effects of, 4 Social factors, 65 Social functioning, measurement of, 23 Social influence, 189–191 Social Intuitionist Approach, 111 Social judgment, 119 Social knowledge conceptual, 131–132 defined, 129 Social learning theory, 187 Social liking, 70 Social memory, 38 Social motivation, 38 Social neuroscience, 2, 104 Social norms, 99, 215–227 Social ranking, 149 Social Responsiveness Scale (SRS), 23 Sociocultural influences, 124, 129 Sociomoral knowledge, 129–132

bindex.indd 254

Somatic marker hypothesis brain regions involved in decision making, 162–163 characteristics of, 111, 133, 161–164 critique of, 173–179 decision making process and, 171–172 development of, 159 functional neuroimaging support of, 170–171 neurotransmitters and, 169–170 testing, 165–171 Somatic markers, in decision-making process as executive processes, 173–174 feedback, impact on, 175–179 framework, 177–178 Somatic marker theory, 178–179. See also Somatic marker hypothesis Somatosensory cortex, 53, 74 Somatovisceral afference model of emotion (SAME), 167 Spinal cord functions of, 175–176, 179 injuries, 67–68 SRD gene, 26 Stereotypes, 96–97, 99–100 Stress-coping behavior, 42 Striatum, 95 STS gene, 26 Substance abuse. See Drug abuse Substantia nigra, 95 Suicide, 8 SULT gene, 26 Superior temporal sulcus (STS), 53–54, 56, 67, 69, 222–223 Sympathy, 4, 124, 128 Systemizing Quotient (SQ), 23 TAC gene, 25 Tactile sensation observations, 74 Temperoparietal area, 54 Temporal discounting, 237 Temporal lobe, 95, 101 Temporal poles (TPs), 53–54, 151, 222 Temporoparietal junction (TPJ), 2, 53–55, 56, 66, 74, 119, 127, 222–223 Testosterone, 26, 28 Thalamus, 94 Theory of mind (ToM), 49, 66, 147, 216 Time discounting, 237–238

7/25/2011 10:10:03 AM

INDEX

Trait empathy, 23, 70 judgments, 86 Transcranial magnetic resonance (TMS) studies episodic future thinking, 86–87 mirror neuron systems, 67–68, 71 moral judgment, 104 repetitive (rTMS), 71 Transcription, 11 Transcriptsome analysis, 10 Transgression, 112 Transitive hierarchy formation, 207 Trolley-type dilemmas, 133 TRPV gene, 25 Trust, 40, 134 Trust Game (TG), 114, 220–221, 225 TSPO gene, 26 Twin studies autism spectrum conditions, 21–24 pair bonding, 39 UCSC Genome Browser, 9 Ultimatum Game (UG), 221–225 Utilitarian response, 114, 132–133 Vagus nerve, 175 Values, 125 Vasopressin functions of, 2, 30, 227 pair-bonding behavior, 38–39, 42 VEGF gene, 25 Ventral frontopolar cortex, 129 Ventral MPFC (vMPFC), 55–56

bindex.indd 255

255

Ventral putamen, 149 Ventral striatum, 144–146, 162 Ventral tegmental area, 126 Ventrolateral PFC (VLPFC), 95, 100, 102 Ventromedial prefrontal cortex (vmPFC) brain injury in, 113 characterized, 2–3, 160–163 decision making process and, 164–171 emotion and moral behavior studies, 113–118 future thinking, 81–82, 84–87, 94 lesions, 192 moral motivations, 127 moral reasoning, 133, 135 moral sentiments, 129 somatic marker hypothesis and, 170–171, 174–177 VGF gene, 25 V1PR gene, 25, 28 Vulnerability, disease, 5, 7–8 WFS gene, 25, 28–30 White matter, 94 Widowhood, 42 Winner effects, hierarchy formation characteristics of, 204–206, 208–209, 211 empirical study, green swordtail case, 209–211 individual recognition and, 206–207 joint effects, 208–209 Wisconsin card sort test, 161 Working memry, 162, 164, 174 Yoni task, 146

7/25/2011 10:10:03 AM