Risky Decision Making in Psychological Disorders [1 ed.] 0128150025, 9780128150023

Table of contents :
Cover
Risky Decision Making in Psychological Disorders
Copyright
Contents
Acknowledgments
I: The assessment of risky decision making
1 An introduction to risky decision making
Decision making, risk, and uncertainty
Risky decision making
Dual-process models
The somatic marker hypothesis and the neuroscience of risk
Risk-taking behavior
Introduction to the remaining chapters
2 Measurement methods
Risky decision making measures
Adult Decision making Competence
Angling risk task
Balloon analogue risk task
Blackjack task
Bomb risk elicitation task
Cambridge (Rogers) gambling task
Card-guessing task
Chicken game
Choice dilemmas questionnaire
Columbia card task
Cups task
Delay and probability discounting tasks
Devils task/knife switches
Dynamic experiments for estimating preferences (DEEP)
Framing spinner task
Game of dice task
Iowa gambling task
Mirror drawing risk-taking task
Multioutcome risky decision task
Nonsymbolic economic decision making task
Probabilistic gambling task
Reyna and ellis risk task
Risk propensity task
Risky gains task
Sequential investment task
Stoplight game
Two-outcome risky decision task
Wheel of fortune task
Risk propensity and risk attitude measures
Cognitive appraisal of risky events
Decision styles scale
Dohmen scale
Domain-specific risk-taking scale
Evaluation of risk scale
Everyday risk inventory
General decision making style
General risk propensity scale
Passive risk-taking scale
Risk propensity scale
Risk-taking propensity
Stimulating-instrumental risk inventory
Demographic factors in test performance
Age
Sex
Modeling decision making on behavioral tasks
3 Reliability and validity
Reliability
Test–retest
Parallel or multiple forms
Intermethod
Internal consistency
Split-half reliability
Reliability conclusion
Validity
Validity conclusion
Factors affecting reliability and validity
Conclusion
4 Neuroscience and associations with other executive functions
Executive functions: theories and constructs
Assessing executive functions
Impairments in executive functions
Is decision making an executive function?
Correlations between measures of decision making and executive functions
Neuroscience of decision making
What neuroimaging teaches us about decision making processes
Conclusion
II: Risky decision making across psychological disorders
Preface to section II: Organization of the remaining chapters
5 Anxiety: state-dependent stress, generalized anxiety, social anxiety, posttraumatic stress disorder, and obsessive–compul...
The current literature: generalized anxiety
Risk-taking behavior
Delay discounting and risky decision making
The current literature: social anxiety
Risk-taking behavior
Delay discounting and risky decision making
The current literature: posttraumatic stress disorder
Risk-taking behavior
Delay discounting and risky decision making
The current literature: obsessive–compulsive disorder
Risk-taking behavior
Delay discounting and risky decision making
The current literature: trait anxiety and other nondiagnosable types of anxiety
The current literature: state-dependent stress
Performance on other executive function tasks
Neuroimaging
Generalized anxiety disorder
Social anxiety
Posttraumatic stress disorder
Obsessive–compulsive disorder
Acute stress
Potential mechanisms
State-dependent fluctuations in anxiety
Overall executive dysfunction
Cautiousness and indecisiveness
Effects of impulsivity versus behavioral inhibition
Alterations in neural processing of rewards and learning from feedback
Conclusion and future directions
Participant-related factors
6 Disruptions of mood: positive and negative affect, depressive disorders, and bipolar disorders
The current literature: risk-taking behaviors
Depressive symptoms and disorders
Mania, hypomania, and the bipolar disorders
The influence of suicidal ideation and behaviors
The current literature: risky decision making
Depressive symptoms and disorders
Mania, hypomania, and the bipolar disorders
The influence of suicidal ideation and behaviors
Conclusion
The current literature: delay discounting, and reward responsiveness
Depressive symptoms and disorders
Mania, hypomania, and the bipolar disorders
The influence of suicidal ideation and behaviors
Performance on other executive function tasks
Neuroimaging
Potential mechanisms
State-dependent mood
Overall executive dysfunction
Alterations in neural processing of rewards and learning from feedback
Behavioral activation system hyperactivity in bipolar disorder
Participant-related factors
Conclusion and future directions
7 Disordered eating behaviors: anorexia, bulimia, binge eating, and obesity
The current literature: risk-taking behaviors
The current literature: risky decision making
Anorexia nervosa
Bulimia nervosa
Obesity
Binge-eating disorder
Disordered eating behaviors
Comparisons between eating disorder diagnostic groups
The current literature: delay discounting and reward responsiveness
Performance on other executive function tasks
Neuroimaging
Potential mechanisms
Overall executive dysfunction
Comorbidity
Alterations in neural processing of rewards and learning from feedback
Impulsivity
Conclusion and future directions
8 Sleep deprivation and sleep-related disorders
The current literature: risk-taking behaviors
Conclusion
The current literature: risky decision making
Iowa Gambling Task
Balloon Analogue Risk Task
Other tasks
Summary
The current literature: delay discounting and reward responsiveness
Performance on other executive function tasks
Neuroimaging
Potential mechanisms
Overall executive dysfunction
Alterations in neural processing of rewards and learning from feedback
Conclusion and future directions
Participant-related factors
9 Impulsivity and attention-deficit/hyperactivity disorder
The current literature: risk-taking behaviors
The current literature: risky decision making
Balloon Analogue Risk Task
Cambridge Gamble Task
Game of Dice Task
Iowa Gambling Task
Other risky decision making tasks
Summary
The current literature: delay discounting and reward responsiveness
Delay discounting
Reward responsiveness
What factors could be affecting risky decision making in attention-deficit/hyperactivity disorder?
The influence of medication
Comorbid diagnoses
Impulsivity as a personality characteristic
Demographic factors
The influence of retrospective self-report, effort, and potential malingering
Performance on other executive function tasks
Performance on other tasks when decision making was impaired
Performance on other tasks when decision making was not impaired
Neuroimaging
Potential mechanisms
Overall executive dysfunction
Alterations in neural processing of rewards and learning from feedback
Conclusions and future directions
Type of decision making task and comparison to other executive tasks
Participant-related factors
10 Addictive behaviors: gambling and substances of abuse
Pathological gambling
Delay discounting
Risky decision making
Neuroimaging
Relationship with other executive functions
Theories
Impulsivity
Difficulties with learning from reward or loss and perception of risks
Urge to gamble and craving behaviors
Gambling problem severity and type
State and participant variables
Comorbid diagnoses
Treatment implications
Other behavioral addictions
Alcohol use disorder
Risky decision making
Delay discounting and reward responsiveness
Neuroimaging
Nicotine or tobacco use disorder
Risky decision making
Delay discounting and reward responsiveness
Neuroimaging
Cannabis use disorder
Risky decision making
Delay discounting and reward responsiveness
Neuroimaging
Opioid-related disorders
Risky decision making
Delay discounting and reward responsiveness
Neuroimaging
Stimulant use disorders
Risky decision making
Delay discounting and reward responsiveness
Neuroimaging
Ecstasy or MDMA use
Theories of risky decision making across substances of dependence
Impaired executive functions
Altered processing of risks and rewards and difficulties learning from feedback
Impulsivity
Substance use expectancies, satiation, and cue-induced craving
Comorbidities and polysubstance use
Treatment implications
Conclusions
11 Schizophrenia and delusional disorders
The current literature: risk-taking behaviors
The current literature: risky decision making
Balloon Analogue Risk Task
Game of Dice Task
Iowa Gambling Task
Other risky decision making tasks
Summary
The current literature: delay discounting and reward responsiveness
What factors could be affecting risky decision making in schizophrenia spectrum disorders?
The influence of medication
Current symptoms
Other diagnostic considerations
Control group
Performance on other executive function tasks
Neuroimaging
Potential mechanisms
Deficit in learning from feedback
Deficit in assessing the magnitude of rewards/losses and reward responsiveness
Deficit in rewards guiding learning
Deficit in planning for the future
Conclusions and future directions
Delusion proneness and levels of schizophrenia spectrum disorder symptoms
A focus on the development of normative data
12 Conclusions and future directions
Etiologies of risky decision making across disorders
Impaired executive functions
Type/System I versus Type/System II decision making
Reward pathway activation or deficiency
Focus on immediate versus long-term outcomes
“State” versus “trait” factors
Impaired feedback processing
Current issues affecting understanding of risky decision making in psychological disorders
Characteristics of the patient participants
Characteristics of the control participants
Characteristics of the risky decision making tasks
The influence of comorbidities
The influence of treatment
Treatment implications
Future directions for the field
Assessment of ecological validity and task reliability
Development of parallel versions of tasks
Examination of multiple measures in the same study
Which came first: risky decision making or the psychological disorder
Cognitive modeling techniques
References
Index
Back Cover

Citation preview

RISKY DECISION MAKING IN PSYCHOLOGICAL DISORDERS

RISKY DECISION MAKING IN PSYCHOLOGICAL DISORDERS MELISSA T. BUELOW The Ohio State University, OH, United States

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-815002-3 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Nikki Levy Editorial Project Manager: Megan Ashdown Production Project Manager: Omer Mukthar Cover Designer: Victoria Pearson Typeset by MPS Limited, Chennai, India

Contents Acknowledgments

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I THE ASSESSMENT OF RISKY DECISION MAKING 1. An introduction to risky decision making Decision making, risk, and uncertainty Risk-taking behavior Introduction to the remaining chapters

3 11 12

2. Measurement methods Risky decision making measures Risk propensity and risk attitude measures Demographic factors in test performance Modeling decision making on behavioral tasks

15 30 34 35

3. Reliability and validity Reliability Validity Factors affecting reliability and validity Conclusion

40 47 58 59

4. Neuroscience and associations with other executive functions Executive functions: theories and constructs Impairments in executive functions Is decision making an executive function? Correlations between measures of decision making and executive functions Neuroscience of decision making What neuroimaging teaches us about decision making processes Conclusion

v

61 69 70 71 80 88 90

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Contents

II RISKY DECISION MAKING ACROSS PSYCHOLOGICAL DISORDERS Preface to Section II: Organization of the Remaining Chapters

5. Anxiety: state-dependent stress, generalized anxiety, social anxiety, posttraumatic stress disorder, and obsessive compulsive disorder The current literature: generalized anxiety The current literature: social anxiety The current literature: posttraumatic stress disorder The current literature: obsessive compulsive disorder The current literature: trait anxiety and other nondiagnosable types of anxiety The current literature: state-dependent stress Performance on other executive function tasks Neuroimaging Potential mechanisms Conclusion and future directions

98 100 101 103 105 105 107 107 109 111

6. Disruptions of mood: positive and negative affect, depressive disorders, and bipolar disorders The current literature: risk-taking behaviors The current literature: risky decision making The current literature: delay discounting, and reward responsiveness Performance on other executive function tasks Neuroimaging Potential mechanisms Conclusion and future directions

114 117 123 125 125 127 133

7. Disordered eating behaviors: anorexia, bulimia, binge eating, and obesity The current literature: risk-taking behaviors The current literature: risky decision making The current literature: delay discounting and reward responsiveness Performance on other executive function tasks Neuroimaging Potential mechanisms Conclusion and future directions

137 138 141 142 143 144 147

Contents

vii

8. Sleep deprivation and sleep-related disorders The current literature: risk-taking behaviors The current literature: risky decision making The current literature: delay discounting and reward responsiveness Performance on other executive function tasks Neuroimaging Potential mechanisms Conclusion and future directions

150 153 156 157 158 158 160

9. Impulsivity and attention-deficit/hyperactivity disorder The current literature: risk-taking behaviors The current literature: risky decision making The current literature: delay discounting and reward responsiveness What factors could be affecting risky decision making in attention-deficit/ hyperactivity disorder? Performance on other executive function tasks Neuroimaging Potential mechanisms Conclusions and future directions

164 167 171 176 181 183 184 186

10. Addictive behaviors: gambling and substances of abuse Pathological gambling Other behavioral addictions Alcohol use disorder Nicotine or tobacco use disorder Cannabis use disorder Opioid-related disorders Stimulant use disorders Ecstasy or MDMA use Theories of risky decision making across substances of dependence Treatment implications Conclusions

190 196 197 200 202 204 206 208 209 213 214

11. Schizophrenia and delusional disorders The current literature: risk-taking behaviors The current literature: risky decision making The current literature: delay discounting and reward responsiveness What factors could be affecting risky decision making in schizophrenia spectrum disorders? Performance on other executive function tasks Neuroimaging Potential mechanisms Conclusions and future directions

216 218 223 224 227 228 229 231

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Contents

12. Conclusions and future directions Etiologies of risky decision making across disorders Current issues affecting understanding of risky decision making in psychological disorders Treatment implications Future directions for the field

References Index

235 238 240 241

245 391

Acknowledgments Several individuals provided support and encouragement during the development of this project. I would like to thank those undergraduate student researchers who helped with the literature review process (Celeste, Cortney, Alyssa, and Ashley) and Wes for providing feedback on drafts. I would also like to thank my department colleagues and friends for their encouragement: Brad, Chris, Jen, Jim, Julie, and Melissa. Finally, I would like to thank my family, Charlie and Paul in particular, for their support and encouragement throughout this process.

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C H A P T E R

1 An introduction to risky decision making The act of making a decision permeates all facets of life. From seemingly mundane decisions, such as what to wear or what to eat, to larger scale decisions such as whether to undergo surgery or not, we face decisions every day that can have significant effects on both our present and future situations. How do we make these decisions? What factors go into the decision making process to lead us to act in a risk-averse versus risk-seeking manner? What happens in our frontal lobe and reward pathway as we are deciding to take a risk? Being able to think through both the immediate and long-term consequences of a given decision can lead to more or less optimal decisions. In addition, maintaining sight of both potential gains (benefits) and potential losses (risks) can lead to a more balanced evaluation of the available options. And, sometimes, having a particular psychiatric diagnosis can affect this decision making process. Individuals diagnosed with anxiety, depression, schizophrenia, and other psychological disorders need to make important decisions about treatment options, such as opting for a medication trial versus psychotherapy or in some cases a surgical treatment. But they also still engage in the same daily and larger scale decisions that individuals without these diagnoses make. How are decision making and risk-taking affected by having one or more psychological disorders? In the rest of this chapter, I will further examine the construct of decision making and risky decision making in particular. I will also discuss how decision making naturally relates to involvement in risk-taking behaviors.

Decision making, risk, and uncertainty To start this journey, we first need to define the term decision making. At the most basic level, a decision involves a choice between two

Risky Decision Making in Psychological Disorders DOI: https://doi.org/10.1016/B978-0-12-815002-3.00001-2

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© 2020 Elsevier Inc. All rights reserved.

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options. We engage in decision making when we are asked to judge if a letter that flashed on a computer screen was an “X” (yes or no decision). We engage in decision making when we decide which key card to match an item to on the Wisconsin Card Sort Task. We engage in decision making, though likely between more than two options, when we decide which car to lease or buy. But decision making involves a number of factors, including examination of not just potential gains and losses but when they could occur (immediate vs at some point in the future) and their probability of occurrence as well as external factors such as receiving feedback on the decision’s outcome and whether one is pressured to make a decision (e.g., time-limited decision making) (Defoe, Figner, & van Aken, 2015). To make the best possible decision, one must also accurately perceive the probabilities and risks associated with each option (Damasio, 1994). Multiple theories exist to explain how we make decisions. Although an in-depth examination of these theories is beyond the scope of this chapter, reviews can be found in the following sources, among others: Gilovich, Griffin, and Kahneman (2002), Kahneman (2011), Tversky and Kahneman (1974, 1983, 1986) (see Busemeyer & Stout, 2002, for an application to the Iowa Gambling Task). Some of the earliest theories held that we choose the most optimal option based on it having the highest expected value. Let us use an example. Say we are going to roll a die to win some money. If we roll a 1, we would receive $1.00; roll a 2, $2.00; and so on. We could determine the probability of winning exactly $3.00 by taking the total number of winning rolls (in this case, just one since we only win $3.00 by rolling a 3) and dividing it by the total number of possible rolls (in this case, six). Our probability of winning $3.00 is 1/6, or 16.67%. Now let us say that we were asked to pay money to roll the die, in hopes of winning more money than we paid for this opportunity. What should we do? Per theories based on expected value, we should choose the option with the highest expected value. In this example, our new options are to “pay” or to “not pay.” For the pay option to have a higher expected value than the not pay option, our winnings would need to outweigh our costs. Let us say that a roll cost $1 and the winning amounts were the same as above (1 5 $1.00, 2 5 $2.00, etc.). What should we decide? If we roll the die one time, there are six possible outcomes. Five of those outcomes would result in our winning more money than we paid to play. In this case, we have a 5/6 chance (83.33%) of earning money by rolling and a 1/6 chance (16.67%) of breaking even. Most people would decide to roll the die for $1.00 in this example. What if we were asked to pay $4.00 to roll the die? In this case, we have a 2/6 chance (33.33%) of earning money by rolling a 5 or a 6. We have a 3/6 chance (50%) of losing money by rolling a 1, 2, or 3 and a 1/6 (16.67%) chance of breaking even by rolling a 4. In this

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case the probability of losing money outweighs the probability of winning money, leading to a higher expected value in the “not pay” option. Rational decision-makers should apply this logic to all decisions, always opting for the decision with the higher expected value. But our decisions are not always rational. In fact, we often choose an option that does not have the highest expected value. Why? These questions led to the idea of expected utility and later prospect theory. The idea of utility dates back to Bernoulli who in 1738 (1954) introduced the idea that not all decisions are based on a rational examination of the expected value of each option. Instead, one’s subjective perception of the benefits of an option can influence decisions. Personal value, or utility, associated with an option can weigh just as heavily as expected value, in turn potentially biasing decisions. We should make decisions by evaluating the probabilities of the events and assess their expected value, choosing the one with the highest expected value. But we don’t. Our decisions are biased and not entirely consistent with rational processes (Kahneman & Tversky, 1972). Rational decision making occurs when the same decision rules were applied in every situation. How we decided whether to pay $4.00 to roll a die should be the same process we use to decide which car to purchase or which college to attend. But, typically, we do not apply this same logic and reasoning to each decision making situation. The subjective value (utility) associated with different options in different decisions can bias—positively or negatively—the decision making process. The involvement of utility in decisions led to the development of prospect theory (Kahneman, 2011; Kahneman & Tversky, 1979). Prospect theory assesses how decisions are made based on how the outcome will differ from the individual’s current state, and how subjective value (utility) affects the processing of probabilities. Prospect theory focuses on what decisions will actually be made, rather than what decisions should be made based on expected values. In prospect theory, decisions are made as a function of the individual’s current state. Gains and losses (risks and benefits) are not viewed solely based on their magnitude and probability, but instead, on the likelihood they will improve or worsen one’s current state. If in the pay-to-play die roll example I only had $5.00 in my bank account, I would likely take fewer risks than if I had $5000.00 in my bank account. The subjective value of that $4.00 risk depends on my current financial state and can lead to a more risk-averse (not pay) versus risk-seeking (pay) decision. Examinations of utility in general and prospect theory more specifically led to an understanding that how decisions are framed can affect outcomes. Specifically, framing a question in terms of gains versus framing a question in terms of losses can change how risk-seeking or risk-averse of a decision is made. In their classic “Asian disease” study,

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Tversky and Kahneman (1981) asked one group of participants to decide between two treatment options for an outbreak affecting 600 people: Option 1 would result in 200 of those affected being saved, whereas Option 2 had a 33% chance all 600 will be saved and a 67% chance no one will be saved. The second group of participants decided between Option 3, which results in the deaths of 400 of those affected, and Option 4, with a 33% probability of no deaths and a 67% probability of all dying. Reading these two scenarios, Options 1 and 3 are the same, and Options 2 and 4 are the same based on probabilities of success (life) and failure (death). But the participants made very different decisions in each set. The first group of participants were more likely to choose Option 1, whereas the second group of participants were more likely to choose Option 4. Why? Framing. When deciding between Options 1 and 2, the gain-frame that people “will be saved” (Tversky & Kahneman, 1981, p. 453) can lead to a more risk-averse decision making outcome than when deciding within a loss-frame [people “will die” (p. 453)]. Before talking more about risk and risky decisions, let us first examine other cognitive processes involved in decision making. Although expected value and utility, as well as an assessment of risks and benefits, are all crucial to decision making, other cognitive processes are just as important. In fact, Reyna and Brainerd (2011) outline the decision making process in such a way as to solidify its status as an executive function (a theme of Chapter 4: Neuroscience and associations with other executive functions). Decision making involves, at least in part, a process of storing knowledge from previous decisions and more generally, accessing that knowledge in the current decision making situation, then using that information in conjunction with one’s values and situational factors to arrive at a conclusion. In addition, Weber and Johnson (2009), in their review of the psychology and neuroscience of judgment and decision making, reiterate the importance of attention and memory in decision making. They also stress that emotions can affect decisions (see upcoming section). Other cognitive abilities affect decision making. Being able to hold onto the necessary information required to effectively evaluate different options requires attention and working memory skills. To the extent that attention may be limited or distracted in a given situation, decision making could suffer. Decision making also relies on longer term memory access to determine if and how a similar decision played out in a previous situation. Critically evaluating different options, such as by examining the relative pros and cons of each, relies on attention, memory, and executive functions. Although the main focus of this volume will be on decision making itself, decision making could not occur without the contributions of these other cognitive functions.

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Risky decision making What is risk? Risky decision making? Different definitions of each exist. Risk can be thought of as the opposite of a “sure thing.” If a sure thing has a guaranteed outcome, then a risk has a non-guaranteed outcome. We incorporate an understanding of risk into our decisions when we acknowledge that there is a probability of a particular event occurring and that this probability could vary depending on different decision options. Risk introduces uncertainty and the unknown into decision making. Taking a risk can be viewed as preferring a nonguaranteed or risky option to the sure thing or as choosing the option with the lower expected value over the option with the higher expected value (Reyna & Huettel, 2014a, 2014b). Risk can occur when individuals choose an uncertain over a certain option or choose an option with a greater level of variability in outcome (Chua Chow & Sarin, 2002; Figner & Weber, 2011; Weber, 2010). Taking a risk can also have a more clinical implication, as individuals take a risk when they decide to engage in a behavior knowing there is a potential for harm (e.g., Wallach, Kogan, & Bem, 1962). Or we may end up taking a risk because we just do not have all the information that we need. All in all, despite different definitions of risk, it does appear that risky decisions involve some element of uncertainty in terms of the expected outcome whereas riskless decisions are those with a known outcome (e.g., Kahneman & Tversky, 1984). Risky decision making, then, involves making a decision without full knowledge of the outcomes. Risky decision making involves at least some element of uncertainty and some element of risk. There may be a set probability of a large gain, but that outcome is unknown at the time of the decision and is likely offset by a probability of a large loss. A decision still needs to be made, leading to risk-seeking or risk-averse decisions. But what affects this uncertain or ambiguous decision making process? Situational factors, emotions, and cognitive resources, for starters. But some unconscious or automatic processes can affect decision making, leading to our understanding of dualprocess models.

Dual-process models As previously stated, emotions can affect decisions. One’s mood during decision making can affect the favorability of some options, and the potential for improvement in mood after a decision is made can affect the decision (e.g., Weber & Johnson, 2009). Emotions also underlie an automatic, unconscious form of decision making that is distinct from but related to the cognitive, conscious form of decision making. Several different terms for these two processes exist, but all fit under

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the overarching guideline of a dual-process model (e.g., Evans, 2008; Evans & Stanovich, 2013; Reyna, 2004; Tversky & Kahneman, 1983). The dual-process model states that decision making is guided by two different processes: a hot, Type I (System I) system and a cold, Type II (System II) system. System I is the rapid-response system. It occurs automatically when triggered by the current situation, acts without cognitive resources, and is described as unconscious, fast, implicit, automatic, impulsive, quick, requiring little effort, effortless, and innate (Kahneman, 2011; Metcalfe & Mischel, 1999; Stanovich, West, & Toplak, 2011; Zelazo & Muller, 2011). Emotions are involved in System I decision making, whereas cognition guides System II decision making. System II is more deliberate, requires cognitive resources, and is described as conscious, slow, processed, involving agency and choice, concentrated, reasoned, self-control, and limited in capacity. System I may rely on heuristics to arrive at quick decisions. Heuristics are rules or guidelines that can be used to guide decision making, resulting in a more efficient process (Busemeyer & Townsend, 1993; Payne, Bettman, & Johnson, 1988). They can be beneficial, in part because they can decrease the amount of cognitive resources required to make a decision (Simon, 1955). But, heuristics can also introduce bias into decisions (Kahneman & Tversky, 1973). Tversky and Kahneman (1974) listed several heuristics that can introduce bias, including (1) representativeness (one may assess the likelihood of a particular result based on how similar the situation is to a previous one with the preferred result), (2) availability (one may base decisions in part on how readily available other examples/situations similar to the current one come to mind), and (3) adjustment and anchoring (one may bias decisions based on the initial starting point, or anchor, and resulting adjustments to it during decision making). These and other automatically initiated heuristics could lead to a nonoptimal decision by System I unless it is overridden by System 2. How does one override this automatic, mandatory System I? It needs to be interrupted and suppressed while a better, more reasoned response is created by System II (e.g., Stanovich et al., 2011). But not all is doom and gloom with regard to System I. There is evidence to suggest that this system is more efficient (De Neys, 2006) and less sensitive to working memory restrictions (De Neys, 2006; De Neys & Schaeken, 2007) than System II. When System II is overburdened, such as when multiple factors are overwhelming attentional resources, bias could actually creep in and impair the decision (De Neys, Schaeken, & d’Ydewalle, 2005). Thus there are cases in which the quick, efficient System I response leads to a better or more accurate decision than the longer, logical, System II response (De Neys & Pennycook, 2019). These two systems really do attempt their best to arrive at an efficient but also appropriate response to any decision making situation.

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The somatic marker hypothesis and the neuroscience of risk Risky decision making researchers often use the terms hot and cold decision making to refer to the System I and System II processes. Hot decision making emphasizes the idea that emotions can guide decision making. Sometimes these emotions can emerge as a gut feeling or instinct (e.g., the System I response; Bechara, Damasio, Tranel, & Damasio, 1997; Damasio, 1994). This idea sets the foundation for the somatic marker hypothesis (Damasio, 1994), which informed our understanding of the emotional side of decision making. At its most basic level, the somatic marker hypothesis states that we experience changes in our physiological and affective state as a function of different events in our lives. Representations of these emotional states are retained in long-term memory storage. When a similar situation arises in the future, the memories associated with the previous decision are accessed—along with those emotional states, now termed somatic markers. Somatic markers are then used as a kind of implicit knowledge that helps guide current decision making, most notably in situations where there is some level of ambiguity or uncertainty (e.g., Brand, Recknor, Grabenhorst, & Bechara, 2007; Maia & McClelland, 2004). In situations where the risks/ probabilities are known versus uncertain, more explicit knowledge (i.e., cold decision making or System II) instead guides decisions. This interaction between hot and cold, implicit and explicit, and System I and System II is at the heart of any decision with some element of risk to it (Wood & Bechara, 2014). Although covered in greater detail in an upcoming chapter (Chapter 4: Neuroscience and associations with other executive functions), we know that multiple cortical and subcortical structures are involved in these two decision making systems. The prefrontal cortex, the most anterior portion of the frontal lobe, is associated with both System I and System II cognitive processes. Although specific findings can vary, multiple neuroimaging studies support the idea that more cognitive-based (System II or cold) decision making occurs in the dorsolateral prefrontal cortex whereas more emotion-based (System I or hot) decision making occurs in the ventromedial prefrontal cortex and its connections to limbic system structures such as the amygdala (e.g., Adolphs & Tranel, 2004; Bechara, Damasio, Damasio, & Lee, 1999). How did we learn about the neuroscience of risky decision making? Case examples led researchers to hone in on the prefrontal cortex, with later neuroimaging supporting these initial suppositions. The neuroscience of risky decision making and the somatic marker hypothesis has its origins in the case of Phineas Gage (e.g., Damasio, Grabowski, Frank, Galaburda, & Damasio, 1994; Harlow, 1848, 1868). In 1848 Phineas Gage was working as a railroad foreman in Vermont.

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One day, while preparing an explosive to remove rock in the path of the railroad, Gage was distracted and an explosion occurred. The tamping iron he was holding (approximately 2 m in length, 3 cm in diameter) was propelled through his skull, entering his left cheek and exiting the top-right portion of his skull (Figs. 1.1 and 1.2). Gage, still conscious, was taken to Dr. John Harlow for treatment. He survived this injury, living until the 1860s when he died of complications due to seizures (Damasio, 1994). It is a misnomer to say that Gage survived this incident intact. Although he lived, he was a very different person than he was before his injury. Per Dr. Harlow, Gage no longer acted like himself. He was able to walk, talk, and think, but he also was now impulsive, had difficulties planning ahead, and had difficulties controlling his emotions. Examination of Gage’s skull in the years since his death indicated that the tamping iron excised or otherwise significantly damaged large portions of the orbitofrontal cortex, an area we now know is linked with emotion-based and social decision making (Chapter 4: Neuroscience and associations with other executive functions). A later case provided additional support for a link between the orbitofrontal cortex and emotion-based and social decision making. Patient

FIGURE 1.1. Phineas Gage and the tamping iron. Originally from the collection of Jack and Beverly Wilgus, and now in the Warren Anatomical Museum, Harvard Medical School.

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FIGURE 1.2.

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Dr. Harlow’s (1868) depiction of the tamping iron’s path.

EVR (Eslinger & Damasio, 1985) began experiencing personality changes that led to a diagnosis of orbitofrontal meningioma. The meningioma was surgically removed, but difficulties with real-world decision making persisted. He had difficulties holding down a job, failed to think through long-term consequences of his decisions, and experienced significant difficulties with daily activities including making minor decisions. However, his performance on clinical measures of different cognitive abilities, including executive functions, was within normal limits. There was no evidence of impairment on validated cognitive tasks despite evidence of significant real-world limitations. This case, among others, led to a rapid expansion of research into behavioral assessments of decision making. In particular, patient EVR, coupled with a better understanding of the cognitive changes and neurological damage experienced by Phineas Gage, led to the development of the somatic marker hypothesis and evidence that cognition-based and emotion-based systems (e.g., hot and cold) interact in decision making.

Risk-taking behavior Risk-taking behaviors occur because we make a decision to engage in the behavior (Furby & Beyth-Marom, 1992; Reyna & Farley, 2006).

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Emotions, impulsivity, a failure to plan ahead—these and other reasons—can lead to greater involvement in risk-taking behaviors. A greater focus on the immediate, usually positive, outcomes and a lesser focus on the longer-term, potentially more negative outcomes is associated with greater rates of involvement in risk-taking behaviors (e.g., Bickel, Odum, & Madden, 1999; Bogg & Roberts, 2004; Madden, Petry, Badger, & Bickel, 1997; Mitchell, 1999). More generally, the tendency to increase the value of an immediate reward and steadily diminish the value of a more distant reward the further into the future it appears is termed delay discounting (Madden & Bickel, 2010). It can also appear as choice impulsivity, which is evident when an individual chooses a smaller sooner reward over a larger later reward (Hamilton et al., 2015). Impulsivity— the tendency to act on a whim (Eysenck & Eysenck, 1977)—can also affect risky behaviors. As we will see in Chapter 4, Neuroscience and associations with other executive functions, activation of the brain’s reward pathway can also lead to continued involvement in risk-taking behaviors (Jessor, 1991; Levy & Glimcher, 2011; Porcelli & Delgado, 2009b) due to changes and adaptations in how risks and rewards are processed. Just as we saw there is no universal definition of risky decision making, there is also no one definition of risk-taking behavior (Schonberg, Fox, & Poldrack, 2011). Taking a risk, to an economist, can refer to deciding on an option with uncertainty or some level of ambiguity to it. To a clinician, risk-taking may be more focused on the potential negative health consequences of a behavior (e.g., Defoe, Figner, & van Aken, 2015). For the purposes of this volume, risk-taking behavior will be defined in this clinical realm, as a behavior that can potentially result in negative consequences for physical and mental health in the future. Risk-taking behaviors can vary across individuals but are thought to include such behaviors as overuse of alcohol or binge drinking, use of illegal substances, use of substances in potentially dangerous situations (such as driving), engaging in sexual activity without protection against pregnancy or sexually transmitted infection, skydiving, mountain climbing, reckless driving, and playing the stock market. This is by no means an exhaustive list but instead reflects the breadth of different behaviors classified as risky. What will become evident in the remaining chapters is that the definition of risk-taking, and how it is assessed, can vary widely by study.

Introduction to the remaining chapters Both self-report and behavioral measures are frequently used to study risky decision making and real-world risk-taking behavior. In Chapter 2,

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Measurement methods, I provide information about some of the most common measurement methods for assessing risky decision making and two factors involved in the process: (1) attitudes and (2) propensity toward risk. As will be evident, numerous measures of these constructs exist, with differences in the specific type of decision making assessed by each one. For example, the behavioral measures often vary as a function of the assessment of risk and uncertainty (De Groot & Thurik, 2018) and whether decisions made at the start of the task have implications for later within-task decisions (i.e., the extent to which learning occurs). In Chapter 3, Reliability and validity, the evidence for or against reliability and validity of these measures is examined. A common theme across measures is whether lab-based assessments of risk-taking correlate with real-world risk-taking, or the extent to which tasks show ecological validity versus this validity being assumed. Chapter 4, Neuroscience and associations with other executive functions, tackles questions of whether decision making is an executive function, whether task performance correlates with measures of other executive functions, and which brain structures are involved in hot versus cold (or System I vs System II) decision processes. After that, the remaining chapters delve into the research regarding risky decision making and risk-taking behavior as a function of several primary categories of psychopathology. The current status of the literature will be reviewed, with an emphasis on our current understanding of risk-taking behavior, risky decision making, and neuroimaging of risk-related constructs in these disorders. Each chapter will present several theories that underlie risky decision making, as well as examine other factors that could be affecting decisions (e.g., disease severity, medication, and other treatment status). The final chapter will tie together the common threads from each chapter and present information about the next steps in risky decision making research, including cognitive modeling techniques and how our knowledge can inform the treatment process.

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2 Measurement methods Multiple measures exist to assess risky decision making, or at least a portion of the risky decision making construct. Some of these measures are study-specific, utilized in only one study. Others are more prevalent in the research literature—standardized, implemented across studies, and in some cases developed into clinical instruments. Both self-report and behavioral measures will be examined, as will assessments of the related constructs of risk propensity and risk attitude. Measures purported to assess decision making under risk, delay discounting, and reward responsiveness are included as they all tap into a component—small or large—of the overall risky decision making construct. Some measuresdiscussed could be classified as measures of impulsivity and/or measures of risky decision making. They are included in order to provide a context for later discussion of risky decision making in various psychological disorders. Finally, the content of these tasks will be addressed in the present chapter, whereas task psychometrics (reliability, validity) will be addressed in Chapter 3, Reliability and validity, and the neuroscience of the tasks in Chapter 4, Neuroscience and associations with other executive functions. What will become readily apparent across these three chapters is that tasks believed to assess risky decision making do not necessarily correlate with one another, leading to concerns about how the construct is defined across studies. It should be noted that this is not an exhaustive review of all previously utilized risky decision making measures, but rather a compilation of some of the most commonly used tasks in the risk-taking and risky decision making literature.

Risky decision making measures Adult Decision making Competence The adult decision making competence scale (ADMC) was developed to assess accuracy and consistency in decisions (Bruine de Bruin,

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Parker, & Fischhoff, 2007; Parker, Bruine de Bruin, Fischhoff, & Weller, 2018; Parker & Fischhoff, 2005). Originally containing seven subscales, the standard ADMC contains six intercorrelated scales. Resistance to framing has participants respond to a series of dilemmas, choosing which of the two options constitutes the best decision. The questions are first posed within a gain frame, then the same questions are posed within a loss frame. The consistency in responses despite the question framing is assessed. Recognizing social norms asks participants if it is “sometimes OK” to engage in a series of different actions (Bruine de Bruin et al., 2007). A correlation is then calculated between responses to these items and to the same items in which the participant estimated the percentage of 100 similarly aged individuals who would say the behavior is OK. To assess under/overconfidence, participants respond to a series of true/false statements then rate their confidence in the decision. Applying decision rules examines the accuracy of individual’s decisions, assessing the number of correct responses to a series of decisions. On the consistency in risk perception scale, consistency in judgments of the likelihood of events occurring in the next year versus next five years is assessed. Finally, the resistance to sunk costs scale has participants respond to a series of scenarios that pit past expenditures against new circumstances.

Angling risk task The Angling Risk Task (ART; Pleskac, 2008) was developed to assess the influence of learning on decision making on the Balloon Analogue Risk Task (BART) and similar tasks. Pleskac argued that the theory behind the Devil’s Task, BART, and Iowa Gambling Task (IGT), for example, failed to account for participants learning from their experiences on previous selections. Multiple factors are varied in the ART, allowing for an examination of the specific influence of learning in the decision making process. The specifics of the ART are similar to the BART. Participants are told that they will be participating in a fishing tournament in which they will earn money for caught fish. The pond is stocked with blue and red fish. If a red fish is caught, they will earn 5 cents. If instead the blue fish is caught, the trial will be over and all money earned on that trial will be lost. In order to keep the earned money, participants must stop fishing and click the “Collect” button prior to catching a blue fish. On the BART the number of possible pumps per balloon and explosion points is unknown to the participants. On the ART, this information may or may not be known to the participants. Each game trial starts with one blue fish and n 2 1 red fish (Pleskac, 2008). If the participants are fishing on a sunny/clear day, then they see the number of fish available and do not need to learn this

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information. If instead the participants are fishing on a cloudy day, they must use trial-and-error feedback to learn the general probability of catching a blue versus red fish. Two additional manipulations can occur. If the tournament is catch-and-keep, the participants sample fish without replacement and a running tally of the fish caught is shown on the screen. If the tournament is catch-and-release, the participants sample fish with replacement and the tally is not shown on the screen. In keeping with the similarities to the BART, the total number of fish was set to 128, with the maximum beneficial number of caught fish per trial set to 64. The number of trials is variable but typically set to 30 (Pleskac, 2008). The primary outcome variable assessed is the average number of casts per trial, adjusted just for the trials in which the blue fish was not caught (i.e., the trial did not end early). In the original validation study, Pleskac (2008) found that participants made more selections/took more risks when it was sunny/clear than cloudy and in the catch-and-keep versus the catch-and-release condition. He argued that this task showed participants react to changes during the decision making process, in turn changing their representations of the task to reassess the current decision making strategy.

Balloon analogue risk task The BART (Lejuez et al., 2002) was designed to assess risk-taking behavior in adolescents and young adults. Participants are tasked with earning money by pumping up a series of 30 balloons. Each pump of the balloon earns 5 cents, but participants will lose this earned money if the balloon pops. In order to keep the earned money, participants should stop pumping the balloon before it pops and click the “Collect $ $$” button to bank the earned money for that balloon. Unknown to the participants, balloons can pop after 1 128 pumps, with an average break point of 64 pumps. No explicit information about the probability a given balloon will pop at a particular point is given to participants, and in fact, one of the initial balloons is typically set to pop very early (i.e., under 10 pumps) to show that the balloons do, in fact, pop. As the pumps progress on a given balloon, the relative benefit of each pump decreases, while the relative risk increases. Thus, risky decision making occurs when participants continue to pump up the balloons (Lejuez et al., 2002). That said, risky decision making is conflated with monetary gain. Participants engage in riskier behavior the more times they pump up a particular balloon, but they also earn a greater monetary reward for a greater number of pumps (provided the balloon does not pop). Several modifications of the BART are seen across studies. In the original validation study (Lejuez et al., 2002), and utilized in several studies

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afterward, three different colored balloons were used (90 trials total). Each balloon color represented a different average break point (small, medium, and large) so that researchers could examine how risk-taking changed with greater opportunities to take risks. An automatic version of the BART was utilized across several neuroimaging studies. On this version, participants do not pump up the balloons themselves but instead watch while the BART is completed for them after indicating how many times to pump up a balloon. Finally, some versions of the BART utilize a set number of trials (usually 30), whereas others consist of as many trials as possible completed in a set time period.

Blackjack task The Blackjack Task mimics a real-world game of blackjack. Although the specific details of the task vary by study, a general format is followed. Participants receive a hand of blackjack and see one of the two cards the dealer was dealt. Based on the visible cards, participants bet on the outcome of the hand while deciding to hit or stay. The outcome could be a win (monetary gain), a loss (monetary loss), or a push (no change in monetary status).

Bomb risk elicitation task On the Bomb Risk Elicitation Task (Crosetto & Filippin, 2013), participants are tasked with collecting boxes without collecting the one with a bomb in it. Participants see an array of 100 boxes, one of which contains a bomb. They are asked how many boxes they want to collect. The location of the bomb is randomly assigned at the start of the trial. Two versions of the task exist. In the static version, participants just indicate the total number of boxes to collect. The presence/absence of the bomb is revealed at the end of the trial. In the dynamic version, one box is removed per second until participants hit “Stop.” The presence/absence of the bomb is not revealed until the end of the entire task. For both the versions the risk-neutral or risk-avoidant individual can maximize potential by choosing 50 boxes on each trial.

Cambridge (Rogers) gambling task The Cambridge Gambling Task (CGT) was developed to take into account some of the concerns with the IGT (Rogers et al., 1999). Participants see an array of 10 boxes/squares, some of which are red and others are blue. They are tasked with guessing which color box a token is hidden in and then placing a bet that their guess is correct.

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The ratio of red to blue squares varies in each round (1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, and 9:1). Participants start with 100 tokens and can bet 5%, 25%, 50%, 75%, or 95% of their earnings on each trial. Child version: Cake Gamble (Gambling) Task. The Cake Gambling Task is a child-friendly version of the Cambridge (Rogers) Gambling Task (Van Leijenhorst, Westenberg, & Crone, 2008). The boxes holding a token are replaced with two different flavors of cake, one representing a low-risk gamble and the other a high-risk gamble. Participants bet on which flavor of cake will be randomly selected, with the low-risk bet remaining constant but the high-risk bet varying on each trial. Probabilities of selecting the higher risk gamble (17%, 33%, and 50%) are varied throughout the task.

Card-guessing task On one variation of the Card-Guessing Task, participants guess whether a playing card presented facedown is higher or lower than a "5" (Delgado, Nystrom, Fissell, Noll, & Fiez, 2000). Correct guesses earn the participant money, whereas incorrect guess can cost the participant money (e.g., Tricomi, Delgado, McCandliss, McClelland, & Fiez, 2006). On a different variation of the Card-Guessing Task (Van Hoorn, Crone, & Van Leijenhorst, 2016), participants are instead dealt two cards. One appears faceup, while the other appears facedown. Participants first guess whether the second card will be a higher or lower card than the first one, then place a bet on their guess. A correct guess results in a gain of double chips, while an incorrect guess results in a loss of the chips that were bet.

Chicken game The chicken game (Gardner & Steinberg, 2005; Steinberg et al., 2008) is similar to the soon-to-be-described Stoplight Task and is based at least in part on the real-life game of chicken that car drivers were at one time known to play. Participants earn money by driving a virtual car as far as possible but stopping before the clock/timer/meter runs out. In some versions of the task, this timer is represented by a yellow light that can turn to a red light. In other versions, there is instead a visual meter running during the task (Bjork, Momenan, Smith, & Hommer, 2008). Continuing to go past the red light or end of the meter results in a loss on the trial.

Choice dilemmas questionnaire On the Choice Dilemmas (Kogan & Dorros, 1978; Kogan & Wallach, 1964), participants view a series of real-world dilemmas. In each, the

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participant is tasked with providing their friend with feedback about which of two options they should take. One option involves a safer option that may not be optimal (e.g., working for an established corporation with job security but less independence), whereas the other option involves risk but potentially more rewarding outcomes (in this same example, opening one’s own law firm with preferred cases, but the possibility of no new clients; Pruitt & Cosentino, 1975). Participants choose between these two options, indicating whether they would tell the friend to choose the riskier option with a 1/10, 3/10, 5/10, 7/10, 9/10, or 10/10 chance of success associated with that option. The lowest probability with which they would advise the friend to choose the risky option is considered the participant’s level of risk on this task. Modifications were made to the task since its creation in order to decrease gender bias in the original scenarios (e.g., Kogan & Dorros, 1978).

Columbia card task The Columbia Card Task (CCT; Figner & Voelki, 2004, and revised in Figner & Weber, 2011) assesses both “hot” and “cold” components of the risky decision making process via parallel versions of the task. In the “hot” version of the task, participants view a series of 32 cards that when turned over reveal either a smiley face (win) or a sad face (loss). On each trial, participants are given information about the number of loss cards (1 or 3), the amount gained per win card (10 or 30 points), and the amount to be lost if a loss card is turned over (250 or 750 points). Participants turn over cards, one at a time, earning points for each win card. If a loss card is turned over, the trial ends and the loss amount is applied to the accrued points. To bank the most points on a particular trial, participants should stop turning over cards before a loss card is selected and instead click a button to end the trial. The goal of the task is to win as many points as possible. It is believed that participants should utilize the information about the potential wins and losses on a given trial to maximize their likelihood of success (e.g., banking more points) on that trial. Due to the immediate feedback given to participants after turning over each card, and the knowledge of an increasing likelihood a loss card will be selected, the CCT-hot version is thought to measure emotionally based, “hot” or Type I decision making processes (Figner, Mackinlay, Wilkening, & Weber, 2009). On the parallel version of the task, the CCT-cold, participants again view a series of 32 cards that contain both loss and win cards. They are again given information about the number of loss cards, amount of losses, and amount of grains. But, participants do not turn over any cards on the CCT-cold. Instead, they indicate how many cards, 0 32, they would like to turn over on a

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given trial. No immediate feedback is given as to how many win/loss cards they turned over prior to the next trial starting. Thus it is believed that this version of the CCT focuses instead on calculated, deliberative, “cold” or Type II decision making processes (Figner et al., 2009).

Cups task The Cups Task was designed to assess risky decision making in children (Levin & Hart, 2003) but has been used across a variety of participant ages. Participants choose between a set of boxes (cups) on the left side of the screen and a set of boxes on the right side of the screen (the terms "boxes" and "cups" are used interchangeably as researchers use either term when describing the task). There could be an array of 2, 3, or 5 cups on either side of the screen. In the Gain version of the task, one side of the screen has cups that all contain 1 prize (or $0.25). The other side of the screen has boxes that contain either 2 prizes or 0 prizes ($0.00 or multiple $0.25 coins). In the Loss version of the task, one side contains boxes with 21 prize and the other side contains boxes with either 22 or 0 prizes. After choosing one side, participants either receive the 1 prize or select one cup to turn over from the 2/0 prize side, which could result in either a no win (no change) or a win. Choosing the side with the 1 prize is considered the risk-averse option, whereas choosing the side with either a 2 or 0 prize is considered the risk-taking option. Later versions of the task changed the probabilities associated with a win/loss, in that a particular trial might have a 50% chance of winning and another trial might have an 80% chance of winning (Study 2; Levin & Hart, 2003).

Delay and probability discounting tasks There are multiple versions of a delay discounting, temporal discounting, or probability discounting task. Across these tasks, several commonalities are found. Participants are given a choice between a smaller amount of money (or points, prizes, etc.) received immediately and a larger amount of money (or other incentive) at a future time point. Variations in the amount of the immediate and delayed rewards, as well as in the delay interval, occur on each trial (see Scheres, de Water, & Mies, 2013, for a review of how the discounting tasks vary). Briefly, these tasks can vary based on the presence of real versus hypothetical rewards, the probability of the reward occurring at the delay, and the type of reward utilized (most often, money, food, and substances of abuse). In addition, variations on the task utilize the threat of immediate or delayed losses, as well as combinations of gains and losses (e.g., win-now, lose-later; lose-now, win-later; Ostaszewski, 2007).

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On probability discounting tasks (e.g., Scheres et al., 2006), participants receive a choice not between an immediate and delayed reward, but rather between a smaller certain reward and a larger uncertain reward. The probability associated with the larger reward varies.

Devils task/knife switches The Devil’s Task (also sometimes referred to as the Knife Switches Task) has multiple components in common with the previously described BART. On the task, participants see an array of boxes (7 10 on average), one of which holds a devil/knife (Slovic, 1966). In the original task, participants had just one trial in which they could open as many or as few boxes as they wanted to. If the box was a safe box, the participant received candy as a reward. If it contained the devil/knife, the participant lost all the previously earned candy. In later modifications of the task, multiple trials were utilized. On each, the probability of the devil/knife being chosen increased on each trial, meaning the optimal risk-taking strategy would be to open half of the boxes (similar to the optimal breaking point on the BART equaling half the total possible pumps) (Buchel, Brassen, Yacubian, Kalisch, & Sommer, 2011).

Dynamic experiments for estimating preferences (DEEP) DEEP Risk was designed to assess time- and risk elements of preferences in risky decisions (Toubia, Johnson, Evgeniou, & Delquie, 2013). An adaptive questionnaire design is used, in which later questions were altered depending on previous responses. The types of questions are split between ones assessing cumulative prospect theory (Kahneman & Tversky, 1979) and ones assessing time discounting models (Frederick, Loewenstein, & O’Donoghue, 2002). On each question, participants see a gamble between two options. They are asked the maximum amount they could win from the gamble or the most likely outcome to occur. To assess prospect theory, they are also asked which gamble they would rather play, given a set of probabilities associated with each gamble. For example, participants might choose between a 90% chance of winning $40 and a 10% chance of winning $10 (Toubia et al., 2013). To assess time discounting, they were asked which of two monetary amounts was a more attractive decision. For example, participants might choose between obtaining $250 today and $300 in a week (Toubia et al., 2013). The items in the DEEP Risk task mimic those seen across multiple other risky decision making and delay/temporal discounting tasks, but the DEEP questions were designed to utilize Bayesian methods to estimate model parameters.

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Framing spinner task On the Framing Spinner Task, participants choose from a set of five gambles (Eckel & Grossman, 2002). Each gamble has two different outcomes (50% probability associated with each). In the original task, these gambles had a fixed probability with varying payoffs: (1) $10 or $16; (2) $18 or $24; (3) $26 or $32; (4) $34 or $40; and (5) $42 or $48. In other versions, the probabilities were varied but the payoff was fixed (e.g., Holt & Laury, 2002). Participants chose one of the gambles as their wager. The die was rolled (original version) or the spinner spinned to determine if the participant won or lost on that trial. Risk-avoidant individuals opted for less extreme payoffs and framing the choices as gains or losses did not affect risk-taking. In the version presented by Reyna and Ellis (2004), only two spinners were used. One was entirely red and represented the lower “sure” bet. The other spinner was red and blue, with the proportion in each color varying by percent chance of winning (50%, 66%, and 75%). The rewards available also varied ($5, $20, and $150).

Game of dice task The Game of Dice Task (GDT; Brand et al., 2005) was developed to take into consideration some of the limitations of the IGT. On the GDT, participants are tasked with betting on the roll of a die. They can choose a single number (1-number sequence), or a set of two (2-number sequence), three (3-number sequence), or four (4-number sequence) numbers. Across the 12 18 trials, depending on the specific GDT format, participants earn money if the bet included the rolled number. Single number ($1000 bet) and 2-number ($500 bet) sequences are considered the risky decisions, while 3-number ($200 bet) and 4-number ($100 bet) sequences are considered the safer decisions. At the start of the task, participants are explicitly told the level of risk (i.e., the bet amount) associated with each sequence. They could then calculate the probability associated with winning each type of sequence, in turn calculating the best possible type of decision on each trial. After each selection, participants see the actual die roll and are given feedback as to whether they won or lost, and how that bet affected the overall bank. Typically, scores on the GDT are calculated by subtracting the total number of disadvantageous selections (1- and 2-number sequences) from the total number of advantageous selections (3- and 4number sequences), and the total money earned is also examined.

Iowa gambling task The IGT (Bechara, Damasio, Damasio, & Anderson, 1994) was originally designed to assess decision making impairments in individuals

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with ventromedial prefrontal cortex damage. The task creators saw a series of patients with documented damage to the frontal lobe who “passed” all lab-based measures of executive functions. Despite this, their families described multiple instances of real-world decision making impairments. For example, patient EVR underwent surgical removal of a tumor that resulted in bilateral orbitofrontal cortex damage (Eslinger & Damasio, 1985). Following this injury, EVR went from being a successful financial worker, married with two children, to being divorced, living with his parents, and unemployed. Neuropsychological testing revealed above average to superior performance across tasks, yet EVR continued to have difficulties making decisions in the real world. The IGT represents the first attempt to create a lab-based decision making task that could be used in the clinical evaluation of frontal lobe patients. On the IGT, participants are tasked with maximizing profit across 100 selections from four decks of cards. They are given a $2000 credit to start the game and are told that any “winnings” would be deducted from this credit before determining the ending total. They are not given much information about the decks, other than to say that some decks are better than others. After each selection, participants always win some money, but they might also lose some money. That loss might be less than the amount won on that card, or it might be more than the amount won on that card. Based on the long-term outcomes associated with each deck, the task creators labeled two decks (A, B) “disadvantageous” and two decks (C, D) “advantageous” (Bechara et al., 1994). On each selection from Deck A, participants win a larger amount of money ($100 on average). But, they also experience losses on 50% of the trials. After 10 selections from Deck A, participants have incurred a net loss of $250. Thus the long-term outcomes of Deck A are disadvantageous, as they lead to a decrease in money over time. Selections from Deck B also result in a larger immediate win ($100 on average) but can result in a loss on 10% of trials. Those losses are larger in magnitude than the losses in Deck A, as 10 selections from Deck B result in a net loss of $250 and long-term disadvantageous outcomes. Decks C and D, the advantageous decks, both have smaller immediate gains ($50 on average), but less is lost across time. Participants will lose on 50% of Deck C selections, but those immediate losses do not outweigh the amount won on the trials. Participants also lose on 10% of Deck D selections, but again those immediate losses do not outweigh the amount won on the trials. After 10 selections from Deck C or Deck D, participants have incurred a net gain of $250. The task creators envisioned this focus on long-term consequences as the deciding factor in whether or not participants learned to decide advantageously or not. However, more recent research has suggested that there are in fact two competing decision making strategies on the task.

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In one, participants focus on the long-term consequences associated with each deck, leading to Decks C and D being termed advantageous. We see across multiple studies, with various patient populations, that healthy control participants can learn to choose from Decks C and D and avoid Decks A and B, whereas participants with frontal lobe damage, amygdala damage, various neurological and psychological disorders, and at younger and older ages continue to select from Decks A and B (i.e., engage in risky decision making) despite the consequences of their selections (see Buelow & Suhr, 2009, for a review). The second strategy is that participants instead focus on the frequency of losses in each deck, choosing to minimize the frequency of losses as their advantageous decision making strategy (e.g., Lin et al., 2009). Multiple studies have shown that healthy control participants (i.e., those with no psychological or neurological diagnoses) show a preference for Decks B and D, the low loss frequency decks, over Decks A and C, the high loss frequency decks, leading to the development of the Soochow Gambling Task (SGT) variant of the IGT. Modified Version: Soochow Gambling Task (SGT). The SGT (Chiu et al., 2008) was developed as an alternative metric to the IGT, taking into account the confound between frequency of losses and long-term outcomes on the decks. On the SGT, participants again select cards from one of four decks of cards (A, B, C, D). Over the course of five selections from Deck A, participants incur one loss resulting in an overall loss of $250 (negative expected value). Five selections from Deck B result in an overall loss of $250, but the immediate gains are lower than in Deck A and the single loss is also lower. For Decks C and D, losses occur on 4/5 of the trials, with a one-time gain resulting in a net profit of $250 (positive expected value). As with Decks A and B, the immediate losses in Deck C are larger than those in Deck D, as are the one-time gains. Thus Decks A and B are considered “bad” decks and Decks C and D “good” decks (Chiu et al., 2008). This task was created to examine the extent to which individuals decide based on gain-loss frequency (which would result in greater A and B selections) or based on expected values (resulting in greater C and D selections), as there is a prominent “Deck B phenomenon” (Lin, Chiu, Lee, & Hsieh, 2007) seen across multiple IGT studies in healthy control participants. Initial SGT studies found participants preferred Decks A and B to C and D, indicating a decision making style based on perceived gain/loss frequency rather than the better decision making strategy of expected value. This decision making strategy continued despite larger and smaller sizes of the gains (Lin, Chiu, & Huang, 2009). Child Version: Hungry Donkey Task (HDT). The HDT is a childfriendly version of the IGT (Crone & van der Molen, 2004). Instead of earning money for each deck selection, participants are tasked with

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earning apples to feed a hungry donkey. Behind each of the four doors (A,B,C,D) are varying numbers of green apples (wins) and red apples (losses). Participants are told that they should select from a door and will be shown the number of red and/or green apples behind the door. Different variations on the total number of trials exist, ranging from 40 to 100 trials. Similar to the IGT, participants should learn, through trialand-error feedback, to choose from the doors with more green than red apples, and to avoid doors with more red than green apples. Two doors (C, D) provide not only smaller amounts of green apples but also smaller amounts of red apples, leading to an overall net gain in the long term. The remaining two doors (A, B) provide greater amounts of green apples, but also greater amounts of red apples, leading to an overall net loss in the long term. Child Version: Gambling Game Task. The gambling game is a modification of both the HDT and IGT (van Duijvenvoorde, Jansen, Bredman, & Huizenga, 2012). Instead of turning over cards or feeding a hungry donkey, participants see four machines that are provide loss and gain balls. They are tasked with choosing from the machines to maximize the number of gain balls and minimize the number of loss balls. Two machines result in low gains but also low losses (long-term positive outcomes) and two machines result in high gains and high losses (long-term negative outcomes). After each selection, a ball was randomly chosen from the selected machine and either a loss or gain occurred. Two versions of the task exist. In one, the frequency of loss, gain amount, and loss amount information are provided to the participant. In another, this information is hidden from the participants other than for the immediate feedback following a selection.

Mirror drawing risk-taking task This task utilizes the mirror drawing technique, in which participants trace a figure or draw a line while looking into a mirror instead of at the paper. In particular, this task has participants draw a line between two parallel lines, without touching either line (Kreitler & Zigler, 1990). Each drawing also had four peaks that formed a zigzag shape. The task was divided into three parts and participants were given a choice at each part. Participants could choose between a less risky task, earning a smaller reward, or a riskier task, potentially earning a significantly larger reward (double the smaller reward).

Multioutcome risky decision task Lopes and Oden (1999) created a task to assess cumulative prospect theory (Tversky & Kahneman, 1992) and security-potential/aspiration

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theory (Lopes, 1987). Participants choose between a series of paired lotteries, each with 100 tickets in it. The lotteries had five potential outcomes and were paired randomly in a set. Some of the lottery decisions are made within a gain frame and some decisions are made within a loss frame. Within each set of lotteries, one of three expected values are utilized: (1) standard lottery (expected values of $100 or 2 $100), (2) shifted lottery (expected values of $150 or 2 $150), and (3) scaled lottery (expected values of $114.50 or 2 $114.50).

Nonsymbolic economic decision making task On the Nonsymbolic Economic Decision Making Task (Paulsen, Carter, Platt, Huettel, & Brannon, 2012; Paulsen, Platt, Huettel, & Brannon, 2011), participants see a series of three types of choices: safe safe, safe risky, and risky risky. On the safe safe trials, participants choose between two certain options. On the safe risky trials, participants choose between a safe option and a gamble with two equally likely outcomes. The safe and risky options have equivalent expected values but the valuation of the gamble varied on each trial. On the risky risky trials, participants instead chose between two gambles with different expected values and valuations.

Probabilistic gambling task On the probabilistic gambling task (Burnett, Bault, Coricelli, & Blakemore, 2010), participants see two wheels of fortune on the computer screen and are tasked with choosing one wheel in an attempt to maximize earnings. Each wheel has two colors on it, with the size of each color corresponding to the probability of that color being chosen when the wheel is spun. Possible wins and losses were also provided by numeric values next to the wheel pieces. On each wheel, probabilities of winning/losing were set to 0.50/0.50 or 0.20/0.80 and the sizes of gains/losses to 1 / 2 200 or 1 / 2 50. After choosing one of the two wheels, the wheel is spun and participants receive feedback about the outcome. The feedback could be partial, in that only the outcome of the chosen wheel is provided, or complete, in that the outcome of the chosen and unchosen wheels are both shown.

Reyna and ellis risk task On this task (Reyna & Ellis, 1994), participants choose between two spinners. One is considered a sure thing and the other is considered a gamble. The sure thing is represented by a transparent bag with the prize visible.

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If the participant chooses the sure thing, they receive that prize. The gamble consists of a spinning wheel with two options: winning a larger prize (visible in a transparent bag) and winning nothing. The area on the spinner corresponds with the probability of winning nothing (1/3, 2/3, 3/4), and the expected value of the prizes also varies (1, 4, or 30 prizes).

Risk propensity task On the Risk Propensity Task (Aquado, Rubio, & Lucı´a, 2011), participants see a series of 30 trials split into three blocks. On each trial a set of six tokens (one red, five white) appears randomly among a set of squares. Participants are tasked with betting on where the token will appear. They can bet up to five tokens on up to five of the six empty squares. Increasing the number of bets per trial increases the probability of a win, but the gain amount is lower with a higher number of bets. Thus the optimal expected value occurs when bets are placed on three of the six empty squares.

Risky gains task On the Risky Gains Task (Paulus, Rogalsky, Simmons, Feinstein, & Stein, 2003), participants are tasked with responding to a sequence of numbers that appear on the computer screen. The numbers always appear in the order 20, followed by 40, followed by 80. One appears every second. Participants can click to choose one of those three numbers, winning either 20, 40, or 80 points. Selecting 20 always results in a win (safe choice). Selecting 40 could result in 40 points, and selecting 80 could result in 80 points. However, if the 40 or 80 appears in red when clicked, then the participant instead loses the 40(80) points. The probabilities associated with winning/losing at each level are such that a participant could obtain the same final score by only selecting the 20 option versus selecting the 40/80 options.

Sequential investment task In the Sequential Investment Task (Frey, Rieskamp, & Hertwig, 2015), participants decide whether to sell or keep a set of 48 shares across three different markets. There are three trials represented by three days of trading. Depending on the current price per share, participants decide whether to sell or keep their shares. If they decide not to sell, the shares will instead sell at the final selling point for that day. Three factors can vary (Frey et al., 2015): (1) market modality, (2) level of exploration, and (3) feedback. The task is programmed to either have one peak

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price per day (unimodal) or two peak prices per day (bimodal). It can also vary in whether the peak occurs during the early, middle, or later part of trading. Finally, participants may receive only partial feedback about the pricing (up until they decide to sell) or full feedback about how the price changed after the decision to sell. Comparing these versions of the task indicated that receiving full feedback was critical to learning the best time to sell on each market day.

Stoplight game Two variations on the Stoplight Task are examined. In one (Reilly, Greenwald, & Johanson, 2006), participants try to reach 100 combinations of X Y button presses before a red light appears. After starting at the sight of the green light, they receive an indication of their progress after every 10 combinations. At an unknown point, a yellow light will replace the green light. The yellow light will remain for a random amount of time, but it always signals that a red light is coming next. The participant has two options when the yellow light appears. They could stop pressing the X Y keys resulting in a push outcome (no points won or lost). They could also choose to continue pressing the X Y keys to try to reach the 100 combination mark before the red light turns on. If the participant makes it (successful risk), they are rewarded with 25 points. If the participant does not make it (unsuccessful risk), they lose 25 points. On a different variation of the Stoplight Task (Chein, Albert, O’Brien, Uckert, & Steinberg, 2011; Steinberg et al., 2008), participants are instead tasked with reaching the end of a virtual straight road as quickly as possible. Along the road are a series of intersections with stoplights (20 32 lights depending on the study). At each stoplight the light turns yellow and participants can choose to stop or go. If they choose to go and avoid crashing into another car (successful risk), there is no delay in progression. If they choose to go and instead crash into another car (unsuccessful risk), there is a significant delay in progression. If they choose to stop (safe option), there is a smaller delay in progression.

Two-outcome risky decision task This task has participants choose between two options with equal expected values (Lauriola, Levin, & Hart, 2007). One option is a sure thing, whereas the other option represents a gamble with probabilities varying from 0.02, 0.05, 0.25, and 0.50 across trials. Expected values also vary ($1 $1000) to assess consistency in risk preferences across trials. If the gamble is chosen, participants risk losing (or failing to earn) money on a given trial.

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Wheel of fortune task The Wheel of Fortune Task (Ernst, Dickstein, et al., 2004; Ernst, Nelson, et al., 2004) was designed to create a task that differed from the contemporaneous CGT in the following ways: (1) utilizing a familiar format that even younger participants would be familiar with; (2) examining responses to rewards and losses differently (useful for neuroimaging studies); (3) varying both the probability of winning/losing and the magnitude of the potential wins and losses; and (4) assessing decision making confidence in the time between making a decision and receiving feedback about that decision. Participants see a set of wheels. Each wheel has two potential outcomes, with associated probabilities for each outcome. For example, one wheel might have potential wins of either $4 (10% probability) or $0.50 (90% probability). The probabilities are typically set to 90/10, 80/20, 70/30, 60/40, or 50/50, with one additional wheel showing the control or “sure thing” (Elman et al., 2009; Shad, Bidesi, Chen, Ernst, & Rao, 2011). Participants choose the option they think will occur on a wheel during the selection phase (Rao et al., 2011). In the anticipation phase, participants estimate their confidence in the decision before the outcome is given. In the feedback phase, participants see the wheel spin and learn if they won or lost on that trial. Across studies, the number of trials, magnitude of rewards, and probabilities of reward vary. In addition, some studies also include an element of ambiguity, in that a portion (25%, 50%, or 75%) or the entire (100%) wheel is obscured and the probabilities of each outcome are unknown (e.g., Blankenstein, Crone, van den Bos, & van Duijvenvoorde, 2016).

Risk propensity and risk attitude measures Cognitive appraisal of risky events The Cognitive Appraisal of Risky Events (CARE) was designed to assess outcome expectancies and risk-taking behavior in adolescents and young adults (Fromme, Katz, & Rivet, 1997; Katz, Fromme, & D’Amico, 2000). The task was based on the theory that every decision to engage in a behavior with potential health risks takes into consideration the perception of potential risks and potential benefits associated with that behavior. Thus the CARE has three scales: expected risks, expected benefits, and frequency of involvement (although in some cases this is expected future involvement rather than recent involvement). Each scale is then divided into six subscales based on the type of risk-taking behavior: illicit drug use, aggressive/illegal behaviors, risky sexual behaviors, alcohol use, involvement in high-risk sports, and risky

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academic/work behaviors. Higher scores on the frequency of involvement scale indicate greater involvement in the specific risk-taking behavior. Higher scores on the expected benefits and expected risks scales indicate greater levels of each.

Decision styles scale On the Decision Styles Scale (Hamilton, Shih, & Mohammed, 2016), levels of rational and intuitive decision making styles are assessed. Participants respond to a series of 10 questions utilizing a 5-point scale indicating agreement with each item. Rational items focus on decisions made after sufficient information gathering and exploration of alternative. Intuitive items focus on decisions made following initial instincts and gut feelings.

Dohmen scale The Dohmen Scale was developed as a one-item measure of risktaking propensity (Dohmen et al., 2011). Participants use a 10-point scale to rate the extent to which they take risks (higher score) versus avoid risks (lower score).

Domain-specific risk-taking scale The Domain-Specific Risk-Taking (DOSPERT) was designed to assess real-world risk-taking behaviors and the perceptions of risk associated with those behaviors (Blais & Weber, 2006; Weber, Blais, & Betz, 2002). The task is made up of three subscales: likelihood of engaging in the behavior, perceived risk associated with the behavior, and perceived benefit associated with the behavior. Just like with the CARE, these scales were broken down into subscales based on the type of risk-taking behavior assessed: ethical (including illegal behaviors), financial (including gambling), health/safety (including substance use), recreational, and social. Higher scores indicate greater levels of risk, benefit, or involvement in the behavior.

Evaluation of risk scale On the Evaluation of Risk Scale (EVAR; Sicard, Jouve, Blin, & Mathieu, 1999; for an English translation, see Killgore, Vo, Castro, & Hoge, 2006), participants respond to a series of 24 items assessing responses to a described situation. For each item, participants indicate on a horizontal line their level of agreement with one of the two anchor points for the slider. To ease scoring, the EVAR-B was created

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and utilizes a series of 25 fill-in bubbles rather than a slider (Killgore, Castro, & Hoge, 2010).

Everyday risk inventory On the Everyday Risk Inventory (Steketee & Frost, 1994), participants respond to a series of 32 items assessing real-world risk-taking behaviors. These behaviors were intended to represent mild everyday risks and primarily included items related to driving or public health behaviors (e.g., going outside in the cold without a coat, petting an unfamiliar dog).

General decision making style The General Decision Making Style inventory (Scott & Bruce, 1995) was created to assess several decision making approaches that individuals engage in across situations. Utilizing a 5-point scale, participants rate their level of agreement with a series of 25 statements. Five statements measure each of the five decision making styles. Those high in rational decision making engage in a thorough search for information prior to make a decision. Those high in intuitive decision making focus instead on their initial gut reactions and “feelings.” Highly dependent decision-makers rely on advice from others before making a decision, whereas highly avoidant decision-makers put off or postpone the decision as long as possible. Finally, the spontaneous style, revealed during the initial factor analysis (Scott & Bruce, 1995), is represented by an "as-fast-as-possible" style.

General risk propensity scale On the General Risk Propensity Scale, participants self-report their level of risk-taking propensity by responding to a series of eight questions (Zhang, Highhouse, & Nye, 2019). For each item, participants rate their level of agreement on a 5-point scale.

Passive risk-taking scale Keinan and Bereby-Meyer (2012) created a self-report scale to assess the tendency toward passive risk-taking (PRT), or the inclination to opt not to act in order to decrease risk. PRT was based on previous research indicating that a preference for the status quo (Kahneman, Knetsch, & Thaler, 1991; Samuelson & Zeckhauser, 1988) and inaction inertia (Tykocinsky & Pittman, 1998) could lead to the decision to do nothing rather than to do something. The 25 items of the PRT are split into three

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subscales assess PRT in the medical, ethical, and resource domains. Responses are scored based on the extent to which participants would act on the described event using a 7-point scale.

Risk propensity scale On the 6-item risk propensity scale (Nicholson, Soane, FentonO’Creevy, & Willman, 2005), participants self-report their level of risk across six domains: career, financial, health, recreational, safety, and social. Responses are given using a 5-point scale to indicate agreement/ disagreement with each statement. In addition, level of risk-taking involvement is estimated for both past behaviors and current behaviors for the six items. In a separate risk propensity scale (Meertens & Lion, 2008), seven items are used to assess level of orientation to risk. Six items use a 9point scale ranging from strongly disagree to strongly agree and one item uses a 9-point scale ranging from risk-avoidant to risk-seeking.

Risk-taking propensity Multiple risk-taking propensity measures also exist. In one, risktaking propensity (Jackson, Hourany, & Vidmar, 1972) is assessed through responses to 152 items assessing ethical, monetary, physical, and social risk-taking behaviors. For each item, participants rate their level of agreement with the statement on a 9-point scale. A separate risk-taking propensity measure, researched by MacCrimmon and Wehrung (1985), ties together responses to hypothetical risky situations, responses to real-world risky behaviors, and self-reported risktaking propensity. Responses to the self-report and behavioral items can then be examined for concurrent validity of the risk-taking propensity construct.

Stimulating-instrumental risk inventory The Stimulating-Instrumental Risk Inventory was created to assess two components of the decision making process: stimulating and instrumental (Zaleskiewicz, 2001). The stimulating component is viewed as uncontrollable, focused on emotions and potential gains, and occurring through unconscious processing of information. The instrumental components is viewed as the opposite: controllable, focused on cognitions and potential losses, and occurring through conscious processing of information. These appear to overlap with what others refer to as hot and cold, System I and System II, or Type I and

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Type II decision making processes (see Chapter 1: An introduction to risky decision making). The participant responds to a set of 17 items using a Likert-type scale indicating the extent to which the statement describes them. Higher scores on the stimulating risk-taking items are associated with excitement related to engaging in risk-taking behaviors, whereas higher scores on the instrumental risk-taking items are associated with risk-taking only when there is a high probability of a positive outcome.

Demographic factors in test performance Age Multiple studies examined how risky decision making changes with increasing age, given known higher levels of risk-taking behaviors in adolescents and young adults and questions of accurate risk assessment in these and younger age groups. Across tasks, risky decision making decreases with increasing age on the ADMC (Bangma, Fuermaier, Tucha, Tucha, & Koerts, 2017; Bruine de Bruin, Parker, & Fischhoff, 2012; Del Missier et al., 2017), blackjack task (West, Tiernan, Kieffaber, Bailey, & Anderson, 2014), CCT (Figner et al., 2009; Somerville et al., 2018), CGT (Deakin, Aitken, Dowson, Robbins, & Sahakian, 2004), BART (Koscielniak, Rydzewska, & Sedek, 2016), delay discounting tasks (Green, Myerson, & Ostaszewski, 1999; Whelan & McHugh, 2009), HDT (Cortes-Patino, Soares-Filho, & Acosta-Barreto, 2017; Crone, Bunge, Latenstein, & van der Molen, 2005; Crone & van der Molen, 2004, 2007; Groppe & Elsner, 2017), Stoplight Task (Kim-Spoon et al., 2016), Reyna and Ellis Task (Reyna & Ellis, 1994), Risky Gains Task (Lee, Leung, Fox, Gao, & Chan, 2008), Choice Dilemmas (Syndicus, Wiese, & van Treeck, 2018), Nonsymbolic Economic Decision Making (Paulsen et al., 2011, 2012), and probabilistic gambling task (Burnett et al., 2010). However, others find no correlation (Aı¨te et al., 2012; Bishara et al., 2009; Bornovalova, Daughters, Hernandez, Richards, & Lejuez, 2005; Bruine de Bruin et al., 2007; Buelow, 2015; Canavan, Forselius, Bessette, & Morgan, 2014; Cheng & Lee, 2012; de Water, Cillessen, & Scheres, 2014; Eshel, Nelson, Blair, Pine, & Ernst, 2007; Hamilton, Felton, Risco, Lejuez, & MacPherson, 2014; Harden et al., 2018; Huang, Wood, Berger, & Hanoch, 2013; Kirby, Petry, & Bickel, 1999; Lejuez, Aklin, Daughters, Zvolensky, Kahler, & Gwadz, 2007; Reynolds, Patak, & Shroff, 2007; Reynolds, Petak, Shroff, Penfold, et al., 2007; Syndicus et al., 2018; van Duijvenvoorde et al., 2015; Van Leijenhorst et al., 2010, 2008), and some even found greater

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risk-taking with increasing age on the BART (Collado, Felton, MacPherson, & Lejuez, 2014; Duell et al., 2016; Euser, Greaves-Lord, et al., 2013; Humphrey & Dumontheil, 2016) and mirror risk-taking task (Kreitler & Zigler, 1990).

Sex Across multiple studies with multiple measures of risk-taking behavior and risk-taking propensity, males engage in riskier behaviors/decisions than females (Alarco´n, Cservenka, & Nagel, 2017; Dohmen et al., 2011; Duell et al., 2016; Groppe & Elsner, 2017; Howat-Rodrigues, Tokumaru, & Izar, 2018; Keage & Loetscher, 2018; Kirby & Marakovic, 1996; Kogan & Dorros, 1978; LaLiberte & Grekin, 2015; Lejuez, Aklin, Jones, et al., 2003; Lejuez et al., 2002; Magnan & Hinsz, 2005; Markiewicz & Weber, 2013; Marini & Stickle, 2010; Nicholson et al., 2005; Zaleskiewicz, 2001; Zhou et al., 2014). Studies with the IGT and SGT, on the other hand, often find the opposite: males decide more advantageously than females (e.g., Bolla, Eldreth, Matochik, & Cadet, 2004; Byrne & Worthy, 2016; van den Bos et al., 2012; van den Bos, Homberg, & de Visser, 2013). In these studies, however, this sex difference is typically based on the tendency of female participants to choose decks with a lower frequency of losses (one of which is the disadvantageous Deck B) over decks with long-term positive outcomes. Finally, despite these frequently found sex differences in decision making and risk-taking behavior, others report no such differences in decision making across tasks (Bishara et al., 2009; Bonniot-Cabanac & Cabanac, 2009; Bornovalova et al., 2005; Buelow, 2015; Canavan et al., 2014; Cheng & Lee, 2012; Euser, Evans, GreavesLord, Huizink, & Franken, 2013; Hamilton et al., 2014; Kreitler & Zigler, 1990; Lejuez et al., 2007; Meertens & Lion, 2008; Reingen, 1976; Syndicus et al., 2018; van Duijvenvoorde et al., 2015; Zhang et al., 2017).

Modeling decision making on behavioral tasks There has been a push in recent years to go beyond the “standard” outcome indicators on behavioral decision making tasks, instead developing cognitive models to help explain why decision making might be impaired or intact for a given individual. These models and there potential application to future intervention research will be discussed in greater detail in Chapter 12, Conclusions and future directions, but I would like to introduce the topic here. The majority of the cognitive models examined to date focus on decision making on the ART, BART, IGT, or SGT. Busemeyer and

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Stout (2002) were one of the first to decompose performance on the IGT into its individual parts, based on the expectancy valence (EV) model. Per their EV model, the pattern of wins and losses across decisions on the IGT create a reaction, or valence. Based on these valences, participants in turn decide what deck to choose from on the next trial. In addition, the model assumes that participants treat gain and loss information differently. Although evidence exists supporting the EV model on the IGT, more recent research instead favors the prospect valence (PV) model of responding on the IGT. Per the PV model, which was developed as a way to better fit the IGT data and is based on the EV model (Ahn, Busemeyer, Wagenmakers, & Stout, 2008), participants decide which deck to choose from in part based on utility (consistent with EV), updated expectations using feedback/learning (consistent with EV), and the evaluation of the outcome from each trial (different from EV). In this evaluation, and what sets PV apart from EV, participants evaluate each selection with a lowered sensitivity to larger magnitude outcomes and with different sensitivity to gains and losses. In the EV model, only the different sensitivity to gains and losses appears. Across multiple studies attempting to fit these models to the IGT and SGT, the PV model is generally a better fit to the IGT than the EV model, and both models better fit decisions on the IGT than on the SGT (Ahn et al., 2008; Byrne & Worthy, 2016; Dai, Kerestes, Upton, Busemeyer, & Stout, 2015; Fridberg et al., 2010). The benefit of using these models to understand performance on the IGT is that researchers can examine if, how, and why decisions changed over the course of the task, rather than just knowing that the final score was a particular value (e.g., Prause & Lawyer, 2014). There are many ways participants might arrive at the same overall decision making strategy on a given task. For example, the PV model can show how learning occurs (Ahn et al., 2008), that participants evaluate gain and loss information separately first before combining that feedback to decide on the next selection (Dai et al., 2015), that participants tend to stay when they gain and shift when they lose (Lin, Lin, Song, Huang, & Chiu, 2016), and can dissect participants’ sensitivity to losses, sensitivity to gains, consistency of decisions, and relative reliance on recent versus distant feedback in making the next decision (Fridberg et al., 2010). These models can at times better differentiate patients from healthy controls than using the standard scoring metrics (e.g., Fridberg et al., 2010; Lin et al., 2016). Pleskac (2008) developed a model to explain sequential risk-taking tasks, such as the BART and ART. In this model, participants evaluate their available options at the start of each trial and determine an end point that will hopefully maximize wins. They then start the trial, continuously evaluating whether they should continue or stop based on that previously determined end point. The trial feedback then is used to

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update their understanding of the probabilities associated with decisions on the task and will change the decision making strategy on the next trial. Analyzing this model’s fit with the ART indicated participants were in fact changing how they approached a new trial based on the results of the previous trial (Pleskac, 2008). However, most other researchers utilize the four-parameter model, sometimes referred to as the Bayesian sequential risk-taking model, proposed by Wallsten et al. (2005), to explain performance on the BART. Per this model, decision making follows a series of several steps. First, participants have a prior belief about the likelihood a balloon will pop. Based on a given trial, this belief is updated and changes the next decision. In other words, learning occurs. The four parameters included in this model are (1) reward sensitivity, (2) behavioral consistency, (3) initial risk perception, and (4) confidence in initial risk perception (see Wallsten et al., 2005; Seaman, Stillman, Howard, & Howard, 2015, for more information). This model typically fits the data well across studies and shows a greater ability to detect between-group differences than the standard BART scoring. For example, older individuals experienced a higher initial risk perception but lower confidence in this perception than younger individuals despite finding no significant between-group differences in BART final score (Rolison, Hanoch, & Wood, 2012; Seaman et al., 2015). In addition, the parameters of reward sensitivity and behavioral consistency distinguished between those with higher versus lower levels of alcohol consumption when the BART final score did not (van Ravenzwaaij, Dutilh, & Wagenmakers, 2011). The reward sensitivity parameter can even be used to show changes from pre- to posttreatment for a substance use disorder (Khodadadi, Dezfouli, Fakhari, & Ekhtiari, 2010). Several consistencies emerge across studies, models, and tasks. Some of these factors include having a greater weighting of information about gains than about losses (e.g., Yechiam & Busemeyer, 2008), having a greater weighting of information in recent versus more distant trials, updating knowledge/perception of the task or stimulus based on recent feedback (e.g., Pleskac, 2008), sensitivity to losses, and consistency in decisions across trials (e.g., Bishara et al., 2009). As we will see in the next two chapters, few relationships are consistently seen across performance on risky decision making tasks, calling into question their validity. But, to the extent that the lack in correlations is a function of the standard scoring metrics not being correlated across tasks, while the underlying cognitive processes are correlated, utilizing these cognitive modeling approaches can shed light on common and divergent decision making processes affecting performance across tasks.

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3 Reliability and validity I have now provided information about different meanings of the terms “decision making” and “risky decision making,” specified the limits of the remaining chapters in this book, and described a number of risky decision making tasks. Before discussing these tasks and our knowledge of decision making impairments further, it is important to take a look at the psychometric properties of these tasks. Consistency in test scores across administrations (reliability) and accurate assessment of a well-defined construct (validity) are both essential to the development and use of assessment instruments. A task that is not reliable cannot be valid, and lowered reliability can limit inferences made from the task to real-world behaviors. As we will see, reliability and validity are not always adequately assessed for the various behavioral decision making tasks. First, reliability (test retest, parallel or alternative forms, internal consistency) will be examined, as well as the potential for practice effects when the same task is administered across time. Next, evidence for task validity (content, criterion, construct, ecological) will be assessed, including the extent of shared variance (or lack thereof) between similar risky decision making tasks. Finally, factors that can affect the assessment of and level of reliability and validity data will be examined. A thorough review of the concepts of reliability and validity is beyond the scope of this chapter but can be accessed via research methods and introductory statistics textbooks. The following sources are recommended reading regarding reliability (Cozby & Bates, 2012; Murphy & Davidshofter, 2001; Rosenthal & Rosnow, 1991) and validity (Clark & Watson, 1995; Cozby & Bates, 2012; Cronbach & Meehl, 1955; Rosenthal & Rosnow, 1991), as they helped guide the materials presented in this chapter.

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Reliability The presenting issue related to reliability is whether or not the same person taking an assessment instrument multiple times scores in a consistent manner across administrations. Reliability can be examined in multiple ways, including test retest, internal consistency, split-half, interrater, parallel or multiple forms, and intermethod. As the behavioral risky decision making tasks that are the primary focus of this book produce quantitative data, interrater reliability will not be addressed here. Instead, I will focus on the evidence (or lack thereof) of the remaining forms of reliability. Across all forms of reliability, there are multiple factors that can affect performance on tasks, in turn altering our assessment of reliability. Participants may develop a stable response set, continually selecting the same option (“A” or “True”) without fully reading a question. Changes in the testing environment, such as with regard to temperature, level of light, or background noise, could distract the participant during testing. In addition, changes in the participants themselves could also increase measurement error. Most commonly, these changes are seen through increased levels of fatigue and hunger/thirst, as well as lower the levels of motivation or even the development of a health complication such as headache. Measurement error can also be introduced when task instructions are difficult to understand, which can be difficult to assess for some of the tasks we’ve discussed that rely on ambiguity to assess the construct. The remaining subsections delve into the evidence for and against these forms of reliability across tasks. It should be noted, however, that no information could be found regarding reliability of the following tasks: Angling Risk Task, Blackjack, Bomb Risk Elicitation Task (BRET), Cake Gambling Task, Card Guessing Task, Chicken Game, Devil’s Task/Knife Switches, Dynamic Experiments for Estimating Preferences (DEEP Risk), Framing Spinner, Soochow Gambling Task, Hungry Donkey Task, Gambling Game Task, Mirror Drawing Risk-Taking Task, Multi-Outcome Risky Decision Task, Nonsymbolic Economic Decision Making Task, Probabilistic Gambling Task, Reyna & Ellis Risk Task, Risky Gains Task, Sequential Investment Task, Stoplight Game, TwoOutcome Risky Decision Task, and Wheel of Fortune Task.

Test retest Test retest reliability is one of the most commonly reported forms of reliability across clinical and research measures. To assess test retest reliability, individuals are administered a task at Time 1, then are

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readministered the same task at a later date (Time 2). A correlation is then calculated between these two scores to assess the stability or consistency of the score across time. A test retest correlation of 0.80 or higher can provide evidence of the test’s reliability (Cozby & Bates, 2012). Several factors could affect test retest reliability, leading to unusually high or low correlations between scores across time. One such factor is the time interval between assessments. Many cognitive tests have strong practice effects, such that completing the task at Time 1 will lead to an improvement in performance at Time 2. A longer test retest interval is suggested to combat this effect. Increasing the window of time between test administrations is believed to decrease any lingering practice effects that could affect the subsequent administration. However, increasing the interval between test administration raises the issue of the stability of the construct being measured. How stable is risky decision making, for example, across time? There is variability in the stability of constructs, leading to recommended test retest windows that range from a few weeks (e.g., mood symptoms) to several months (e.g., general intelligence and personality traits). An additional issue limiting the assessment of test retest reliability in the risky decision making field is that the completion of a task at Time 1 can be a very different experience than completing that same task at Time 2. Take the Iowa Gambling Task (IGT), for example. At the first administration, participants are given very vague information about the task (see Chapter 2: Measurement methods, for greater detail). They have to figure out how the task works through trial-and-error learning across the 100 trials. By the end of the task, most participants have at least a vague sense of the relative risks and benefits associated with each of the four tasks. If this same task is administered 6 weeks, 6 months, or even 1 year later, participants still retained information about how the task “worked” and had better overall (i.e., less risky) performance on it (e.g., Verdejo-Garcia et al., 2007). Therefore on some behavioral decision making tasks, it is possible that a true estimate of test retest reliability, as well as parallel forms reliability, may be more difficult to come by. The relative dearth of such studies has a negative impact on clinical practice, as practitioners may be unaware of the extent of practice effects across repeat administrations or may misinterpret high scores as indicative of a relative strength rather than evidence of a previous administration of the task. Turning to specific risky decision making tasks, several patterns emerged. First, there is more published information about test retest reliability for paper-and-pencil based measures than for behavioral measures. Second, reliability estimates can vary significantly across studies and across time intervals. What follows is a summary of the test retest data for specific risky decision making measures.

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Adult Decision Making Competence (ADMC). Both 9-day and 2-month test retest reliabilities are presented for the ADMC. After 9 days (Bruine de Bruin et al., 2007), test retest reliabilities ranged from small (0.28 for path independence, later dropped from the measure) to more moderate-strong estimates: 0.46 (recognizing social norms), 0.47 (under/ overconfidence), 0.51 (consistency in risk perception), 0.58 (resistance to framing), 0.61 (resistance to sunk costs), and 0.77 (applying decision rules). These estimates are rather consistent with the 2-month test retest reliabilities presented by Peng et al. (2018): 0.44 (under/overconfidence), 0.47 (consistency in risk perception), 0.55 (recognizing social norms), 0.59 (resistance to framing), 0.60 (resistance to sunk costs), and 0.78 (applying decision rules). Balloon Analogue Risk Task (BART). Test retest reliability was assessed over a range of time intervals. Repeat administration in the same testing session showed moderate to strong correlations (0.62 0.82; Lejuez, Aklin, Jones, et al., 2003). When that time interval is increased, the correlations remain moderate to strong after 9 days (0.79; Weafer, Baggott, & de Wit, 2013), 2 weeks (0.77; White, Lejuez, & de Wit, 2008), and 3 weeks (0.69, Buelow & Barnhart, 2018b; 0.66 0.76, Xu, Korczykowski, Zhu, & Rao, 2013). However, there is also evidence of increased risk-taking behavior at Time 2 compared to Time 1 (Buelow & Barnhart, 2018b; Goudriaan et al., 2010). At even larger time intervals (3 months, Forster, Finn, & Brown, 2016; 1 5 years, Collado, Felton, MacPherson, & Lejuez, 2014; MacPherson, Magidson, et al., 2010; MacPherson, Reynolds, et al., 2010; Qu, Galvan, Fuligni, Lieberman, & Telzer, 2015), the moderate-to-strong correlations continue. Cognitive Appraisal of Risky Events (CARE). Fromme et al. (1997) reported moderate to strong 10-day test retest reliabilities across the six CARE subscales. Although specific subscale scores were not presented, the range of test retest correlations was 0.51 0.65 for the expected reward scales and 0.58 0.79 for the expected benefit scales. No reliability was assessed for the frequency of involvement scale. Columbia Card Task (CCT). Three-week test retest reliability on the CCT was estimated to be 0.57 (Buelow & Barnhart, 2018b). No differences were seen in the level of risk-taking behaviors between the two time points. Cups Task. Levin et al. (2007) conducted a 3-year follow-up on their original study participants (Levin & Hart, 2003). Correlations between task performance ranged from 0.29 (parents) to 0.38 (children), both of which were significant. Delay Discounting Tasks. Several different delay and probability discounting tasks exist, and for ease of interpretation, the results of any reliability and validity data across task versions are combined in this chapter. Across studies, test retest reliabilities appear to be moderate

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to strong over 7 days (0.62 0.82; Johnson & Bruner, 2013), 5 6 weeks (0.64 0.77; Beck & Triplett, 2009; Kirby, 2009), 3 months (0.45 0.86; Ohmura, Takahashi, Kitamura, & Wehr, 2006), 1 2 years (0.63 0.76; Anokhin, Golosheykin, & Mulligan, 2015; Kirby, 2009), and 6 years (Audrain-McGovern, Rodriguez, Epstein, et al., 2009). Domain-Specific Risk-Taking (DOSPERT). Weber et al. (2002) reported significant variations in reliability across the five DOSPERT riskbehavior subscales across a 1-month interval: 0.44 (financial), 0.58 (social), 0.72 (ethics), 0.75 (health), and 0.80 (recreational). The reliabilities were also moderate for the risk-perception subscales: 0.42 (financial), 0.47 (social), 0.56 (recreational), 0.66 (health), and 0.67 (ethics). Game of Dice Task (GDT). Three-week test retest reliability on the GDT was estimated to be 0.49 (Buelow & Barnhart, 2018b). An assessment of the patterns of performance on the task at the two time points indicated that participants were less risky at Time 2 than at Time 1, potentially pointing to information learned about the task at Time 1 influencing subsequent behavior at Time 2. Choice Dilemmas. Test retest reliability is moderately strong for both males (0.78) and females (0.82) (Wallach, Kogan, & Bem, 1962). Everyday Risk Inventory. Two-to-three-week test retest reliability for the measures is high (0.93; Steketee & Frost, 1994). General Decision Making Style. One-month test retest reliability was low for the rational subscale (0.28) but moderate to strong for the remaining scales (0.61 0.77) (Spicer & Sadler-Smith, 2005). IGT. Interestingly, the clinical manual for the IGT does not reference data on reliability of the measure (Bechara, 2007). Several authors, however, examined consistency in IGT performance across time, with evidence of strong practice effects persisting for months after original testing. For example, improved performance is seen at the second testing session compared to the first testing session, with delays of 1 3 weeks (Buelow & Barnhart, 2018b; Ernst, Grant, et al., 2003; Ernst, Kimes, et al., 2003; Xu, Korczykowski, Zhu, & Rao, 2013), 1 6 months (Bechara et al., 1994; Burdick, Braga, Gopin, & Malhotra, 2014; Verdejo-Garcia et al., 2007), and even across time in the same session (Lejuez, Aklin, Jones, et al., 2003; Lejuez, Aklin, Zvolensky, et al., 2003; Lin, Song, Chen, Lee, & Chiu, 2013). Specific test retest values are in the low to moderate (Buelow & Barnhart, 2018b; Cardoso et al., 2010; Hulka et al., 2015; Xu et al., 2013) range (time interval: 3 weeks to 1 year). Passive Risk-Taking. Performance on the passive risk-taking scale was highly reliable across both 3-week (0.83 0.90; Keinan & Bereby-Meyer, 2012) and 8-week (0.54 0.78; Riva, Gorini, Cutica, Mazzocco, & Pravettoni, 2015) intervals.

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Parallel or multiple forms For tasks with strong practice effects, utilizing a parallel form could lessen these effects on repeat administrations. To assess parallel forms (or multiple forms) reliability, one version of the task is administered. At a later point the parallel version of the task is administered and a correlation is calculated between scores on the two test versions. Since two different versions of the task are used, a shorter intertask trial can be used, even administering both forms in the same testing session. However, very few of the commonly used tasks have an alternate form. There are multiple versions of the CCT (hot, cold; Figner et al., 2009); however, varying evidence exists as to if these are assessing overlapping components of risky decision making. The IGT has a parallel form, the E-F-G-H version (e.g., Bechara et al., 2000; Bechara, Dolan, & Hindes, 2002; Must et al., 2006), which mimics the original A-B-C-D version except with immediate larger punishments and longer-term larger rewards. However, again it is unclear to what extent these can be considered parallel forms when there are inconsistencies in results when both are administered to the same sample (e.g., VerdejoGarcia, Lopez-Torrecillas, Calander, Delgado-Rodriguez, & Bechara, 2009).

Intermethod An alternative to the parallel forms reliability is intermethod reliability. Instead of using two versions of the same task, two different decision making tasks could be administered at different time points to assess changes in the construct over time. However, this method is infrequently used in the current risky decision making research. As will be discussed in greater detail in the validity section, few correlations are seen between different decision making tasks, pointing to their assessing different, rather than overlapping, components of the decision making construct.

Internal consistency Internal consistency focuses on the items encompassing a task itself, assessing the correlation between an item and each other item in the task. Because of this, most studies that present internal consistency data are for tasks involving self-report data (rather than responses on a behavioral task). ADMC. Differences are seen in internal consistency values across adolescent and adult samples. Among adolescents and youths, internal consistency values are in the weak to moderate range [0.13 (resistance to sunk costs) 0.76 (under/overconfidence), Parker et al., 2018], whereas consistency is moderate to strong in adult samples (0.45 0.87; Bavolar, 2013; Blacksmith, Behrend, Dalal, & Hayes, 2019;

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Del Missier, Mantyla, & Bruine de Bruin, 2010; Del Missier, Mantyla, & Bruine de Bruin, 2012; Del Missier et al., 2013; Del Missier et al., 2017; Parker et al., 2018; Peng et al., 2019; Weller, Levin, Rose, & Bossard, 2012). Low internal consistency was found for the resistance to sunk costs and resistance to framing subscales (Parker & Fischhoff, 2005), and the low-performing path independence scale (0.25, Bruine de Bruin et al., 2007) was removed from the ADMC following early studies. Factor analyses support a single factor model (Bavolar, 2013; Parker et al., 2018; Peng et al., 2018; but see Blacksmith et al., 2019). CARE. Overall, internal consistency is moderate to strong across the frequency of involvement, expected risks, and expected benefit scales (0.64 0.94; Buelow & Brunell, 2014; Chandler & Pronin, 2012; D’Amico et al., 2015; Ellison & Levy, 2012; Fromme et al., 1997; Kelly et al., 2005; Maisto et al., 2004; Messman-Moore, Walsh, & DiLillo, 2010; Poon, 2016; Reynolds et al., 2015; Wright, Squires, Goodness, Maisto, & Palfai, 2013). Choice Dilemmas. Only one study provided an estimate of internal consistency for responses and only a moderate alpha value was found (0.50; Ronay & Kim, 2006). DOSPERT. Internal consistency was examined across the risk-taking, expected benefits, and expected risks scales. Alpha values are generally moderate to strong for the expected risk (0.70 0.81, Lozano et al., 2017; 0.71 0.84, Weber et al., 2002), expected benefit (0.66 0.89, Lozano et al., 2017), and risk-taking scales (0.63 0.80, Hu & Xie, 2012; 0.64 0.85, Lozano et al., 2017; 0.69 0.83, Weber et al., 2002). Everyday Risk Inventory. Internal consistency was high across multiple samples in the initial validation study (0.83 0.91; Steketee & Frost, 1994) as well as in the initial English translation (0.78; Killgore, Vo, Castro, & Hoge, 2006). General Decision Making Style. Overall, moderate to high internal consistency is seen across studies and across subscales (0.62 0.96) (Avsec, 2012; Baiocco, Laghi, & D’Alessio, 2009; Bavolar & Orosova, 2015; Chermack & Nimon, 2008; Curseu & Schruijer, 2012; Loo, 2000; Scott & Bruce, 1995; Spicer & Sadler-Smith, 2005). Stimulating-Instrumental Risk Inventory. Internal consistency is moderate to high on both the stimulating and instrumental subscales (0.67 0.80 across three samples; Zaleskiewicz, 2001).

Split-half reliability Only one test administration is needed to assess split-half reliability. Performance on a portion of the task is compared to performance on a

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different portion of the task. Depending on the exact nature of the task, this could be a correlation between the first half and last half, or between the even and odd items. It is important to keep in mind, however, that reliability analyses such as those assessing split-half reliability may not be appropriate to judge the reliability of measures in which participants need to learn from feedback on previous trials. It is likely for this reason that relatively few tasks have published split-half reliability information. BART. Assessments of split-half reliability on the BART typically focus on the pattern of pumps per balloon across blocks of trials. Moderate to strong correlations are typically seen across blocks (r 5 0.61 0.91, Ashenhurst, Jentsch, & Ray, 2011; Collado et al., 2017; Felton, Collado, Shadur, Lejuez, & MacPherson, 2015; Lahat et al., 2012; Lejuez et al., 2007; Schmitz, Manske, Preckel, & Wilhelm, 2016). Some researchers did not provide correlations between blocks of trials but instead examined potential differences in pumps across blocks. Results are quite varied. Increased pumps per balloon (i.e., greater risk-taking behavior) are seen as the task progresses (Benjamin & Robbins, 2007; Celio & Lisman, 2014; DeMartini et al., 2014; Euser et al., 2013; Koppel et al., 2017; Lejuez et al., 2002; Lim, Yuen, & Tong, 2015). On the other hand, decreased pumps are also seen as the task progresses (Ashenhurst, Bujarski, Jentsch, & Ray, 2014; Zhang & Gu, 2018), while still others find no differences across blocks (Canavan et al., 2014; Erskine-Shaw, Monk, Qureshi, & Heim, 2017; Lejuez, Aklin, Bornovalova, & Moolchan, 2005). Cambridge Gambling Task (CGT). Correlations of 0.85 0.94 were found between the first and last halves of the CGT (Monterosso et al., 2001). IGT. Very few split-half reliability assessments are seen with the IGT. A correlation of 0.80 was found between the first and last half of the IGT (Monterosso et al., 2001), and low to moderate correlations were seen between odd and even blocks of trials (0.44 0.46; Gansler, Jerram, Vannorsdall, & Schretlen, 2011a). Risk Propensity Task. Interblock correlations were examined, with evidence of moderate to strong correlations across blocks (0.57 0.69 at Time 1, 0.69 0.90 at Time 2; Aquado et al., 2011).

Reliability conclusion There is a relative lack of reliability data across measures, especially when compared to the amount of validity data about to be presented. Although a number of tasks, primarily those that are paper-and-pencil or questionnaire-based, show moderate to high test retest reliability and internal consistency, this data is missing for a number of behavioral

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tasks. When test retest reliability data exists, it points to a concern as to whether these tasks show practice effects that can bias future responses. On the GDT and IGT, for example, Time 2 performance is improved over that of Time 1, pointing to learning occurring and being retained across time. Evidence for within-task improvements in decision making, such as on the BART and IGT, also show that task-based learning could negatively affect the ability to readminister the “same” task in the future. There is a need for clinicians to have access to tasks that can be administered at multiple time points, such as to track improvement in decision making as a function of clinical intervention, but it is unclear what tasks (or tasks) are reliable and valid for this purpose. The next section will provide more information about relationships between different tasks (intermethod), but in general there are not consistent, significant correlations between performance on various behavioral tasks.

Validity When we administer a behavioral decision making task, are we actually measuring risky decision making? How do we know? These are the major questions behind examinations of task validity. First, the different types of validity will be introduced, then the evidence in favor (or against) these validities will be examined by task. The main types of validity that will be examined are content, criterion (concurrent, predictive), construct (convergent and discriminant), and ecological. With the exception of ecological validity, these validities were presented in Cronbach and Meehl (1955) as vital to the validation of any new measure. Evidence for these validities can come from examinations of group-based differences in performance, correlational and factor analyses, and data regarding reliability and internal consistency of the measures. When a new measure is developed, it should be grounded in the relevant theory and include items that measure the critical components of that theory (Clark & Watson, 1995). Assessing the content of the task is the focus of content validity. Content validity focuses on the particular items in the measure, examining whether the items assess the major content areas related to that theory. For example, a new measure of depression should include items assessing all criteria for depression, rather than just a few of the criteria. In the decision making realm, the accurate assessment of content validity will depend on how the task creators defined the construct being assessed. Given the breadth of the decision making literature, it would be nearly impossible to develop a valid measure that accurately assesses “decision making.” Instead, a measure should accurately and fully assess the subcomponent of

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decision making that it is theoretically tied to. Expert judges may be required to fully examine the content validity of a task. Criterion validity instead focuses on what the test outcome/score means. If a new measure of depression is created, does it actually differentiate between those with depression and those without? Correlate with or predict a diagnosis of depression? If a new measure of risky decision making is created, to what extent does it distinguish between those with known decision making impairments and those without? Two subcomponents of criterion validity specify whether this ability to predict or distinguish between groups occurs at the same time (concurrent) or at a later point in time (predictive) compared to the—in our case—risky decision making assessment. We can see evidence of criterion validity when researchers examine relationships between risky decision making task performance and real-world risk-taking behaviors, either measured at the same time (concurrent) or at two different time points (predictive, if the risk-taking assessment occurs at a future date). Although some evidence of criterion validity will be assessed in this chapter, a significant portion will be presented in Chapter 10, Addictive behaviors: gambling and substances of abuse, where both substance use behaviors and substance use disorders will be examined. A related component to criterion validity is ecological validity, or the examination of how well a lab-based task can assess or predict real-world behaviors. Ecological validity concerns are typically raised regarding behavioral decision making tasks as they may not mimic real-world decision making situations. For example, how often do you turn over a series of four decks of cards with unknown win/loss ratios to maximize your profit over 100 trials? Try not to pop a balloon while earning money along the way? But, to the extent that performance on these tasks is associated with real-world risky behaviors, both criterion and ecological validity are shown. Finally, construct validity focuses on the overall relationship of a measure to the construct it is assessing and is typically shown through consistent evidence across a series of studies. Does this new measure actually assess the construct it is thought to assess? Does a new risky decision making task actually assess risky decision making, or rather one or several specific components of this large construct? Convergent and divergent validity are used to assess construct validity of a task (Campbell & Fiske, 1959). Any new measure of a particular construct should be related to other measures of that same or similar constructs (convergent) and should not be related to measures of “conceptually distinct” behaviors (divergent). One concern with regard to convergent and divergent validity is the potential for high correlations between risky decision making and the personality characteristics of impulsivity and sensation seeking. Some measures even refer to themselves

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as measures of impulsive decision making, emphasizing the likely shared variance between measures of these constructs. However, largescale factor and meta-analyses provide evidence of convergence among self-report measures of impulsivity but divergence between self-report and behavioral measures of impulsivity or related characteristics (e.g., Cyders & Coskunpinar, 2012; Duckworth & Kern, 2011; Reynolds, Ortengren, Richards, & de Wit, 2006; Reynolds, Richards, & de Wit, 2006; Sharma, Markon, & Clark, 2014). Thus in the upcoming examination of task validity, attention will be paid to relationships with impulsivity and related personality characteristics, other decision making tasks, and real-world decision making impairments. The following is an examination of evidence of the validity of the decision making tasks presented in the previous chapter. Of the tasks described in the previous chapter, published information on validity could not be found for the following: Card Guessing Task, DEEP Risk, Gambling Game, Nonsymbolic Economic Decision Making, Probabilistic Gambling Task, Reyna & Ellis Risk Task, Risky Gains Task, Sequential Investment Task, and SGT. ADMC. The ADMC was created by selecting tasks fitting a theoretical set of decision making skills in adolescents and young adults (Parker & Fischhoff, 2005). The primary means of assessing validity of the ADMC is through correlations with other behavioral tasks. Performance on the ADMC correlated with performance on the CGT (Peng et al., 2019), the CCT-hot (Jamieson & Mendes, 2016), and with the IGT (Mantyla, Still, Gullberg, & Del Missier, 2012; Peng et al., 2019), but not with a risky choice task (Parker & Weller, 2015) or the BART (Mantyla et al., 2012). In terms of relationships to real-world behaviors, worse decision making on the ADMC is associated with a greater likelihood of engaging in realworld behaviors with minor to serious negative outcomes (Bruine de Bruin et al., 2007; Parker, Bruine de Bruin, & Fischhoff, 2015; Peng et al., 2019; Weller, Leve, Kim, Bhimji, & Fisher, 2015; Weller et al., 2012). Scores on the applying decision rules scale also predicted attention-deficit/hyperactivity disorder diagnosis (Mantyla et al., 2012), as well as greater interpersonal difficulties two years later (Weller et al., 2015). ART. Few studies directly provide evidence of the validity of the ART. Riskier decisions on the ART are associated with substance use and other real-world risk-taking behaviors (Pleskac, 2008). To date, no correlations were examined between performance on this and other risky decision making tasks. BART. Rather mixed results are found when correlations between impulsivity, sensation seeking, and the BART are examined. About half of the previous research finds no significant correlation with impulsivity (Aklin, Lejuez, Zvolensky, Kahler, & Gwadz, 2005; Bagge, Littlefield, Rosellini, & Coffey, 2013; Cavalca et al., 2013; Clay et al., 2018;

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Coffey, Schumacher, Baschnagel, Hawk, & Holloman, 2011; Collado et al., 2014; Ellingson, Potenza, & Pearlson, 2018; Euser et al., 2013; Ferrey & Mishra 2014; Hopko et al., 2006; Hunt, Hopko, Bare, Lejuez, & Robinson, 2005; Kerwin, Farris, & Hantula, 2012; Ledgerwood, Alessi, Phoenix, & Petry, 2009; Lejuez et al., 2007; LoBue et al., 2014; MacKillop et al., 2014; Marini & Stickle, 2010; Mishra & Lalumiere, 2011; Pietruska & Armony, 2013; Reddy et al., 2014; Reynolds, Richards, Dassinger, & de Wit, 2004; Reynolds, Ortengren, et al., 2006; Ryan, MacKillop, & Carpenter, 2013; Sohn, Kang, Namkoong, & Kim, 2014; Webster & Crysel, 2012) and sensation seeking (Aklin et al., 2005; Collado et al., 2014; Ellingson et al., 2018; Hopko et al., 2006; Ledgerwood et al., 2009; MacPherson, Magidson, et al., 2010; MacPherson, Reynolds, et al., 2010; Mishra & Lalumiere, 2011; Skeel, Pilarski, Pytlak, & Neudecker, 2008; Webster & Crysel, 2012). The remaining studies instead find significant correlations with impulsivity (Ciccarelli, Malinconico, Griffiths, Nigro, & Cosenza, 2016; Hulvershorn et al., 2015; Lejuez et al., 2002; Lejuez et al., 2005; Lorian & Grisham, 2010) and sensation seeking (Brailovskaia, Schillack, Assion, Horn, & Margraf, 2018; Byrne & Worthy, 2016; Cui, Colasante, Malti, Ribeaud, & Eisner, 2016; Felton et al., 2015; Ferrey & Mishra, 2014; Lawyer, 2013; Lejuez et al., 2002; Lejuez et al., 2007; Loman et al., 2014; MacKillop et al., 2014; Marini & Stickle, 2010; Mishra & Lalumiere, 2010; Mishra, Lalumiere, & Williams, 2017; Pleskac, 2008). Evidence in favor of the construct validity of the BART comes from correlations with other risky decision making measures. The BART is not correlated with the IGT (Aklin et al., 2005; Balaguero, Vicente, Molina, Tormos, & Rovira, 2014; Bishara et al., 2009; Buelow & Barnhart, 2018b; Gonzalez et al., 2012; Harden et al., 2018; Le Bas, Hughes, & Stout, 2015; Lejuez, Aklin, Jones, et al., 2003; Mantyla et al., 2012; Xu et al., 2013), ADMC (Mantyla et al., 2012), CCT (Buelow & Barnhart, 2018b), GDT (Buelow & Barnhart, 2018b; Goudriaan et al., 2010; Pletzer & Ortner, 2016), or delay discounting task (Barnhart & Buelow, 2017a; Bornovalova et al., 2005; Ciccarelli, Malinconico, et al., 2016; Coffey et al., 2011; Fernie, Cole, Goudie, & Field, 2010; Gonzalez et al., 2012; Johnson, Tharp, Peckham, Sanchez, & Carver, 2016; Ledgerwood et al., 2009; Sohn et al., 2014; Xu et al., 2013). However, others instead find significant relationships between decisions made on the BART and decisions made on the CGT (Henninger, Madden, & Huettel, 2010; MacKillop et al., 2014), games of blackjack (Crysel et al., 2013; Webster & Crysel, 2012), stoplight task (Harden et al., 2017; Harden et al., 2018), CCT (Brunell & Buelow, 2017), IGT (Adoue et al., 2015; Brown et al., 2015; Harden et al., 2017; Harden et al., 2018; Skeel, Neudecker, Pilarski, & Pytlak, 2007; Upton, Bishara, Ahn, & Stout, 2011), and delay discounting tasks (Courtney et al., 2012; Crysel et al., 2013; Harden et al., 2017; Kerwin et al., 2012;

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MacKillop et al., 2014; Mishra & Lalumiere, 2011; Reynolds, Ortengren, et al., 2006; Reynolds, Richards, et al., 2006). Multiple studies examined the ability to the BART to predict both concurrent and future involvement in various real-world risk-taking behaviors. Risky decision making on the BART is correlated with risky behaviors including, tobacco use (Larsen et al., 2014; Lejuez et al., 2005; Lejuez, Aklin, Jones, et al., 2003), alcohol use (Ashenhurst et al., 2011; Claus & Hutchison, 2012; Courtney et al., 2012; DeMartini et al., 2014; Fernie et al., 2010), other substance use (Aklin et al., 2005, 2012; Bornovalova et al., 2005; Hanson, Thayer, & Tapert, 2014; Hopko et al., 2006; Pleskac, 2008), gambling (Ciccarelli, Malinconico, et al., 2016; Lawyer, 2013; MacKillop et al., 2014), HIV-risk behaviors (Bornovalova, Gwadz, Kahler, Aklin, & Lejuez, 2008; Schuster, Crane, Mermelstein, & Gonzalez, 2012), and risky driving behaviors (Chee, Lee, Patomella, & Falkmer, 2017; Cheng & Lee, 2012), as well as with risk-taking behaviors more generally (Aklin et al., 2005; Collado et al., 2014; Derefinko et al., 2014; Lejuez et al., 2002; Lejuez, Simmons, Aklin, Daughters, & Dvir, 2004; Lejuez et al., 2007; Lorian & Grisham, 2010; Pietruska & Armony, 2013; Qu et al., 2015; Swogger, Walsh, Lejuez, & Kosson, 2010). The BART also predicted future marijuana use (Felton et al., 2015), alcohol use (Fernie et al., 2013), and antisocial behaviors (Bai & Lee, 2017). However, again there are studies that show no relationship between BART performance and concurrent risk-taking behaviors, including tobacco, alcohol, and other substance use (Bogg, Fukunaga, Finn, & Brown, 2012; Clay et al., 2018; Ellingson et al., 2018; Euser, Evans, et al., 2013; Euser, Greaves-Lord, et al., 2013; Hamilton et al., 2014; Lawyer, 2013; Rose, Jones, Clarke, & Christiansen, 2014; Ryan, Dube, & Potter, 2013; Ryan, MacKillop, et al., 2013; Skeel et al., 2007; Skeel et al., 2008; Weafer, Milich, & Fillmore, 2011; Woerner, Kopetz, Lechner, & Lejuez, 2016), gambling (Betancourt et al., 2012; Mishra & Lalumiere, 2010; Pletzer & Ortner, 2016), risky driving (Le Bas et al., 2015), and risky sexual behaviors (Woerner et al., 2016), as well as more generally (Brailovskaia et al., 2018; Deuter et al., 2017; Mamerow, Frey, & Mata, 2016; Syndicus et al., 2018; Szrek, Chao, Ramlagan, & Peltzer, 2012). Blackjack Task. Performance on a blackjack task is correlated with the BART (Crysel, Crosier, & Webster, 2013; Webster & Crysel, 2012) but not a delay discounting task (Crysel et al., 2013). In addition, performance on a blackjack task is correlated with impulsivity and sensation seeking (Crysel et al., 2013; Webster & Crysel, 2012) and probable pathological gambling (Baboushkin, Hardoon, Derevensky, & Gupta, 2001). In addition, those who lost at blackjack reported taking greater risks on the DOSPERT (Kostek & Ashrafioun, 2014). BRET. Performance on the BRET is more comparable to the investment game than the multiple price list or lottery choice

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(Crosetto & Filippin, 2013). Performance on the BRET was not correlated with self-reported risk-taking on the DOSPERT in Crosetto and Filippin (2016), but it was in Gurdal, Kuzubas, and Saltoglu (2017). Cake Gamble (Gambling) Task. Performance on this task is correlated with sensation seeking (van Leijenhorst et al., 2008) but not with temporal discounting rates (de Water et al., 2014; de Water, Burk, Cillessen, & Scheres, 2016). In addition, performance is correlated with increased rates of alcohol, tobacco, and marijuana use (de Water et al., 2016). Cambridge Gamble (Gambling) Task. Performance on the CGT is correlated with performance on the IGT (D’Acremont & Van der Linden, 2006; Monterosso et al., 2001) and delay discounting tasks (Li, Shi, et al., 2016; but see Monterosso et al., 2001). Although some find correlations between the CGT and drug use (Monterosso et al., 2011) and HIV-risk behaviors (Wilson & Vassileva, 2016), others find no correlations with real-world risk-taking behaviors (Giesbrecht et al., 2014; Pollak, Shalit, & Aran, 2018). Chicken Game. No correlation is seen between performance on the chicken game and on a risky card game (Howat-Rodrigues et al., 2018). Choice Dilemmas Questionnaire. Level of riskiness on the choice dilemmas task is correlated with sensation seeking and performance on the implicit risk task (Ronay & Kim, 2006), but not with the big five personality traits (Syndicus, 2018) or the BART (Ronay & Kim, 2006; Syndicus et al., 2018). In addition, a significant number of participants give an incorrect response when asked who took the greater risk in the scenario, indicating potential issues understanding the task and/or estimating probabilities correctly (Reingen, 1976). This difficulty could negatively impact the validity of the task, but adjusting the instructions does decrease the error rate (Hale, Boster, & Monggeau, 1991). Performance on the choice dilemmas is associated with risk-taking behaviors on the DOSPERT [Syndicus, 2018 (Study 1 but not Study 2)], as well as realworld suicidal (Kochansky, 1973) and alcohol use (Hopthrow, de Moura, Meleady, Abrams, & Swift, 2014) behaviors. CARE. Given that the CARE assesses risk-taking behaviors, evidence for the ecological and construct validity of the task comes from correlations between the measure and other assessments of real-world risktaking behaviors. The original task was created by first having students keep track of their risky behaviors and self-rated level of risk associated with each behavior for 10 days (Fromme et al., 1997). Study researchers then also rated the risk of each behavior, and the resultant risky behaviors were then tested in a separate group of undergraduate students and subjected to factor analysis. Responses on the CARE are correlated with sensation seeking and impulsivity (Fromme et al., 1997; Brown et al., 2015), but not with performance on the Wheel of Fortune task (Roy et al., 2011). Outcome expectancies are significantly correlated with alcohol and

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other substance use problems (Fromme et al., 1997; Lienemann & Lamb, 2013; Kelly et al., 2005; Montes, Witkiewitz, Pearson, & Leventhal, 2019) as well as other real-world risk-taking behaviors (Dvorak, Wray, Kuvaas, & Kilwein, 2013; Heinz, de Wit, Lilje, & Kassel, 2013; Metrik, Caswell, Magill, Monti, & Kahler, 2016; Nickoletti & Taussig, 2006). CCT. Construct validity evidence for the CCT is centered on distinguishing between the cold and hot versions of the task, as the cold version is thought to assess deliberative decision making while the hot version assesses affective decision making. Greater affective responses, including heart rate frequency and skin conductance responses, are seen on the CCT-hot but not the CCT-cold (Figner et al., 2009; Holper & Murphy, 2014; Kluwe-Schiavon et al., 2015). By contrast, participants on the CCTcold used more information and paid more attention to loss and gain information than on the CCT-hot (Markiewicz & Kubinska, 2015). Additional evidence for construct validity comes from associations with other decision making tasks. Inconsistent results are found. The CCT-hot correlates with the BART (Brunell & Buelow, 2017; Buelow & Blaine, 2015) and early IGT trials (Brunell & Buelow, 2017). The CCT-cold correlates with the later IGT trials (Brunell et al., 2017) (but see Buelow & Barnhart, 2018b), but not with the BART or GDT (Buelow & Barnhart, 2018b). Relatively few studies examine ecological validity of the task. Correlations are found between the CCT and impulsivity, sensation seeking, and behavioral inhibition (Buelow, 2015; Penolazzi, Gremigni, & Russo, 2012), but task performance does not predict alcohol use (LaLiberte & Grekin, 2015). Cups Task. Experiencing losses (i.e., taking greater risks) is correlated with impulsivity (Levin & Hart, 2003). Delay Discounting Tasks. Across different delay and probability discounting tasks, significant correlations are seen between responses and level of impulsivity (Kirby et al., 1999; Mobini, Grant, Kass, & Yeomans, 2007; Ostaszewski, 1996; Richards, Zhang, Mitchell, & de Wit, 1999); however, others find no relationship with impulsivity or sensation seeking (Dom, De Wilde, Hulstijn, & Sabbe, 2007; McCarthy et al., 2016; Ostaszewski, 1996). Inconsistent correlations are also seen with the IGT (Dom et al., 2007; Monterosso et al., 2001; Olson, Hooper, Collins, & Luciana, 2007), CGT (Monterosso et al., 2001), and BART (Meda et al., 2009). Several lines of research provide evidence in the favor of the validity of the measure. Changes in the discounting rate are seen as a function of increasing size of the delayed reward (Bialaszek & Ostaszewski, 2012; Green, Myerson, & McFadden, 1997; Jones & Oaksford, 2011; Kirby, 1997; Kirby & Marakovic, 1996; Mitchell & Wilson, 2012; Myerson, Green, & Morris, 2011), length of the delay interval (Ostaszewski, 2007; Scheres, de Water, et al., 2013; Scheres, Tontsch, & Thoeny, 2013), and

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whether losses or gains are examined (Ostaszewski & Karzel, 2002; Shead & Hodgins, 2009; Shelley, 1994). Multiple studies suggest that using hypothetical rewards does not lead to different responses than using real rewards (see Scheres, de Water, et al., 2013; Scheres, Tontsch, et al., 2013, for a review; Lagorio & Madden, 2005; Lawyer, Schoepflin, Green, & Jenks, 2011; Madden, Begotka, Raiff, & Kastern, 2003). Multiple studies examined relationships between delay discounting and real-world substance use and other risky behaviors. Evidence shows that those with steeper delay discounting (e.g., a greater tendency to choose the immediate over a delayed reward) have higher rates of concurrent and future nicotine (Audrain-McGovern, Rodriguez, Epstein, et al., 2009; Bickel et al., 1999; Fields, Leraas, Collins, & Reynolds, 2009; Krishnan-Sarin et al., 2007; Reynolds, 2004; Reynolds, Richards, Dassinger, et al., 2004; Reynolds, Richards, Horn, & Karraker, 2004; Reynolds, Petak, Shroff, 2007; Reynolds, Patak, Shroff, Penfold, et al., 2007) and other substance abuse behaviors (Daugherty & Brase, 2010; Kirby, Petry, & Bickel, 1999; Kollins, 2003; MacKillop et al., 2011; Petry, 2003). Relationships are also seen with risky health (Daugherty & Brase, 2010) and gambling (Dixon, Marley, & Jacobs, 2003; Petry, 2001a; Reynolds, 2006) behaviors. There are several studies, however, indicating no relationship between delay discounting and risk-taking behaviors (Businelle, McVay, Kendzor, & Copeland, 2010; Fernie et al., 2010; Reynolds et al., 2003; Sweitzer et al., 2008). Devil’s Task/Knife Switches. Little evidence exists of the construct validity of the task, but risk-taking on the measure is associated with real-world risk-taking behaviors (Hoffrage, Weber, Hertwig, & Chase, 2003; Montgomery & Landers, 1974; Slovic, 1966). Dohmen Measure. Responses to the one-item Dohmen measure are correlated with sensation seeking (Brailovskaia et al., 2018) and can predict performance on a lottery task (Dohmen et al., 2011) and involvement in risky behaviors (Dohmen et al., 2011; Szrek et al., 2012). DOSPERT Scale. Risk-taking on the DOSPERT is associated with increased impulsivity (Lozano et al., 2017) and sensation seeking (Brailovskaia et al., 2018; Lozano et al., 2017; Weber et al., 2002). In addition, scores are correlated with the BART (Courtney et al., 2012; but see Brailovskaia et al., 2018) and a delay discounting task (Courtney et al., 2012; Mishra & Lalumiere, 2017). The DOSPERT is a better predictor of real-world risk-taking behaviors than the BART across several studies (Brailovskaia et al., 2018; Szrek et al., 2012). DOSPERT scores are correlated with real-world problem gambling (Mishra et al., 2017), and dishonesty (Zimerman, Shalvi, & Bereby-Meyer, 2014). Evaluation of Risk. Significant correlations are seen with impulsivity (Sicard, Taillemite, Jouve, & Blin, 2003) but not sensation seeking (Killgore, Grugle, Killgore, & Balkin, 2010; Killgore, Kelley, & Balkin, 2010).

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Total scores on the Evaluation of Risk scale are correlated with real-world alcohol use, aggressive/angry behavior, and recent risk-taking behaviors (Killgore, Castro, et al., 2010; Killgore, Vo, et al., 2006). Everyday Risk Inventory. Scores on the measure are correlated with sensation seeking (Steketee & Frost, 1994), but no other evidence was found for construct or ecological validity. Framing Spinner. Limited evidence exists of the construct or ecological validity of this task, other than a lack of correlation between risk-taking on the task and sensation seeking (Eckel & Grossman, 2002). In addition, risk-taking on the framing spinner does predict real-world risktaking (Reyna et al., 2011), but additional research is needed to fully assess its ecological validity. GDT. Of the four studies assessing correlations between the GDT and other risky decision making tasks, only one finds a correlation with the later IGT trials (Brand, Recknor, et al., 2007). The others find no relationship between the GDT and the BART, CCT, or IGT (Buelow & Barnhart, 2018b; Cogo et al., 2014; Pletzer & Ortner, 2016). Ecological validity is not often examined with the GDT, and the few existing studies show it is not sensitive to eating behaviors (Van den Eynde et al., 2012; Wu et al., 2013) or alexithymia (Zhang et al., 2017) but does correlate with real-world gambling behaviors (Pletzer & Ortner, 2016). General Risk Propensity Scale. Responses on the measure are correlated with extraversion, risk-tolerance, and risk-taking behaviors as reported with the DOSPERT, in addition to predicting future risk-taking behaviors (Zhang & Gu, 2018). General Decision Making Style. Decision making styles are correlated with impulsivity and sensation seeking (Avsec, 2012; Baiocco et al., 2009) but not social desirability (Loo, 2000). In addition, scores are correlated with most of the ADMC subscales (Bavolar & Orosova, 2015). HDT. Very little evidence exists regarding the psychometric properties of the HDT. As the task is modeled after the IGT, there seems to be a baseline assumption that the IGT validity studies “hold” for the simplified HDT. No correlations are seen between the HDT and performance on delay discounting tasks (Groppe & Elsner, 2017; WintherSkogli, Egeland, Andersen, Hovik, & Oie, 2014; but see Groppe & Elsner, 2014), but the pattern of adult performance on the task itself mimics the patterns seen among adults taking the standard IGT (Crone & van der Molen, 2004). Few studies examine ecological validity of the HDT, with only minimal evidence it predicts future difficulties restarting eating behaviors (Groppe & Elsner, 2015; but see Groppe & Elsner, 2014). IGT. Of the tasks described in this book, the IGT has arguably the largest body of research examining its validity. Numerous personality traits and other individual-differences characteristics are examined in

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correlation with task performance; however, I will limit discussion here to impulsivity, sensation seeking, and behavioral activation/inhibition to remain consistent with the data on other measures. In addition, the clinical manual (Bechara, 2007) devotes a chapter to validity of the measure. As is the case with several other instruments, mixed relationships are seen between personality and task performance. Although many find those higher in sensation seeking, impulsivity, and/or behavioral activation make riskier decisions on the task (e.g., Buelow & Suhr, 2013; Burdick, Roy, & Raver, 2013; Crone, Vendel, & van der Molen, 2003; Davis, Patte, Tweed, & Curtis, 2007; Franken & Muris, 2005; Franken, van Strien, Nijs, & Muris, 2008; Suhr & Tsanadis, 2007; Upton et al., 2011; Van Honk, Hermans, Putman, Montagne, & Schulter, 2002; Xu et al., 2013), others fail to find such correlations (e.g., Hammers & Suhr, 2010; Reynolds, Basso, Miller, Whiteside, & Combs, 2019; Young, Gudjonsson, Goodwin, Perkins, & Morris, 2013; Zermatten, van der Linden, d’Acremont, Jermann, & Bechara, 2005). Similar mixed relationships are seen between IGT and other risky decision making task performances. Scores on the IGT are correlated with scores on the BART (Skeel et al., 2007; Upton, Bishara, Ahn, & Stout, 2011; Xu et al., 2013; but see Aklin et al., 2005; Buelow & Blaine, 2015; Lejuez, Aklin, Jones, et al., 2003), a delay discounting task (Xu et al., 2013), and the GDT (Brand, Franke-Sievert, Jacoby, Markowitsch, & Tuschen-Caffier, 2007; but see Starcke, Tushen-Caffier, Markowitsch, & Brand, 2009), but not the CCT-hot (Buelow & Blaine, 2015). Turning to relationships between task performance and risky realworld behaviors, most studies support the validity of the IGT. Correlations are seen between the IGT and concurrent and future risktaking behaviors (Bechara & Martin, 2004; Best, Williams, & Coccaro, 2002; Cavedini, Riboldi, Keller, D’Annucci, & Bellodi, 2002; Nejtek, Kaiser, Zhang, & Djokovic, 2013; Reynolds, Basso, Miller, Whiteside, & Combs, 2019; Ursache & Raver, 2015; Verdejo-Garcia et al., 2006); however, again some inconsistencies are seen (e.g., Adinoff et al., 2003; Businelle, Apperson, Kendzor, Terlecki, & Copeland, 2008; Field et al., 2006; Iudicello et al., 2013). This topic will be addressed in greater detail in the substance use disorders chapter, as the majority of early IGT studies examined substance dependence when not assessing those with focal neurological impairments. However, at least some mention should be made here of the literature examining potential validity concerns. First, there are at least two frequently utilized versions of the IGT (with additional variations appearing across some studies). The first is the original IGT (Bechara et al., 1994) and the second is the clinical version of the IGT (Bechara, 2007), which is based on revisions made to the task in the early 2000s. Although some evidence suggests no difference in scores or predictive Risky Decision Making in Psychological Disorders

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utility between these versions (e.g., Bechara, Tranel, Damasio, & Damasio, 2000b), others find differences in deck selections across versions (e.g., Buelow & Barnhart, 2018a). A second validity concern deals with the behavior of healthy control participants on the task. A number of researchers find a preference for Deck B, a disadvantageous deck, among individuals with no known psychological or neurological disorder (for reviews, see Chiu, Huang, Duann, & Lin, 2018; Steingroever, Wetzels, Horstmann, Neumann, & Wagenmakers, 2013). Other participants require additional trials (over 100 or 200 trials) to learn to decide advantageously (Lin et al., 2013). If there is such variability in performance among individuals who should be able to learn the risk and benefit ratios on this task, then the validity of the task among those with diagnosable disorders is called into question. Of course, scores on this and other measures of risky decision making should be interpreted in the context of other cognitive test results to more fully understand the individual’s current decision making difficulties. Mirror Drawing Risk-Taking Task. Those better able to delay gratification on a delay discounting task are less risky on this task (Kreitler & Zigler, 1990). Multi-Outcome Risky Decision Task. No correlation is seen between task performance and impulsivity, nor between task performance and real-world gambling behaviors (Bonniot-Cabanac & Cabanac, 2009). Passive Risk-Taking. Performance on the passive risk-taking scale is correlated with impulsivity but not sensation seeking (Keinan & Bereby-Meyer, 2012). Lending credence to the validity of the task, active risk-taking is rated as riskier than passive risk-taking (Keinan & Bereby-Meyer, 2017). In addition, responses are correlated with both the DOSPERT and real-world passive risky behaviors (Keinan & Bereby-Meyer, 2012). Risk Propensity. Performance on the risk propensity measure is correlated with sensation seeking, the everyday risk inventory (Meertens & Lion, 2008), and the DOSPERT (Horcajo et al., 2014; Koerner et al., 2017). However, no correlation is seen with a risky gambles task (Meertens & Lion, 2008). Risk Propensity Task. The measure is correlated with a betting dice task, self-reported risk-taking behavior, and sensation seeking (Aquado et al., 2011). Stimulating-Instrumental Risk Inventory. Both stimulating risk-taking and instrumental risk-taking are correlated with arousal-seeking, impulsivity, and sensation seeking (Zaleskiewicz, 2001). Scores on the stimulating risk-taking scale are correlated with all DOSPERT subscales, whereas scores on the instrumental risk-taking scale are only correlated with DOSPERT financial and social risk-taking behaviors (Zaleskiewicz, 2001). In addition, scores on the measure predicted gambling behaviors on the DOSPERT (Markiewicz & Weber, 2013). Risky Decision Making in Psychological Disorders

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Stoplight Task. Increased impulsivity is associated with riskier choices (i.e., greater decisions to go rather than stop) on the Stoplight task (Reilly et al., 2006; Steinberg et al., 2008). Riskier decisions are associated with greater real-world substance use in adolescents but not adults (Kim-Spoon et al., 2016). Two-Outcome Risky Decision Task. Performance on the two-outcome task is correlated with performance on the multioutcome risky decision task (Lauriola, Levin, & Hart, 2007). Task performance is also correlated with hypothetical real-world risk scenarios, but not with financial risktaking on the DOSPERT (Lauriola et al., 2007). Wheel of Fortune. Correlations are seen between risk-taking on the Wheel of Fortune and behavioral activation (reward dependence; Rao et al., 2011). Riskier decisions on the task are also associated with realworld substance use difficulties (Rao et al., 2011), gambling problems (Ulrich et al., 2016), and risk-taking behaviors more generally (Roy et al., 2011). In addition, individuals who are more tolerant of ambiguity on a variant of the task in which part of the wheel is obscured in turn reported greater risk-taking behaviors (Blankenstein et al., 2016).

Validity conclusion Across tasks, there does appear to be evidence in favor of the validity of behavioral tasks to assess the construct of risky decision making. Tasks correlate with measures of impulsivity and sensation seeking, but each appears to capture unique variance in risk-taking behavior (i.e., convergent and divergent validity). Relationships between various risky decision making tasks are quite varied, which may be due at least in part to these measures tapping into different components of the risky decision making construct. What is concerning, however, are inconsistencies in whether task performance is associated with concurrent or future risk-taking behaviors.

Factors affecting reliability and validity External factors can have an influence on reliability of test instruments, which could, in turn, negatively affect assessment of task validity. Situational factors (e.g., level of instruction given prior to the start of the task, environmental conditions during testing, and the level of fatigue/hunger/motivation) could account for some between-study differences in reliability and validity as well. As will be seen in upcoming chapters, state-dependent stress and negative mood, independent of anxiety and mood disorder diagnoses, can affect task performance in

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either a positive or negative direction. Variations in how the task is presented (e.g., number of trials, win/loss ratios, reward/loss magnitude, presence/absence of monetary pay-outs) and sample sizes could also lead to between-study variations in conclusions and are likely at least partially responsible for the mixed findings seen in the remaining chapters.

Conclusion Collapsing across studies of both reliability and validity, a lot of work still needs to be done. Multiple tests are lacking in adequate assessment of their test-retest reliability and the possibility of practice effects, a concern for clinicians who are interested in how decision making difficulties change over time or in response to treatment/intervention. Are all of these measures assessing different things, such that there are no intermethod approaches to deal with practice effects? Are measures empirically validated if there is no reliability data? Validity data? Although only one task is available as a clinical instrument (with normative data) at the time of writing (IGT), multiple tasks could be utilized to help inform diagnosis and treatment. For this to happen, however, adequate reliability and validity data is needed. Finally, there are not consistent, significant correlations between performance on various behavioral tasks, leaving one to wonder what exactly each task measures.

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C H A P T E R

4 Neuroscience and associations with other executive functions There is a general assumption that decision making is an executive function, and as such, that behavioral decision making tasks measure a type of executive function (e.g., Clark, Iverson, & Goodwin, 2001a, 2001b; Roca, Torralva, Lopez, et al., 2008; Roca, Torralva, Meli, et al., 2008; Torralva et al., 2009; Verdejo-Garcia et al., 2009). As should be evident from the preceding chapters, decision making is a complex construct that encompasses both hot (or System I/Type I) and cold (or System II/Type II) components. Even within these two overarching categories, there are multiple components that comprise the decision making process. To the extent that these components rely on or tap into other higher order cognitive processes or recruit other systems (such as attention and memory), decision making can be considered an executive function. But what are executive functions? How do we know they are linked to functioning of the brain’s frontal lobe? And just what is the relationship of decision making to the other executive functions? To explore these topics further the rest of this chapter will discuss (1) the concept of executive functions and common assessment instruments, (2) disorders known to affect executive functions and the frontal lobe more generally, (3) relationships (or lack thereof) between measures of executive functions and risky decision making, and (4) the neuroscience of executive functions in general and of decision making more specifically.

Executive functions: theories and constructs If you were to ask 10 different neuropsychologists to describe executive functions, you would likely receive 10 different responses.

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Although we agree on what executive functions are and how we can assess them, the precise definition of the construct can vary. For example, here is one definition from Lezak, Howieson, and Loring (2004): As the most complex of behaviors, executive functions are intrinsic to the ability to respond in an adaptive manner to novel situations and are also the basis of many cognitive, emotional, and social skills. . . .The executive functions can be conceptualized as having four components: 1) volition, 2) planning, 3) purposive action, and 4) effective performance (p. 611).

Describing executive functions as complex is common to the varying definitions that exist. Other definitions of executive functions describe them as the “executive” or “supervisor” of the other cognitive functions (e.g., Eslinger & Chakara, 2004), able to change, adapt, or modulate the processes of other cognitive functions such as attention, memory, and visuospatial perception (e.g., Purves et al., 2013). At their most basic, executive functions can be described as the cognitive processes that allow for goals to be met (Anderson, Jacobs, & Anderson, 2008; Fuster, 2008). Executive functions aid in the identification of a goal, the assessment of the required resources to achieve that goal, the initiation of the plan to achieve the goal, and the monitoring of steps toward that goal (with the potential for adaptions along the way). Some refer to this system as a supervisory attention system, as it first creates the plan (selecting from a variety of different available responses) and then adapts the steps as necessary along the way (Eslinger & Chakara, 2004; Shallice, 1988). What types of cognitive abilities are considered in the realm of executive functions? The simple answer: it varies. Across sources (e.g., Eslinger & Chakara, 2004; Lezak et al., 2004; Purves et al., 2013; Strauss, Sherman, & Spreen, 2006), the most common executive functions include volition, self-regulation, purposive action, effective performance, initiation, planning, organization, inhibition, shifting-set, working memory, cognitive flexibility, idea generation, abstract reasoning, sustained attention, nonverbal reasoning, formation of concepts, categorization, and the effective use of feedback (described in more detail in an upcoming section). With all of these subcomponents, it is no wonder how difficult it is to narrow down executive functions to a unified definition. An additional factor influencing this difficulty is whether executive functions represent one unified construct (Grafman, 2002) or a set of individual but related constructs (Miyake et al., 2000). Miyake et al. (2000) presented one of the most-cited representations of executive functions to date. They conducted a confirmatory factor analysis on three executive functions, using the following tasks (1) inhibition: antisaccade task, stop-signal task, Stroop task (all three required participants to inhibit a naturally occurring response); (2) shifting:

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plus minus task, number letter task, local global task (all three involved shifting between different rules throughout the task); and (3) updating: keep track task, tone monitoring task, letter memory task (all three involved keeping track of all presented items and then reporting information about just one of the items). The results of the confirmatory factor analysis indicated support for a three-factor model, with inhibition, shifting (or cognitive flexibility), and updating as separate but related factors and no support for a one-factor model (in which these three were all part of a unitary construct). Thus we tend to think of executive functions as related or correlated constructs that collectively are used to identify a goal, create a plan to achieve that goal, implement the plan, monitor movement toward the goal, and adjust accordingly (e.g., Cummings & Miller, 2007).

Assessing executive functions How do we assess executive functions? This question is difficult to answer with just one task, as we have seen how broad the category of executive functions can be. A common mistake is to administer one measure that taps into one executive function and, if performance falls below expectation, indicate executive functions are impaired. Just as with assessment of memory, attention, visuospatial perception, language, and motor/praxis, assessment of executive functions relies on the administration of multiple measures of the construct to more fully understand the individual’s pattern of strengths and weaknesses. In this section, I will first provide greater detail about some of the more commonly assessed executive functions before then describing tasks used to assess that particular construct. For more detail about specific executive functions and common measurement methods, I refer the reader to these sources: Lezak et al. (2004), Kreutzer, DeLuca, and Caplan (2011), Cummings and Miller (2007), and Spreen et al. (2006) (see Table 4.1). Planning and organization. If the first step toward attaining a goal is to determine what that goal is, then the next step is to begin putting together a plan to achieve that goal. An individual must identify the subgoals that are needed in order to achieve the overarching goal, create an order for these subgoals to be attained, and obtain the necessary information regarding previous experiences and currently available resources to carry out the plan. Or, in other words, the individual has to come up with an organized plan listing the steps needed to carry out a path toward the goal. Planning and organization are often assessed with the use of a figure copy, clock drawing, or tower task. In a clock drawing task (Strauss et al., 2006), participants are typically given a blank piece of

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TABLE 4.1 Summary of executive functions and frontal lobe structures. Causes real-world impairments in

Causes impairments on measures of

Lateral prefrontal cortex (including dorsolateral and ventrolateral prefrontal cortices)

• Shifting-sets (including perseveration) • Organization • Planning (including impulsiveness) • Monitoring and regulation

• • • • •

Medial prefrontal cortex (including ventromedial prefrontal cortex and orbitofrontal cortex)

• Social judgment and empathy • Abrupt changes in personality • Goal-directed behaviors • Disinhibition (including inappropriate effect and behaviors) • Difficulties learning rewarding and punishing values of a stimulus

• Decision making • Self-regulation and learning from feedback • Inhibition • Emotion-based learning

Anterior cingulate cortex

• Processing emotions • Monitoring and cognitive control • Motivated behaviors

• Emotion-based learning • Decision making • Avoiding errors

Damage to

Working memory Attention and vigilance Planning/organization Cognitive flexibility Set-shifting and inhibitory control

paper and asked to draw a clock, put all the numbers on it, and place the hands to reflect a particular time given by the clinician. Poorly planned and organized clock drawings might be reflected in clocks that are too small for all the numbers to fit inside or clocks with numbers written so largely the individual runs out of room for them. Arguably the most used figure copy task is the Rey Osterrieth complex figure (Strauss et al., 2006). On this task, participants are given a depiction of a complex figure and asked to draw it as accurately as possible. Later, trials assess memory for the figure itself, but the individual’s approach to the initial figure copy can indicate difficulties with planning and organization. Tower tasks (e.g., Shallice, 1982), which can vary according to the specific rules for moving pieces, task participants with changing the locations of a set of beads (or disks) from their starting

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locations to the target locations in as few a number of moves as possible. Some of the restrictions on these moves involve only moving one piece at a time and only placing smaller pieces on top of larger pieces. To minimize the number of moves on each trial, participants need to plan ahead to where a particular piece will need to go in order to match the final design. Planning difficulties can lead to a greater number of moves on these tasks. Initiation. After a goal is identified and a plan is created, the individual needs to initiate, or begin, the action plan. The already-organized plan needs to be initiated while other competing actions, or habits, need to be suppressed. Some individuals can, however, experience difficulties with initiation. Most commonly, this initiation difficulty appears in the context of apathy or other motivational struggles. Apathy, which is related to but distinct from depression, refers to difficulties with initiation and maintenance of behaviors. Apathy can occur due to different psychological and neurological disorders (such as Parkinson’s disease), as well as insults to the anterior cingulate cortex and surrounding structures (e.g., Le Heron, Apps, & Husain, 2018). Apathy can be assessed via self-report instruments, but this may lead to an underestimate of the current level of difficulties. Some measures, such as the Frontal Systems Behavior Scale (FrSBe; Grace & Malloy, 2001), include both a self-report and a familyreport option so as to obtain a variety of views on the individual’s current level of apathy. Initiation difficulties might manifest as slowed initial responses to fluency tasks. Word fluency tasks typically involve an individual verbally coming up with as many items that match a given prompt within a short time limit. That prompt might entail a particular letter, a particular semantic category, or in versions that involve elements of cognitive flexibility, switching between two different categories. Individuals with initiation difficulties may be very slow to present their first word to a particular prompt or may exhaust their responses relatively early in the task (fail to maintain set). Self-regulation and learning from feedback. To achieve that overarching goal, individuals have to seek out and incorporate feedback along the way. If aspects of the environment change, the plan may need to be adjusted in some way (see shifting or cognitive flexibility section). These changes may also be necessary if the individual finds themself veering off-course along the way. Of course, in order to self-regulate, individuals must seek out feedback and then utilize it effectively. If the feedback is received but not interpreted, goal attainment can be negatively affected. To further elaborate on this process with examples of cognitive tests, let us first introduce shifting and cognitive flexibility as these abilities work together in the feedback response process. Shifting or cognitive flexibility. Plans frequently need to be updated, indicating the individual should have some level of flexibility in the

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path toward the goal. Shifting and cognitive flexibility refer to this process of adaptation and sometimes both are referred to as mental flexibility (Kreutzer et al., 2011). If the rules change, the cognitively-flexible individual is able to change their behavior. If the individual receives feedback that the goal will not be met, the cognitively-flexible individual is able to shift resources and adjust the plan—or even abandon the plan—as needed to improve the likelihood of successful goal attainment. Those who are unable to use feedback to adjust may become “stuck in set,” referred to as perseveration. Perseveration can be assessed through a simple motor task, such as by correctly repeating a series of alternating movements, or by a simple drawing task, such as by correctly copying a figure with alternating shapes. Individuals who have difficulty with shifting can perseverate on one of the motor movements or copied shapes, repeating this same item over and over again. Shifting, cognitive flexibility, and learning from feedback can also be assessed with more complex tasks. The previously described tower tasks, in addition to assessing planning and organization, can provide information about how the individual responds to feedback about their strategy use thus far. The Trail Making Test is used as both a measure of processing speed (Part A) and as a measure of inhibition and shifting (Part B). In Part A, participants draw a line connecting all of the numbers in order as quickly as possible. In Part B, participants instead alternate between numbers and letters in order. To the extent that a participant perseverates on numbers and ignores the letters in Part B, for example, impaired cognitive flexibility and response to feedback are shown. One of the more complex executive function measures, tapping into inhibitory control, cognitive flexibility, learning from feedback, and problem-solving skills, is the Wisconsin Card Sorting Task (WCST) (see Strauss et al., 2006, for more details). It is also the executive function task most frequently paired with behavioral risky decision making tasks across research studies. On the WCST, participants are told to match a series of cards to one of the four key cards. They are not given any information about how to match the cards, but instead, need to rely on feedback given by the examiner (“correct” or “incorrect”) to learn the rule. If the rules were to change, the participant would need to again use trial-and-error feedback to learn the new strategy. Impairments in shifting, cognitive flexibility, and learning from feedback could result in someone perseverating on just one sorting principle. Inhibition and inhibitory control. Inhibition is a necessary component to achieve a goal. Developing a plan involves creating a series of steps designed to meet that goal. Inherent to this process is that some steps (or behaviors, habits, etc.) will not be chosen for this plan. Those processes need to be inhibited (or turned off, ignored) in order for the plan

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to progress. For example, in the previously described Trail Making Test, the prototypical response of connecting 1 to 2 needs to be turned off in Part B in order to turn on the correct response of connecting 1 to A. The automatic impulse must be turned off in order to succeed. Two other tasks where we see the benefit of inhibitory control (or impairment when such control is lacking) are stop-signal and Stroop tasks. On a stop-signal task, participants may learn a response, such as clicking a button when a certain letter or object appears. After learning this response a new instruction is given. This new instruction could be to continue providing the learned response, unless a different signal (usually a sound) appears. If that new signal appears, the participant should inhibit the previously learned response. Other variations on this task use the terminology go/no-go or go/stop task (see Verbruggen & Logan, 2008, for more details on these designs). On the Stroop, participants usually complete a series of two or three trials in order to determine the relative effects of processing speed versus inhibitory control (see MacLeod, 1991, for a review of Stroop research). In one trial, participants may be asked to read a list of words as quickly as possible. In another trial, they may be instead asked to name the color of ink a series of symbols is printed in. On the final, incongruent trial, participants are asked to name the color of ink a word is printed in, ignoring the incongruent color-word that is printed there. For example, the correct response for the word “blue” printed in red ink would be “red.” Responses typically slow down, with increasing numbers of errors, due to difficulties inhibiting the prepotent response of reading the written information. In other versions of the Stroop task, this trial appears with both congruent (word and ink color match) and incongruent trials to tease apart the effect of inhibitory control without the first two trials. Fluency or response/idea generation. The ability to come up with different ideas or responses can lead to improvements in the planning process, as the individual will have more options to choose from in determining the best path to a goal. The ability to come up with different responses or ideas is an executive function, although it also relies heavily on language and processing speed components. In addition, individuals must be able to retrieve previously learned information from memory storage. In clinical practice, fluency is typically assessed with verbal or figural fluency tasks (Strauss et al., 2006). On a verbal fluency task, patients may be asked to come up with as many words that start with a particular letter of the alphabet (phonemic) or belong to a particular category (semantic). On a figural fluency task such as the Ruff Figural Fluency Test (Ruff, 1996), patients instead draw as many figures as possible within a given dot configuration. Difficulties with fluency or response generation would result in fewer generated items on each task.

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Abstract reasoning. Inherent in the goal-setting and attainment process is the ability to think abstractly instead of concretely, looking for different solutions to a problem. Individuals who can come up with new, novel steps to achieve a goal may have more options to try when a previously developed plan needs to be amended. Abstract reasoning may require the individual to forgo concrete evidence in favor of identifying patterns across theoretical ideas (Kreutzer et al., 2011). Assessing abstract reasoning can occur with tasks such as Similarities and Matrix Reasoning from the Wechsler scales (Wechsler, 2008) or Proverbs from the D-KEFS (Delis et al., 2001). In Similarities, patients are given two words and asked how they are alike. Abstract reasons are given higher scores than concrete reasons. In Matrix Reasoning, patients see an image with a part missing and must deduce the correct missing item from the response options. In Proverbs, patients are asked to interpret a proverb, with more abstract interpretations scored higher than more concrete interpretations. Problem-solving. Problem-solving can honestly refer to the entire goalsetting process. The goal could be seen as a problem that needs to be solved, with the other previously described executive functions a part of the problem-solving process. Different potential solutions are generated and then either retained or discarded in favor of a more applicable solution. Problem-solving, or difficulties wherein, can be seen on measures such as the WCST and tower tasks. In addition, the category test (e.g., DeFilippis & McCampbell, 1997) assesses problem-solving, concept formation, and response to feedback. On this task, participants see a series of four images and a set of four numbers. They are asked to determine the number that best represents the concept depicted by the four images. Feedback is presented after each trial so that the participant can adjust their conceptualization of the task on subsequent trials. Concept formation. To the extent that individuals develop a hypothesis to explain a series of events or the occurrence of a single event, the individuals are engaging in concept formation. It can rely on the activation of previously learned information, in order to determine if it is relevant to understanding a novel situation. Individuals may need to rely on other executive and related cognitive functions to engage in efficient concept formation, including selective and sustained attention and setshifting. The category test, as previously described, assesses concept formation in addition to problem-solving. Processing speed. Although not an executive function per se, processing speed interacts with a number of executive functions and other cognitive abilities to affect successful task completion and goal attainment. Processing speed is an index of how efficient an individual is, with the particular domain dependent on the task. For example, a verbal fluency task might assess elements of cognitive processing speed.

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Trail Making Test Part A might assess elements of psychomotor processing speed. A task such as Symbol Digit Modalities (Smith, 1982) can also tap into psychomotor processing speed, as it requires participants to recode a series of numbers into their corresponding symbols. Slowed processing speed can be due in part to not only frontal lobe impairments but also to subcortical and other structure involvement. Hence, processing speed is related to but not entirely an executive function. Sustained attention or vigilance. Just as with processing speed, sustained attention and vigilance can affect executive function task performance. Tasks with multiple trials, or that take place over a lengthy period of time, required continued attention (or vigilance) to the task. Sustained attention and vigilance are vital to successful completion of tasks with changing demands, stimuli, or feedback. These abilities are typically assessed with a task such as the continuous performance test (CPT), but the effects of sustained attention difficulties can be seen on Trail Making, the WCST, and various feedback-based decision making tasks. Working memory. Working memory is often used interchangeably with short-term memory, yet to a neuropsychologist, working memory requires an additional component of manipulating the information that short-term memory does not require. Our understanding of working memory is largely dependent on Baddeley’s (1986) model. Working memory is divided into three systems that collectively allow us to hold onto bits of information in temporary storage to work with them in some way. Two of these systems, the visuospatial sketchpad and phonological loop, are responsible for temporarily storing recent verbal and visual information. The third system, and most relevant to the neuroscience of decision making, is the central executive. The central executive dictates where attentional resources should go, such as to keeping track of feedback during a sequential risky decision making task. Working memory is most often assessed with digit span and letter span tasks.

Impairments in executive functions We have seen what different executive functions exist and described some of the more common tasks that can assess these cognitive abilities. But how do impaired executive functions manifest in real-world settings, as well as in the lab or clinical evaluation? Although not an exhaustive list (see Strauss et al., 2006, for more information), impairments in one or more executive functions could result in • poor initiation • poor motivation

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poor planning and organization poor ability to inhibit poor problem-solving skills diminished judgment diminished ability to shift between tasks difficulties with inhibition (e.g., verbal or physical outbursts) poor working memory perseveration difficulties adapting to feedback carelessness impulsiveness poor decision making increased risk-taking behavior

What affects executive functions and the frontal lobes with which they are associated? Multiple neurological and psychiatric disorders can lead to impaired executive functions. Some of the most common neurological causes of diminished executive functions are traumatic brain injuries, infections (e.g., herpes simplex encephalitis), hydrocephalus and resultant mass effect, tumors, multiple sclerosis, Parkinson’s disease, vascular disease (including ischemic and hemorrhagic strokes and ruptured aneurysms affecting the frontal lobe), and degenerative disorders such as the dementias (most notably: frontotemporal dementia, primary progressive aphasia, and the frontal variant of Alzheimer’s disease). In terms of psychiatric disorders, the most common diagnoses associated with frontal/executive impairments are bipolar disorder, major depressive disorder, obsessive compulsive disorder, schizophrenia, and attention-deficit/hyperactivity disorder.

Is decision making an executive function? As stated in the beginning of this chapter, there is a strong assumption that decision making is an executive function. Now that we have taken a tour of the different types of executive functions, let us revisit that assumption. A decision involves a choice between two or more options. Risky decisions involve some element of the unknown (ambiguity), in which potential consequences of a decision may be unclear during the decision making process. Some risky decision making tasks involve sequential decisions, in which participants need to utilize feedback from previous trials to learn how to adapt their decision making style to be more advantageous over continued trials. As part of this adaptation, they might need to inhibit an impulse to, for example, go for the higher immediate reward in favor of the smaller immediate but

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Correlations between measures of decision making and executive functions

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larger delayed reward. If the rules of the decision making task are vague, individuals will need to be flexible, try out multiple theories as to what outcomes different decisions lead to, and pay attention to any feedback. Relying on the ability to access previously stored information can also benefit decision making. All in all, it sounds like decision making must rely on a number of different executive functions to lead to an optimal decision making strategy. Let us now look at what the data indicates: are executive function and decision making task performances correlated?

Correlations between measures of decision making and executive functions Before turning to decision making correlations, let us briefly examine whether the different executive functions are correlated with one another. As Miyake et al. (2000) indicated, executive functions are a set of related but distinct cognitive abilities. As such, we should find that tasks assessing the exact same construct (e.g., fluency) should be more correlated than tasks assessing different constructs (e.g., fluency versus problem-solving). However, the data are a bit more complex than that. Many of the executive function measures, as we saw briefly in the previous sections, tap into not just one executive function but multiple. Since the literature on correlations between executive function measures is extensive, I will provide a few representative results here. Verbal fluency tasks are often highly correlated with one another; however, low correlations are seen between fluency tasks across different domains (figural, phonemic, semantic; Demakis & Harrison, 1997; Hanks et al., 1996; Troyer, 2000). Performance on the Stroop is correlated with performance on measures of attention, vigilance, and working memory (including the Trail Making Test and CPT; Strauss et al., 2006). Trail Making is also correlated with working memory (Paced Auditory Serial Addition Test) and processing speed (Symbol Digit Modalities), as well as with the WCST (O’Donnell et al., 1994). Despite correlations between the WCST and a number of other executive function measures, this task does not correlate with measures sensitive to feedback processing and problem-solving (Booklet Category Test; Golden et al., 1997; Perrine, 1993). In addition, the WCST loads on a separate factor across different exploratory and confirmatory factor analyses (Boone, Ponton, Gorsuch, Gonzalez, & Miller, 1998; Fisk & Sharp, 2004; Miyake et al., 2000; Paolo et al., 1995), indicating the likely influence of both domain-specific (problem-solving, learning from feedback) and domain-general (executive function, working memory) processes on this and other tasks.

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Finally, it is also possible we might not see strong correlations between measures of risky decision making and measures of other executive functions due to the previously described hot/cold (Type I/Type II) distinction. Per these theories, logic, planning, organization, reasoning, and rationalization are considered “cold” processes (Zelazo & Muller, 2011), whereas affective and motivational processes, such as those that can be invoked during risky decision making, are considered “hot” processes (Ardila, 2008; Brock, Rimm-Kaufman, Nathanson, & Grimm, 2009; Chan, Shum, Toulopoulou, & Chen, 2008; Seguin, Arseneault, & Tremblay, 2007). To the extent that a hot task taps into cold processes, the decision making and other executive function tasks should show some relationship to each other. If the hot task does not rely on these cold processes, little relationship is anticipated across measures. What do these correlations with decision making tasks look like? Table 4.2 contains a summary of studies assessing the correlation between a decision making task and one or more other executive function tasks. I will also devote a section in each of the upcoming chapters to evidence for or against correlations between these tasks as a function of the specific category of psychological disorders. I should also note that for a number of tasks, no information could be found regarding relationships to other executive tasks. On the Adult Decision Making Competence (ADMC), correlations are found with measures of inhibition (but not the Stroop) and working memory, as well as with processing speed and general intelligence. The Balloon Analogue Risk Task (BART), in general, shows few correlations with executive function tasks (Clay et al., 2018; Martini et al., 2018), including on factor analyses (Barnhart & Buelow, 2017a; Duckworth et al., 2011). Despite the overall lack of relationships with other executive functions, some correlations did emerge with the WCST and working memory tasks. In general, performance on the Columbia Card Task (CCT) is associated with measures of cognitive flexibility, inhibitory control, working memory, and planning. Delay-discounting tasks are associated with inhibition, planning, and overall intelligence, but not with attention/vigilance and some probability-discounting tasks. The results for the GDT are quite mixed, with support for and against relationships with cognitive flexibility, planning, and overall intelligence. The vast majority of the literature focuses on relationships between the Iowa Gambling Task (IGT) and measures of executive functions. According to the IGT manual (Bechara, 2007), decision making on the task is associated with performance on measures of category- and setshifting and cognitive flexibility. Despite both decision making and executive functions being linked to frontal lobe functioning (see next section), individuals with damage to the frontal lobe can experience

Risky Decision Making in Psychological Disorders

TABLE 4.2 Summary of relationships between decision making tasks and other executive measures. Decision making task

Cognitive domain

Cognitive measure

Results

Authors

ADMC

Inhibitory control

Stroop

No correlation

Del Missier et al. (2012) Mantyla et al. (2012)

Inhibition

Stop signal

Correlation

Del Missier et al. (2012)

Processing speed

Digit symbol substitution

Correlation

Del Missier et al. (2017)

Working memory

Digit span

Correlation

Bangma et al. (2017)

N-Back

Correlation

Del Missier et al. (2012) Del Missier et al. (2017) Del Missier et al. (2013)

Overall (general) intelligence

Correlation

Bruine de Bruin et al. (2007) Del Missier et al. (2012) Weller et al. (2015)

B1ART

Inhibitory control

Stroop

No correlation

Barnhart and Buelow (2017a) Romer et al. (2016)

Inhibition

Go/no-go

Correlation

Duell et al. (2016)

No correlation

Derefinko et al. (2014) Fernie et al. (2010) Telzer et al. (2013) (Continued)

TABLE 4.2 (Continued) Decision making task

Cognitive domain

Cognitive measure

Results

Authors

Stop signal

No correlation

Courtney et al. (2012) Dougherty et al. (2015) Fernie et al. (2010) Gonzalez et al. (2012) Sohn et al. (2014)

Planning, shifting, abstract reasoning, perseveration

Wisconsin Card Sorting Test

Correlation

Campbell, Samartgis, and Crowe (2013) Duell et al. (2016)

Tower of London

No correlation

Johnson, Tharp, et al. (2016).

Processing speed

Simple Reaction Time task

Correlation

Henninger et al. (2010)

Working memory

Auditory Consonant Trigrams

No correlation

Bogg et al. (2012)

Digit span

No correlation

Romer et al. (2016)

Corsi blocks

Correlation

Romer et al. (2016)

2-Back

Correlation

Romer et al. (2016)

Correlation

Duell et al. (2016)

Overall (general) intelligence

MacKillop et al. (2014) No correlation

Bishara et al. (2009) Gonzalez et al. (2012)

CCT

Cognitive flexibility

Trail Making Test-B

Correlation

Huang et al. (2015)

Inhibitory control

Stroop

Correlation

Huang et al. (2015)

Inhibition

Go/no-go

No correlation

Lee et al. (2018)

Planning, shifting, abstract reasoning, perseveration

Wisconsin Card Sorting Test

Correlation

Buelow (2015)

Working memory

Digit span backward

Correlation

Buelow (2015) Figner et al. (2009)

Delay-discounting task

Attention, vigilance

Continuous performance test

No correlation

McCarthy et al. (2016)

Inhibition

Go/no-go

Correlation

Olson et al. (2007)

Probability discounting

Probability-discounting task

Correlation

Holt et al. (2003) Richards et al. (1999)

No correlation

Ohmura et al. (2005) Olson et al. (2007)

Planning, shifting, abstract reasoning, perseveration

Card Sort Task

Correlation

Antonini, Becker, Tamm, and Epstein (2015)

Visuospatial short-term memory

Spatial span

Correlation

Antonini et al. (2015)

Correlation

Olson et al. (2007)

Overall (general) intelligence GDT

Attention, working memory

Digit span

No correlation

Radomski et al. (2015)

Cognitive flexibility

Trail Making Test-B

Correlation

Schiebener et al. (2011)

No correlation

Fond et al. (2013)

No correlation

Wu et al. (2013)

Inhibition

Stop signal

(Continued)

TABLE 4.2 (Continued) Decision making task

Cognitive domain

Cognitive measure

Results

Authors

Planning

Tower of Hanoi

Correlation

Brand, Roth-Bauer, Driessen, and Markowski (2008)

No correlation

Radomski et al. (2015)

Correlation

Brand, Kalbe, Kracht, et al. (2004)

Planning, shifting, abstract reasoning, perseveration

Wisconsin Card Sorting Task

Brand, Labudda, et al. (2004b) Brand, Fujiwara, et al. (2005) Brand, Kalbe, et al. (2005) Brand et al. (2008) Brand et al. (2009) Brand et al. (2013) Brand et al. (2014) Ma et al. (2013) Schiebener et al. (2011) Schiebener et al. (2015)

Overall (general) intelligence

No correlation

Radomski et al. (2015)

No correlation

Cogo et al. (2014) Schiebener et al. (2015)

HDT

Cognitive flexibility

Trail Making Test-B

No correlation

Winther Skogli et al. (2014)

Inhibition

Stroop

No correlation

Groppe et al. (2014) Groppe and Elsner (2017) Winther Skogli et al. (2014)

Planning

Tower test

No correlation

Winther Skogli et al. (2014)

Shifting

Cognitive flexibility task

Correlation

Groppe et al. (2014) Groppe and Elsner (2017)

Verbal fluency

Letter fluency

No correlation

Winther Skogli et al. (2014)

Working memory

Digit span backward

Correlation

Groppe et al. (2014) Groppe and Elsner (2017)

IGT

Letter number sequencing

No correlation

Winther Skogli et al. (2014)

Attention

Index comprised brief test of attention, digit span, continuous performance test-2

Correlation

Gansler et al. (2011a)

Attention, working memory

Digit span

No correlation

Besnard et al. (2015) Escartin et al. (2012) Reynolds et al. (2018)

Cognitive flexibility

Trail Making Test-B

Correlation

Bonatti et al. (2008) Iudicello et al. (2013) Levine et al. (2005) (Continued)

TABLE 4.2 (Continued) Decision making task

Cognitive domain

Cognitive measure

Results

Authors

No correlation

Besnard et al. (2015) Fonseca et al. (2012) Zelazo et al. (2003)

Inhibitory control

Planning

Planning, shifting, abstract reasoning, perseveration

Stroop

Correlation

Sigurdardottir et al. (2010)

No correlation

Besnard et al. (2015)

Planning test

Correlation

Bonatti et al. (2008)

Tower of London, Tower of Hanoi

No correlation

Brand et al. (2007b)

Wisconsin Card Sorting Test

No correlation

Bechara, Damasio, Tranel, and Anderson (1998) Bechara et al. (2001) Grant et al. (2000) Lee et al. (2007) Levine et al. (2005) Overman et al. (2004) Reynolds et al. (2018) Ritter et al. (2004) Rotherham-Fuller et al. (2004) Shurman et al. (2005)

Correlation

Brand, Recknor, et al. (2007) Escartin et al. (2012) Gansler et al. (2011a) Gansler et al. (2011b) Iudicello et al. (2013) Scheffer et al. (2011) Zelazo et al. (2003)

Berg Card Sort Task

No correlation

Antonini et al. (2015)

Modified Card Sort Task

No correlation

Besnard et al. (2015)

Processing speed

Trail Making Test-A

Correlation

Bonatti et al. (2008)

Processing Speed

Symbol Digit Modalities Test

Correlation

Levine et al. (2005)

Verbal fluency

Letter, semantic

Correlation

Escartin et al. (2012) Sigurdardottir et al. (2010)

Visuospatial short-term memory

Spatial span

No correlation

Antonini et al. (2015)

Working memory

Arithmetic

No correlation

Escartin et al. (2012)

Letter number sequencing

No correlation

Escartin et al. (2012)

Delay task

No correlation

Bechara et al. (1998)

Various tasks

No correlation

Bechara (2007)

Overall (general) intelligence

Brand, Recknor, et al. (2007) Correlation

Bar-On, Tranel, and Denburg (2003) Gansler et al. (2011a)

ADMC, Adult decision making competence; BART, Balloon Analog Risk Task; CCT, Columbia Card Task; GDT, Game of Dice Task; HDT, Hungry Donkey Task; IGT, Iowa Gambling Task.

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decision making deficits without impairments of tests of memory or other executive functions (Brazzelli, Colombo, Della Sala, & Spinnler, 1994; Eslinger & Damasio, 1985; Shallice & Burgess, 1991). However, examining the literature to date, correlations between the IGT and other executive function tasks vary widely, both within a particular measure and across different measures/domains of executive functions. For tasks such as Trail Making, the Stroop, and the WCST, significant evidence exists both in favor of relationships with the IGT and in favor of no relationships with the IGT. The same holds, but to a smaller degree, with the Hungry Donkey Task (children’s variant of the IGT). These significant variations in correlations across decision making and other executive functions tasks may reflect the difficulty in pinpointing precisely what each task measures and the cognitive abilities driving that behavior. Or, they may reflect differences in the frontal lobe structures thought to guide specific cognitive processes. If, for example, the dorsolateral prefrontal cortex is tied to problem-solving and monitoring, tasks tapping these cognitive abilities may not be correlated with affectively-based tasks that are associated with the ventromedial prefrontal cortex. In the next section, I will further discuss the neuroscience behind decision making in particular and executive functions more generally.

Neuroscience of decision making How do we know that decision making and executive functions are tied to the frontal lobe? It all began (figuratively) with Phineas Gage. As described in greater detail in Chapter 1, An introduction to risky decision making, Phineas Gage displayed significant changes in his personality and ability to interact with others following damage to portions of his orbitofrontal cortex. He failed to examine the long-term consequences of his behaviors, displaying an impulsive, rash decision making style. His changed behaviors overlap with patients such as E.V.R. (Eslinger & Damasio, 1985), who demonstrated real-world decision making deficits yet scored with the average or higher range on measures of frontal lobe functioning. The knowledge that real-world decision making impairments, a sort of “myopia for the future” (Bechara et al., 2002), are linked with frontal lobe functioning yet are not evident on standard tasks led to the development of the IGT and other tasks. These tasks allow us to understand the extent to which emotion is involved in various types of decisions, as well as the specific contributions of the prefrontal cortex and other linked structures. Executive functions are linked with frontal lobe functioning and the prefrontal cortex in particular (Stuss & Alexander, 2000; Stuss & Levine,

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Neuroscience of decision making

2002); however, there are differences in the structure function relationship across this large structure. As can be seen in Table 4.1, the lateral portions (including dorsolateral), the medial portions (including ventromedial), and the anterior cingulate cortex are associated with different cognitive abilities. The lateral prefrontal cortex (Fig. 4.1) is responsible for a number of the goal-directed activities that can take place, such as planning and organization. Damage here can lead to difficulties with flexibility, task completion, and attention (e.g., Manes et al., 2002). The medial prefrontal cortex (Fig. 4.2), as we saw in our discussion of Phineas Gage and Patient EVR, is responsible for goal-directed behaviors but with more of a social, personality, and reward-based component added in. The anterior cingulate is associated with not only cognitive processes that include error monitoring, but also processing emotions and motivations tied to a behavior (e.g., Blakemore, Rees, & Frith, 1998; Botvinick, Nystrom, Fissel, Carter, & Cohen, 1999; Frank, Woroch, & Curran, 2005; Kennerley, Walton, Behrens, Buckley, & Rushworth, 2006; Kerns et al., 2004). Some even differentiate between the contributions of dorsolateral and ventromedial prefrontal cortices to executive functions by noting that the dorsolateral region is associated with more calculated cognitive tasks (cold/System I/Type I) and the ventromedial region with more emotion-based and behavioral tasks (hot/System II/Type II) (Krain et al., 2006). In addition, and as previously discussed, individuals with damage to the ventromedial prefrontal cortex can display vast real-world decision making deficits yet score

Dorsolateral prefrontal cortex

Precentral Central sulcus gyrus Postcentral gyrus Parietal lobe Dorsal Anterior

Posterior Ventral

Occipital lobe Ventrolateral prefrontal cortex Temporal lobe Lateral sulcus

FIGURE 4.1 Brain lateral view.

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Dorsomedial prefrontal cortex

Anterior cingulate cortex Ventromedial prefrontal cortex

FIGURE 4.2 Brain medial view.

within normal limits on standard clinical measures (e.g., Bechara et al., 1994; Eslinger & Damasio, 1985), indicating a dissociation between some measures of executive functions depending on the location of structural damage. For example, damage to lateral regions could result in a dysexecutive syndrome whereas damage to medial regions a disinhibition syndrome (e.g., Grace & Malloy, 2001). Yet these structures do not operate independently of one another or of other cortical and subcortical structures. Multiple pathways connect portions of the prefrontal cortex with portions of the brain’s reward pathway, which can significantly influence decision making. Multiple studies of patients with damage to various frontal lobe and other structures pinpoint portions of the brain’s reward pathway as involved in risky decision making (Fig. 4.3). The brain’s reward pathway, comprised the dopaminergic mesocortical and mesolimbic pathways, ties together portions of the frontal lobe, limbic system, midbrain, and other areas to assign reward value to particular stimuli and in turn affect risk-taking behavior (Galvan, 2012; Glimcher, Camerer, Fehr, & Poldrack, 2009). The mesolimbic pathway links structures from the limbic system with structures from the midbrain and includes the ventral tegmental area of the midbrain, the amygdala, and the ventral striatum (including the nucleus accumbens). The mesocortical pathway instead links structures from the midbrain with cortical structures and includes the ventral tegmental area and prefrontal cortex (including ventromedial and dorsolateral portions). Involvement in risk-taking behavior is

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Striatum

Frontal cortex

Nucleus accumbens VTA

Hippocampus

Amygdala

FIGURE 4.3 Brain reward pathways.

correlated with activation of structures along this pathway (Jessor, 1991; Levy & Glimcher, 2011; Porcelli & Delgao, 2009a, 2009b; Reyna, 2012). In addition, engaging in risky decision making tasks also can activate this pathway (Knutson, Adams, Fong, & Hommer, 2001; Knutson et al., 2003; Sheffer et al., 2013). Structural research. Risky decision making is seen among individuals with damage to a number of frontal lobe structures. Most notably, those with damage to the orbitofrontal or ventromedial prefrontal cortex show riskier decision making across tasks (Anderson, Bechara, Damasio, Tranel, & Damasio, 1999; Bechara, 2003, 2004; Bechara & Damasio, 2002; Bechara, Damasio, & Damasio, 2000; Bechara, Tranel, Damasio, & Damasio, 2000b; Bechara et al., 1994, 1998; Bechara, Tranel, Damasio, & Damasio, 1996; Fellows, 2004; Fujiwara et al., 2008; Rogers, 1999b; Strenziok et al., 2011; Waters-Wood et al., 2012; Winstanley, Theobald, Cardinal, & Robbins, 2004; Xiao, Bechara, et al., 2013). Additional impairments are seen when the amygdala (Bechara, 2004; Bechara et al., 1999; Hanten et al., 2006; Weller, Levin, Shiv, & Bechara, 2007), thalamus (Tranel, Bechara, & Damasio, 2000), or nucleus accumbens (Cardinal, Pennicott, Sugathapala, Robbins, & Everitt, 2001) are affected. Risky decision making is not seen when the temporal and occipital lobes are the only affected areas (Bechara, Damasio, et al., 2000), whereas the results are mixed with regard to the dorsolateral prefrontal cortex. While some

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studies suggest no decision making impairments (Bechara et al., 1998), others find impaired decision making occurs when the dorsolateral prefrontal cortex (predominately right hemisphere) is affected (Bechara, 2004). Still others find evidence that large frontal lesions, which include but are not limited to the ventromedial prefrontal cortex, are necessary to impair decision making (Al-Khindi, Macdonald, & Schweizer, 2014; Clark et al., 2003; Eslinger & Damasio, 1985; Fellows & Farah, 2005; Levine et al., 2005; Manes et al., 2002; Rahmen et al., 1999; Roca et al., 2010; Rogers et al., 1999a; Scheffer et al., 2011; Satish, Streufert, & Eslinger, 1999; Shallice & Burgess, 1991; Sigurdardottir et al., 2010). Functional research. The results of functional neuroimaging studies largely support these structural findings. Activation in the anterior cingulate cortex, ventromedial prefrontal cortex, portions of the striatum, insula, amygdala, and nucleus accumbens are associated with the decision making process (Table 4.3). In addition, the dorsolateral and dorsomedial prefrontal cortices are activated during risky decision making tasks, consistent with the previously stated theory that the ventromedial prefrontal cortex is sensitive but not specific to decision making impairments. In addition, multiple studies suggest that risky decision making is associated with activity in the prefrontal cortex more generally (Bailey & West, 2017; Cho et al., 2015; Galvan & Peris, 2014; Gansler et al., 2012; Gianotti et al., 2009; Shad, Bidesi, Chen, Ernst, et al., 2011; Shad, Bidesi, Chen, Thomas, et al., 2011; Sheffer et al., 2013; Van Leijenhorst et al., 2010), as well as with structures in the reward pathway (Bickel et al., 1999; Ernst & Paulus, 2005; Eshel et al., 2007; Smoski et al., 2009). Taken together, the results of structural and functional research point to involvement of the prefrontal cortex and portions of the reward pathway in risky decision making, with specificity of task performance to the ventromedial prefrontal cortex ambiguous. By task. The number of structural and functional studies varies significantly across decision making task. Only one study utilized neuroimaging during the ADMC. Completion of the task activated neural areas involved in language, memory, emotion, and executive functions (Talukdar et al., 2018). Processing of feedback after each hand of blackjack was correlated with activation in the medial frontal lobe (Bailey et al., 2017). Suboptimal decisions on the blackjack task were associated with increased activity in the anterior cingulate cortex (Hewig et al., 2008). During the CCT, activation changes were seen in the thalamus, anterior insula, and dorsomedial prefrontal cortex as the level of risk increased (van Duijvenvoorde et al., 2015). Among children, adolescents, and young adults, risky decisions on the Cake Gambling Task were associated with activation in the medial prefrontal cortex (Van Leijenhorst et al., 2010). Higher scores on the CARE were associated with lower activation in the right orbitofrontal cortex and ventromedial

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TABLE 4.3

Functional neuroimaging studies of risky decision making tasks.

Structure

Authors

Anterior cingulate cortex

Bjork et al. (2008) Eshel, Nelson, Blair, Pine, and Ernst (2007) Fukui, Murai, Fukuyama, Hayashi, and Hanakawa (2005) Hewig et al. (2008) Pletzer and Ortner (2016) Roy et al. (2011) Shad, Bidesi, Chen, Ernst, et al. (2011) Tucker et al. (2004)

Ventromedial prefrontal cortex

Adinoff et al. (2003) Bolla et al. (2003) Dalley, Mar, Economidou, and Robbins (2008) Ernst et al. (2002) Grant, Bonson, Contoreggi, and London (1999) Lawrence, Jollant, O’Daly, Zelaya, and Phillips (2009) Marcos-Pallares, Mohammadi, Samii, and Munte (2010) Paulsen, Platt, Huettel, and Brannon (2012) Tucker et al. (2004) Windmann et al. (2006)

Striatum

Boileau et al. (2014) Buchel et al. (2011) Galvan and Peris (2014) Kahn, Peake, Dishion, Stormshak, and Pfeifer (2014) Linnet, Peterson, Doudet, Gjedde, and Moller (2010) Marcos-Pallares, Mohammadi, Samii, and Munte (2010) McClure, Laibson, Loewenstein, and Cohen (2004) Nusslock et al. (2012) Pinto, Steinglass, Greene, Weber, and Simpson (2014) Roy et al. (2011) Steeves et al. (2009) (Continued)

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TABLE 4.3 (Continued) Structure

Authors

Insula

Gansler, Jerram, Vannorsdall, and Schretlen (2012) Paulsen, Carter, Platt, Huettel, and Brannon (2012) Pletzer and Ortner (2016) Roy et al. (2011) van Duijvenvoorde et al. (2015)

Amygdala

Paulsen, Carter, Platt, Huettel, and Brannon (2012) Xiao et al. (2013a)

Nucleus accumbens

Dalley, Mar, Economidou, and Robbins (2008)

Dorsolateral prefrontal cortex

Bolla et al. (2003) Christakou, Brammer, Giampietro, and Rubia (2009) Gorini, Lucchiari, Russell-Edu, and Pravettoni (2014) Hoffmann et al. (2018) Holper, ten Brincke, Wolf, and Murphy (2014) McClure, Laibson, Loewenstein, and Cohen (2004) Roy et al. (2011) Sheffer et al. (2013) Tucker et al. (2004)

Dorsomedial prefrontal cortex

van Duijvenvoorde et al. (2015)

prefrontal cortex during response inhibition on an emotional go/no-go task (Brown et al., 2015), providing evidence that some risk-taking behaviors could be due to difficulties inhibiting a potentially harmful response. On the Devil’s Task (Knife Switches), Buchel et al. (2011) found that participants who experienced a large missed opportunity (i.e., stopped opening boxes well before the devil was found) tended to open more boxes on the next round, a behavior associated with changes in lateral ventral striatum activation. Risk-taking on the task was also associated with changes in prefrontal cortex activation between the right and left hemispheres (Gianotti et al., 2009). Risky decisions (i.e., to go rather than stop) on the stoplight task were preceded by increased activity in the ventral striatum, whereas safe decisions were preceded by activity in the right inferior frontal gyrus (Kahn et al., 2014). Riskier decisions are seen on the Cups task among individuals with lesions to the amygdala (gain trials only), insula, and ventromedial prefrontal cortex

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(Weller et al., 2007; Weller, Levin, Shiv, & Bechara, 2009). In addition, riskier choices on this task are correlated with increased frontostriatal activation (Galvan & Peris, 2014). On the chicken game, making a risky decision (i.e., choosing to continue despite the risk the clock runs out and all earned money is lost) is associated with lowered dorsal anterior cingulate cortex activation among individuals with a substance use disorder (Bjork et al., 2008). Risk-taking propensity was associated with inferior frontal gyrus and dorsolateral prefrontal cortex activation (Zhou et al., 2014). Increased activity was seen in the amygdala, hippocampus, and insula during more risk-averse decision making, but increased activity was instead seen in the ventromedial prefrontal cortex during more risk-seeking decision making on the nonsymbolic economic decision making task (Paulsen et al., 2012). Making a risky decision on the Risky Gains task was associated with greater activation in the anterior cingulate cortex, inferior frontal gyrus, superior temporal gyrus, cerebellum, thalamus, dorsal striatum, and dorsolateral and ventromedial prefrontal cortices (Arce, Miller, Feinstein, Stein, & Paulus, 2006; Gowin et al., 2014; Lee et al., 2008; Paulus et al., 2003). Being more risk-averse in one’s decision making on this task was instead associated with greater insula activation (Arce et al., 2006). The Wheel of Fortune task is often used to assess risky decision making in fMRI studies. As such, there is evidence that risky decisions on the task are associated with increased activation in areas associated with the reward pathway, including the striatum, amygdala, anterior cingulate cortex, dorsolateral and ventrolateral prefrontal cortices, thalamus, nucleus accumbens, and orbitofrontal cortex (Addicott et al., 2012; Alarco´n et al., 2017; Cservenka & Nagel, 2012; Elman et al., 2009; Ernst, Dickstein, et al., 2004; Ernst, Nelson, et al., 2004; Eshel et al., 2007; Roy et al., 2011; Shad, Bidesi, Chen, Ernst, et al., 2011; Smith et al., 2009). Multiple studies examined changes in neural activation as a function of performance on the BART. In general, risk-taking on the BART is associated with increased activation in the prefrontal cortex (including ventromedial, dorsomedial, and dorsolateral), dorsal anterior cingulate cortex, nucleus accumbens, dorsal and ventral striatum, anterior insula, and cerebellum (Chiu et al., 2012; Claus et al., 2012, 2018; Elsey et al., 2016; Fukunaga, Brown, & Bogg, 2012; Galvan et al., 2013; Lighthall et al., 2012; Pletzer & Ortner, 2016; Qu et al., 2015; Telzer, Fuligni, Lieberman, & Galvan, 2013; Weber et al., 2014; Yarosh et al., 2014; Yu, Mamerow, Lei, Fang, & Mata, 2016; Yu, Li, et al., 2017; Zhang & Gu, 2018). These encompass both reward pathways and those involved in cognitive control. Atrophy in the right orbitofrontal cortex (Strenziok et al., 2011), as well as smaller medial orbitofrontal cortex volume (Peper et al., 2013) were also associated with greater risk-taking on the BART. Activation in the dorsolateral prefrontal cortex and right anterior

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insula was also associated with less risky performance on the BART (Gorini et al., 2014; Hoffmann et al., 2018). Performance on various delay and probability-discounting tasks was also examined with functional neuroimaging techniques; however, inconsistencies are seen across studies. In general, the lateral and medial prefrontal cortex, including orbitofrontal cortex, and nucleus accumbens are associated with performance on delay-discounting tasks (Cardinal et al., 2001; Cho et al., 2013; Dalley et al., 2008; Marcos-Pallares et al., 2010; Sellitto, Ciaramelli, & di Pellegrino, 2010; Winstanley et al., 2004). While some find the ventral striatum activates to the immediate rather than delayed option (Wittmann, Lovero, Lane, & Paulus, 2010), others find the ventral striatum, in combination with the ventromedial prefrontal cortex, is more associated with interpretation of delayed rather than immediate options (Prevost et al., 2010). Those who tended to discount future larger rewards in favor of sooner smaller ones had greater activation in the nucleus accumbens and portions of the prefrontal cortex to the sooner than to the larger reward (Ballard & Knutson, 2009; MacKillop et al., 2012; McClure et al., 2004). Activation in the striatum and thalamus was associated with processing immediate losses and the medial prefrontal cortex with immediate gains (Xu, Liang, Wang, Li, & Jiang, 2009), yet others suggest the ventral striatum is associated with interpreting the value of immediate gains (Peters et al., 2011; Sripada, Gonzalez, Luan Phan, & Liberzon, 2011). Finally, there is an extensive literature to suggest that the IGT is linked with prefrontal cortex functioning. Briefly, impaired performance on the IGT is associated with functioning in the amygdala and ventromedial prefrontal cortex, with large lesions affected these areas also causing impairments (e.g., Bechara, 2003; Bechara & Damasio, 2005; Bechara et al., 1996, 1998, 1999; Bechara, Tranel, et al., 2000; Bolla et al., 2003; Clark et al., 2003; Ernst et al., 2002; Fujiwara et al., 2008; Manes et al., 2002; Xiao et al., 2013a). That said, impaired performance on the IGT is also seen when individuals experience other executive function impairment, most notably in working memory, as well as memory impairment, leading some to suggest lowered IGT performance is sensitive but not specific to frontal lobe impairment (e.g., Alvarez & Emory, 2006).

What neuroimaging teaches us about decision making processes Improvements in functional neuroimaging technology allow researchers not just to examine if activation changes during a task, but to assess why activation changes during one task but not (or differently from) another. Particular structures may activate in response to some elements of a task (e.g., immediate reward) but not others (e.g., delayed

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reward). In general, hot decision making processes are linked with the orbitofrontal and ventromedial prefrontal cortex (Krain et al., 2006; Krawcyzk, 2002; Rolls & Grabenhorst, 2008; Sonuga-Barke, 2005; Zelazo & Muller, 2011) and cold processes with the dorsolateral prefrontal cortex (Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Krain et al., 2006; Zelazo & Muller, 2011). To the extent that more complex decision making is needed on a particular task (e.g., integrating multiple sources of information), the orbitofrontal cortex and hippocampus are also implicated (Krawcyzk, 2002; Rolls, 1999). One element of decision making that can vary across tasks is the tendency to focus on immediate versus delayed or future consequences. The overall anticipation of future events, needed to successfully guide decision making, is linked with activation in the anterior cingulate cortex and insula (Fukui et al., 2005; Lin, Chiu, Cheng, & Hsieh, 2008). A greater focus on immediate gains, including processing the magnitude of their rewards to aid decisions, is associated with functioning in the insula, portions of the midbrain, amygdala, and medial prefrontal cortex (Knutson et al., 2001; Ludwig et al., 2015; McClure et al., 2004; Sripada et al., 2011; Wittmann et al., 2010). But, the anterior cingulate cortex, ventral striatum, and medial prefrontal cortex are associated with processing reward magnitudes more generally (Bartra, McGuire, & Kable, 2013; Bush et al., 2002; Prevost et al., 2010; Shad, Bidesi, Chen, Ernst, et al., 2011; Shad, Bidesi, Chen, Thomas, et al., 2011), as well as increasing motivation (Chamberlain et al., 2008; Figee et al., 2011; O’Doherty, Buchanan, Seymour, & Dolan, 2006; Peters & Buchel, 2011; Remijnse et al., 2006; Schultz, Dayan, & Montague, 1997; Sripada et al., 2011) and adjusting behavior to avoid negative future outcomes (Rolls, 1996). The amygdala and ventral striatum are linked with processing losses and tying an emotional valence to those losses to guide future decisions (Charpentier, De Neve, Li, Roiser, & Sharot, 2016; Depue & Collins, 1999; Haber & Knutson, 2010; Hariri, 2009), consistent with other research suggesting rewards and punishments are processed differently in the brain (Gotffried & Dolan, 2004; Haruno et al., 2004; Knutson et al., 2003; Liu et al., 2007; O’Doherty, Critchley, Deichmann, & Dolan, 2003; Smith et al., 2002; Wrase et al., 2007). A second component that is critical to decision making is understanding and learning from feedback. Structures involved in processing feedback and learning from it include the inferior parietal lobule and affective structures (Aron, Robbins, & Poldrack, 2004; Shohamy et al., 2004), including the ventral striatum, amygdala, and orbitofrontal cortex (Adolphs & Tranel, 2004; Banis, Geerligs, & Lorist, 2014; Fellows, 2006). To successfully learn from feedback, one must incorporate a number of other functions. They must anticipate rewards (nucleus accumbens, anterior cingulate cortex, ventral striatum; Elliott, Frith, & Dolan, 1997;

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Elliott & Dolan, 1998; Ikemoto & Panksepp, 1999; Knutson et al., 2001). They must be sensitive to losses and whether these losses are continuing to build across time (prefrontal cortex; Christakou et al., 2009). The feedback may be dependent on framing and context, which relies on the ventral prefrontal cortex, amygdala, and insula for interpretation (Baxter & Murray, 2002; Elliott, Friston, & Dolan, 2000; Gottfried, O’Doherty, & Dolan, 2003; Pickens et al., 2003; Rogers et al., 2004; Taylor et al., 2006). And finally, learning from and adapting to feedback may require some processing of uncertain information or ambiguous situations (orbitofrontal cortex, amygdala; Hsu, Bhatt, Adolphs, Tranel, & Camerer, 2005; Kim, Somerville, Johnstone, Alexander, & Whalen 2003; Reyna & Huettel, 2014a, 2014b; Sachdev & Malhi, 2005; Whalen et al., 2001).

Conclusion Decision making is an executive function and relies on executive and other cognitive functions to occur. Multiple structural and functional neuroimaging studies indicate that decision making is linked to functioning of the prefrontal cortex and its associated structures, with some differences between hot/System I and cold/System II processes. Most notably, the medial prefrontal cortex (ventromedial/orbitofrontal in particular) and portions of the brain’s reward pathway (including nucleus accumbens, amygdala, and striatum) are implicated in emotion-based or sequential decision making, whereas more lateral sections of the prefrontal cortex are implicated in other executive functions. But, despite similarities in the structures guiding decision making across tasks, not all decision making tasks are correlated with one another or with other executive function tasks. Although concerns will be raised in the remaining chapters about across-study variations in the sample size, sample characteristics, and adaptations to a particular task, it is possible other factors influence this lack of consistent between-task correlations. It may be that lack of correlations in decision making task performance is due to the reliance on examination of total task scores. As we have seen and will discuss again in the final chapter, cognitive modeling techniques allow for a fine-grained analysis of overlapping and nonoverlapping aspects assessed across various tasks. In addition, the lack of consistent correlations between decision making and other executive tasks points to the dissociation of the specific qualities needed to succeed on a particular task. These newer cognitive models could begin to incorporate nondecision making tasks that involve similar cognitive processes, such as the WCST or tower tasks, to differentiate between the generalized cognitive functions that affect performance across tasks and the specific functions affecting a particular task.

Risky Decision Making in Psychological Disorders

Preface to section II: Organization of the remaining chapters Section II shifts away from the more general information presented in Section I and toward specific information about risky decision making and risk-taking behavior as a function of common psychological disorders. In Chapter 5, Anxiety: state-dependent stress, generalized anxiety, social anxiety, posttraumatic stress disorder, and obsessive compulsive disorder, the focus is on how risky decisions manifest among individuals with anxiety disorders. Although both obsessive compulsive disorder and posttraumatic stress disorder were split-off from the anxiety disorders in the DSM-5, most of the research to date was conducted while these were classified as anxiety disorders and thus will be discussed in this chapter. In Chapter 6, Disruptions of mood: positive and negative affect, depressive disorders, and bipolar disorders, the focus shifts to bipolar and unipolar depressive disorders, with additional attention paid to state fluctuations in mood (such as through mood induction experimental manipulations) and the presence/absence of suicidal ideation. Both eating disorders (anorexia nervosa, bulimia nervosa, binge eating disorder) and disordered-eating behaviors are addressed in Chapter 7, Disordered eating behaviors: anorexia, bulimia, binge eating, and obesity, with additional discussion of risky decision making in obesity. In Chapter 8, Sleep deprivation and sleep-related disorders, the effects of acute and chronic sleep deprivation on decision making are examined. Chapter 9, Impulsivity and attention-deficit/hyperactivity disorder, investigates differences in decision making and risk-taking behaviors as a function of both a diagnosis of attention-deficit hyperactivity disorder and the personality characteristic of impulsivity. The influences of addictive behaviors, including substance use disorders and pathological gambling behaviors, are assessed in Chapter 10, Addictive behaviors: gambling and substances of abuse. Finally, Chapter 11, Schizophrenia and delusional disorders, examines decision making among those with schizophrenia spectrum disorders. The final chapter brings together some of the key findings and theories of risk-taking behavior across disorders, further examines the contributions of cognitive modeling analyses

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to our understanding of specific decision making processes, and provides some clinical intervention suggestions for patients exhibiting decisionmaking difficulties. In each chapter, several potential causes for increased risky decisions and involvement in risk-taking behavior will be examined. To the extent possible, influences of psychotropic medications and comorbid diagnoses are assessed. In some cases (Chapter 11: Schizophrenia and delusional disorders), concerns regarding the performance level for the “healthy control” comparison group are examined. Results of structural and functional neuroimaging studies with patient populations are assessed in light of the hypotheses regarding the causes of risk-taking. In some cases the potential mechanisms underlying risk-taking behavior appear across multiple disorders. Some, such as overall impaired executive functions or the hot/cold (System 1/System 2) distinction, may not be surprising given the information presented in Section I. Others, such as impairments in planning for the future, will be explained in greater detail in the corresponding chapter. Some of the more common hypotheses for impaired decision making, as will become evident in Section II, are difficulties processing and learning from feedback, lowered sensitivity to losses, and increased sensitivity to rewards. Two components that deserve some additional explanation here are impulsivity and the behavioral activation and inhibition systems. Impulsivity is a commonly used word without a unified, accepted meaning. In fact, defining impulsivity often relies on the use of a series of descriptors, such as disinhibition, difficulties with self-control, sensation seeking, hyperactivity, discounting future rewards, anticipatory responding, the inability to wait, and nonplanning (e.g., Barnhart & Buelow, 2017a, 2017b; Bechara et al., 1994; Cloninger, Svrakic, & Przybeck, 1993; Depue & Collins, 1999; Milich & Kramer, 1984; Reynolds, Ortengren, et al., 2006). Complicating the process of arriving at a unified definition of impulsivity is the strong research base suggesting not all self-report and behavioral measures of the construct relate to one another (e.g., Barnhart & Buelow, 2017a, 2017b; Duckworth & Kern, 2011; Reynolds, Ortengren, et al., 2006; Reynolds, Richards, et al., 2006). For the purposes of the following section, impulsivity is thought to encapsulate all of the abovementioned characteristics and can be thought of as both a failure to plan ahead and a tendency to act without thinking (or thinking sufficiently). The behavioral activation system (BAS) and behavioral inhibition system (BIS) work together to influence whether the decision to approach or avoid a situation. The BIS/BAS theory comes from Gray’s (1987) reinforcement sensitivity theory of personality, which focuses on how individuals respond to signals of reward versus signals of punishment. For some individuals, perceiving a signal of an actual or potential threat,

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punishment, or loss can lead to increased levels of anxiety. That anxiety can lead to an attempt to avoid the potential threat (escape). Per Gray’s theory, the BIS system is activated when a potential threat is detected, which can in turn activate the fight-or-flight system (Gray, 1990; Gray & McNaughton, 2000). When, instead, a signal of potential reward or gain is perceived, the BAS system may be activated, in turn leading to reward-based approach behaviors. The BIS and BAS work together to allow for reward-approach and punishment-escape behaviors to occur (Franken & Muris, 2006; Gray, 1990, 2000). To the extent that an individual experiences high BIS and low BAS, risk-averse and inhibited decisions (i.e., fewer risk-taking behaviors) will likely occur. On the other hand, experiencing low BIS and high BAS could lead to risk-seeking and impulsive behaviors (Carver & White, 1994; Carver, 2004; Corr, 2001, 2002; Kambouropoulos & Staiger, 2004). Collectively, these systems can be thought of as one way that reinforcement-based learning occurs, or that goal conflicts (between the competing BIS and BAS systems) can be resolved (Bijttebier, Beck, Claes, & Vandereycken, 2009). How impulsivity, the behavioral inhibition and activation systems, and reward-based learning, among other processes, affect risk-taking behavior, and risky decision making will be explored throughout the remaining chapters.

Risky Decision Making in Psychological Disorders

C H A P T E R

5 Anxiety: state-dependent stress, generalized anxiety, social anxiety, posttraumatic stress disorder, and obsessive compulsive disorder Anxiety is one of the more common symptoms that all individuals can experience at some point in their lifetimes. Anxiety can fluctuate based on characteristics of the environment. For example, a college student might feel a high level of anxiety before the first biology exam of a new semester. After the test is over, that anxiety level is likely to decrease—at least until a notification is received that the test grade is available for viewing. Some use the terms “state-dependent stress” and “state-dependent anxiety” interchangeably, but both refer to this fluctuating, acute state of anxiety. For others, anxiety can be longer lasting. It can occur due to a detected threat in the environment, fear about a stimulus, worry about the future, or fear of causing harm. Fear is often present-focused and can trigger the fight-or-flight system to turn on. Individuals in a current state of fear can experience any number of sympathetic nervous system symptoms, including rapid heart rate, sweating, changes in respiration rate, and racing thoughts. This fight-or-flight response was created in our evolutionary past to help us evade threats to our physical survival by “amping up” our bodily resources to fight (or flee) and survive. Unfortunately, this same system is activated even when the feared item or situation is not a direct threat to survival and can lead to the development of an anxiety disorder. In the DSM-V (APA, 2013), the following diagnoses are included in the anxiety disorder category: separation anxiety disorder, selective

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mutism, specific phobia, social anxiety (SA) disorder, panic disorder, and generalized anxiety disorder (GAD). Of these, separation anxiety disorder and selective mutism are both new to this category of diagnoses and primarily originate in childhood; for these reasons, they are outside the scope of this chapter. Panic disorder and specific phobia likely tap more into the fear component of anxiety disorders, rather than the anxiousvigilant component of some of the other disorders, which may be why very little research exists as to potential decision making impairments as a function of these disorders. This chapter will focus on decision making impairments in SA and GAD, as well as two other disorders that until the DSM-V were in this category. Obsessive compulsive disorder (OCD), now listed in the OCD and related disorder category, contains many of the same features as the other anxiety disorders, including an overestimated sense of danger or threat, recurrent thoughts, and avoidance of anxiety-provoking situations. Posttraumatic stress disorder (PTSD) is now in the trauma- and stressor-related disorders category, despite also having a number of fear and anxiety components in common with the other anxiety disorders. In all of these disorders, there is a tendency to overestimate the potential danger or threat in a given situation, leading to an increased level of anxiety and subsequent negative effects on emotion and cognition. In the sections that follow, I will examine the current literature on risk-taking behaviors, risky decision making, and delay discounting among those diagnosed with SA, GAD, OCD, or PTSD. In addition, I will discuss research with individuals who do not meet diagnostic criteria for one of these disorders but are currently experiencing high levels of anxiety. I will also discuss how manipulating anxiety in the lab can affect subsequent decision making task performance. At the end of the chapter, I will combine the data from the four diagnoses studied to assess the support for and against several theories of decision making impairments as a function of anxiety disorder diagnosis.

The current literature: generalized anxiety GAD occurs in approximately 0.9% 2.9% of the US population (0.4% 3.6% worldwide; APA, 2013). The central feature of GAD is worry—persistent worry that is difficult to control and can lead to concentration difficulties. The worry can be all-encompassing a “preoccupation” with the potential for unpredictable negative events at some point in the future (e.g., Mueller, Nguyen, Ray, & Borkovec, 2010). This concern about an unknown in the future can translate into an intolerance for uncertainty in the present (De Bruin, Rassin, & Muris, 2006; Dugas, Gagnon, Ladouceur, & Freeston, 1998; Dugas et al., 2005; Rosen

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& Knauper, 2009) that can in turn negatively affect decision making (Ladouceur, Talbot, & Dugas, 1997; Tallis, Eysenck, & Mathews, 1991). In addition, avoidance of thinking about uncertain events or of signals of danger (Dorfman Rosen, Pine, & Ernst, 2016), coupled with attentional resources spent looking for cues of danger (Britton, Lissek, Grillon, Norcross, & Pine, 2011; Mathews & MacLeod, 1986; Mogg & Bradley, 1998), could collectively negatively affect decisions. Finally, individuals with a history of worry and anxiety can experience alterations in their responses to both rewards and punishments (e.g., BarHaim et al., 2009; DeVido et al., 2009; Hardin et al., 2007; Paulus & Yu, 2012), in turn leading to potentially risk-averse decisions (e.g., Lorian & Grisham, 2010; Maner et al., 2007; Maner & Schmidt, 2006).

Risk-taking behavior Relatively few studies specifically examine rates of risk-taking behaviors among those with a diagnosis of GAD. Lowered rates of risktaking behaviors (Brown, Lubman, & Paxton, 2010; Lorian, Titov, & Grisham, 2012) and greater risk aversion across health-risk behaviors (Lorian & Grisham, 2010; Maner et al., 2007; Maner & Schmidt, 2006; Mueller et al., 2010; Steketee & Frost, 1994) are frequently seen. But, a GAD diagnosis can also predict age of first alcohol, tobacco, and marijuana use (Marmorstein et al., 2010).

Delay discounting and risky decision making Anxiety could have different relationships with decision making, leading to either improved or worsened decision making (Borkovec & Roemer, 1995; Wittchen et al., 2000; Worthy, Byrne, & Fields, 2014). GAD can lead to greater errors on decision making tasks when rewards are involved (DeVido et al., 2009), or result in no significant differences from nonanxious controls (Krain et al., 2008). When specific tasks are assessed, participants with GAD or high levels of worry do not show any differences from controls on the Balloon Analogue Risk Task (BART) (Buelow & Barnhart, 2017) or the Iowa Gambling Task (IGT) (Buelow & Barnhart, 2017; Castro e Couto et al., 2012; Drost, Spinhoven, Kruijt, & Van der Does, 2014; Kirsch & Windmann, 2009; Wild, Freeston, Heary, & Rodgers, 2014). That said, others find worry improves performance on the IGT (Garon, Moore, & Waschbusch, 2006; Mueller et al., 2010; Pajkossy, Dezso, & Paprika, 2009; Peters & Slovic, 2000; Schmitt, Brinkley, & Newman, 1999; Smoski et al., 2008; Van Honk et al., 2002; Werner, Duschek, & Schandry, 2009) and Cups Task (Galvan & Peris, 2014). Still others find impaired performance on the

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IGT and Wheel of Fortune Task as a function of high worry and GAD symptoms (de Visser et al., 2010; Dorfman et al., 2016; Miu, Heilman, & Houser, 2008; Pajkossy et al., 2009).

The current literature: social anxiety SA prevalence is relatively high in the United States (7% 12%) compared to other areas of the world (0.5% 2.0%; APA, 2013; Kessler et al., 1994; Kessler, Chiu, Demler, & Walters, 2005). Individuals experiencing SA express a fear of negative evaluation by others, leading to an avoidance of social situations where there is potential for evaluation. Although at first thought, one would likely assume that individuals with high levels of SA, or social phobia, engage in fewer risk-taking or risk-seeking behaviors, research suggests otherwise. The “prototypical” individual with SA is risk-averse and low in novelty-seeking behaviors (e.g., Gilbert, 2001; Leary, 2001); however, up to 21% of individuals with SA instead show the opposite: risk-seeking behaviors (Erwin et al., 2003; Kachin et al., 2001; Kashdan et al., 2006; Kashdan et al., 2010; Vohs, Baumeister, & Ciarocco, 2005). Cluster analyses support this distinction, as one cluster shows low rates of risk-taking behaviors and a tendency toward behavioral inhibition, whereas a smaller cluster shows high rates of risk-taking behaviors coupled with a tendency toward behavioral activation or reward-seeking (Kashdan & Hofmann, 2008; Kashdan et al., 2009; Nicholls, Staiger, Williams, Richardson, & Kambouropoulos, 2014).

Risk-taking behavior Increased risk-taking behaviors are seen among individuals with SA, specifically with regard to risky sexual behaviors and substance use (Buckner, Eggleston, & Schmidt, 2006; Burke & Stephens, 1999; Erwin, Heimberg, Schneier, & Liebowitz, 2003; Gullette & Lyons, 2005; Hanby, Fales, Nangle, Serwik, & Hedrich, 2012; Hoyle, Fejfar, & Miller, 2000; Kachin, Newman, & Pincus, 2001; Kashdan, Collins, & Elhai, 2006; Kashdan, Elhai, & Breen, 2008; Kashdan & McKnight, 2010; Kashdan, McKnight, Richey, & Hofmann, 2009; Kidorf & Lang, 1999; Lorian & Grisham, 2010; Lorian, Mahoney, & Grisham, 2012; Rahm-Knigge, Prince, & Conner, 2018; Rounds, Beck, & Grant, 2007). In fact, a diagnosis of SA predicts first use of alcohol, tobacco, and marijuana (Kaplow, Curran, Angold, & Costello, 2001; Marmorstein, White, Loeber, & StouthamerLoeber, 2010; Sartor et al., 2006). Greater rates of substance abuse and dependence are also seen (Buckner, Bonn-Miller, Zvolensky, & Schmidt, 2007; Buckner, Heimberg, Ecker, & Vinci, 2013; Burke & Stephens, 1999;

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Grant et al., 2004; Kushner, Sher, & Erikson, 1999; Kushner, Abrams, & Borchardt, 2000; Sonntag, Wittchen, Hofler, & Stein, 2000; Swendsen et al., 1998), which can be counterintuitive. However, some argue that the increase in risk-taking behaviors might be an attempt to avoid or otherwise escape feelings of anxiety (Burke & Stephens, 1999; Ham & Hope, 2005) or as a form of self-medication (Chutuape & De Wit, 1995). On the other hand, SA is also associated with lower rates of risk-taking behaviors (Bruch et al., 1992; Eggleston, Woolaway-Bickel, & Schmidt, 2004; Lorian & Grisham, 2011) or no relationship with health-risk behaviors (Johnson et al., 2000; McGee, Williams, & Stanton, 1998).

Delay discounting and risky decision making Varied results are seen when behavioral tasks are used. Individuals with SA exhibit lowered risky decision making on the Cups Task (Galvan & Peris, 2014) and the BART (Lorian & Grisham, 2010). Individuals with SA may interpret ambiguous or vague information as negative (Amir, Foa, & Coles,1998; Downey & Feldman, 1996), leading to risk-averse decisions. When angry and happy faces are placed on the IGT cards, participants with SA choose less from advantageous decks when the angry faces are on them (Pittig, Pawlikowski, Craske, & Alpers, 2014). Variations in decision making occurr as a function of reward magnitude, in that socially anxious participants are more sensitive to reward magnitude in their decisions than nonanxious participants (Richards et al., 2015). Anxiety could lead to less risky decision making due to an increased sensitivity to threats and risks (Lerner & Keltner, 2001). Finally, evidence exists that delay discounting is steeper in SA (Rounds et al., 2007) and does not differ between socially anxious individuals and controls (Jenks & Lawyer, 2015).

The current literature: posttraumatic stress disorder Although PTSD is no longer categorized as an anxiety disorder, but rather as a stress-related disorder, in the DSM-V, most of the research conducted on risky decision making in PTSD occurred while it was categorized as a form of anxiety. In addition, multiple symptoms of the diagnosis are consistent with symptoms of other anxiety disorders, including avoidance of situations that cause increased anxiety and intrusive, repetitive thoughts. In addition, symptoms consistent with risk-taking behaviors are now included in the diagnostic criteria for PTSD (Weiss, Tull, & Grate, 2014), indicating a need to understand risky decisions in this population. With regard to PTSD, fewer studies

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examined the influence of the disorder, or the subclinical symptoms, on relay decision making and delay discounting despite evidence of greater rates of impulsivity as a function of the disorder (Joseph, Dalgleish, Thrasher, & Tule, 1997; Kotler, Iancu, Efroni, & Amir, 2001; Oquendo et al., 2005).

Risk-taking behavior Both a diagnosis of PTSD and higher levels of PTSD symptoms (with or without the formal diagnosis) are associated with greater levels of alcohol, nicotine, and other drug use; risky sexual behaviors; risky or unsafe driving behaviors; increased rates of gambling; and aggressive behaviors (Aarstad-Martin & Boyraz, 2017; Adler, Britt, Castro, McGurk, & Bliese, 2011; Epstein, Saunders, Kilpatrick, & Resnick, 1998; Jakupcak et al., 2007; Karatzias & Chouliara, 2009; Kelley et al., 2012; Kessler, Chiu, et al., 2005; Kuhn, Drescher, Ruzek, & Rosen, 2010; Lasser et al., 2000; Netto et al., 2013; Peltzer & Pengpida, 2014; Reisner, Mimiaga, Safren, & Mayer, 2009; Rutter, Weatherill, Krill, & Orazem, 2013; Shipherd, Stafford, & Tanner; 2005; Strom et al., 2012; Weiss, Tull, Borne, & Gratz; 2013; Widome, Kehle, Carlson, Laska, Gulden, & Lust; 2011). These increased rates of risk-taking behaviors were also seen in adolescents (Borders, McAndrew, Quigley, & Chandler, 2012). In addition, high rates of alcohol abuse and dependence are seen among those meeting criteria for PTSD (Baker et al., 2009; Javidi & Yadollahie, 2012; Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995). In some cases, the alcohol use might not rise to the level of a diagnosable disorder but is still problematic for the individual (Berenz et al., 2016; Boyraz et al., 2018; Read, Wardell, & Colder, 2013; Read, Griffin, Wardell, & Ouimette, 2014). It is possible that alcohol was used as a coping mechanism (Khantzian, 2003).

Delay discounting and risky decision making As previously stated, very few studies directly examined performance on a delay discounting measure in those with PTSD. Engelmann et al. (2013) found a comorbid diagnosis of PTSD mitigated the steep delay discounting seen among individuals with a primary diagnosis of major depressive disorder (MDD). In general, PTSD symptoms are associated with less effective or riskier decision making in lab-based settings (e.g., Killgore, Cotting, et al., 2008; Stoltenberg et al., 2011; Yeater, Hoyt, Leiting, & Lopez, 2016). Multiple behavioral tasks are used to assess risky decision making among individuals with PTSD or PTSD symptoms. Greater PTSD symptoms and riskier decisions on the BART

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(Danielson, Ruggiero, Daughters, & Lejuez, 2010; Heinz et al., 2016), and risk-taking on the BART decreased following treatment for PTSD (Weiss, Tull, & Gratz, 2014). When the standard IGT is used, few differences are seen as a function of PTSD diagnosis or cooccurring dissociative symptoms (Dretsch et al., 2012; Roca, Hart, Kimbrell, & Freeman, 2006). On a version of the IGT in which immediate losses are presented instead of immediate gains, impaired decision making is seen among those with PTSD compared to controls (Dretsch et al., 2012). Hopper et al. (2008) found that veterans with a diagnosis of PTSD experienced both lowered expectations of and lowered satisfaction with received rewards on the Wheel of Fortune Task. On the Risky Gains Task, no differences emerged in risky decisions as a function of either PTSD or MDD diagnosis (Engelmann et al., 2013). Finally, performance on the CGT was riskier among individuals with both PTSD symptoms and gambling behaviors than among individuals with just gambling behaviors, indicating the additive negative effect of PTSD symptoms on decision making (Leppink & Grant, 2015).

The current literature: obsessive compulsive disorder OCD symptoms can have their onset anytime from the pediatric to adult years (Pauls, Abramovitch, Rauch, & Geller, 2014), with prevalence rates falling in the 1% 3% range (Apter et al., 1996; Fullana et al., 2009; Pauls, Alsobrook, Goodman, Rasmussen, & Leckman, 1995; Ruscio, Stein, Chiu, & Kessler, 2010; Valleni-Basile et al., 1994). The most common symptoms are the presence of repetitive and persistent thoughts (obsessions) and/or repetitive behaviors one feels driven to act on (compulsions). Individuals with a diagnosis of OCD show consistent impairments across measures of executive functions (Abbruzzese, Bellodi, Ferri, & Scarone, 1995; Abbruzzese, Ferri, & Scarone, 1997; Abramovitch, Abramowitz, & Mittleman, 2013; Cavallaro et al., 2003; Kashyap, Kumar, Kandavel, & Reddy, 2012; Kuelz, Hohagen, & Voderholzer, 2004; Lennertz et al., 2012; Olley, Malhi, & Sachdev, 2007; van den Heuvel et al., 2005). Some of these difficulties include failure of response inhibition (Chamberlain, Fineberg, Menzies, et al., 2007; Morein-Zamir, Fineberg, Robbins, & Sahakian, 2010; Penades et al., 2005), with resulting higher levels of impulsivity and difficulties with impulse control (Benatti, Dell’Osso, Arici, Hollander, & Altamura, 2013; Ettelt et al., 2007; Fontenelle, Mendlowicz, & Versiani, 2005; Patton, Stanford, & Barratt, 1995; Summerfeldt et al., 2004). That said, individuals with OCD also show difficulties tolerating uncertainty (Fear & Healy, 1997; Frost & Shows, 1993; Stern et al., 2013; Tolin et al., 2003), with lowered confidence in their decisions that combine to lead to

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greater information gathering prior to a decision being made (Fear & Healy, 1997; Foa et al., 2003; Milner, Beech, & Walker, 1971; Pelissier & O’Connor, 2002; Reese, McNally, & Wilhelm, 2011). Taken together, these executive function indicators could point toward either risk-averse or risk-seeking performance on decision making measures.

Risk-taking behavior Relatively few studies directly examined real-world risk-taking behaviors among those with a diagnosis of OCD, instead focusing on behavioral assessment of risky decision making in a lab-based setting. However, both higher rates (Semple et al., 2011) and lower rates (Cicolini & Rees, 2003) of risk-taking behaviors are seen in OCD samples.

Delay discounting and risky decision making In general, individuals with OCD take longer to make a decision across tasks (Banca et al., 2015; Figee et al., 2011; Foa et al., 2003; Sip, Muratore, & Stern, 2016). The results across behavioral tasks vary. Lower levels of risk-taking behaviors are found on the BART (Sohn, Kang, Namkoong, & Kim, 2014), but the results with the GDT consistently show no benefit or impairment on the task as a function of OCD diagnosis or symptoms (Kim et al., 2015; Starcke et al., 2009; Starcke et al., 2010; Zhang, Dong, et al., 2015). The results are mixed on the CGT: individuals with OCD show both impaired (Dittrich, Johansen, Landro, & Fineberg, 2011; Dittrich & Johansen, 2013) and intact (Chamberlain, Fineberg, Blackwell, et al., 2007; Chamberlain, Fineberg, Menzies, et al., 2007; Hybel et al., 2017; Starcke et al., 2010; Watkins et al., 2005) decision making on this task. The results with the IGT are quite mixed. Researchers find both no differences between healthy controls (HC) and those with OCD (Boisseau, Thompson-Brenner, Pratt, Farchione, & Barlow, 2013; Borges et al., 2011; Dittrich et al., 2011; Jollant, Buresi, et al., 2007; Krishna et al., 2011; Lawrence et al., 2006; Nielen et al., 2002; Whitney, Fastenau, Evans, & Lysaker, 2004), whereas others find those with OCD engage in worse decision making on the task than HC (Cavallaro et al., 2003; Cavedini, Zorzi, Piccinni, Cavallini, & Bellodi, 2010; Cavedini et al., 2012; Grassi et al., 2015; Kashyap et al., 2012; Kashyap, Kumar, Kandavel, & Reddy, 2013; Kim et al., 2015; Kodaira et al., 2012; Long et al 2012; Martoni et al 2015; da Rocha, Alvarenga, Malloy-Diniz, & Correa, 2011; da Rocha, Malloy-Diniz, Lage, & Correa, 2011; Starcke et al., 2009; Starcke et al., 2010; Zhu et al., 2014). Two studies (Cavedini, Riboldi, D’Annucci, et al., 2002; Cavedini et al., 2004) found that

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performance on the IGT was associated with treatment responsivity. Those with OCD who decided advantageously on the IGT had better treatment outcomes than those who decided disadvantageously on the IGT. With regard to delay discounting, one study found no differences in discount rates among those with OCD and HC (Pinto et al., 2014). Others instead find that those with various compulsions engaged in greater discounting of delayed rewards than controls (Ong et al., 2018; Weatherly, 2012a).

The current literature: trait anxiety and other nondiagnosable types of anxiety Some literature examines risk-taking behavior and decision making among individuals with subthreshold anxiety symptoms or other nondiagnosable forms of anxiety. However, the findings are mixed. No relationship is found between trait anxiety, worry, and risk-taking behaviors (Browning, Behrens, Jocham, O’Reilly, & Bishop, 2015; Buelow & Barnhart, 2017; Dickstein et al., 2010; Elsey et al., 2016). However, some instead find risk-avoidant decisions among those who self-report high anxiety or anxiety sensitivity (Broman-Fulks, Urbaniak, Bondy, & Toomey, 2014; Giorgetta et al., 2012; Grecucci et al., 2012; Lorian, Mahoney, et al., 2012; Maner et al., 2007; Ortega, Ramirez, Colmenero, & del Rosario Garcia-Viedma, 2017; Raghunathan & Pham, 1999; Werner, Duschek, & Schandry, 2009). Still others find higher stress/anxiety are associated with riskier decisions on the BART and Blackjack Tasks (Jordan, Sivanathan, & Galinsky, 2011) and on the IGT (Hart, Schwabach, & Solomon, 2010; Miu et al., 2008; Zhang, Xiao, & Gu, 2017), as well as steeper delay discounting (Xia, Gu, Zhang, & Luo, 2017). Taken together, diagnosable and nondiagnosable forms of anxiety can have an effect on risky decision making on behavioral tasks.

The current literature: state-dependent stress Multiple studies examine the influence of a situational stressor on risky decision making. Several of the most common methods of inducing stress (anxiety) in the lab are the cold pressor task (e.g., von Baeyer et al., 2005) and the Trier Social Stress test (e.g., Kirschbaum, Pirke, & Hellhammer, 1993). In the cold pressor task, participants are asked to place their arm in very cold water for a set amount of time. This experience induces a feeling of intense pain that begins to dissipate after the arm is removed from the water. The cold pressor task is known to

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induce stress and anxiety and can also be used as a threat of future pain. In the Trier Social Stress test, participants are randomly assigned to either a control condition or to stress condition. In the stress condition, participants are told that they have just a few minutes to prepare before giving a speech, either in front of a group of people or in front of a camera (with the recording later viewed by others). Higher levels of stress are reported in those assigned to the stress versus control condition (Jenks & Lawyer, 2015). Utilizing these and other stress/anxiety manipulations, researchers find rather mixed results. No effect of acute stress is seen on delay discounting (Jenks & Lawyer, 2015; Krause-Utz et al., 2016; Rounds, Beck, & Grant, 2007 [level of anxiety predicted responses not the group manipulation]), the GDT (Gathmann et al., 2014), or other tasks (Cano-Lopez, Cano-Lopez, Hidalgo, & Gonzalez-Bono, 2016; Lempert, Porcelli, Delgado, & Tricomi, 2012; Robinson, Bond, & Roiser, 2015; Sokol-Hessner et al., 2016). On the other hand, a greater body of research supports a link between acute lab-created anxiety and increased delay discounting (i.e., preference for more immediate but smaller rewards; Kimura et al., 2013; Lempert et al., 2012). In addition, acute stress can negatively affect performance on the Wheel of Fortune Task (depending on the reward parameters; Clark, Li, et al., 2012; Richards et al., 2015), BART (Reynolds et al., 2013; Syndicus, Wiese, & van Treeck, 2018), the Cups Task (Galvan & McGlennen, 2012), GDT (Liebherr, Schubert, Averbeck, & Brand, 2018; Pabst, Brand, & Wolf, 2013; Pabst, Schoofs, Pawlikowski, Brand, & Wolf, 2013; Starcke, Wolf, Markowitsch, & Brand, 2008), a lottery game (FeldmanHall et al., 2015), and the IGT (more disadvantageous selections; Leonello & Jones, 2016; Preston, Buchanan, Stansfield, & Bechara, 2007; Simonovic, Stupple, Gale, & Sheffield, 2017; Starcke, Agorku, & Brand, 2017; Verdejo-Garcia et al., 2015; Wemm & Wulfert, 2017). On the BART, men increase their risk-taking following a cold pressor manipulation, but women do not (Kluen, Agorastos, Wiedemann, & Schwabe, 2017; Lighthall, Mather, & Gorlick, 2009; Lighthall et al., 2012). Similar sex differences are seen with the IGT following a cold pressor task (Preston et al., 2007; van den Bos, Harteveld, & Stoop, 2009). On the other hand, stress can also lower risk-taking on the BART (Wise, Phung, Labuschagne, & Stout, 2015), the Cups Task (Uy & Galvan, 2017), the ADMC (Shields, Lam, Trainor, & Yonelinas, 2016), and on a lottery task immediately following the stressor (Bendahan et al., 2017; Cahlikova & Cingl, 2017). Overall, stress can have a negative effect on the decision making process. In their review (Starcke & Brand, 2012) and later metaanalysis (Starcke & Brand, 2016), a small but significant relationship exists between reward-seeking and risk-taking behaviors as a function of induced stress/anxiety (d 5 0.17). Stress negatively affects the ability to

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learn from rewards and punishments (Bogdan & Pizzagalli, 2006; Cavanagh, Frank, & Allen, 2011; Petzold et al., 2010) and how quickly a decision is made (Porcelli & Delgao, 2009a, 2009b; van den Bos et al., 2009). These difficulties could explain why we often see differences between decisions in a gain frame versus a loss frame following a stress induction (e.g., Mather & Lighthall, 2012; Yamakawa et al., 2016). In addition, others suggest that anxiety decreases the informationgathering process, leading individuals to base their decisions on less information or lower quality information (Yang, Saini, & Freling, 2015). Coupled with these specific decision making errors, stress can negatively affect executive functions more generally (Al’Absi, Hugdahl, & Lovallo, 2002; Hsu et al., 2003; McCormick, Lewis, Somley, & Kahan, 2007) leading to a greater difficulty working with the information required to make an informed decision.

Performance on other executive function tasks Most studies assessing risky decision making in anxiety disorders and acute stress manipulations focused on just decision making, with few relationships with other measures of executive functions examined. Of those that did assess relationships between decision making tasks and other executive function measures, decision making is largely independent of other task performance (Borges et al., 2011; de Visser et al., 2010; Heinz et al., 2016; Kim et al., 2015; but see Cavallaro et al., 2003). Significantly more research is needed to examine the relationship between decision making and other cognitive abilities among those experiencing high levels of anxiety, as our current understanding is limited as to whether the decision making difficulties seen across studies are independent of known executive function difficulties in these disorders (see below for more detail).

Neuroimaging Generalized anxiety disorder Individuals with GAD have changes in prefrontal cortex functioning (Hoehn-Saric, McLeod, & Zimmerli, 1989; Hoehn-Saric, Lee, McLeod, & Wong, 2005; Nutt, 2001; Stein, Westenberg, & Liebowitz, 2002; Thayer & Lane, 2000; Whalen et al., 2007), and to the left hemisphere more generally (Heller et al., 1997), which may affect performance on tasks (including risky decision making measures) that tap into the PFC. Increased activation is seen in the prefrontal cortex and amygdala in response to

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risky versus nonrisky (Galvan & Peris, 2014) and uncertain versus more certain (Krain et al., 2008) decisions.

Social anxiety Few neuroimaging studies were conducted among individuals with SA. Although one found greater activation in the prefrontal cortex and amygdala in response to risky versus nonrisky decisions (Galvan & Peris, 2014), others find abnormalities (Sripada et al., 2013), including lowered activation levels (Schneier, Blanco, Antia, & Liebowitz, 2002), in response to risky decisions. This lowered or abnormal activation may lead to greater difficulties differentiating between risky and nonrisky choices in the decision process.

Posttraumatic stress disorder Among those with PTSD compared to controls, activation changes are seen in the ventral striatum (Elman et al., 2009), prefrontal cortex (Bremner, Staib, Kaloupek, & Southwick, 1999; Shin, Rauch, & Pitman, 2006), and amygdala (Admon et al., 2013; LeDoux, 2007; Pessoa, 2009), which could explain difficulties processing and responding to reward and punishment feedback.

Obsessive compulsive disorder Several theories of OCD focus on dysfunction in the connections between the prefrontal cortex (dorsal and orbitofrontal in particular), striatum, and amygdala (Del Casale et al., 2011; Graybiel & Rauch, 2000; Menzies et al., 2008; Milad & Rauch, 2012; Saxena, Brody, Schwartz, & Baxter, 1998; Saxena & Rauch, 2000; Tibbo & Warneke, 1999; van den Heuvel et al., 2004; Whiteside, Port, & Abramowitz, 2004). Specific to risky decision making in OCD, risk aversion on tasks was associated with altered activation patterns in the limbic-frontal connections (Admon et al., 2012; Zhu et al., 2014), including in the nucleus accumbens (Figee et al., 2011) and anterior cingulate (Hauser et al., 2017).

Acute stress Increasing acute stress increases activation in the prefrontal cortex (Cerqueira, Almeida, & Sousa, 2008; Gathmann et al., 2014; Kern et al., 2008) and portions of the dorsal striatum (Lighthall et al., 2012), structures implicated in reward processing and learning from feedback. However, among those with decreased risk-taking behavior following

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an acute stress manipulation, lowered ventromedial prefrontal cortex and ventral striatum activity are seen, likely pointing to greater encoding of potential risks than potential benefits (Engelmann, Meyer, Fehr, & Ruff, 2015; Ossewaarde et al., 2011).

Potential mechanisms State-dependent fluctuations in anxiety Manipulating stress (state anxiety) in-the-moment can affect decision making. It is also likely that some elements of state-dependent anxiety are activated during cognitive evaluations of those with anxiety disorders. This interaction of state and “trait” anxiety could account for some of the fluctuations in findings noted across studies. For example, someone with a diagnosis of OCD who typically shows risk-avoidant behaviors might perform poorly on a lab-based decision making measure if they experience a significant increase in state anxiety at the time of testing. For someone who already exhibits decision making impairments, state-dependent anxiety could accentuate that difficulty even more. However, given that there are inconsistencies even among the induced stress literature, it is likely that this is only one factor of several that affect risky decision making among those with various anxiety disorders.

Overall executive dysfunction Impairments across types of executive functions are common across the anxiety disorder spectrum (e.g., Brandes et al., 2002; Chamberlain, Fineberg, Menzies, et al., 2007; Mohlman & DeVito, 2017; Morein-Zamir et al., 2010; O’Toole & Pedersen, 2011; Penades et al., 2005; Vasterling et al., 2002; Yehuda, Golier, Tischler, Stavitsky, & Harvey, 2005). As we saw in the neuroimaging section, most anxiety disorders have cooccurring alterations in prefrontal cortex functioning, consistent with impaired executive functions. One might, then, argue that any impairments in decision making are then a consequence of this executive dysfunction, as decision making is also linked with frontal lobe functioning. However, very few researchers specifically report results of both decision making and other executive function tasks within the same sample of participants. To truly determine if executive dysfunction is the cause or correlate of risky decision making across anxiety disorders, multiples studies are needed in which a battery of executive measures are examined in relation to risky decision making task performance.

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Cautiousness and indecisiveness Individuals experiencing high levels of anxiety may experience difficulties arriving at a decision. They may be so concerned about making the wrong decision, leading to some negative evaluation or outcome, that they ultimately cannot decide or spend a significant portion of time devoted to the decision making process. Individuals with PTSD can show avoidance behaviors when there is a concern about a potential threat (Engelmann et al., 2013), such as could occur in a decision making task where there is a possible loss if the wrong choice is selected. Among those with a diagnosis of OCD, individuals may be so cautious—so risk-averse—that they continually seek out new evidence, almost as a compulsion, which prevents a decision from being made (Rotge et al., 2008; Stern et al., 2013; Steketee & Frost, 1994). Although this cautiousness could eventually lead to a more advantageous decision with enough time, it may not mimic real-world situations that call for more immediate decisions to be made and might also contribute to the continuance of compulsive symptoms.

Effects of impulsivity versus behavioral inhibition It is also possible that impulsivity may play a role in some of the risk-taking behaviors and risky decisions seen. Multiple analyses show evidence of a subset (approximately 20%) of individuals with SA who display risk-seeking, impulsive behaviors. In addition, higher rates of impulsivity are seen among individuals with OCD. As we will see in Chapter 9, there is a strong link between impulsivity and risky decision making, both as a function of attention-deficit/hyperactivity disorder and of subclinical levels of impulsivity. It should be noted, however, that there are differences between impulsivity and the compulsions seen in OCD (e.g., Hollander, 2005). In addition, there are different forms of impulsivity, including attentional, physical, and cognitive. There is some research to suggest that elements of cognitive impulsivity might correlate strongly with symptoms of OCD (Abramovitch & McKay, 2016; Stein, Hollander, Simeon, & Cohen, 1994), which may account for studies showing high rates of impulsivity in OCD. Future studies should differentiate between different types of impulsivity to further elucidate the potential relationship between impulsivity, anxiety, and risk-taking behavior. It is also possible that alterations in the activation of the behavioral inhibition system (BIS) affect decisions across the anxiety disorders. Individuals with GAD and SA, for example, are highly attuned to potential signs of danger (negative events, negative evaluations) in a given situation. This focus on danger could map onto an overactive BIS,

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which is on guard for signals of punishment. When the BIS identifies such a potential punishment, it may trigger avoidance behaviors as a means of escaping that action. This reaction could lead to risk-averse behaviors—and decisions—among those with GAD and SA. However, more information is needed about the relationships between BIS, behavioral activation system (BAS) (see next section), anxiety, and decision making is needed before coming to a conclusion as to this possible mechanism driving decision making.

Alterations in neural processing of rewards and learning from feedback One of the more common theories explaining impaired decision making in anxiety is that individuals are experiencing difficulties accurately processing reward information, difficulties learning from positive and negative feedback, or both. Descriptions of both PTSD and OCD indicate the brain’s reward pathway, and a reward deficiency, is involved in the disorders (e.g., Admon et al., 2012; Blum et al., 2000; Cavedini, Gorini, & Bellodi, 2006; Comings & Blum, 2000; Del Casale et al., 2011; Figee et al., 2011; Schafer, Vaitl, & Shienle, 2010). In addition, the nucleus accumbens, a component of the reward pathway, is a target for deep brain stimulation surgery in OCD (e.g., Denys et al., 2010; Huff et al., 2010; Sturm et al., 2003). Individuals may be more sensitive to the benefits of an immediate reward that they do not consider the potential negative (or positive) long-term consequences of choosing that option (Altman & Shankman, 2009; Cavedini, Riboldi, D’Annucci, et al., 2002). It is possible that some of these difficulties processing reward information and focus on immediate consequences are due to an overactivation of the BAS. BAS activates at the indication of a reward, leading to risk-seeking behaviors. If the reward pathway is also overstimulated by a potential immediate reward, these two systems could combine to rule out consideration of long-term consequences or alternative options. In addition, on a number of behavioral tasks, participants must weigh the feedback provided about both wins and losses to arrive at the optimal decision making strategy for the remainder of the task. Placing too much emphasis on either the reward-based or the loss-based feedback can lead toward impaired decisions on either the too risk-seeking or too riskaverse ends of the spectrum.

Conclusion and future directions All in all, there are significant inconsistencies in risky behavior across both anxiety type and behavioral decision making task used. As most

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studies utilize just one measure of risky decision making in a particular study, it is difficult to tell whether these inconsistencies are due to the specific measure used to assess decision making or have to do with fluctuations in the level of anxiety, particular symptom of anxiety, or other participant-related factor that varies across studies. In addition, there is a lack of information about how decision making relates to other executive functions within this population, limiting our understanding of the precise mechanisms leading to both decision making impairments and decision making improvements as a function of different forms of anxiety.

Participant-related factors Finally, several participant-level factors may account for some of the variations in results seen across studies. Sample sizes vary significantly, with most falling in the 20 40 per group range. As we will see across other chapters, small sample sizes can fail to take into account variations in decision making task performance across factors such as age, sex, and educational history. As there were differences in decision making and risk-taking behavior between male and female participants (e.g., Auerbach, Kertz, & Gardiner, 2012; Danielson et al., 2010; de Visser et al., 2010; Dorfman et al., 2016) and we know there are sexbased differences in decision making patterns on at least some tasks (e.g., van den Bos, Homberg, & de Visser, 2013), the demographics of future participants should be taken into consideration in analyses and in determining an appropriate sample size. The level of current anxiety symptoms was not always accounted for in previous studies. It is possible that some of the inconsistencies in findings could be related to whether participants were experiencing high or low levels of anxiety at the time of testing. Finally, comorbidities are common among individuals with anxiety disorders, most commonly with other anxiety disorders, mood disorders, and substance use disorders. As will be seen in the next chapter, manic and depressive mood symptoms can negatively affect decision making. The links between anxiety, substance use, and decision making are particularly strong, and thus the studies examining these cooccurring disorders are presented in a later chapter.

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C H A P T E R

6 Disruptions of mood: positive and negative affect, depressive disorders, and bipolar disorders Individuals can experience fluctuations in mood that range across a spectrum. On one end of this spectrum are the depressive symptoms: sadness, negative mood, anhedonia, etc. On the other end of the spectrum are the manic and hypomanic symptoms: expansive mood, irritability, inflated self-esteem, etc. These symptoms can fluctuate over time, but when they exist more often than not for at least 4 (hypomanic episode) to 14 (major depressive episode) days, mood disorder diagnoses are possible. In the DSM-V (APA, 2013), diagnoses are split into two overall categories based on the specific symptom set. Individuals experiencing both manic/hypomanic episodes and major depressive episodes will likely fall into the bipolar and related disorders category, which includes bipolar I disorder, bipolar II disorder, and cyclothymic disorder. Individuals experiencing unipolar depressive symptoms will instead be most likely diagnosed under the depressive disorders category, which includes disruptive mood dysregulation disorder, major depressive disorder, persistent depressive disorder (dysthymia), and premenstrual dysphoric disorder (APA, 2013). Bipolar spectrum disorders have a lifetime prevalence rate between 1% and 5% (APA, 2013; Ferrari et al., 2016; Hadjipavlou, Bond, & Yatham, 2012; Merikangas & Pato, 2009), whereas rates for major depressive disorder are often higher (7% 16%; APA, 2013; Judd et al., 2002; Kessler et al., 2003). The negative views of the self, world, and future, coupled with anhedonia and a lack of positive expectations for the present and future, seen in depression (Beck, Rush, Shaw, & Emery, 1979; Macleod & Salaminiou, 2001) could lead to impaired decision making that has a greater focus on immediate versus long-term outcomes. In addition, increased risk-taking

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behavior is featured in the diagnostic criteria for the bipolar spectrum disorders, specifically during the hypomanic or manic episode. A secondary component linked with both the bipolar and unipolar mood disorders, and with risky decision making more generally, is suicidal ideation. The diagnosis of a depressive or bipolar disorder significantly increases the risk of suicide behaviors or attempt (Azorin et al., 2009; Beautrais, Joyce, & Mulder, 1998; Conwell, Duberstein, Cox, Herrmann, Forbes, & Caine, 1996; Conwell, Duberstein, & Caine, 2002; Fergusson & Lynskey, 1995; Gould et al., 1998; Neves, Correa, & Malloy-Diniz, 2009; Oquendo, Currier, Liu, Hasin, Grant, & Blanco, 2010; Pompili et al., 2008), as up to 36% of those with a bipolar diagnosis report a lifetime history of suicide attempt (APA, 2013; MullerOerlinghausen, Berghofer, & Bauer, 2002). The focus on the present/ immediate versus the future/long-term seen in those experiencing suicidal ideation could also result in impaired decision making on behavioral tasks. In the sections that follow, I will examine the current literature on risk-taking behaviors, risky decision making, and delay discounting among individuals with a history of depressive and manic/hypomanic symptoms, as well as among those with diagnoses within the bipolar or depressive disorder categories. I will examine the additive effect of current or past suicidal behaviors to affect decision making, as well as examine evidence regarding the behavioral activation system (BAS) hyperactivity hypothesis of bipolar disorder. Finally, I will tie in the current literature with the research investigating the role of impulsivity in risky decision making (Chapter 9: Impulsivity and attention-deficit/ hyperactivity disorder) to determine if impulsivity may be one of the key underlying components affecting decision making in this population.

The current literature: risk-taking behaviors Depressive symptoms and disorders Increased depressive symptoms are associated with a number of risky behaviors, including drug and alcohol use and misuse (Kealy, Ogrodniczuk, Rice, & Oliffe, 2018; Nagoshi, 1999; Patock-Peckham, Hutchinson, Cheong, & Nagoshi, 1998; Taniguchi, Shacham, Onen, Grubb, & Overton, 2014), current nicotine use (Audrain-McGovern, Rodriguez, & Kassel, 2009; Costello, Erkanli, Federman, & Angold, 1999; Fleming, Kim, Harachi, & Catalano, 2002; Taniguchi et al., 2014), and aggressive behaviors (Kealy et al., 2018). In addition, greater depressive symptoms are associated with multiple risky sexual behaviors, including decreased condom use (Shrier, Harris, Sternberg, &

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Beardslee, 2001), earlier onset of sexual activity (Longmore, Manning, Giordano, & Rudolph, 2004; Spriggs & Halpern, 2008), and other risky sexual behaviors (Lin, Lee, & Yang, 2017; Taniguchi et al., 2014). Depressive symptoms also increase the motivation to drink alcohol (Eissenberg, 2004; Palfai, Monti, Ostafin, & Hutchison, 2000) and avoidance of negative affect is provided as a common reason for alcohol use (Cooper, Russell, Skinner, Frone, & Mudar, 1992). These risky behaviors decrease as depressive symptoms improve (Rotheram-Borus, Rosario, Reid, & Koopman, 1995). Among those with a diagnosis of major depressive disorder (MDD), a similar pattern emerges. A diagnosis of MDD is associated with alcohol and nicotine use (Grant, Dawson, et al., 2004; Katon et al., 2010; Katon, 2011; Kessler, Chiu, et al., 2005; Lasser, Boyd, Woolhandler, Himmelstein, McCormick, & Bor 2000; Lin et al., 2004), other drug use (Lubman, Allen, Rogers, Cementon, & Bonomo, 2007; Rohde, Noell, Ochs, & Seeley, 2001; Stein, Solomon, Herman, Anderson, & Miller, 2003), and risky sexual behaviors (Alvy et al., 2011; Beidas, Birkett, Newcomb, & Mustanski, 2012; Bradley, Remien, & Dolezal, 2008; Crepaz & Marks, 2001; Lubman, Allen, Rogers, et al., 2007; Rohde et al., 2001). With regard to alcohol use, those with MDD more often report that they use alcohol to suppress emotions (Cohn, Cobb, Hagman, Cameron, Ehlke, & Mitchell, 2014) and have increased risk of developing an alcohol use disorder later in life (McCarty et al., 2009). Taken together, a clear pattern of increased risk-taking behaviors emerges across the depressive symptom spectrum.

Mania, hypomania, and the bipolar disorders As with depression, previous research examined the relationship between manic symptoms, hypomanic symptoms, and the bipolar disorders and involvement in various risk-taking behaviors. Individuals at risk of a manic episode show increased risky sexual behaviors (Goodwin & Jamison, 1990; Meade, Fitzmaurice, Sanchez, Griffin, McDonald, & Weiss, 2011). In addition, symptoms of mania and hypomania are associated with increased risky sexual behaviors (Brown et al., 2010; Dvorak, Wray, Kuvaas, & Kilwein, 2013; Marengo, Martino, Igoa, Fassi, et al., 2015; Marengo, Martino, Igoa, Scapola, et al., 2015; Meade, Graff, Griffin, & Weiss, 2008; Stewart et al., 2012). A diagnosis of bipolar I or bipolar II disorder is associated with increased involvement in risk-taking behaviors (Blanco et al., 2008; Christopher, McCabe, & Fisher, 2012; Fletcher, Parker, Paterson, & Synnott, 2013; Magalhaes, Kapczinski, & Kauer-SantAnna, 2009; Rosa et al., 2009). More specifically, individuals with a diagnosis of bipolar

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disorder engage in increased risky sexual behaviors (Di Nicola et al., 2010; Fletcher et al., 2013; Krantz et al., 2018; Marengo, Martino, Igoa, Fassi, et al., 2015; Marengo, Martino, Igoa, Scapola, et al., 2015; Meade et al., 2011; Meade, Bevilacqua, & Key, 2012; Reinharth, Braga, & Serper, 2017), gambling behaviors (Di Nicola et al., 2010; McIntyre, McElroy, Konarski, Soczynska, Wilkins, & Kennedy, 2007; Reinharth et al., 2017), increased and excessive alcohol and other substance use (de Moraes et al., 2013; Fletcher et al., 2013; Grant, Stinson, et al., 2004; Reinharth et al., 2017; Richardson, 2011), and risky/reckless driving (Fletcher et al., 2013; Reinharth et al., 2017; Serretti & Olgiati, 2005). Again, a consistent pattern emerges of increased risk-taking behavior among those with a bipolar disorder and those experiencing or at risk of mania.

The influence of suicidal ideation and behaviors Suicidal ideation can occur among individuals experiencing either depressive or manic/hypomanic episodes, in addition to other mental health difficulties. Suicidal ideation, as well as a history of suicidal behavior or attempt, is independently associated with higher rates of risk-taking behaviors (King et al., 2001). A history of suicidal behaviors/ideation is associated with increased nicotine use (Ammerman, Steinberg, & McCloskey, 2018; Bae, Ye, Chen, Rivers, & Singh, 2005; Garnefski & Jan De Wilde, 1998; Woods, Lin, Middleman, Beckford, Chase, & DuRant, 1997), alcohol and other drug use (Afifi, Cox, & Katz, 2007; Ammerman et al., 2018; Anteghini, Fonseca, Ireland, & Blum, 2001; Bae et al., 2005; Garnefski et al., 1998; Kandel, 1988; Levy & Deykin, 1989), gambling (Afifi et al., 2007; Garnefski et al., 1998), risky sexual behaviors (Ammerman et al., 2018; Lin et al., 2017), and aggressive behaviors (Afifi et al., 2007; Anteghini et al., 2001; Bridge et al., 2015; Sosin et al., 1995; Woods et al., 1997). A concern emerges as to the directionality of these relationships. Do the mood symptoms and suicidal ideation precede the increased risktaking behaviors, or do the increased risk-taking behaviors precede the mood symptoms and in particular the suicidal behaviors? Individuals with a history of alcohol use disorder are at increased risk of later developing MDD (Grant et al., 2004) and substance use also increases risk of suicidal behaviors (Borowsky, Ireland, & Resnick, 2001; Brent et al., 1993; Eaton, Foti, Brener, Crosby, Flores, & Kann, 2011; O’Donnell, Stueve, & Wilson-Simmons, 2005; Rosenberg, Jankowski, Sengupta, Wolfe, Wolford, & Rosenberg, 2005; Shaffer, Garland, Gould, Fisher, & Trautman, 1988; Shaffer et al., 1996). Increased risk of suicidal behaviors is also seen among those with a history of aggressive

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(Eaton et al., 2011; Garrison, McKeown, Valois, & Vincent, 1993) and risky sexual behaviors (Eaton et al., 2011; Rosenberg et al., 2005). Evidence exists to support both directionalities. Mood disorders can affect the decision to engage in risk-taking behaviors, and engaging in risk-taking behaviors can affect one’s mood.

The current literature: risky decision making Depressive symptoms and disorders To date, most research has focused on the presence/absence of risky decision making during an active major depressive episode rather than in remitted MDD. In general, riskier or worse decision making is seen among individuals with a diagnosis of MDD compared to healthy controls (Adida et al., 2011; Cella, Dymond, & Cooper, 2010; Chamberlain & Sahakian, 2006; Jollant et al., 2005; Malloy-Diniz et al., 2009; Murphy et al., 2001; Must, Szabo, Bodi, Szasz, Janka, & Keri, 2006). However, others instead find either no differences in decision making (Hancock, Moffoot, & O’Carroll, 1996; Hockey, Maule, Clough, & Bdzola, 2000) or less risky decisions in the context of depression versus a more positive mood state (Garcia-Retamero, Okan, & Maldonado, 2015; Isen, Means, Patrick, & Nowicki, 1982; Isen, 1997; Nygren, Isen, Taylor, & Dulin, 1996). Anhedonia (Must, Horvath, Nemeth, & Janka, 2013) and alexithymia (Bagby & Taylor, 1997) have both been put forth as potential causes of impaired decision making in depression. With regard to specific behavioral decision making tasks, the majority of research focuses on the Iowa Gambling Task (IGT), followed by the Cambridge Gambling Task (CGT), Game of Dice Task (GDT), and Wheel of Fortune task. On the IGT the results are quite mixed. While some find that those with depression prefer disadvantageous decks [Cella et al., 2010; Han et al., 2012 (only male participants); Kornreich et al., 2013; Must et al., 2006; Westheide et al., 2008], others find the opposite (better performance in depression; Dalgleish et al., 2004; Smoski, Lynch, Rosenthal, Cheavens, Chapman, & Krishnan, 2008) or no group differences in performance [Deisenhammer, Schmid, Kemmler, Moser, & Delazer, 2018; Gorlyn et al., 2013; Hegedus, Szkaliczki, Gal, Ando, Janka, & Almos, 2018; Ho, Hsu, Lu, Gossop, & Chen, 2018; McGovern, Alexopoulos, Yuen, Morimoto, & GunningDixon, 2014; Westheide et al., 2007 (remitted MDE)]. Evidence also supports anhedonia (Beck, Stinson, Thearle, Krakoff, & Gluck, 2017), alexithymia (Zhang et al., 2017), and apathy (McGovern et al., 2014) as potential causes of these impairments. On the CGT, no differences are seen in performance between those with depression and controls

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(Moreines, McClintock, Kelley, Holtzheimer, & Mayberg, 2014; Murphy et al., 2001) and those with a family history of depression actually show lower risk-taking than those without a family history (Mannie, Williams, Browning, & Cowen, 2015). Results with the GDT are mixed, finding both riskier decisions in depression (Deisenhammer et al., 2018) and no difference in decisions in alexithymia (Zhang et al., 2017). Safer performance is seen on the BART among those with depression compared to those with anxiety (Ortega, Ramirez, Colmenero, & del Rosario Garcia-Viedma, 2017) and controls (Hevey, Thomas, Laureano-Schelten, Looney, & Booth, 2017), although no impact of depressive symptoms is seen on the Wheel of Fortune task (Felder et al., 2012; Shad, Bidesi, Chen, Ernst, et al., 2011). Taken together, the research into behavioral decision making task impairments as a function of depressive symptoms is quite varied. A relatively equal number of studies find no difference between healthy controls and those with active symptoms or a history of depression as that find significantly riskier or impaired decision making in depression. Several theories exist regarding decision making in depression. One theory is that individuals are experiencing difficulties learning from rewards/positive events (Cella et al., 2010; Depue & Iacono, 1989; Elliott, Sahakian, McKay, Herrod, Robbins, & Paykel, 1996; Eshel & Roiser, 2010; Must et al., 2006; Steele, Kumar, & Ebmeier, 2007; Tomarken & Keener, 1998) that could, in turn, lead to difficulties changing behavior in the presence of rewards (Eshel & Roiser, 2010) and experiencing motivation during reward-based tasks (Martin-Soelch, 2009). This responsivity to reward is a critical component in most behavioral decision making tasks and to the decision making process more generally (Der-Avakian & Markou, 2012). Impaired response to reward could lead to a lack of focus or motivation for future reward, in turn affecting decisions. It is also possible that instead of a focus on rewards, the potential for a negative consequence or punishment drives decision making in depression. Individuals experiencing depressive symptoms, including anhedonia, may experience higher sensitivity to negative feedback and signals of punishment (Elliott et al., 1996) as well as overestimate the likelihood of a negative event occurring in the future (Carleton et al., 2012). This sensitivity to negative events can lead to an increased focus on losses and punishments, in turn causing losses to have more of an impact on future decisions than gains. For example, an individual with depression may respond to a large win on the IGT by anticipating that an upcoming negative event will negate that win (Must et al., 2013). This negative anticipation could then bias upcoming decisions as individuals are less able to adjust the decision making strategy to the actual feedback rather than anticipated outcomes. However, this focus on negative outcomes could lead to risk avoidance

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(Allen & Badcock, 2003) that potentially results in a more accurate decision making strategy. Support for this hypothesis comes from the elaboration likelihood model (Petty & Cacioppo, 1986), which posits two routes of processing information: the central route and the peripheral route. The peripheral route is a more “surface-level” processing of information and is sensitive to cues in the environment, whereas the central route involves a more elaborate processing of information. Being in a positive mood can lead to greater peripheral processing, whereas being in a negative mood can lead to greater central processing (Tellis, 1998). A bias toward processing more negative information than positive information in depression (e.g., Smoski et al., 2008) could lead to greater central processing, which could in turn lead to more accurate decisions among those with depression compared to controls. There is evidence for this “depressive realism” occurring among those with mild levels of depression (Haaga & Beck, 1995; Lewinsohn, Mischel, Chaplin, & Barton, 1980). On the BART, we have some evidence that depressed participants respond to an explosion (a punishment) by becoming more risk-averse on the next trials (which is not seen in healthy controls; Hevey et al., 2017). We know that emotions guide various components of the decision making process (Edell & Burke, 1987; Forges, 1994, 1995; Maner et al., 2005; Nygren et al., 1996; Petty, Schumann, Richman, & Strathman, 1993), but it is unclear whether difficulties processing positive information versus an overfocus on negative information guides decision making in depression.

Mania, hypomania, and the bipolar disorders Research into risky decision making among those with a diagnosis of bipolar disorder focuses on differences according to the phase of the disorder. Risky decision making is seen among individuals in an acute manic phase (Adida et al., 2008; Clark, Iversen, & Goodwin, 2001a; Minassian, Paulus, & Perry, 2004; Murphy et al., 2001; Rubinsztein et al., 2001; Yechiam, Hayden, Bodkins, O’Donnell, & Hetrick, 2008), acute depressive phase (Murphy et al., 2001; Roiser, Cannon, et al., 2009; Rubinsztein, Michael, Underwood, Tempest, & Sahakian, 2006; Yechiam et al., 2008), and euthymic or remitted phase (Christodoulou, Lewis, Ploubidis, & Frangou, 2006; Clark, Iversen, & Goodwin, 2002; Gorrindo, Blair, Budhani, Dickstein, Pine, & Leibenluft, 2005; Jollant, Guillaume, Jaussent, Bellivier, et al., 2007; Rubinsztein, Michael, Paykel, & Sahakian, 2000; Yechiam et al., 2008). That said, others find no impairment in decision making among those with a bipolar diagnosis (Saunders, Goodwin, & Rogers, 2016), including during the euthymic phase (Clark et al., 2002; Rubinsztein et al., 2000).

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Most commonly, behavioral task performance was assessed with the IGT, BART, CGT, and Wheel of Fortune task. The results with the IGT are quite mixed, as a relatively equal number of studies find risky decision making among those with bipolar disorder compared to controls [Adida et al., 2008, 2011, 2015; Brambilla et al., 2013; Burdick et al., 2014; Christodoulou et al., 2006; Clark et al., 2001a; Cotrena, Branco, Shansis, & Fonseca, 2016 (Bipolar II only); Jollant, Guillaume, Jaussent, Bellivier, et al., 2007; Malloy-Diniz et al., 2009; Powers et al., 2013] compared to those that find no differences in decision making between the two groups (Caletti et al., 2013; Edge, Johnson, Ng, & Carver, 2013; Gilbert et al., 2011; Gkintoni, Pallis, Bitsios, & Giakoumaki, 2017; Martino et al., 2011; Ono et al., 2015; Yechiam et al., 2008). However, a metaanalysis of IGT performance in euthymic bipolar participants found a small but significant effect (euthymic bipolar riskier than controls; Edge et al., 2013). Mixed findings are seen when the BART is utilized. Both riskier performance (Hidiroglu et al., 2013) and risk-averse performance (Reddy et al., 2014) are associated with a diagnosis of bipolar disorder and those taking antipsychotic medications are more risk-averse on the task (Reddy et al., 2014). In a study by Holmes et al. (2009), however, riskier decisions are only seen among those with bipolar and cooccurring alcohol use disorder. On the Wheel of Fortune task, those with bipolar disorder endorse lowered confidence in their decisions and more dissatisfaction with losses than controls (Ernst, Dickstein, et al., 2004). Riskier decisions are also seen on the CGT across all phases of bipolar disorder (Bauer et al., 2017; Bauer et al., 2018; Linke, King, Poupon, Hennerici, Gass, & Wessa, 2013; Murphy et al., 2001; Roiser, Cannon, et al., 2009; Rubinsztein et al., 2006; Scholz, Houenou, Kollmann, Duclap, Poupon, & Wessa, 2016; Wu, Passos, et al., 2016) and among first-degree relatives of those with the diagnosis (Wessa et al., 2015). Treatment may have an effect on risky decision making among those with bipolar disorder. Treatment with repetitive transcranial magnetic stimulation (rTMS) (Myczkowski et al., 2018) and lithium (Adida et al., 2015) improves performance on the IGT, whereas treatment with pramipexole (Burdick et al., 2014) and benzodiazepines (Adida et al., 2015; Adida et al., 2011) impairs performance. In addition, symptom severity is associated with task performance (Murphy et al., 2001). Several theories exist to explain both risky and intact decision making among those with bipolar disorder. Those with bipolar disorder may have difficulties adapting to changing task demands and feedback, such as following negative feedback. These difficulties may be associated with impaired learning, memory, and planning/organizational skills in the disorder (Kulkarni, Jain, Janardhan Reddy, Kumar, & Kandavel, 2010). Impaired shifting could also be related to differences in attention to reward versus to losses. If an individual focuses more on gains than

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losses, subsequent decision making may be biased in favor of larger gains despite the larger losses that could also be associated with those decisions. If an individual focuses more on losses, such as to minimize future experiences of loses, then decision making may be improved. Among individuals with bipolar disorder, greater attention to losses is seen (Brambilla et al., 2013; Powers et al., 2013; but see Edge et al., 2013). But, individuals do not always learn from those losses (Adida et al., 2008) or rely more on win feedback than loss feedback to guide subsequent decisions (Burdick et al., 2014). Finally, response inhibition, or lack thereof, could account for these group differences. Individuals with bipolar disorder have difficulties inhibiting thoughts and behaviors (Akiskal & Benazzi, 2005; Altshuler et al., 2004; Goodwin & Jamison, 1990; Martinez-Aran et al., 2004; Zubieta, Huguelet, O’Neil, & Giordani, 2001). If attention is paid to loss feedback, the inability to inhibit responding to the “thrill” of a win could still negatively affect decision making.

The influence of suicidal ideation and behaviors In general, significant decision making impairments are seen among individuals with a history of suicidal ideation or suicidal behaviors/ attempts compared to controls without this history (Adams, Giffen, & Garfield, 1973; Bridge et al., 2012; Bruine de Bruin, Dombrovski, Parker, & Szanto, 2015; Chamberlain, Odlaug, Schreiber, & Grant, 2013; Clark et al., 2011; Dombrovski et al., 2010; Housden, O’Sullivan, Joyce, Lees, & Roiser, 2010; Kochansky, 1973; Malloy-Diniz, Neves, Abrantes, Fuentes, & Correa, 2009; Martino, Strejilevich, Torralva, & Manes, 2011; Oldershaw et al., 2009; Richard-Devantoy, Olie, Guillaume, Bechara, Courtet, & Jollant, 2013; Szanto et al., 2015; Voon et al., 2010; Weintraub et al., 2006). These findings are supported by the results of a recent metaanalysis (Richard-Devantoy, Berlim, & Jollant, 2014) and there is also evidence that decision making impairments are greater among those with a history of more violent suicide attempts (Jollant et al., 2005). In terms of specific tasks, the majority of previous research focuses on performance on the IGT and CGT. Those with a history of suicide attempt are riskier on the IGT compared to healthy controls (Bridge et al., 2012; Jollant et al., 2005; Jollant, Guillaume, Jaussent, Castelnau, et al., 2007; Jollant et al., 2010; Jollant, Guillaume, Jaussent, Bechara, & Courtet, 2013; Olie et al., 2015; Pustilnik, Elkana, Vatine, Franko, & Hamdan, 2017; Westheide et al., 2008) or nonsuicidal psychiatric controls (Jollant et al., 2010). Relatives of individuals with a history of suicide are riskier on the IGT than controls (Ding et al., 2017; Hoehne, Richard-Devantoy, Ding, Turecki, & Jollant, 2015). In addition,

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participants with bipolar disorder (de Moraes et al., 2013; RichardDevantoy et al., 2014), unipolar depression (Hegedus et al., 2018; Richard-Devantoy, Olie, Guillaume, & Courtet, 2016; Richard-Devantoy et al., 2014), or traumatic brain injury (Homaifar, Brenner, Forster, & Nagamoto, 2012) and a history of suicide attempt exhibit worse/riskier decision making on the IGT compared to those with the diagnosis but no suicide attempt history. Performance is not correlated with other clinical or cognitive variables (Bridge et al., 2012; Jollant, Guillaume, Jaussent, Castelnau, et al., 2007; Richard-Devantoy et al., 2013), pointing to the unique contribution of suicidal ideation to risky decision making. But others find no group differences in performance on the IGT as a function of suicidal behavior (Gorlyn, Keilp, Oquendo, Burke, & Mann, 2013; Legris, Links, van Reekum, Tannock, & Toplak, 2012) or other self-injury (Janis & Nock, 2009). In addition, controlling for current mood symptoms, psychotropic medication use, age, and gender negated any differences on the IGT as a function of suicidal ideation (Sheftall et al., 2015; Wyart, Jaussent, Ritchie, Abbar, Jollant, & Courtet, 2016). Using the CGT, decision making deficits are again seen among those with a history of suicide attempt compared to those without a history of attempt (Ackerman et al., 2014; Clark et al., 2011; but see Dombrovski et al., 2012, for a lack of group differences). Several theories are proposed to account for increased risky decision making on behavioral tasks among those with a history of suicidal behaviors or attempts. Attention may be more sensitive to immediate-/present-focused consequences versus long-term/future-focused consequences, resulting in impaired decision making or a myopia for the future (Dombrovski et al., 2011; Jollant, Guillaume, Jaussent, Castelnau, et al., 2007). Difficulties with learning, and especially learning from positive and negative feedback to change one’s decision making strategy, can also account for these impairments. Individuals with suicidal ideation/behaviors may fail to learn to decide advantageously following feedback about wins and losses (Dombrovski et al., 2013; Jollant et al., 2005; Malloy-Diniz et al., 2009; Martino et al., 2011), or fail to pay attention to critical information that could adjust decision making strategies (Ackerman et al., 2014; Clark et al., 2011; Dombrovski et al., 2010).

Conclusion Across both the unipolar and bipolar mood disorders, no consistent pattern emerges in terms of overall risky/impaired versus risk-averse decision making. Task performance varies by current mood symptoms, the presence/absence of medication treatment, and the specific task used to assess decision making. But, a clear additive negative

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contribution of suicidal ideation emerged. A relative focus on negative versus positive feedback, difficulties shifting sets and learning from feedback, difficulties with response inhibition, a focus on immediate versus long-term outcomes, and the varied sample sizes across studies could account for some of the mixed findings seen to date. Another factor that is infrequently assessed but could be at least partially affecting findings across studies is impulsivity. As will be discussed in a later chapter impulsivity is both a personality characteristic and a symptom of several DSM-V disorders, including bipolar disorder (Adida et al., 2008; Cherek & Lane, 1999; Moeller, Barratt, Dougherty, Schmitz, & Swann, 2001; Najt, Perez, Sanches, Peluso, Glahn, & Soares, 2007; Peluso et al., 2007; Swann, Dougherty, Pazzaglia, Pham, & Moeller, 2004; Swann, Pazzaglia, Nicholls, Dougherty, & Moeller, 2003; Swann, 2009). Greater levels of impulsivity are also seen among those with a history of suicide attempt (Dervic, Brent, & Oquendo, 2008; Dougherty, Mathias, Marsh, Papageorgiou, Swann, & Moeller, 2004; Logan, Schachar, & Tannock, 1997; Maser et al., 2002; McGirr, Renaud, Bureau, Seguin, Lesage, & Turecki, 2008; Swann, Dougherty, Pazzaglia, Pham, Steinberg, & Moeller, 2005). As few studies assess both the personality characteristic of impulsivity and levels of depressive and manic symptoms, it is unclear to what extent impulsivity may account for some of the risky decisions seen among those with unipolar or bipolar mood disorders.

The current literature: delay discounting, and reward responsiveness Depressive symptoms and disorders Multiple researchers examined changes in delay discounting as a function of depressive symptoms, including anhedonia, or a diagnosis of depression. Greater depressive symptoms (or lower overall mood) are associated with steeper discounting of delayed rewards (i.e., a stronger preference for smaller immediate gains over larger but delayed rewards) (Caceda, Durand, et al., 2014; Dennhardt & Murphy, 2011; Henriques, Glowacki, & Davidson, 1994; Imhoff, Harris, Weiser, & Reynolds, 2014; Juhasz et al., 2009; Levin, Haeger, Ong, & Twohig, 2018; Ludwig et al., 2015; Pizzagalli, Iosifescu, Hallett, Ratner, & Fava, 2008; Pulcu et al., 2014; Rezvanfard, Ekhtiari, Azarakhsh, Djavid, & Kaviani, 2010; Rounds, Beck, & Grant, 2007; Szuhany, MacKenzie, & Otto, 2018; Weafer, Baggott, & de Wit, 2013; Yoon et al., 2007). This finding holds for individuals currently experiencing symptoms of grief (Maccallum & Bonanno, 2016) and those with low levels of serotonin (Schweighofer et al., 2008) but is diminished in those with remitted

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depression compared to those with current symptoms (Pulcu et al., 2014). However, others instead find that greater depressive symptoms (Dombrovski et al., 2011; Lempert & Pizzagalli, 2010; Urosevic, Youngstrom, Collins, Jensen, & Luciana, 2016) and anhedonia (Lempert & Pizzagalli, 2010) are associated with less discounting of delayed rewards. Abnormal reward processing is seen in MDD (Beddington et al., 2008), which could help one to explain some of these findings. In addition, difficulties are seen evaluating large rewards after a delay (Pulcu et al., 2014), which may be due to a greater focus on shorter term decision making strategies due to impaired future-planning in depression. This difficulty with future-planning is seen among individuals experiencing prolonged grief, in that they have difficulty imagining positive events in the future (Maccallum & Bryant, 2010; Robinaugh & McNally, 2013). Difficulties with future-orientation could contribute to a more intense focus on immediate gains over larger but more distant gains in depression.

Mania, hypomania, and the bipolar disorders By contrast, fewer studies examine delay discounting among those with a bipolar disorder or history of manic/hypomanic symptoms. Those with a diagnosis of bipolar disorder discount delayed rewards more steeply than controls (Ahn et al., 2011; Urosevic et al., 2016) and at a similar rate to those with a diagnosis of schizophrenia or schizoaffective disorder (Ahn et al., 2011). These findings are not associated with current treatment (medications or psychotherapy) (Urosevic et al., 2016). Lowered reward processing and lowered self-control are also seen among those with bipolar disorder (Eichhorst et al., 2006; Nieuwenstein, Aleman, & de Haan, 2001), which could account for difficulties processing the magnitude of and waiting for the delayed reward. These differences in delay discounting are also seen among those without a diagnosis of bipolar disorder: individuals with high hypomanic personality traits (Mason, O’Sullivan, Blackburn, Bentall, & El-Deredy, 2011) and at risk of bipolar disorder (Wessa, Kollman, Linke, Schonfelder, & Kanske, 2015) also prefer immediate to delayed rewards.

The influence of suicidal ideation and behaviors Compared to a control group without a history of suicidal behaviors or ideation, individuals with a history of suicide attempt, behavior, or ideation prefer immediate rewards to larger but distant rewards (Caceda, Durand, et al., 2014; Dombrovski et al., 2011; Dombrovski, Siegle, Szanto,

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Clark, Reynolds, & Aizenstein, 2012; Liu, Vassileva, Gonzalez, & Martin, 2012; Mathias et al., 2011). Delay discounting is steeper among those with depression and either suicidal ideation or suicide attempt compared to nonsuicidal individuals with depression (Caceda, Durand, et al., 2014; Pulcu et al., 2014), so the presence of depression itself cannot solely account for this preference for immediate reward. There is evidence that individuals with suicidal ideation prefer immediate outcomes over a future event (Dombrovski et al., 2011; Krysinskia, Heller, & De Leo, 2006; van Herringen, Bijttebier, & Godfrin, 2011), indicating that an inability to focus on future outcomes (independent of the reward component) may contribute to steeper delay discounting on tasks in this population.

Performance on other executive function tasks To date, relatively few studies directly compare performance on a behavioral risky decision making task to performance on other executive function measures within the same sample. A negative correlation is seen between performance on the IGT and the go/no go task among currently depressed individuals with and without suicidal ideation (Westheide et al., 2008). Performance on the IGT is typically not correlated with overall estimated intelligence (Bridge et al., 2012; Jollant, Guillaume, Jaussent, Bellivier, et al., 2007; Martino et al., 2011; RichardDevantoy et al., 2013; but see Adida et al., 2011) but is with performance on the Wisconsin Card Sort Task (Brambilla et al., 2013). Others find no correlations between decision making and other tasks (Hoehne et al., 2015). More often than not, if multiple executive function tasks are administered, no correlations are reported between the tasks, leaving our understanding of decision making impairments in the context of other executive impairments significantly lacking.

Neuroimaging As behavioral decision making task performance is generally associated with the prefrontal cortex, and the ventromedial or orbitofrontal cortex more specifically (Fukui, Kington, Raymont, & Shergill, 2005; Jollant et al., 2010; Lawrence, Jollant, O’Daly, Zelaya, & Phillips, 2009; Li, Lu, D’Argembeau, Ng, & Bechara, 2010), most neuroimaging studies examine the contribution of these areas and the mesocorticolimbic system more generally. In general, changes in mood state can affect activity in portions of the prefrontal cortex and anterior cingulate cortex (Drevets & Raichle, 1998). Major depressive disorder affects functional activity in some of the same structures as are implicated in risky decision making, including the

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anterior cingulate cortex (Ernst & Paulus, 2005; Pizzagalli, 2011). Abnormal activity in the prefrontal cortex is seen among individuals experiencing major depressive disorder (Rogers et al., 2004), alexithymia (Sutherland, Carroll, Salmeron, Ross, & Stein, 2013), and anhedonia (Gorwood, 2008), in general as well as in response to emotional stimuli (Heller et al., 2009; Light et al., 2011; Ludwig et al., 2015). Abnormal activity is also seen in the orbitofrontal cortex (Ballmaier et al., 2004; Lacerda et al., 2004; Richard-Devantoy, Ding, Lepage, Turecki, & Jollant, 2016), nucleus accumbens (Cohen, Axmacher, Lenartz, Elger, Sturm, & Schlaepfer, 2009), and anterior cingulate cortex (Diekhof, Falkai, & Gruber, 2008; Pizzagalli, 2011; Smoski et al., 2009). These abnormal activation patterns are seen in response to positively valenced emotional stimuli (Epstein et al., 2006; Keedwell, Andrew, Williams, Brammer, & Phillips, 2005; Schaefer, Putnam, Benca, & Davidson, 2006), as well as during reward anticipation on various tasks (Felder et al., 2012; Forbes et al., 2009; Knutson, Bhanji, Cooney, Atlas, & Gotlib, 2008; Pizzagalli et al., 2009; Shad, Bidesi, Chen, Ernst, et al., 2011; Smoski et al., 2009), suggesting a potential hyporesponsivity to reward in portions of the mesocorticolimbic pathway (Smoski et al., 2009) as well as an overreactivity to potential punishments (Dombrovski et al., 2013). Altered reactivity in the mesocorticolimbic reward pathway can affect motivation and rewardbased learning (Pizzagalli, 2014), leading to impaired performance on feedback and learning-based decision making tasks. Among those with a bipolar disorder or high levels of manic/hypomanic symptoms, alterations are seen in activation in the orbitofrontal cortex in general and during reward anticipation (Ono et al., 2015; Singh et al., 2013). Additional activation changes are seen in the prefrontal cortex more generally (Ballard & Knutson, 2009; Frangou, Kington, Raymont, & Shergill, 2008; Jogia, Dima, Kumari, & Frangou, 2012; Ono et al., 2015), as well as in the frontostriatal pathway (Hariri, Brown, Williamson, Flory, de Wit, & Manuck, 2006; Mason, Trujillo-Barreto, Bentall, & El-Deredy, 2016) and frontotemporal regions (Kulkarni et al., 2010). These activation changes are associated with impaired performance on decision making tasks (Linke et al., 2013; Scholz et al., 2016). In addition, these activation changes are consistent with reward hypersensitivity and a preference for immediate gains (Abler, Greenhouse, Ongur, Walter, & Heckers, 2007; O’Sullivan, Szczepanowski, El-Deredy, Mason, & Bentall, 2011) and appear in response to anticipated reward but not anticipated loss (Harmon-Jones et al., 2008). Thus we see altered processing of gains and losses at both ends of the mood spectrum, with this altered processing affecting reward-based decision making. Neuroimaging research also supports involvement of the prefrontal cortex in decision making in those with a history of suicide attempt. Those with a history of suicidal behaviors show changes in activation in

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basal ganglia (Ahearn et al., 2001; Di Maio, Squitieri, Napolitano, Campanella, Trofatter, & Conneally, 1993; Dombrovski et al., 2012; Foncke, Schuurman, & Speelman, 2006; Soulas, Gurruchaga, Palfi, Cesaro, Nguyen, & Fenelon, 2008; Vang, Ryding, Traskman-Bendz, van Westen, & Lindstrom, 2010; Voon et al., 2008) and their connections with the decision making pathways (Ahearn et al., 2001; Bowden et al., 1997; Dombrovski et al., 2012; Dombrovski, Szanto, Clark, Reynolds, & Siegle, 2013; Jollant et al., 2010; Vang et al., 2010; Voon et al., 2008). In addition, activation changes in the ventromedial prefrontal (Arango, Underwood, & Mann, 1997; Audenaert et al., 2002; Jollant et al., 2008; Mann et al., 2000; Oquendo et al., 2003; van Herringen et al., 2003) and orbitofrontal (Mann, 1998; Monkul et al., 1997; Oquendo et al., 2003; van Herringen et al., 2003) cortices are also seen, which could be due in part to serotonin distributions in these areas (Arango et al., 1997; Arango, Underwood, & Mann, 2002; Mann, 1998; Underwood, Kassir, Bakalian, Galfalvy, Mann, & Arango, 2012). In addition, structural changes in the prefrontal cortex increase risk for suicidal behavior (Benedetti et al., 2011; Ding et al., 2015; Dumais et al., 2005; Gvion & Apter, 2011; McGirr & Turecki, 2007; Monkul et al., 2007; Oquendo et al., 2003; Wagner, Schultz, Koch, Schachtzabel, Sauer, & Schlosser, 2012). These activation and structural changes reflect difficulties changing one’s decisions to match an adapting environment (Dombrovski et al., 2010, 2013; Richard-Devantoy et al., 2013). When one needs to change decision making strategies based on moment-to-moment feedback, this can be difficult. In terms of sensitivity to gains and losses, conflicting results are seen. Olie et al. (2015) find that those with a history of suicide attempt show increased orbitofrontal cortex activation to wins than losses, whereas Jollant et al. (2010) show no activation differences between gains and losses.

Potential mechanisms State-dependent mood One theory for the risky decision making impairments seen across studies is that they do not reflect impairments specific to mood disorder symptoms, but rather impairments due to state-dependent fluctuations in mood (e.g., state vs “trait” mood). Current mood can influence a variety of cognitive functions that can affect the decision making process. In addition, nonclinical fluctuations in mood are associated with changes in activation in the ventral prefrontal cortex that mimics those changes seen in mood disorders (Baker, Frith, & Dolan, 1997). Emotions affect decisions (Bechara et al., 1997; Cohen & Blum, 2002; Kahneman, 2003; Wagar & Dixon, 2006; Zajonc, 1980). Damasio’s (1994) somatic marker

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hypothesis suggests that individuals develop affective, physiological signals in response to rewards and punishments, guiding future decisions. There is evidence to suggest that these somatic markers, and therefore mood states, guide decision making on the IGT, at least during the early “under uncertainty” trials (Maia & McClelland, 2004; Wagar & Dixon, 2006). Emotions may also guide decision making more in uncertain situations than when the risks and benefits associated with different decisions are more explicit, such as on the GDT. Emotions can alter other cognitive functions that can affect the decision making process, leading to riskier decisions. One’s current mood can influence the information-gathering and information-processing stages (Bolt, Goschke, & Kuhl, 2003; Hanze & Hesse, 1993; Isen & Means, 1983; Ruder & Bless, 2003). Increased deliberation can be seen prior to a decision among those in a more negative mood (Bless & Schwarz, 1999), which could improve decision making compared to the reliance on heuristics that can occur among those in more positive moods (Bless et al., 1996; Bodenhausen, Kramer, & Susser, 1994). In general, state positive (Nygren et al., 1996; Roiser, Farmer, et al., 2009) and negative (Buelow & Suhr, 2013; Kaplan et al., 2006; Miu, Heilman, & Houser, 2008; Roiser, Cannon, et al., 2009; Smoski et al., 2008; Suhr & Tsanadis, 2007; Tice, Bratslavsky, & Baumeister, 2001; Werner, Duschek, & Schandry, 2009) mood can influence the decision making process across different behavioral tasks. Several studies specifically manipulated mood state immediately prior to completion of a decision making task. Although some found inducing a positive or negative mood did not affect decision making (Clark, Iverson, & Goodwin, 2001b; Heilman, Crisan, Houser, Miclea, & Miu, 2010), others found improved decision making following induced positive (Bagneux, Bollon, & Dantzer, 2012; de Vries, Holland, & Witteman, 2008) or negative (Bagneux et al., 2012; Buelow, Okdie, & Blaine, 2013; Chou, Lee, & Ho, 2007; Fernandez-Serrano, Moreno-Lopez, Perez-Garcia, Viedma-del Jesus, Sanchez-Barrera, & Verdejo-Garcia, 2011; Harle & Sanfey, 2007; Lerner, Small, & Loewenstein, 2003; Yuen & Lee, 2003) mood. These findings point to the importance of assessing not just overall mood state (such as with measures of depressive and manic symptoms) but also state affect immediately prior to decision making. In-the-moment affect may explain some of the inconsistencies seen across risky decision making studies.

Overall executive dysfunction A second theory for why we see decision making impairments among those with unipolar and bipolar mood disorders is that they are a

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consequence of overall executive dysfunction, rather than a separate entity. Among those with active depressive symptoms, impairments are seen in set-shifting and problem solving (Must et al., 2006; Naismith et al., 2003), inhibition (Richard-Devantoy, Ding, et al., 2016), and other executive functions (Austin, Mitchell, & Goodwin, 2001; Bortolato, Carvalho, & McIntyre, 2014; Burt, Zembar, & Niederehe, 1995; Cotrena, Branco, Kochhann, Shansis, & Fonseca, 2016; Ottowitz, Dougherty, & Savage, 2002; Roiser & Sahakian, 2013; Samame, Szmulewicz, Valerio, Martino, & Strejilevich, 2017; Snyder, 2013; Snyder, Miyake, & Hankin, 2015). These impairments extend to those with remitted depression (Kessing, 1998; Neu, Bajbouj, Schilling, Godemann, Berman, & Schlattmann, 2005; O’Brien, Lloyd, McKeith, Gholkar, & Ferrier, 2004; Paelecke-Habermann, Pohl, & Leplow, 2005; Paradiso, Lamberty, Garvey, & Robinson, 1997; Reischies & Neu, 2000; Weiland-Fiedler et al., 2004; but see Biringer et al., 2005; Marcos, Salamero, Gutierrez, Catalan, Gasto, & Lazaro, 1994). These executive function impairments are also seen in those with bipolar disorders (Bourne et al., 2013; Depp et al., 2012; Malloy-Diniz et al., 2009; Torres, Boudreau, & Yatham, 2007; Wingo, Harvey, & Baldessarini, 2009). Individuals with a history of or recent suicidal ideation/behaviors also experience impaired executive functions. Impairments in cognitive inhibition (Richard-Devanoty et al., 2014), cognitive flexibility and problem solving (Bartfai, Winborg, Nordstrom, & Asberg, 1990; Beck, Steer, Beck, & Newman, 1993; Gibbs, Dombrovski, Morse, Siegle, Houck, & Szanto, 2009; Pollock & Williams, 1998; Pollock & Williams, 2004), and other executive functions (e.g., Bredemeier & Miller, 2015; Chamberlain et al., 2013; Dombrovski et al., 2008, 2010; Jollant et al., 2005; Keilp, Sackheim, Brodsky, Oquendo, Malone, & Mann, 2001; Keilp, Gorlyn, Oquendo, Burke, & Mann, 2008; Keilp, Wyatt, Gorlyn, Oquendo, Burke, & Mann, 2014; King, Conwell, Cox, Henderson, Denning, & Caine, 2000; Marzuk, Hartwell, Leon, & Portera, 2005; see Richard-Devantoy et al., 2014, for a metaanalysis) are seen as a function of suicidal ideation/ behaviors. Executive functions are linked to functioning of the prefrontal cortex. Impairments in various executive functions can also affect functioning of portions of the prefrontal cortex, including the orbitofrontal cortex (Ballmaier et al., 2004; Lacerda et al., 2004; Rogers et al., 2004). If decision making is an executive function and is also linked with functioning of the prefrontal cortex, then it stands to reason that decision making would also be affected when other executive functions are affected. What is not clear, however, is whether these other executive impairments are a correlate or cause of decision making impairments. As we previously saw, few studies assess both decision making and other executive functions in the same study. Those that do may not assess

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potential relationships between performance across tasks or across time in a longitudinal design. It is therefore difficult to know whether one difficulty (e.g., set shifting, problem solving) preceded another difficulty (e.g., decision making). We do know, however, that impaired executive functions can lead to increased impulsivity among those with a history of suicidal behaviors (Logan et al., 1997; McGirr et al., 2008), lending some support to overall executive dysfunction prompting riskier decision making. Finally, impaired executive functions could interact with impairments in emotion regulation (Bredemeier & Miller, 2015) or reward processing to collectively lead to risky decision making among those with mood disorders and/or suicidal ideation. As many of the behavioral decision making tasks rely on reward-based learning or reward-based outcomes, difficulties processing these rewards or regulating emotional responses to rewards and losses could negatively affect decisions.

Alterations in neural processing of rewards and learning from feedback A key component of the riskier decisions and increased risk-taking seen among those with mood spectrum symptoms is likely an alteration in neural processing of rewards and punishments. This alteration can affect feedback-based learning, as individuals may place more weight on risky rewards than on potential negative outcomes. Difficulties with emotional, behavioral, and cognitive regulation are seen in those with depressive (Bagby & Taylor, 1997; Johnstone, van Reekum, Urry, Kalin, & Davidson, 2007) and bipolar (Black et al., 2014; Chandler, Wakeley, Goodwin, & Rogers, 2009; Dvorak et al., 2013; Swann, 2010) symptoms. In addition, altered reactions are seen to positive/rewarding stimuli in bipolar disorder and suicidal behavior (Eisner, Johnson, & Carver, 2008; Ernst, Mechawar, & Turecki, 2009; Hayden et al., 2008; Johnson, Ruggero, & Carver, 2005; Leibenluft, Charney, & Pine, 2003; Leibenluft, Charney, Towbin, Bhangoo, & Pine, 2003; Meyer, Beevers, Johnson, & Simmons, 2007; Murphy et al., 2001; Pizzagalli, Goetz, Ostacher, Iosifescu, & Perlis, 2008; van Herringen et al., 2011). Among those with depression the altered sensitivity seems to be instead toward decreased reward responsivity (Aupperle & Paulus, 2010; Elman et al., 2005; Hopper et al., 2008; Sailer et al., 2008; Tremblay et al., 2005) and greater negative or punishment sensitivity (Berenbaum & Oltmanns, 1992; Rawal, Riglin, Ng-Knight, Collishaw, Thapar, & Rice, 2014)—even to the point of punishment overreactivity (Elliott et al., 1996; Elliott, Sahakian, Herrod, Robbins, & Paykel, 1997; Murphy, Michael, Robbins, & Sahakian, 2003; Nelson & Craighead, 1977; Steffens, Wagner, Levy,

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Horn, & Krishnan, 2001). Individuals may be very cautious, choosing to avoid potential punishments as much as possible especially after a large “win” (Padrao, Mallorqui, Cucurell, Marco-Pallares, & RodriguezFornells, 2013). These changes in positive and negative stimuli processing can affect the ability to learn from win and loss feedback, a critical component to most of the behavioral risky decision making tasks utilized in the literature. A hypersensitivity to gains/wins could result in a focus on immediate outcomes and a difficulty fully assessing the relative strengths of and then waiting for delayed outcomes (Eisner et al., 2008; Hayden et al., 2008; Johnson et al., 2005; Mason et al., 2011). This focus on immediate outcomes could lead to difficulties utilizing the win and loss feedback to adapt future decisions (Chandler et al., 2009; Dombrovski et al., 2010; Jollant et al., 2007; Pizzagelli et al., 2008a) and to learn rewardbased contingencies that might change as the task progresses (Cella et al., 2010; Dickstein et al., 2004; Dombrovski et al., 2010; Gorrindo et al., 2005; McGirr, Dombrovski, Butters, Clark, & Szanto, 2012). Individuals who are highly risk-averse may alter their response styles after any punishment or signal of punishment (Murphy et al., 2003). On tasks such as the IGT or GDT, where there are both risks and benefits associated with even advantageous decisions, this punishment sensitivity may result in impaired performance as participants do not exert the necessary risk to learn which decks (or number of predicted dice) are better than others. This heightened punishment sensitivity in depression could lead to an overestimate of future negative events (Beck et al., 1979), leading to risk-averse decisions that may not lead to optimal outcomes. On the other hand, heightened reward sensitivity in mania could lead to an overestimate of positive future outcomes (Fulford, Johnson, & Carver, 2008), again leading to a suboptimal decision making strategy in which greater emphasis is placed on positive versus negative decision feedback.

Behavioral activation system hyperactivity in bipolar disorder A final theory of decision making impairments specific to the positive (manic) and negative (depressive) symptoms seen in the bipolar disorders is the BAS hyperactivity hypothesis. Per this hypothesis, manic symptoms are caused, at least in part, by increased BAS activation, whereas depressive symptoms are caused by lowered BAS activation (Alloy & Abramson, 2010; Alloy et al., 2009; Depue & Iacono, 1989; Depue, Krauss, & Spoont, 1987; Johnson, Edge, Holmes, & Carver, 2012; Urosevic, Abramson, Harmon-Jones, & Alloy, 2008). BAS stems from Gray’s (1991) behavioral activation (BAS) and inhibition (BIS) systems.

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The behavioral activation system is sensitive to signals of rewards and constitutes the behavioral approach system. When the BAS system detects a potential reward, increased behaviors to obtain that reward are activated. This process could result in greater delay discounting, as the BAS system would seek out a more immediate versus delayed reward (Alloy et al., 2009). BIS is the opposite. BIS is sensitive to signals of punishment, and those signals lead to greater avoidance behaviors. If BIS is high and BAS is low, as could occur in depression per this theory (see Johnson et al., 2012, for review), then experiencing a loss or the threat of a loss could result in significantly increased risk-averse behavior on subsequent decisions. Thus high BAS (mania) could lead to impulsive decision making, whereas low BAS and high BIS (depression) could lead to less impulsive decision making (Alloy et al., 2009; Gray, 1991). In research practice, evidence exists in support of the BAS hyperactivity hypothesis in bipolar disorder. Individuals prone to symptoms of mania also report higher BAS than those low in mania-proneness (Johnson et al., 2012). These findings extend to those with a diagnosis of bipolar disorder (Alloy et al., 2008; Alloy, Uroˇsevi´c, et al., 2012; Alloy, Bender, et al., 2012; Johnson et al., 2000; Meyer, Johnson, & Carver, 1999; Meyer, Johnson, & Winters, 2001; Nusslock, Abramson, HarmonJones, Alloy, & Hogan, 2007; Salavert et al., 2007), and BAS hypersensitivity can even predict earlier onset of bipolar disorder (Alloy, Bender, et al., 2012b). BAS activates the same dopaminergic reward pathway that is activated during various risky decision making tasks (Bemphol, 2010; Depue & Iacono, 1989; Nusslock et al., 2012), indicating greater BAS activation could be one cause of the riskier decisions seen among those with bipolar disorder diagnoses. This neural activation change can interact with the previously noted reward-based learning difficulties to negatively affect overall decision making task performance.

Participant-related factors A concern that appears across categories of DSM-V disorders is the presence of comorbid diagnoses when decision making is assessed as a function of a specific diagnosis or category of disorders. For example, there is significant comorbidity between substance use disorders, which have known decision making impairments (see Chapter 10: Addictive behaviors: gambling and substances of abuse), and mood disorders (Kandel et al., 1999; Merikangas et al., 2007; Regier et al., 1990; Tull & Gratz, 2013). Decision making impairments and steeper delay discounting are seen among individuals with a mood disorder (Dennhardt & Murphy, 2011; Moody, Franck, & Bickel, 2016) or suicide attempt

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(Liu et al., 2012) and comorbid alcohol and other substance use/abuse, as well as those with a mood and an eating disorder (Abbate-Daga et al., 2015). Thus it is possible that there is an additive or synergistic effect of having two (or more) diagnoses that all are known to affect decision making processes. But, what is not known is if decision making impairments caused by one diagnosis led to the development of a secondary diagnosis. Jollant et al. (2010, 2013) theorized that lower decision making ability may increase vulnerability to future suicidal behavior, lending credence to the notion that decision making impairments could lead to the development of comorbid symptom development. However, future research should examine the temporal nature of these facets through longitudinal study designs. A second participant-related factor to consider in future research is the effect of treatment on decision making among those with mood disorder diagnoses. Very few studies to date examined the effect of treatment on decision making, with evidence of improvements following subcaudate tractotomy (Dalgleish, 2004) and deep brain stimulation surgery (McNeely, Mayberg, Lozano, & Kennedy, 2008; Moreines et al., 2014), whereas repeated transcranial magnetic stimulation (rTMS), lithium, and psychotherapy did not result in more advantageous decision making or other executive functions (Dichter, Felder, Petty, Bizzell, Ernst, & Smoski, 2009; Malhi et al., 2016; Tovar-Perdomo, McGirr, Van den Eynde, Rodrigues dos Santos, & Berlim, 2017). Additional research is needed to tease apart the effects of various treatment approaches on long-term decision making outcomes.

Conclusion and future directions Consistent increases in risk-taking behaviors with a potential for negative short- and long-term health consequences are seen among those with symptoms and diagnoses of mood and bipolar disorders. Inconsistencies instead emerge when risky decision making is assessed with behavioral tasks. Evidence both for and against decision making impairments is seen across tasks and mood disorder symptoms, with no consistent pattern emerging to explain these varied findings. Utilizing the IGT as an example, I examined the level of healthy control performance to see if they were showing worse than anticipated performance (e.g., Steingroever, Wetzels, Horstmann, Neumann, & Wagenmakers, 2013). Across studies, healthy controls preferred advantageous decks for the most part, and the small between-study inconsistencies in performance level cannot explain the disparate findings. Unfortunately, the other commonly used tasks do not have such a standardized way to assess “normal” performance to evaluate healthy controls in such a

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way. But, the rather small sample sizes (typically 15 30 per group) may do more to explain these inconsistencies. Highly powered studies are needed to provide a more stable, reliable assessment of risky decision making in mood and other psychological disorders. These highpowered studies can also help one to elucidate the relationship between risky decision making and state versus “trait” mood.

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7 Disordered eating behaviors: anorexia, bulimia, binge eating, and obesity Eating disorders represent a broad range of food- and eating-related impairments, running along a spectrum of severe caloric restriction on one end (i.e., dietary restriction) to binging behaviors on the other (i.e., binge eating). The disorders discussed in this section include anorexia nervosa (AN), bulimia nervosa (BN), and binge-eating disorder (BED). Obesity, although not a DSM-V disorder, will also be examined due to commonalities with BED and difficulties with behavioral control. In addition, a relatively new line of research examines disordered eating behaviors, a subclinical set of eating behaviors that can put one at risk of a future eating disorder. All of these behaviors can vary in their level of behavioral control (AN-restriction highest; Brooks, Rask-Andersen, Benedict, & Schioth, 2012). Binge eating is the most common eating disorder symptom (Mitchison & Mond, 2015), impacting nearly 8% of the general population (Hay, Mond, Buttner, & Darby, 2008; Hudson, Hiripi, Pope, & Kessler, 2007). Binge eating as a symptom is common to all of the diagnosable eating disorders (APA, 2013). First, let us compare the key signs and symptoms of AN, BN, and BED. The primary features of AN include persistent and severe caloric restriction, fear of weight gain, and altered body image/weight perception (APA, 2013). AN severity can be further specified according to current body mass index (BMI; see next paragraph). Prevalence rates of AN are in the 1% 4% range (APA, 2013; Herpertz-Dahlmann, 2015; Hudson et al., 2007; Keski-Rahkonen et al., 2007) with significant differences in the diagnostic rate between males and females. In BN, individuals experience recurrent binge-eating episodes—discrete periods of time in which the individual eats a significantly larger amount of food than usual coupled with feelings of loss of control during the eating

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episode (APA, 2013). Some describe binge eating as compulsive food intake and a binge can be followed by a purge (Avena, Rada, & Hoebel, 2008; Corwin, 2006). Purging behaviors, also described as inappropriate attempts to compensate for the binges (APA, 2013), can include vomiting, excessive laxative use, over exercise, and fasting. BN is also associated with a negative, distorted sense of self and body shape. BN affects 1% 3% of women (APA, 2013; Hudson, Hiripi, Pope, & Kessler, 2007), but as we will see, subclinical disordered eating behaviors are much more prevalent in young men and women. BED is new to the DSM-V, after being under consideration for inclusion in the DSM-IV. In BED, individuals experience frequent binges and the cooccurring loss of control and feeling of embarrassment or disgust without the compensatory behaviors (APA, 2013). Although predating its official inclusion, upward of 15 million individuals may be affected by BED in the United States alone, with lifetime prevalence rates in the 1% 3% range (APA, 2013; Hudson et al., 2007; Smink, van Hoeken, Oldehinkel, & Hoek, 2014). Obesity is primarily defined and diagnosed on the basis of an individual’s BMI. BMI (body mass index) is based on the relationship between an individual’s weight and height [National Institutes of Health (NIH)] and provides a rough estimate of body fat and risk for cardiovascular diseases. Using BMI, individuals can be categorized as underweight (BMI , 18.5), normal or healthy weight (BMI 18.5 24.9), overweight (BMI 25.0 29.9), or obese (BMI . 30.0; NIH). Prevalence rates can vary by study. For example, de Souza Ferreira and da Veiga (2008) found that 16.2% of their sample was overweight or obese and 2.5% underweight. Up to 18% of adolescents are classified as obese, an increasing rate over the past several years (Ogden, Carroll, Kit, & Flegal, 2012). Disordered eating behaviors do not have a set definition nor set of criteria. They are thought to comprise subclinical eating disorders, existing on the lower end of the eating behavior spectrum than AN, BN, and BED (Dancyger & Garfinkel, 1995; Fairburn & Beglin, 1990; Lowe et al., 1996). Disordered eating behaviors are made up of atypical eating behaviors that can cause impairment in the individual’s life yet do not meet full criteria for one of the previously noted disorders (DiPasquale & Petrie, 2013; Hudson et al., 2007) or may be less intense or severe (Uzun et al., 2006). The presence of disordered eating behaviors can lead to an increased risk for later eating disorder diagnosis (Araujo, Santos, & Nardi, 2010; Kessler et al., 2013; Wade, Wilksch, & Lee, 2012). Disordered eating behaviors can include intentional caloric restriction, binge eating, eating rituals, extreme dieting, diet pill use, fasting, and more (Cooley & Toray, 2001; Croll, Neumark-Sztainer, Story, & Ireland, 2002; Krahn, Kruth, Gomberg, & Drewnowski, 2005; Loth, MacLehose, Buchianeri, Crow, & Neumark-Sztainer, 2014; Wadden, Brownell, &

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Foster, 2002). Rates of disordered eating behaviors are significantly higher than eating disorders. Dieting and extreme dieting behaviors are reported in 50% 80% of adolescent and young adult females (Field, Haines, Rosner, & Willett, 2010; Uzun et al., 2006). Binge-eating behaviors are seen in 28% 37% of participants (Davis & Fischer, 2013; de Souza Ferreira & da Veiga, 2008). Across the different disordered eating behaviors, prevalence rate estimates range from low (11%) to high (56%) (Barnhart & Buelow, 2018; Croll et al., 2002; Cruz-Saez, Pascual, Salaberria, Elxebarria, & Echeburua, 2015; Farchaus Stein, Chen, Corte, Keller, & Trabold, 2013). In the following sections, I examine the current literature on risktaking behaviors, risky decision making, and delay discounting as a function of current eating disorder, obesity, or disordered eating behaviors.

The current literature: risk-taking behaviors The vast majority of the research literature to date focuses on the presence of risk-taking behaviors among overweight and obese individuals, with a relative dearth of information about risk-taking as a function of AN, BN, and BED. In general, diagnosis of an eating disorder is associated with an increased risk of other health-compromising behaviors that can have negative health outcomes (Bulik, Sullivan, & Epstein, 1992; French, Perry, Leon, & Fulkerson, 1995; French, Story, Downes, Resnick, & Blum, 1995; French, Perry, Leon, & Fulkerson, 1994; Killen et al., 1987; Lilenfeld et al., 2006; Neumark-Sztainer, Story, & French, 1996; Neumark-Sztainer, Story, Dixon, & Murray, 1998; Suokas et al., 2014). Lifetime prevalence of substance use among those with a primary diagnosis of AN is 24.6% and among those with BN is 48.7% (Mann et al., 2014). Among those with BN, 65% report using alcohol and 30% illegal drugs (Fischer & le Grange, 2007). Obesity and overweight are associated with a number of health-risk behaviors (e.g., Fonesca, Matos, Guerra, & Pedro, 2009; Mellin, Neumark-Sztainer, Story, Ireland, & Resnick, 2002), including increased alcohol, tobacco, and drug use (Brewer-Smyth, Cornelius, & Pohlig, 2016; Farhat, Iannotti, & Simons-Morton, 2010; Huang, Lanza, WrightVolel, & Anglin, 2013; Nelson, Lust, Story, & Ehlinger, 2009; Pasch, Nelson, Lytle, Moe, & Perry, 2008; Ratcliff et al., 2011; Ross, Graziano, Pacheco-Colon, Coxe, & Gonzalez, 2016; Shan et al., 2010; VeraVillarroel, Piqueras, Kuhne, Cuijpers, & van Straten, 2014). Some instead find decreased alcohol use in obesity and overweight (Gearhardt, Harrison, & McKee, 2012; Kleiner et al., 2004). Increased rates of risky sexual behaviors are also seen among individuals with higher BMI

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(Becnel et al., 2017; Eisenberg, Neumark-Sztainer, & Lust, 2005; Kershaw, Arnold, Lewis, Magriples, & Ickovics, 2011; Leech & Dias, 2012; Moskowitz & Seal, 2010; Ratcliff et al., 2011), though some find this is gender-specific (Akers et al., 2009; Akers et al., 2016) and others find no relationship (Guadamuz et al., 2012). Research suggests that at least one reason for increased rates of tobacco use is due to the belief that tobacco use will lead to weight loss (Farris, Zvolensky, Robles, & Schmidt, 2015). Other research suggests that it is not BMI per se, but rather perceived body weight/image that is associated with increased involvement in risky behaviors (Jiang, Risica, Arias, Perry, & VinerBrown, 2012). Increased rates of risky behaviors are also seen among individuals in the subclinical realm of the disordered eating spectrum. Most of this research was conducted to examine risk-taking behavior rates among those exhibiting extreme dieting or purging behaviors. These disordered eating behaviors are associated with greater alcohol, tobacco, and other substance use (Farchaus Stein, Chen, Corte, Keller, & Trabold, 2013; French et al., 1994; French, Perry, et al., 1995; French, Story, et al., 1995; Gritz & Crane, 1991; Killen et al., 1987; Rafiroiu, Sargent, Parra-Medina, Drane, & Valois, 2003; Thorlton, Park, & Hughes, 2014). The directionality of these relationships is not certain, as Neumark-Sztainer et al. (1997) found that greater risk-taking behaviors predicted unhealthy weight loss (rather than the other way around). All in all, greater levels of substance use and sexual risk behaviors are seen across the entire disordered eating behavior spectrum, with additional research needed into how these behaviors manifest in AN, BN, and BED.

The current literature: risky decision making Anorexia nervosa Individuals with current AN symptomatology show impaired decision making on the Iowa Gambling Task (IGT) compared to controls (Abbate-Daga et al., 2011; Adoue et al., 2015; Aloi et al., 2015; Bodell et al., 2014; Bosanac et al., 2007; Brogan, Hevey, & Pignatti, 2010; Cavedini et al., 2004; Cavedini et al., 2006; Chan et al., 2014; Danner, Sanders, et al., 2012; Fagundo et al., 2012; Fornasari et al., 2014; Galimberti et al., 2013; Liao et al., 2009; Steward et al., 2016; Tchanturia et al., 2007, 2011; Tenconi et al., 2016). In addition, those with AN appear sensitive to punishments, in that they show better learning than controls following the appearance of a punishment on a reward-based task (Bernardoni et al., 2017). There is some evidence to suggest that the subtype of AN matters, as impaired decision making is seen among

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binge-purge but not restrictive subtypes (Danner et al., 2013; but see Garrido & Subira, 2013, for the opposite relationship). That said, some find no differences on the IGT (Giannunzio et al., 2018; Guillaume et al., 2010; Jollant, Buresi, et al., 2007), beads task (McKenna, Fox, & Haddock, 2014), BART (Adoue et al., 2015; Neveu et al., 2016), and other tasks (Butler & Montgomery, 2005). Additional research examines the extent to which performance on decision making tasks improves as a function of treatment or can be used to predict treatment outcomes. Some research identified no improvement in decision making after treatment (i.e., similar performance to pretreatment AN; Bodell et al., 2014; Bosanac et al., 2007; Danner, Sanders, et al., 2012; Ehrlich et al., 2015; Giannunzio et al., 2018). Others instead find improved IGT performance following treatment or AN remission (Lindner, Fichter, & Quadflieg, 2012; Steward et al., 2016; Tchanturia et al., 2007). Still others note that individuals with greater performance on the IGT at initiation of treatment show greater treatment benefit (Cavedini et al., 2006) and longer-term goal maintenance (Steward et al., 2016) than those with lower IGT performance.

Bulimia nervosa Performances on the IGT (Boeka & Lokken, 2006; Brogan et al., 2010; Chan et al., 2014; Degortes, Tenconi, Santonastaso, & Favaro, 2015; Liao et al., 2009), affective go/no-go (Mobbs, van der Linden, d’Acremont, & Perroud, 2008), and Game of Dice (GDT) (Brand, Franke-Sievert, et al., 2007) are impaired among those with BN compared to healthy controls. However, others find no differences between groups on these and other tasks (Guillaume et al., 2010; Neveu et al., 2016; Wu et al., 2013). In fact, those with a diagnosis of BN are more risk-averse than controls on the beads task (Sternheim, Startup, & Schmidt, 2011).

Obesity By far the majority of the risky decision making research concerns individuals with overweight or obesity. These studies can be classified according to their primary weight-related variable of interest: (1) diagnosis of obesity or overweight, (2) BMI, (3) waist circumference, or (4) percent body fat. Impaired decision making is seen, usually with the IGT, as the primary behavioral measure among individuals with larger waist circumferences (Beck et al., 2017) and higher percent body fat (Chang et al., 2016; Stinson, Krakoff, & Gluck, 2018). BMI itself is not consistently correlated with decision making task performance (Brogan, Hevey, O’Callaghan, Yoder, & O’Shea, 2011; Elzakkers et al., 2017).

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The majority of the research to date finds a consistent relationship between obesity and more disadvantageous performance on the Wheel of Fortune task (Navas et al., 2016), the Hungry Donkey Task (Groppe & Elsner, 2017), and the IGT (Brogan et al., 2010, 2011; Danner, Sanders, et al., 2012; Davis, Levitan, Muglia, Bewell, & Kennedy, 2004; Davis, Patte, Curtis, & Reid, 2010; Fagundo et al., 2012; Georgiadou, GrunerLabitzke, Kohler, de Zwaan, & Muller, 2014; Graham, Gluck, Votruba, Krakoff, & Thearle, 2014; Horstmann et al., 2011; Koritzky, Yechiam, Bukay, & Milman, 2012; Mallorqui-Bague et al., 2016; Perpina, Segura, & Sanchez-Reales, 2017; Pignatti et al., 2006; Verbeken, Braet, Bosmans, & Goossens, 2014; Verdejo-Garcia et al., 2010). That said, not all research supports this conclusion. Some find no effect of obesity or overweight on the IGT (Goldschmidt et al., 2018; Kittel, Schmidt, & Hilbert, 2017; Navas et al., 2016). Comparatively, few studies examine decision making as a function of treatment. Those exhibiting successful weight loss maintenance have better GDT performance than those who are less successful following treatment (Brockmeyer, Simon, Becker, & Friederich, 2017). Although the IGT is correlated with weight loss (Witbracht, Laugero, Van Loan, Adams, & Keim, 2012), others find no differences on the IGT in preand post-bariatric surgery participants (Georgiadou et al., 2014).

Binge-eating disorder Relatively few studies examine risky decision making in those with BED, potentially due to its status as a newer diagnosis. A diagnosis of BED is associated with lowered performance on the IGT (Aloi et al., 2015; Danner, Ouwehand, van Haastert, Hornsveld, & de Ridder, 2012), GDT (Svaldi, Brand, & Tuschen-Caffier, 2010), and a reward-driven decision making task (Reiter, Heinze, Schlagenhauf, & Deserno, 2017). Danner et al. (2016) also found impaired IGT performance among individuals with BED, but only after watching a sad versus neutral video clip. A subset of studies compares individuals with obesity to individuals with obesity and BED. In these studies, individuals diagnosed with both obesity and BED show more disadvantageous performance on the IGT than individuals diagnosed with just obesity (Muller et al., 2014), whereas others find that both groups are equally impaired on this task (Davis, Patte, Curtis, & Reid, 2010; Kittel et al., 2017; Manasse et al., 2014).

Disordered eating behaviors The study of risky decision making in disordered eating behaviors is relatively new and depends in part on how disordered eating is

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defined. Worse performance on the IGT is seen with those reporting increased soda consumption (Ames et al., 2014) and among those reporting binge/purge behaviors (Gat-Lazer, Geva, Gur, & Stein, 2017). On the other hand, others find no relationship between these behaviors and GDT or IGT performance (Barnhart & Buelow, 2018; Kanakam, Krug, Collier, & Treasure, 2017).

Comparisons between eating disorder diagnostic groups Although most studies focus on performance comparisons among those with an eating disorder diagnosis to those without, some instead compare performance across eating disorder diagnoses. Some show no differences across specific eating disorders and across tasks (Brogan et al., 2010; Guillaume et al., 2010; Van den Eynde et al., 2012), whereas others find BED engage in riskier decision making than AN (Aloi et al., 2015) and AN engage in riskier decision making than BN (Sternheim et al., 2011). Finally, several metaanalyses examined risky decision making in those with eating disorders more generally and by specific subtype. Both small (Wu, Brockmeyer, et al., 2016; Wu, Passos, et al., 2016; Yang, Shields, Guo, & Liu, 2018) and moderate large (Guillaume et al., 2015) effect sizes are seen, indicating impaired decision making across the eating disorder diagnoses and obesity. A “clear” profile of cognition in those with BN is difficult to determine, due in large part to small sample sizes across studies (van den Eynde et al., 2011). Recovery from AN leads to an improvement in decision making to the level of healthy comparison participants (Guillaume et al., 2015). Despite some null findings, there is a small relationship between BMI and risky decision making (Emery & Levine, 2017).

The current literature: delay discounting and reward responsiveness Relatively few studies examined delay discounting and reward responsiveness outside of individuals with obesity. Individuals with a diagnosis of AN show steeper delay discounting (i.e., prefer smaller sooner to larger later rewards) than healthy controls (Decker, Figner, & Steinglass, 2014); however, not all find this relationship (Ehrlich et al., 2015; King et al., 2016). Still others find the opposite: individuals with acute or recovered AN discount to a lesser degree (i.e., show less of a preference for smaller sooner than larger later rewards) than controls (Ritschel et al., 2015; Steinglass et al., 2012, 2017). A meta-analysis

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confirms these mixed findings in AN (McClelland et al., 2016). Among those with BN, steeper delay discounting is seen compared to healthy controls (Balodis et al., 2014; Kekic et al., 2016; McClelland et al., 2016; but see Simon et al., 2016, for no group differences in delay discounting). BED is also associated with steeper delay discounting (McClelland et al., 2016; Mole et al., 2015; but see Simon et al., 2016). Increased disordered eating behaviors are also associated with greater (steeper) delay discounting (Dong et al., 2016; VanderBroek-Stice, Stojek, Beach, vanDellen, & MacKillop, 2017). Steeper delay discounting is seen in obese and overweight participants compared to control participants (Buono, Whiting, & Sprong, 2015; Davis et al., 2010; Fields, Sabet, & Reynolds, 2013; Hendrickson & Rasmussen, 2013; Hendrickson, Rasmussen, & Lawyer, 2015; Klement et al., 2018; Mole et al., 2015; Rasmussen, Lawyer, & Reilly, 2010; Simmank, Murawski, Bode, & Horstmann, 2015; Stojek & MacKillop, 2017; Weller, Cook, Avsar, & Cox, 2008). Greater discounting is also seen in those with higher BMIs (Dassen, Houben, & Jansen, 2015; Jarmolowicz, Cherry, et al., 2014; Lawyer, Boomhower, & Rasmussen, 2015; Price, Higgs, Maw, & Lee, 2016; Weller et al., 2008) and among those with a greater percent body fat (Lu et al., 2014). But, just as with the other eating disorders, some research finds no differences in delay discounting compared to controls (Groppe & Elsner, 2017; Nederkoorn, Smulders, Havermans, Roefs, & Jansen, 2006; Rasmussen, Lawyer, & Reilly, 2010). One reason for these differing results may be that the type of reward matters. There is some evidence to suggest that discounting could occur for monetary but not food-related rewards (Avila, Toledo, Campos, Diaz, & Corona, 2016; Dassen et al., 2015; Hendrickson & Rasmussen, 2017). Others indicate that the presence of a comorbid diagnosis, gambling disorder, leads to steeper delay discounting than obesity alone (Fields, Sabet, Peal, & Reynolds, 2011). Several meta-analyses examine delay discounting in obesity, showing both small (Bickel et al., 2014) and medium (Amlung, Petker, Jackson, Balodis, & MacKillop, 2016) effects. Rate of delay discounting can predict treatment outcome (Hayes, Eichen, Barch, & Wilfley, 2018). Those with steeper initial discounting of food and monetary rewards show fewer treatment gains (Best et al., 2012).

Performance on other executive function tasks A general pattern emerges in the current literature. When a behavioral decision making task is assessed as part of a larger neuropsychological battery, the authors frequently do not report correlations between tasks (e.g., Abbate-Daga et al., 2011; Cavedini et al., 2004;

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Fagundo et al., 2012; Giannunzio et al., 2018). Among those who did assess and report relationships, inconsistencies are seen. Performance on the GDT is correlated with the Stroop and Trail Making Test-B, two executive function tasks, among those with a primary BN diagnosis (Brand, Franke-Sievert, et al., 2007). IGT scores are both correlated (Georgiadou et al., 2014) and not correlated (Goldschmidt et al., 2018) with a tower task, whereas no correlations are seen with the WCST (Tenconi et al., 2016), Stroop, Corsi blocks, and Trail Making Test (Georgiadou et al., 2014). Individuals with obesity (Perpina et al., 2017; Stinson et al., 2018; Verdejo-Garcia et al., 2010; Yang et al., 2018) and AN (Sarrar et al., 2011) show impairments on measures of problem solving, cognitive flexibility, set shifting, planning, and inhibition. Additional research is needed to determine whether impairments in decision making in those with eating disorders occurs in combination with or separate from impairments in other executive functions.

Neuroimaging Although not specific to one eating disorder, or a diagnosed eating disorder itself, the reward pathway is implicated in risky decision making in disordered eating and in relation to food-related decisions more generally. Increased activation in the striatum (Dong et al., 2016) and prefrontal cortex (Kishinevsky et al., 2012; Uher, Treasure, & Campbell, 2002) are associated with increased disordered eating behaviors and the progression to obesity and other eating disorders. Anticipation of food activates the posterior cingulate cortex and subsequent presentation of the food (i.e., being allowed to eat the anticipated food) activates the medial orbitofrontal cortex (Simon et al., 2016), indicating food itself is rewarding and pointing to involvement of this pathway in eating behaviors. Finally, a recent review indicates a link between delay discounting and altered frontostriatal circuits across eating disorder diagnoses (McClelland et al., 2016), again pointing to involvement of the reward pathway in “normal” and disordered eating behaviors. Numerous studies of participants with AN, BN, BED, and obesity utilize neuroimaging during risky decision making tasks. Patients with AN, compared to healthy controls, show lowered medial orbitofrontal cortex volume that is correlated with impaired IGT performance (Bodell et al., 2014). In addition, altered activation is seen in portions of the reward pathway, including anterior cingulate, striatum, nucleus accumbens, and ventromedial prefrontal cortex, during risky decision making and delay discounting tasks (Kaye, Fudge, & Paulus, 2009; Keating, Tilbrook, Rossell, Enticott, & Fitzgerald, 2012; King et al., 2016; Pietrini et al., 2011; van Kuyck et al., 2009) that can “normalize” following

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treatment (Decker et al., 2014). Other studies point to changes in the connectivity between areas responsible for cognitive control and areas involved in reward and punishment processing (Bernardoni et al., 2017; Canna et al., 2017; Cha et al., 2016; Ehrlich et al., 2015; Scaife, Godier, Reinecke, Harmer, & Park, 2016). These altered functional connections can lead to impairments in processing negative feedback or punishment (Murao et al., 2017) and difficulties distinguishing between positive (reward) and negative feedback (Wagner et al., 2007). Portions of the prefrontal cortex (Boehm et al., 2018) and striatum (Foerde, Steinglass, Shohamy, & Walsh, 2015) are also known to activate to food-related and other rewards. Fewer neuroimaging studies were conducted with participants with active BN, BED, or obesity diagnoses. The presence of active BN is associated with abnormal connections between cognitive control and reward processing/decision making pathways (Canna et al., 2017; Cyr et al., 2016), as well as altered responses to rewards in the reward pathway (Harrison, O’Brien, Lopez, & Treasure, 2010; Wagner et al., 2010). Changes in white matter integrity (Mettler, Shott, Pryor, Yang, & Frank, 2013) and medial orbitofrontal cortex volume (Schafer, Vaitl, & Schienle, 2010) are also seen. Individuals with a diagnosis of BED, obesity, or both show lowered activation in portions of the prefrontal cortex on delay discounting and risky decision making tasks (Hege et al., 2015; Lavagnino, Arnone, Cao, Soares, & Selvaraj, 2016; Stoeckel, Murdaugh, Cox, Cook, & Weller, 2013). In addition, BED is associated with increased medial orbitofrontal cortex volume (Schafer, Vaitl, & Schienle, 2010) and greater BMI with gray matter volume in the left orbitofrontal cortex and ventromedial prefrontal cortex (He et al., 2015) as well as dorsolateral prefrontal cortex structural changes (Horstmann et al., 2011). Changes in the insula are also seen: healthy weight participants show increased activation on the risky gains task whereas overweight participants show lowered activation (Mata, Verdejo-Roman, Soriano-Mas, & Verdejo-Garcia, 2015). Finally, activity in the frontostriatal pathways is linked to treatment outcomes, as individuals with BED who show lower pathway activation have worse treatment outcomes than those with higher activation (Balodis et al., 2014).

Potential mechanisms Overall executive dysfunction Executive dysfunction may be one potential theory to explain increased risky decision making and steeper delay discounting across disordered eating patterns. Since decision making is an executive

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function and linked with functioning of some overlapping prefrontal cortex structures, it would make sense that these impairments are an extension of other executive difficulties. Impaired executive functions are a known risk factor for maladaptive eating behaviors that can lead to overweight and obesity (e.g., Groppe & Elsner, 2015). That said, research does not consistently find executive impairments across the different disorders. Impairments are seen in inhibitory control, planning, problem solving, memory, and cognitive flexibility (Chan et al., 2014; Fagundo et al., 2012; Galimberti et al., 2013; Goldschmidt et al., 2018; Groppe & Elsner, 2017; Manasse et al., 2014; Talbot, Hay, Buckett, & Touyz, 2015). But, on the other hand, no differences are seen on these and similar measures compared to healthy controls (Degortes et al., 2015; Galimberti et al., 2013; Manasse et al., 2014). Giel et al. (2017), in their review, find deficits in inhibitory control among those with obesity and/or binge-eating disorder. Deficits in inhibitory control could explain difficulties refraining from the more immediate or short-term advantageous selection in favor of the longer-term and more advantageous selections. As we saw in the previous sections, performance on executive function and decision making tasks assessed within the same study does not always correlate. Taken together, it appears that executive dysfunction may contribute to impaired decision making across the disordered eating spectrum, but it is not the only factor at play.

Comorbidity Another potential mechanism underlying decision making impairment in disordered eating/eating disorders is the presence of comorbid psychopathology. As we have seen in the previous chapters and will in the upcoming ones, impaired decision making is seen across a number of the diagnostic categories, many of which commonly cooccur with AN, BN, and BED. Depression, anxiety, and substance use disorders are the most common comorbid diagnoses, and all are known to also impair decision making. Individuals with BN frequently exhibit comorbid disorders (62.5%), most commonly mood (Fischer & le Grange, 2007). Yet when these two diagnoses are examined in the same participants, conflicting findings are seen. Mood symptoms both account for more variability (Danner et al., 2016) and less variability (Boeka & Lokken, 2006) in IGT scores than a BN diagnosis. For those with AN the presence of depressive symptoms both further impairs (Abbate-Daga et al., 2015) or does not affect (Adoue et al., 2015; Danner et al., 2013) risky decision making. Depressive mood is also associated with disordered eating (e.g., Polivy & Herman, 1987) and, in addition to substance abuse, is a risk factor for the development of an eating disorder (e.g., Garner, 1993; Rosen, Gross, &

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Vara, 1987). Problem gambling behaviors that co-occur with obesity can lead to worse decision making than obesity alone (Grant, Derbyshire, Leppink, & Chamberlain, 2015). We also saw previously that those with a diagnosed eating disorder are at an increased risk of developing substance use difficulties. It is not certain in many of these studies which came first, the eating disorder or the comorbid disorder.Thus it is unknown at this time what may be more responsible for diminished decision making and may act as a consequence of the primary disorder to further diminish decision making.

Alterations in neural processing of rewards and learning from feedback We know the brain’s reward pathway and connections to the prefrontal cortex are impacted by the presence of an eating disorder. One way this may affect real-world decision making is by affecting the processing of rewards and punishments, leading to difficulties learning from positive and negative feedback and punishments. Altered dopamine signals are associated with the development of an eating disorder or obesity (Frieling et al., 2010; Park, Godier, & Cowdrey, 2014; Stoeckel et al., 2008). Increased dopamine response and subsequent increased reward pathway stimulation is rewarding and can lead to increased involvement in addictive behaviors, including food consumption (Beaver et al., 2006; Kanakam, Krug, Collier, & Treasure, 2017; Keating et al., 2012; O’Hara, Campbell, & Schmidt, 2015; Rothemund et al., 2007; Volkow, Fowler, & Wang, 2002; Wierenga et al., 2014). Alterations in this pathway can lead to an overreaction or heightened sensitivity to rewarding items, such as food, that can lead to risky decisions and ultimately disordered eating (Appelhans et al., 2011; Bartholdy et al., 2017; Brogan et al., 2011; Compan, Walsh, Kaye, & Geliebter, 2015; Dong, Jackson, Wang, & Chen, 2015; Dong et al., 2014; Harrison et al., 2010; Rollins, Dearing, & Epstein, 2010). But, others instead suggest lowered reward processing and enhanced punishment processing (Giannunzio et al., 2018; Harrison, Treasure, & Smillie, 2011; Loxton & Dawes, 2001), or that reward processing are more subservient to cognitive control than is usually seen to occur (Soussignan, Schaal, Rigaud, Royet, & Jiang, 2011). This difference may come down to the specific type of disordered eating, with restrictive behaviors due to increased punishment sensitivity and bingeing behaviors due to increased reward sensitivity, as well as associated differences in focus of decision making (e.g., a preference for short-term over longterm consequences; Volkow & Baler, 2015). All in all, impaired reward system functioning and an overreliance on learning from rewards can underlie eating disorders and may also be a precursor to decreased

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decision making (Berner & Marsh, 2014). Difficulties distinguishing between different types of feedback, as well as between risks and rewards, may also contribute to these impairments (Boisseau et al., 2013; Svaldi et al., 2015; Wagner et al., 2007). For example, individuals with diagnosed eating disorders may process events as positive or rewarding that are not processed that way by those without eating disorders. Severe caloric restriction is typically experienced as negative or punishing but may instead be processed as rewarding in those with restrictive disordered eating behaviors. These neural changes can, in turn, affect realworld decision making.

Impulsivity Impulsivity and the behavioral activation system (BAS) may also be a mechanism for risky decision making in this population. BAS, behavioral inhibition, and impulsivity are all associated with the development of disordered eating and eating disorders, with evidence that impulsivity is associated with increased binge/purge type behaviors and reduced BAS with restricting behaviors (Atalayer, 2018; Bodell, Joiner, & Ialongo, 2012; Burrows, Hides, Brown, Dayas, & Kay-Lambkin, 2017; Drukker et al., 2009; Fields et al., 2013; Fischer, Smith, & Cyders, 2008; Meule, de Zwaan, & Muller, 2017; Schag, Schonleber, Teufel, Zipfel, & Giel, 2013; Verdejo-Garcia et al., 2010; Waxman, 2009). Impulsivity is positively correlated with food intake and obesity (Davis, 2013; Fields et al., 2013; Jasinska et al., 2012; Meule & Platte, 2015; Murphy, Stojek, & Mackillop, 2013), as well as risky decision making within eating disorder populations (Butler & Montgomery, 2005; Gat-Lazer et al., 2017; but see Tchanturia et al., 2011). Impulsivity is also associated with the response to food cues and how rewarding they are perceived to be (Tetley, Brunstrom, & Griffiths, 2010; Volkow, Wang, & Baler, 2011), and treatments that focus on lowering impulsive responses can lead to greater outcomes (Delgado-Rico et al., 2012). Additional research is needed to determine if risky decision making is consistently linked to higher levels of impulsivity across eating disorders or is specific to certain diagnoses, as disorders such as AN-restrictive are less associated with impulsivity and more with strict behavioral control.

Conclusion and future directions Although there are inconsistencies throughout the literature, it does appear that the presence of disordered eating behaviors or a diagnosed eating disorder can have a significant impact on risky decision making,

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delay discounting, and involvement in risk-taking behaviors. Variations are seen across different diagnostic categories, with greater evidence of these difficulties in those with obesity and BED and more inconsistencies among those with AN and BN. However, there are two characteristics of previous studies that might affect results. A number of studies utilize relatively small samples of patients and controls, which could lead to low power to detect significant differences. Many studies are cross-sectional in nature, leaving relatively unknown the longer-term pattern of relationships between decision making and disordered eating behaviors. In addition, multiple studies utilize female-only or predominantly female samples, as well as samples of younger adults or adolescents. These age groups can show decision making difficulties independent of any psychiatric diagnosis and we have also discussed inconsistent gender-based differences across decision making tasks. But, lifetime prevalence rates of any eating disorder diagnosis and of disordered eating behaviors indicate that males do experience disordered eating behaviors. We need a better understanding of how decision making is affected across genders. Some research is beginning to examine whether changing time orientation can change delay discounting and risky decision making, leading to improved treatment outcomes. Engaging in healthy eating patterns can create conflict between short-term and long-term goals (Joireman, Shaffer, Balliet, & Strathman, 2012). Eating healthier foods and healthier portions might, to some, be considered a less appetitive decision in the immediate than eating foods high in sugar or carbohydrates. But these short-term negatives are paired with long-term positives, in that the individual is making healthier lifestyle decisions. Encouraging people to think about the future might shift the decision making time-frame from the immediate to the long-term. In fact, research suggests that thinking about the future can decrease delay discounting (Daniel, Said, Stanton, & Epstein, 2015), increase healthy choices (Adams & Nettle, 2009; Hall & Fong, 2007; Piko & Brassai, 2009), improve treatment outcomes (Jarmolowicz, Cherry, et al., 2014), and lower impulsive decision making patterns (Daniel, Stanton, & Epstein, 2013). Others instead find no correlation between increased future orientation and either delay discounting or health-related outcomes (e.g., BMI; Dassen, Houben, & Jansen, 2015). We do know that treatments that target executive functions more generally can improve weight loss outcomes (e.g., Hayes et al., 2018); however, this is an avenue ripe for future research exploration.

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C H A P T E R

8 Sleep deprivation and sleep-related disorders The DSM-5 classifies sleep-related disorders as “sleep wake disorders” and includes diagnoses of insomnia, hypersomnolence, narcolepsy, obstructive sleep apnea, central sleep apnea, sleep-related hypoventilation, circadian rhythm sleep wake disorders, and the parasomnias [nonrapid eye movement (REM) sleep arousal disorders, nightmare disorder, REM sleep behavior disorder, and restless leg syndrome; American Psychiatric Association (APA), 2013]. Several commonalities exist among the various sleep-related disorders. There is some disturbance in the quantity and/or quality of sleep, which frequently results in excessive daytime sleepiness. Cognitive complaints, such as decreased attention, memory, and executive functions, are common (see next sections). Age of diagnosis varies, as do prevalence rates. Parasomnias such as nightmares, sleepwalking, and sleep terrors can occur in young infants and children, with 1.3% 3.9% of preschool children experiencing frequent nightmares and 1% 5% meeting criteria for the non-REM sleep disorder of sleepwalking (APA, 2013). Rates for the dyssomnias are generally higher, ranging from 0.02% (narcolepsy) to 20% (obstructive sleep apnea in older adults) (APA, 2013). However, a greater number of individuals experience occasional sleep difficulties, or sleep difficulties that occur frequently but do not meet full criteria for a diagnosable disorder. Up to 33% of adults experience at least some symptoms of insomnia (APA, 2013), 10% 30% of children sleepwalk (APA, 2013), 25% of adolescents experience at least one symptom of insomnia (Roberts, Roberts, & Chan, 2008), and most US adolescents and adults sleep less than the recommended number of hours per night (National Sleep Foundation, 2006). Sleep needs can vary by age and other individual characteristics (Mercer, Merritt, & Cowell, 1998), but in general individuals do not obtain an adequate amount of sleep and thus operate in a form of chronic sleep deprivation most of the time.

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In the sections that follow, I examine the current literature on risk-taking behaviors, risky decision making, and delay discounting as a function of diagnosed sleep disorder, self-reported sleep difficulties, and acute sleep deprivation conducted in a lab-based setting. Specific attention will be paid to the potential influence of medications to treat insomnia and other sleep disorders, as well as comorbid diagnoses.

The current literature: risk-taking behaviors Individuals experiencing both acute and chronic sleep deprivation, either due to an unknown or situational factor or due to the diagnosis of a sleep disorder, often engage in risk-taking behaviors with a potential to cause harm to one’s health and well-being. Children, adolescents, and adults who obtain less sleep than is recommended show increased risk-taking behaviors compared to those getting adequate sleep (Anderson, Storfer-Isse, Taylor, Rosen, & Redline, 2008; Beebe et al., 2008; Carskadon, 1989 1990; Dahl, 1996; Dahl & Lewin, 2002; Harrison & Horne, 2000; Laska, Pasch, Lust, Story, & Ehlinger, 2009; O’Brien & Mindell, 2005; Paavonen et al., 2009; Peters et al., 2009; Rossa, Smith, Allan, & Sullivan, 2014; Schnyer, Zeithamova, & Williams, 2009; Telzer et al., 2013a, 2013b). This finding can be due to the excessive daytime sleepiness reported by those with sleep deficiency, as daytime sleepiness is associated with increased risk-taking, impulsivity, and engagement in activities with the potential for harm (Connor, Norton, et al., 2002; Dinges, 1995, 2008; Pack & Schwab, 2005; Swaen, Van Amelsvoort, Bultmann, & Kant, 2003). In addition, Thomas, Monahan, Lukowski, and Cauffman (2015) suggest that it is not the sleep deprivation or fatigue that affects risk-taking behaviors, but rather how each influences working memory and impulse control. However, not all researchers find sleep deprivation increases risk-taking. There is also evidence, though less voluminous, that sleep deprivation can decrease risk-taking (Acheson, Richards, & de Wit, 2007; Chaumet et al., 2009; Killgore, Grugle, et al., 2008; Killgore, Lipizzi, Kamimori, & Balkin, 2007; Stahle et al., 2011). More specific research focuses on subtypes of risk-taking behavior, including substance use, risky sexual behaviors, aggression, and unsafe driving. Multiple studies examined the relationship between acute and chronic sleep deprivation and use of alcohol, tobacco, and other substances. Sleep deprivation is associated with an earlier onset of substance use (Owens, Wang, Lewin, Skora, & Baylor, 2017; Wong, Brower, & Zucker, 2009) as well as more consistent (Sing & Wong, 2010) and higher rates of use across substances (Catrett & Gaultney, 2009; Gau & Soong, 2007; Holmen, Barrett-Connor, Holmen, & Bjermer, 2000;

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Johnson & Breslau, 2001; McKnight-Eily et al., 2011; O’Brien & Mindell, 2005; Pasch, Latimer, Cance, Moe, & Lytle, 2012; Patten, Choi, Gillin, & Pierce, 2000; Reichenberger, Hilmert, Irish, Secor-Turner, & Randall, 2016; Tynjala, Kannas, & Levalahti,1997; Winsler, Deutsch, Vorona, Payne, & Szklo-Coxe, 2015; Wong, Brower, Fitzgerald, & Zucker, 2004; Wong, Robertson, & Dyson, 2015). Sleep difficulties are even associated with increased severity of substance abuse symptoms (Hasler, Bootzin, Cousins, Fridel, & Wenk, 2008; Roberts, Roberts, & Duong, 2008, 2009). Two concerns emerg in this literature, however. One is that there appears to be a dose response relationship between level of sleep deprivation and substance use. Individuals who fall just short of the recommended number of hours of sleep do not show increased risk-taking, but those with significant sleep deprivation do (Meldrum & Restivo, 2014). The second concern relates to the direction of the effect. It is unclear if the sleep difficulties led to increased substance use, or if increased substance use led to sleep difficulties. We know that higher rates of substance use are associated with increased sleep difficulties (Ebrahim, Shapiro, Williams, & Fenwick, 2013; Schierenbeck, Riemann, Berger, & Homyak, 2008), so it is possible that there is a synergistic effect of these two behaviors on one another. Mixed findings are seen when risky sexual behaviors, ones that have a potential negative health consequence, are examined. Although some researchers find sleep deprivation and overall sleep difficulties increase risky sexual behaviors (McKnight-Eily et al., 2011; Meldrum & Restivo, 2014; O’Brien & Mindell, 2005; Yen, King, & Tang, 2010), others find no relationship (Reichenberger et al., 2016). With regard to unsafe driving and pedestrian behaviors, the picture is clearer. Daytime sleepiness, diagnoses of narcolepsy, sleep apnea, insomnia, and hypersomnia, and sleep deprivation more generally are all associated with increased close calls, hits, and accidents in virtual reality pedestrian (Avis, Gamble, & Schwebel, 2014; Davis, Avis, & Schwebel, 2013) and driver (Perier et al., 2014; Pizza, Contardi, Ferlisi, Mondini, & Cirignotta, 2008; Risser, Ware, & Freeman, 2000) simulators. These results mimic those seen using real-world accident records (Danner & Phillips, 2008; Hutchens, Senserrick, Jamieson, Romer, & Winston, 2008; Leger et al., 2014; Leger, Guilleminault, Bader, Levy, & Paillard, 2002; Leger, Massuel, & Metlaine, 2006; Millman, 2005; National Sleep Foundation, 2006; Spaeth, Goel, & Dinges, 2012). That said, several researchers find no relationship between road/pedestrian safety and sleep difficulties (Kotterba et al., 2004; Philip et al., 2010). Leufkens et al. (2014) followed-up on their lack of sleep-related driving impairment, finding instead that treatment with a hypnotic agent impaired the driving ability in all participants, regardless of baseline

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sleep difficulties. The potential influence of hypnotic medications will be discussed at the end of this section. Sleep deprivation also affects aggression, delinquency, and gambling behaviors. Higher rates of aggression and delinquency are seen in those with sleep difficulties compared to those without (Catrett & Gaultney, 2009; Clinkinbeard, Simi, Evans, & Anderson, 2011; Haynes et al., 2006; Ireland & Culpin, 2006; Kamphuis, Meerlo, Koolhaas, & Lancel, 2012; Meldrum & Restivo, 2014; Morrison, McGee, & Stanton, 1992; O’Brien & Mindell, 2005; Scott & Judge, 2006; Umlauf, Bolland, & Lian, 2011; Wong & Brower, 2012; Wong et al., 2015). Higher rates of gambling, both pathological and nonpathological, are also seen among those with sleep difficulties compared to controls (Loft & Loo, 2015; Parhami et al., 2012; Parhami, Siani, Rosenthal, & Fong, 2013). Finally, there is evidence of a relationship between sleep deprivation, mood, and suicidal ideation. Although negative mood is not put forth as a risk-taking behavior, one can argue that suicidal ideation leading to suicidal behavior is a risky behavior with the potential for negative health outcomes. Sleep deprivation not only leads to increased negative mood and sadness (Chen, Burley, & Gotlib, 2012; Lofthouse, Gilchrist, & Splaingard, 2009; McKnight-Eily et al., 2011; Morrison et al., 1992; Pasch, Laska, Lytle, & Moe, 2010; Roberts, Roberts, & Chen, 2001; Wolfson & Carskadon, 1998) but can also increase suicidal ideation and attempts (Liu, 2004; McKnight-Eily et al., 2011; Roberts et al., 2001; Winsler, Deutsch, Vorona, Payne, & Szklo-Coxe, 2015). It is not just sleep quantity but sleep quality that counts in this relationship, as sleep quality is the strongest predictor of college student well-being (Ridner, Newton, Staten, Crawford, & Hall, 2016). But, similar to the concern regarding substance use and sleep, it is unclear whether sleep deprivation leads to increased negative mood or if negative mood leads to sleep deprivation. Insomnia and hypersomnia are both potential symptoms of a major depressive episode and may be the first signs and symptoms of the disorder (Mitru, Millrood, & Mateika, 2002). The directionality of this relationship deserves further investigation.

Conclusion In sum, a clear but not consistent pattern emerges that sleep deprivation, both acute and chronic, is associated with increased involvement in various risk-taking behaviors that can have long-term health consequences. The strength of this relationship may depend in part on the level of sleep difficulties, and some questions remain as to whether sleep is the direct or indirect cause of these increased risky behaviors. In addition, several studies found hangover effects of sleep medications that can

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negatively affect outcomes (Bianchi & Westover, 2014), such as on driving and other tasks with a potential for accidental injury (Roth et al., 2014; Vermeeren et al., 2013; Verster, Veldhuijzen, & Volkerts, 2004; Zhang et al., 2010). However, relatively few studies specifically examine participants who exhibit sleep difficulties but do not currently take sedatives or hypnotics compared to participants who exhibit sleep difficulties and take these medications. The effect of sleep medication versus sleep deprivation itself should be further teased apart in future research.

The current literature: risky decision making Acute and chronic sleep deprivation can both impact decision making. Although there is some evidence that decisions improve following time spent away from the decision (including to sleep; Dijksterhuis, 2004; Dijksterhuis, Bos, Nordgren, & van Baaren, 2006; Lerouge, 2009), the influence of sleep deprivation on risky decision making is quite varied. Two different types of studies are reviewed in this section. One set of studies involves the assessment of decision making among individuals experiencing chronic sleep deprivation, which may be due to a sleep disorder diagnosis. Typically, chronic sleep deprivation and fatigue is associated with worse decision making and difficulties attending to risk (Frings, 2012; McKenna et al., 2007; O’Brien & Mindell, 2005; Paavonen et al., 2009; Peters et al., 2009; Schnyer et al., 2009). The second set of studies involves acute sleep deprivation in a lab-based setting, through overnight (or longer) prolonged wakefulness paradigms. Acute sleep deprivation also typically results in riskier or impaired decision making and worse performance on other tasks (Brown, Tickner, & Simmonds, 1970; Harrison & Horne, 2000; Hartley & Shirley, 1977; Kjellberg, 1975; Menz, Buchel, & Peters, 2012; Sicard, Jouve, & Blin, 2001; Venkatraman, Chuah, Huettel, & Chee, 2007; Venkatraman, Huettel, Chuah, Payne, & Chee, 2011; Wicklow & Espie, 2000; Wilkinson, 1961, 1965). Changes in sleep patterns do appear, in general, to affect decision making (Durmer & Dinges, 2005; Mullette-Gillman, Kumianingsih, & Liu, 2015; Ratcliff & Van Dongen, 2009; Whitney & Hinson, 2010), including on reward-based tasks (Chee & Chuah, 2008; Gujar, Yoo, Hu, & Walker, 2011; Hewig et al., 2010; Holm et al., 2009). More specific information by behavioral task follows.

Iowa Gambling Task Multiple authors examine the influence of self-reported sleep loss or acute lab-based sleep deprivation on performance on the Iowa

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Gambling Task (IGT). The length of sleep deprivation varies from overnight to 72 hours, but the pattern of performance is consistent. Acute sleep deprivation results in riskier decisions on the IGT compared to individuals who sleep as usual prior to testing (Abe, Inoue, Komada, & Hori, 2011; Killgore, 2007; Killgore, Balkin, & Wesensten, 2006; Killgore, Grugle, & Balkin, 2012; Killgore, Killgore, et al., 2007; Killgore, Lipizzi, et al., 2007; Liu & Zhou, 2016; Pace-Schott, Nave, Morgan, & Spencer, 2012), with most of these studies relying on healthy collegeaged participants. That said, several find no effect of acute sleep deprivation (Pace-Schott et al., 2009; Singh, 2013) or self-reported sleepiness (Olson, Weber, Rauch, & Killgore, 2016) on IGT performance. One study (Killgore et al., 2012) examined the potential for psychostimulant medications to restore decision making, to no avail. It should be noted that a number of these studies relied on relatively small sample sizes, with less than 40 participants per sleep group. IGT performance is also examined in the context of diagnosed sleep disorders, including narcolepsy with cataplexy, obstructive sleep apnea, restless leg syndrome, and REM sleep disorders. Participants with a diagnosis of narcolepsy with cataplexy are riskier on the IGT than healthy controls (Bayard, Raffard, & Gely-Nargeot, 2011; Bayard, Langenier, & Dauvilliers, 2013b; Delazer et al., 2011). In each of these studies, approximately half the patients were currently taking medications to treat narcolepsy. Participants with REM sleep disorder (Delazer et al., 2012; Sasai et al., 2012) and restless leg syndrome (Bayard, Langenier, & Dauvilliers, 2013a; Galbiati et al., 2015) also make riskier decisions on the IGT than healthy controls. Although Bayard et al. (2013b) found that dopamine agonist treatment does not affect IGT performance, Galbiati et al. (2015) found significant improvement in decision making following three months of treatment. Among patients with obstructive sleep apnea, the results are more mixed. Although some research suggests sleep apnea leads to riskier decisions (Daurat, Ricarrere, & Tiberge, 2013; McNally, Shear, Tlustos, Amin, & Beebe, 2012), others find no differences between those with the diagnosis and healthy controls (Delazer et al., 2016). However, Delazer et al. (2016) find a correlation between IGT performance and oxygen saturation level during sleep, pointing to a potential cause of decision making impairment among those with sleep disorders.

Balloon Analogue Risk Task The findings with the Balloon Analogue Risk Task (BART) are quite mixed. Some find that sleep deprivation increases risk-taking on the

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BART as assessed through increased explosions or increased average adjusted pumps per balloon (Killgore, Kamimori, & Balkin, 2011; Killgore, Kelley, et al., 2010; Lei et al., 2017; Rossa et al., 2014; Telzer et al., 2013), whereas others find no effect or even a positive effect (i.e., decreased risk-taking) following sleep deprivation or decreased sleep (Acheson et al., 2007; Chaumet et al., 2009; Demos et al., 2016; Killgore, 2007; Killgore, Grugle et al., 2008). Several researchers also investigate the influence of caffeine and psychostimulants on risk-taking following acute sleep deprivation. Caffeine intake postdeprivation reverses previously seen increased risk-taking on the BART (i.e., lower risk-taking; Killgore et al., 2011), but taking a psychostimulant medication can increase risk-taking when sleep deprivation lowers it (Killgore, Grugle et al., 2008).

Other tasks Multiple other behavioral risky decision making tasks are utilized to assess sleep deprivation effects; however, relatively few studies utilize each task compared to those investigating the IGT and BART. Sleep deprivation is associated with worse (or riskier) performance on the Cambridge Gamble Task (Harvanko, Derbyshire, Schreiber, & Grant, 2014), stoplight task (Roehrs, Greenwald, & Roth, 2004), lottery choice task (McKenna, Dickinson, Orff, & Drummond, 2007), blackjack task (Fraser, Conduit, & Phillips, 2013), probabilistic decision task (Maric et al., 2017), and lexical decision task (Mazzetti et al., 2006). In addition, participants take longer and have greater difficulty using emotion and cognition to guide responses to Greene’s moral dilemmas following 77 hours of sleep deprivation (Killgore, Killgore, et al., 2007). However, sleep deprivation results in decreased risk-taking on the Cups Task (Uy & Galvan, 2017) and no difference in performance on the Game of Dice Task compared to controls (Bayard et al., 2013a, 2013b, 2011). Riskier performance is also seen across other decision making and gamble tasks (Aran, Wasserteil, Gross, Mendlovic, & Pollak, 2017; Frings, 2012; Tonetti et al., 2016; Venkatraman et al., 2007, 2011). Risk-taking propensity is also examined in this population. Several researchers utilized the Evaluation of Risks Scale to measure selfreported propensity to take risks. No consistent pattern of responses is seen, as following sleep deprivation researchers find increased (Sicard et al., 2001; Sicard, Jouve, Blin, & Mathieu, 1999) and decreased (Chaumet et al., 2009; Killgore, 2007; Killgore et al., 2011; Killgore, Cotting, et al., 2008; Killgore, Grugle et al., 2008) risk-taking on the measure.

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Summary Overall, it appears there is a link between both acute and chronic sleep deprivation and performance on risky decision making tasks. However, mixed findings are seen across tasks, level of sleep deprivation, and presence of a sleep disorder. In addition, there are several factors that could influence these findings. First, most studies are conducted on small samples of participants. Sample sizes range from 10 to 50 per patient or sleep deprivation group. The reliance on small samples limits power to detect significant small effects and could contribute to the null findings seen across some studies. Second, although some studies remove participants who take medications to treat narcolepsy, insomnia, or other sleep disorder, other studies keep these participants in their samples. The known hangover effects of sleep medications (e.g., Bianchi & Westover, 2014) could also influence performance on risky decision making tasks that rely on learning. Finally, the influence of comorbid diagnoses is unknown. Although some researchers conducting acute sleep deprivation studies exclude participants with a history of other psychiatric or neurological diagnosis, this is not the case with all studies. A large portion of psychiatric, medical, and neurological disorders list sleep difficulties and fatigue as a symptom or side effect. Sleep impairments are seen, for example, in hypomania (Baglioni, Spiegelhalder, Lombardo, & Riemann, 2010), and greater risk-taking behaviors are seen among those with hypomania who sleep poorly compared to sleep well (Brand, Gerber, Puhse, & Holsboer-Trachsler, 2011). Sleep impairment is included in the diagnostic criteria for the depressive disorders and depression is associated with changes in risky decision making. Sleep impairment is also strongly associated with several of the anxiety disorders and anxiety is also linked with risky decision making task performance. Future researchers should more fully examine the individual effect of sleep deprivation on risky decision making versus the synergistic effect of sleep deprivation in the context of another psychiatric disorder.

The current literature: delay discounting and reward responsiveness Relatively fewer studies specifically examined delay discounting (a preference for a smaller, more immediate reward over a larger but temporally distant reward) as a function of acute or chronic sleep deprivation. Although nearly all studies show no effect of sleep or sleep

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deprivation on delay discounting (Acheson et al., 2007; Demos et al., 2016; Libedinsky et al., 2013; Massar & Chee, 2015; Menz et al., 2012), Reynolds and Schiffbauer (2004) find instead that sleep-deprived participants show greater delay discounting than rested participants. In addition, Hewig et al. (2010) find that individuals are more intolerant of delayed rewards following sleep deprivation. It is possible that sleep affects the perception of risks and benefits, leading to changes in reward-based decisions and preferences for immediate versus delayed rewards. Sleep deprivation leads to a preference for higher gains, which also increases risk (Holm et al., 2009; Rossa et al., 2014; Venkatraman et al., 2007, 2011). Sleep deprivation reduces attention to signals of risk, which can result in riskier decisions being made (Frings, 2012; McKenna et al., 2007; Watling, Armstrong, Obst, & Smith, 2014). Sleep deprivation and the presence of sleep disorders, such as narcolepsy, can influence processing of rewards as well as other cognitive functions involved in rewardbased decision making (Naumann, Bellebaum, & Daum, 2006; Ponz, Khatami, Poryazova, Werth, Boesiger, & Bassetti, 2010; Ponz, Khatami, Poryazova, Werth, Boesiger, et al., 2010; Rieger, Mayer, & Gauggel, 2003; Schwartz et al., 2008). However, Massar and Chee (2015) instead find that sleep-deprived participants are still able to assess the probabilities associated with various events and adjust their assessment of risks/benefits accordingly. As will soon be discussed, it is likely that these changes in risk and benefit sensitivity interact with difficulties learning from feedback to affect decision making following sleep deprivation.

Performance on other executive function tasks Very few of the previously referenced studies report results of decision making tasks in conjunction with other cognitive tasks. Even when multiple executive functions are assessed in the same study, not all report correlations between decision making and other executive tasks (e.g., Daurat et al., 2013). Overall, minimal relationships are seen between performance on the IGT and the information sampling task (Delazer et al., 2011), a tower variant (Delazer et al., 2012), and olfactory function (Sasai et al., 2012), among individuals with sleep-related disorders. Based on such a limited sample of studies, it is difficult to make any conclusions about how decision making fits in with other executive function tasks in those with sleep difficulties. As will be seen, there are consistent decreases in other executive functions as a consequence of sleep deprivation.

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Neuroimaging Structural and functional changes are seen on neuroimaging as a function of acute and chronic sleep deprivation. Sleep loss is associated with decreased hippocampal (Altena et al., 2008; Nofzinger et al., 2004; Riemann et al., 2007) and left orbitofrontal cortex (Altena, Vrenken, Van Der Werf, van den Heuvel, & Van Someren, 2010) volumes, both of which are implicated in risky decision making (e.g., Bechara, Damasio, et al., 2000). Structural changes in the orbitofrontal cortex are also seen in obstructive sleep apnea (Macey et al., 2008). Functional studies show decreased activation to decision making areas in obstructive sleep apnea (Ayalon, Ancoli-Israel, & Drummond, 2009) and narcolepsy (Fadel & Deutch, 2002; Korotkova, Sergeeva, Eriksson, Haas, & Brown, 2003; Ponz, Khatami, Poryazova, Werth, Boesiger, & Bassetti, 2010). Structures showing decreased decision making task-related activity include the prefrontal cortex (Scherfler et al., 2012; Thomas et al., 2000), ventromedial prefrontal cortex (Libedinsky et al., 2011), and ventral tegmental area (Ponz, Khatami, Poryazova, Werth, Boesiger, & Bassetti, 2010; Scherfler et al., 2012), while sleep itself increases activation in the prefrontal cortex and ventromedial prefrontal cortex, improving decision making (Seeley, Smith, MacDonald, & Beninger, 2016). However, others find greater risk-taking on tasks among those with sleep loss is due to increased ventromedial prefrontal cortex and nucleus accumbens activity, leading to greater risk- and reward-seeking behaviors (compared to focusing on losses) (Martin et al., 2015; Venkatraman et al., 2007, 2011). Thus functional imaging evidence suggests that the brain’s reward system is affected by sleep loss and it may be in the form of increased focus on gains and decreased focus on losses.

Potential mechanisms Overall executive dysfunction One theory of risky decision making in acute and chronic sleep deprivation is that it is a function of other cognitive impairments known to occur from sleep loss. Researchers liken the cognitive impairments following sleep loss to the cognitive impairments seen among individuals with a blood alcohol content of .10 (Dawson & Reid, 1997; Roehrs, Burduvali, Bonahoom, Drake, & Roth, 2003; Williamson & Feyer, 2000). Sleep loss, both acute and chronic, is associated with declines in attention and processing speed (Durmer et al., 2005; Harrison & Horne, 2000; O’Brien & Mindell, 2005; Paavonen et al., 2009; Peters et al., 2009), working memory (Harrison & Horne, 2000; Romer et al., 2011; Steenari et al.,

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2003), and various executive functions (Beebe & Gozal, 2002; Cheshire, Engleman, Deary, Shapiro, & Douglas, 1992; Durmer et al., 2005; Ireland & Culpin, 2006; Naismith, Winter, Gotsopoulos, Hickie, & Cistulli, 2004; Naumann et al., 2006; Ponz, Khatami, Poryazova, Werth, Boesiger, & Bassetti, 2010; Ponz, Khatami, Poryazova, Werth, Boesiger, et al., 2010; Ratcliff et al., 2009; Rieger et al., 2003; Sadeh, Gruber, & Raviv, 2003; Sateia, 2003; Schwartz et al., 2008; Telzer et al., 2013; Whitney et al., 2010). These executive deficits are seen in adults (e.g., Horne, 2012; Jackson et al., 2013; Lo et al., 2012; Pilcher & Huffcutt, 1996) as well as children and adolescents (e.g., Bernier, Beauchamp, Bouvette-Turcott, Carlson, & Carrier, 2013; Dewald-Kaufman, Oort, & Meijer, 2013; Gruber, Cassoff, Frenette, Wiebe, & Carrier, 2012; Molfese et al., 2013; Sadeh et al., 2003). Two recent meta-analyses confirm that short-term sleep disruption/deprivation results in lowered performance on assessments of attention and memory (Lim & Dinges, 2010; Whitney, Hinson, Jackson, & Van Dongen, 2015). Intact performance on risky decision making tasks can rely, at least in part, on attention to detail, learning from feedback, and retention of information as the task progresses. These cognitive abilities are affected by sleep loss, which could in turn affect the ability to inhibit impulses such as those to take greater risks (Romer et al., 2011; Steenari et al., 2003). Executive functions are also linked with frontal lobe functioning, which we previously saw and can be affected by sleep deprivation. It is possible that deficits in decision making seen across tasks, diagnosis of sleep disorder, and length of sleep deprivation could be due to overall executive function impairments.

Alterations in neural processing of rewards and learning from feedback A separate theory is that increased risk-taking behavior seen in the real-world and on lab-based behavioral tasks is due to altered processing of rewards and risks in those experiencing sleep deprivation. We know that sleep deprivation affects functioning in reward processing areas of the brain, as well as in the neurons that project to these areas (e.g., Ashton-Jones, Smith, Moorman, & Richardson, 2009; Chee et al., 2011; Gujar et al., 2011; Holm et al., 2009; Libedinsky et al., 2011; Sakurai, 2007; Scherfler et al., 2012; Venkatraman et al., 2011). One way that this change in reward pathway function manifests is as increased responsiveness to reward signals (Martin et al., 2015; Telzer et al., 2013; Venkatraman et al., 2011) and decreased responsiveness to losses or signals of risk (Martin et al., 2015; Venkatraman et al., 2007). These changes in neural processing of potential risks and rewards associated with

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decisions can lead to a failure to avoid risks (Killgore, Balkin et al., 2006) or an interference with learning a decision is risky. One way that individuals learn a decision is risky is through feedback from previous decisions. Tasks such as the IGT rely on trial-by-trial feedback to learn that some decks are riskier than others (e.g., Dunn, Dalgleish, & Lawrence, 2006; Guillaume et al., 2009). Sleep deprivation interferes with feedback-based learning (Hewig et al., 2010; Olson et al., 2014; Whitney et al., 2015; Venkatraman et al., 2007), including on the IGT (Pace-Schott et al., 2011; Seeley et al., 2014, 2016). Sleep deprivation may interfere with the decision making process by shifting attention to the immediate rewards despite the risks associated with them (Martin et al., 2015). Thus sleep deprivation may cause further disruption in the interaction of self-control and reward-seeking/processing that can affect decision making.

Conclusion and future directions Although overall there is clear evidence of increased involvement in real-world risk-taking behaviors among those self-reporting lowered sleep quantity and/or quality, and in those with a diagnosed sleep disorder, this consistency is not seen across behavioral decision making tasks. It is possible that some of this inconsistency is due to the relatively small sample sizes utilized in each study, but another factor is likely the reliance on just one risky decision making task per study. When riskier decisions are made, it appears that a combination of overall executive impairment and difficulties accurately learning from feedback regarding risks and benefits are to blame.

Participant-related factors Several participant-related factors may affect the relationship between sleep deprivation and performance on decision making tasks. First, there is evidence to suggest a dose response relationship, in that greater executive dysfunction is seen among those with a greater quantity of sleep deprivation. While acute sleep deprivation studies standardize the number of hours of sustained wakefulness, studies of those with chronic sleep deprivation do not. Participants self-report their usual sleep patterns, or sleep impairment is assumed given a diagnosis of a sleep disorder. Second, studies often combine participants who take sleep medications with participants who do not, leading to a potential confound. Are cognitive impairments due to the sleep deprivation itself or due to the “hangover” effects of medications the next day? Finally,

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the potential influence of comorbid diagnoses is unclear. What is causing chronic sleep deprivation in those without a diagnosed sleep disorder? Is another medical, psychiatric, or neurological condition causing sleep loss and is it that underlying disorder that is actually affecting decision making? At present, it appears that sleep deprivation does exert a unique influence on behavioral decision making task performance, but whether it has a synergistic effect with other disorders (leading to even worse decision making) is unclear.

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9 Impulsivity and attentiondeficit/hyperactivity disorder Impulsivity can refer to both a personality characteristic that varies across individuals and to a clinical characteristic common in the diagnostic criteria of several disorders including bipolar disorder, borderline personality disorder, and attention-deficit/hyperactivity disorder (ADHD) (Evenden, 1999; Seidl, Pastorek, Troyanskaya, & Scheibel, 2015; Smith et al., 2007). However, no universally accepted definition of impulsivity exists, as the most common components of the characteristic are wide-ranging and include failure to plan ahead, inattention, hyperactivity, disinhibition, nonplanning, inhibitory control, reward seeking, risk taking, discounting of future rewards, and sensation seeking (Cloninger, Svrakic, & Przybeck, 1993; Dalley, Everitt, & Robbins, 2015; Dawe, Gullo, & Loxton, 2004; Depue & Collins, 1999; Duckworth & Kern, 2011; Tellegen, 1982; Winstanley, Eagle, & Robbins, 2006; Zuckerman, Kuhlman, Joireman, Teta, & Kraft, 1993). In this chapter, I will focus on the research literature regarding impulsivity as a personality/individual differences characteristic and impulsivity as part of the ADHD diagnositic criteria. These characteristics of impulsivity, including failure to plan ahead, discounting of future rewards, and difficulties with inhibitory control, could all lead to risky decision making on labbased tasks and in real-world settings. ADHD is one of the most common neuropsychiatric disorders affecting children and adolescents. ADHD affects 11% of children in the United States (Visser et al., 2014) and 7% of children worldwide (Thomas, Sanders, et al., 2015). The National Survey of Children’s Health (2016) estimated that 6.1 million children and adolescents (9.4% of the total population) had ever received a diagnosis of ADHD. In 2013 the American Psychiatric Association revised diagnostic criteria for ADHD in the DSM-V (APA, 2013), but diagnosis continues to rely on symptoms falling into either the inattentive or hyperactive impulsive

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clusters. The inattention symptom set includes difficulties such as making careless mistakes, exhibiting difficulty focusing on tasks, failing to follow instructions or finish activities, showing difficulty with organization and avoiding mentally taxing tasks, losing items needed for an activity or task, being forgetful and/or easily distracted, and not seeming to listen when someone is speaking (APA, 2013). The hyperactive impulsive symptom set instead focuses on symptoms such as fidgeting/squirming, running around and leaving one’s seat at inappropriate times, acting “on the go” or as if “driven by a motor” (p. 60), talking excessively and having difficulty playing/working quietly, and difficulty waiting turns (including talking over someone else). For a diagnosis of ADHD, at least some symptoms must be present prior to age 12, appear in multiple settings (i.e., not just at school), and interfere with functioning. Per the DSM-V (2013), three subtypes can be diagnosed based on the specific cluster of symptoms in the past 6 months: (1) ADHD-predominantly inattentive (individuals exhibit inattentive but not hyperactive impulsive symptoms), (2) ADHD-predominantly hyperactive impulsive (individuals exhibit hyperactive impulsive but not inattentive symptoms), or (3) combined (individuals exhibit both inattentive and hyperactive impulsive symptoms). In the sections that follow, I will examine the current literature on risk-taking behaviors, risky decision making, and delay discounting among adolescents and adults with a history of ADHD. I will also compare these findings to those in individuals with higher levels of impulsivity but not a diagnosis of ADHD. In addition, I will examine the evidence in favor of several competing hypotheses as to why those with ADHD and/or high levels of impulsivity exhibit greater rates of decision making impairments.

The current literature: risk-taking behaviors Individuals with ADHD are frequently involved in behaviors that have a high risk for negative health and other outcomes. A diagnosis of ADHD (Connor, Glatt, Lopez, Jackson, & Melloni, 2002), and of hyperactive symptoms in particular (Scime & Norvilitis, 2006), is associated with greater levels of aggressive behaviors. In a recent study, secondary school students were asked to self-report their level of involvement in risky online behaviors (Vural, Uncu, & Zinnur, 2015). Students with greater levels of ADHD symptoms reported a greater likelihood of surfing the internet to chat with strangers, as well as meeting in person with strangers they met online, than students with lower levels of ADHD symptoms. A diagnosis of ADHD can also affect perceptions of safety when crossing a busy road. Individuals aged 13 17 completed a virtual reality

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task in which they were told to cross a road when they felt the gap in traffic was safe. Those with a diagnosis of ADHD had a lower margin of safety to cross the road, walked slower across the road, and made more unsafe crossings (with a greater number of collisions) than those without a diagnosis of ADHD (Clancy, Rucklidge, & Owen, 2006). Risky driving behaviors are also quite common among adolescents and adults with a history of ADHD. Various studies relying on selfreport of risky driving behaviors find that individuals with a history of ADHD engage in riskier driving behaviors, including moving violations, errors, and crashes, than those without a history of ADHD (Barkley, Murphy, Dupaul, & Bush, 2002; Graziano, Reid, Slavec, Paneto, McNamara, & Geffken, 2015; Reimer et al., 2005; Thompson, Molina, Pelham, & Gnagy, 2007). Others find that this relationship is only significant among males (Rosenbloom & Wultz, 2011). Current ADHD diagnosis is also associated with increased levels of anger and aggressive driving behaviors (Richards, Deffenbacher, & Rosen, 2002) and increased use of social networking sites while driving (Turel & Bechara, 2016). These self-report studies are frequently supported by studies utilizing non-self-report techniques. For example, among individuals enrolled in a course to regain a revoked driver’s license, 19.7% 28.5% had a diagnosis of ADHD and this diagnosis was the best predictor of risks taken while driving (Valero et al., 2017). Driving simulator tasks show lower total scores among individuals with ADHD than individuals without ADHD (Barkley et al., 2002). In addition, examination of driving records finds greater incidence of traffic citations, license suspensions, and speeding tickets among those with versus without an ADHD diagnosis (Barkley et al., 2002). Individuals who allow their driving to be video-recorded for 3 months show greater numbers of crashes and minor driving events among individuals with ADHD (Merkel et al., 2016). It is possible that treatment with methylphenidate (MPH) could decrease these risky driving behaviors (Cox et al., 2012); however, additional research is needed before this recommendation can be made. ADHD diagnosis is also related to not just involvement in high-risk activities, but changes in the perception of risks associated with these activities. For example, 7- to 11-year-old boys were asked to rate the level of risk seen in various play activities. Although all of the participants identified hazards in the behaviors, those with a diagnosis of ADHD rated the activities as less risky and identified less severe consequences of the behaviors than those without ADHD. In addition, the participants with ADHD took fewer opportunities to lower the risk of injury than those without ADHD (Farmer & Peterson, 1995). A diagnosis of ADHD is also associated with greater risk of injury, even among children as young as 12 months of age (e.g., Garzon, Huang, & Todd,

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2008; Lahey et al., 1998; Rappley et al., 1999). Among male adolescents, ages 16 25, those with a history of ADHD are more likely than their non-ADHD diagnosed peers to report involvement in motor sports such as racing and ATV or motorcycle riding (Wymbs et al., 2013). A 33-year follow-up study on boys diagnosed with ADHD in childhood found that, as adults, participants with ADHD have a greater number of head injuries, admissions to the emergency room, and riskier driving behaviors than those without a childhood diagnosis of ADHD (Ramos Olazagasti et al., 2013). Two risk-taking behaviors frequently associated with a diagnosis of ADHD are risky sexual behaviors and substance use. Several studies examined risky sexual behaviors as a function of ADHD diagnosis in only one gender. Females with a diagnosis of ADHD report greater involvement in risky sexual behaviors than females without a diagnosis of ADHD (Hosain, Berenson, Tennen, Bauer, & Wu, 2012) and 18- to 26-year-old males with ADHD reported a greater number of sexual partners, more casual sexual encounters, and an earlier initiation of sexual activity than males without ADHD (Flory, Molina, Pelham, Gnagy, & Smith, 2006). Among college students, females with a diagnosis of ADHD are less likely than females without ADHD and than males with and without ADHD to report condom use (Perrigue Huggins, Rooney, & Chronis-Tuscano, 2015). An earlier onset of ADHD symptoms (prior to age 12) is also associated with increased involvement in later risky sexual behaviors as well as increased risk of sexual assault (White & Buehler, 2012). Taken together, a diagnosis of ADHD appears to be associated with riskier sexual behaviors. Strong evidence links ADHD with comorbid substance use, both of legal and illegal substances. Individuals diagnosed with ADHD are at increased risk of substance use (Gudjonsson, Sigurdsson, Sigfusdottir, & Young, 2012) and have a three-times greater risk of developing a substance use disorder (SUD) later in life (Lee, Humphreys, Flory, Liu, & Glass, 2011). In addition, 27% of those with a diagnosis of ADHD have a comorbid diagnosis of SUD (Biederman et al., 1997). ADHD is linked not just with the use of substances but also with an earlier age of initiation of substance use (Dunne, Hearn, Rose, & Latimer, 2014; Wilens, Biederman, Mick, Faraone, & Spencer, 1997) and onset of SUD (Kaye et al., 2014). A diagnosis of ADHD is not even required to see such a link, as even high but subthreshold symptoms of ADHD are associated with greater levels of nicotine and alcohol use behaviors (Malmberg, Edbom, Wargelius, & Larsson, 2011). Recent drug use and risky drug-use behaviors (such as sharing needles or overdose) are greater among individuals with a diagnosis of ADHD than those without this diagnosis (Dunne et al., 2014; Gonzalez, Velez-Pastrana, Varcarcel, Levin, & Albizu-Garcia, 2015). Risky behaviors associated with ADHD medications are also seen. In a survey,

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11% with a diagnosis of ADHD report selling their psychostimulant medications whereas 22% report misusing their medications (Wilens, Gignac, Swezey, Monuteaux, & Biederman, 2006). Collectively, these studies largely show a strong link between a diagnosis of ADHD and later involvement in risk-taking behaviors with potential negative future health consequences. The strongest links appear to be between ADHD and risky sexual, driving, and substance use behaviors. Several issues remain unclear. First, it is unclear to what extent these risk-taking behaviors are alleviated, or at least moderated, by treatment with psychostimulant medications. Most studies employ a mix of individuals with ADHD who had and had not been treated with stimulant medications. Second, it is unclear how involvement in risk-taking behaviors may vary as a function of when the ADHD symptoms started. Are there differences in behaviors between those with a childhood diagnosis and those with a diagnosis in adulthood? There is some evidence that earlier symptom onset is associated with earlier involvement in risktaking behaviors. But, this concern is complicated by the fact that it is unclear if those diagnosed with ADHD in adulthood have undiagnosed ADHD symptoms in childhood versus the symptoms beginning later and possibly due to a different cause. Lastly, the majority of these studies rely on self-reported involvement in risky behaviors. It is possible that participants may not be accurate in their retrospective reports, which could go either in the direction of reporting greater risk-taking or less risk-taking. This final concern can be directly examined, however, by looking at the results of research utilizing behavioral risky decision making tasks that are linked with real-world risk-taking behavior.

The current literature: risky decision making Multiple studies examine risky decision making on lab-based measures, with varying results. The following summarizes these findings by specific measure used.

Balloon Analogue Risk Task Relatively few studies examine performance on the Balloon Analogue Risk Task (BART) among individuals with ADHD. No differences are seen between those with a diagnosis of ADHD and those without a diagnosis across several studies (Mantyla, Still, Gullberg, & Del Missier, 2012; Ryan, Dube, et al., 2013; Weafer, Milich, & Fillmore, 2011). However, Poon and Ho (2016) find greater risk-taking on the BART among individuals with ADHD versus those with a reading disorder or participants

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without either diagnosis. Although few studies examine BART performance as a function of ADHD diagnosis, those that have do not provide a consistent pattern of impaired performance on the task.

Cambridge Gamble Task Several studies examine performance on the CGT in children, adolescents, and adults with ADHD. Compared to controls, medication-naı¨ve boys with ADHD are riskier on the task than their non-ADHD peers (Coghill, Seth, & Matthews, 2014; DeVito et al., 2008). By contrast, Sorensen et al. (2017) find greater delay aversion and worse risk adjustment, but not increased risk proneness, in children with ADHD versus controls. In a sample of adolescents, those with ADHD choose the less likely color more often than controls but also choose smaller bets after the previous selection resulted in a loss (Kroyzer, Gross-Tsur, & Pollak, 2014). In adults, two studies find no differences between those with a history of ADHD and controls (McLean et al., 2004; Pollak, Shalit, & Aran, 2018). Thus it appears that more consistent impairments are seen on this task in individuals with a diagnosis of ADHD in younger versus older age groups.

Game of Dice Task Inconsistent findings are also seen with the Game of Dice Task (GDT). Wilbertz et al. (2012) find no differences in risky decisions on the GDT as a function of ADHD status. On the other hand, Matthies, Philipsen, and Svaldi (2012) show that individuals with ADHD earn less on the GDT than controls and make more 2-dice than 4-dice selections. However, following a boredom induction in their second study, these group differences disappear. Based on just these two studies, it appears that no differences are found on the GDT as a function of ADHD diagnosis when other factors, such as boredom, are taken into consideration.

Iowa Gambling Task Results are varied when assessing performance on the Iowa Gambling Task (IGT) as a function of ADHD diagnosis, which could be due in part to specific characteristics of the participant samples. Several studies find individuals with a diagnosis of ADHD are riskier (i.e., made more selections from the disadvantageous decks and/or earned less money on the task) than controls (Malloy-Diniz, Fuentes, Borges Leite, Correa, & Bechara, 2007; Mantyla et al., 2012; Miller, Sheridan,

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Cardoos, & Hinshaw, 2013). However, others find no discernable differences in task performance between groups (Ernst, Kimes, et al., 2003; Gonzalez-Gadea et al., 2013). Segala, Vasilev, Raynov, Gonzalez, and Vassileva (2015) had participants rate their retrospective childhood ADHD symptoms with the Wender Utah Rating Scale, finding that 20% of the variance in IGT scores is accounted for by this measure. Greater childhood ADHD symptoms are associated with riskier decision making in the present. Performance among children and adolescents is assessed with the IGT, child variants of the IGT, and the Hungry Donkey Task (HDT). On the IGT, Toplak, Jain, and Tannock (2005) find greater Deck B and fewer Deck D selections among adolescents with ADHD versus controls. However, both Hobson, Scott, and Rubia (2011) and Masunami, Okazaki, and Maekawa (2009) instead find no differences on the IGT as a function of ADHD diagnosis. Hobson instead find that the presence of oppositional defiant disorder (ODD) or conduct disorder (CD) accounts for the variability in IGT performance. On modified versions of the IGT, no differences are seen between those with and without a diagnosis of ADHD in one study (Antonini et al., 2015), but boys with hyperactive/impulsive symptoms are riskier than boys without such symptoms in another study (Bubier & Drabick, 2008). It should be noted that in this last study, individuals were not diagnosed with ADHD but rather showed a history of ADHD symptoms as rated by their parents. In addition, a diagnosis of ADHD is not associated with improvements in performance on the IGT across time among adolescents (Ernst, Grant, et al., 2003). On the HDT, no differences are consistently seen between adolescents with ADHD and controls (Geurts, van der Oord, & Crone, 2006; Hovik et al., 2015; Lambek et al., 2010; Winther Skogli, Egeland, Andersen, Hovik, & Oie, 2014; Winther Skogli, Andersen, Hovik, & Oie, 2017). Taken together, the results for the IGT are rather inconsistent. No differences between those with ADHD and healthy controls are consistently seen on the HDT, a standard children’s version of the IGT, but results are mixed with the regular IGT in both adolescents and adults. It is possible that these inconsistencies could be due in part to how the diagnosis of ADHD was made, the gender ratio and age of participants, and the presence/absence of current symptoms and medication use.

Other risky decision making tasks A variety of other decision making tasks are examined in individuals with ADHD. On the ADMC, Mantyla et al. (2012) find that those with ADHD diagnoses are worse than controls on the applying decision rules items, but not on the over/underconfidence section. Riskier performance

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is also seen amongst individuals with ADHD on a make-a-match game (Drechsler, Rizzo, & Steinhausen, 2010). On this task, participants make selections from categories with small (1:2 chance), medium (1:3 chance), or large (1:4 chance) risks. Those with ADHD make riskier selections (i.e., more from the large and less from the small categories) than healthy controls. They choose the less likely but more rewarded options. Participants with ADHD are more impulsive on a two-choice, but not five-choice, impulsivity task compared to controls (Patros, Alderson, Lea, & Tarle, 2017) and choose more from jackpots with larger penalties than did controls (Luman, Oosterlaan, Knol, & Sergeant, 2008). However, as the frequency of penalties increases, performance of those with ADHD is indistinguishable from those without the diagnosis (Luman et al., 2008). Utilizing the BRIEF, a self- or parent-report measure of executive dysfunction, participants with ADHD report greater impairments than controls (self-report, Kroyzer et al., 2014; Winther Skogli et al., 2014; parent-report, Hovik et al., 2015). By contrast, several additional studies find no impairments in decision making as a function of ADHD status. Specifically, no differences are seen on a balloon emotional learning task (Humphreys, Tottenham, & Lee, 2018), the Ultimatum game (Ma, LambregtsRommelse, Buitelaar, Cillessen, & Scheres, 2017), or a probabilistic gambling task (Mesrobian, Villa, Bader, Gotte, & Lintas, 2018). Lunt et al. (2012) find no differences between those with ADHD and controls on a tossing coins task, a dice sequences task, a dice throws task, and the beads task. Across three studies utilizing a gambling task, Pollak et al. (2016) find no impairments in decision making among individuals with ADHD, despite greater self-reported involvement in real-world risky behaviors. In addition, no differences are seen when feedback is versus is not given about their decisions.

Summary When lab-based measures assessing risky decision making are utilized, no consistent pattern of performance emerges. Across the various tasks, some studies find individuals, both children and adults, with a history of ADHD are riskier than individuals without a history of ADHD. Other studies find no differences as a function of ADHD diagnosis. Several reviews and metaanalyses also tackle this topic. Dekkers, Popma, Agelink van Rentergem, Bexkens, and Huizenga (2016) conducted a meta-analysis of risky decision making in ADHD, finding an overall small to medium effect size corresponding to riskier decision making in those with ADHD versus controls. It is possible underpowered studies may have failed to detect significant effects of ADHD on

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risky decision making. The only moderator that emerged was a trend toward comorbid disruptive behavior disorder affecting this relationship, but not internalizing disorders, age, or task explicitness. Mowinckel, Pedersen, Eilertsen, and Biele (2015) find overall deficits across decision making tasks as a function of ADHD diagnosis. Finally, Groen, Gaastra, Lewis-Evans, and Tucha (2013) compare performance on implicit gambling (i.e., IGT, door opening task, BART) and explicit gambling (i.e., CGT, GDT, make-a-match, probabilistic gambling task) tasks, finding a more consistent pattern of impairments on explicit gambling tasks in adults than implicit gambling tasks in adults or either task in children/adolescents. Several confounding factors could affect these results, leading to such different findings across studies. First, not all studies require participants to have a confirmable clinical diagnosis of ADHD. Relying on self-report of an ADHD diagnosis requires participants to remember the exact diagnosis given, by who, and when. In addition, relying on retrospective self-report of childhood symptoms can be prone to exaggeration or inaccurate recall. Next, current level of ADHD symptoms could play a role in these inconsistencies. If individuals are tested as adults, having had a childhood history of ADHD diagnosis, it is possible that those with no current ADHD symptoms may show no impairments on decision making tasks whereas those with continued ADHD symptoms might. In addition, some studies utilize participants who refrain from psychostimulant use prior to testing, whereas others have participants utilizing medication during testing.

The current literature: delay discounting and reward responsiveness Delay discounting Risky decision making can also be assessed through the use of various delay discounting (or temporal discounting) paradigms. A number of studies of individuals with a diagnosis of ADHD rely on the monetary incentive delay task (or MID; Knutson, Adams, Fong, & Hammer, 2001), as it is amenable to use in the fMRI scanner. On the MID, participants complete a series of trials in which they respond to one of a set of cue shapes. These shapes could cue a potential reward (varying magnitudes such as $0.20, $1.00, $5.00; Knutson et al., 2001), a potential punishment, or that there is no reward or punishment associated with that response. Participants are asked to press a button in response to the target that appears following the cue and then receive feedback as to whether they won or lost on that trial. Among children the results are

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quite varied. Those with a diagnosis of ADHD, compared to same-aged children without ADHD, have slower reaction times (Demurie, Roeyers, Baeyens, & Sonuga-Barke, 2012; Van Hulst et al., 2017), faster reaction times (Kappel et al., 2015), greater omission errors (Demurie, Roeyers, Wiersema, & Sonuga-Barke, 2016), and no differences in accuracy (Demurie et al., 2011; Scheres, Milham, Knutson, & Castellanos, 2007). In a variant comparing responses when the reward is money versus social (a smiling face), Demurie et al. (2011) finds that children with ADHD respond faster to a monetary reward than a social cue reward. Among adults a clearer picture emerges. No differences in either accuracy or reaction time emerge as a function of ADHD diagnosis (Edel et al., 2013; Hoogman et al., 2013; Kappel et al., 2015; Stoy et al., 2011; Wilbertz et al., 2013). Results of neuroimaging during the MID show changes in responsiveness to reward anticipation in those with ADHD versus controls. Specifically, those with ADHD show decreased ventral striatal activation (Edel et al., 2013; Hoogman et al., 2013; Kappel et al., 2015; Scheres et al., 2007), but von Rhein et al. (2015) finds greater neural responses during reward anticipation and receipt in those with ADHD. A significant number of previous researchers assessed delay discounting utilizing hypothetical tasks. On these, participants choose between a smaller amount available immediately (usually of money, but sometimes of other items) and a larger amount available after a delay. The delay interval could be assessed in seconds, up to periods of 5 10 years in the future. The amount of money available immediately and in the future is also quite variable, from a few cents up to hundreds of dollars (or other currency equivalent). Similarly to the MID results, no clear pattern of performance emerges among children with ADHD. A number of researchers find no differences in discounting task performance among children with ADHD compared to similarly aged children without ADHD (Costa Dias et al., 2013, 2015; Karalunas et al., 2018; KrauseUtz et al., 2016; Scheres et al., 2006; Scheres, Tontsch, Thoeny, & Kaczkurkin, 2010; Scheres, Tontsch, et al., 2013). On the other hand, others find that children with ADHD discount the delayed reward more (i.e., prefer the smaller, immediate reward) than children without ADHD (monetary incentives: Barkley, Edwards, Laneri, Fletcher, & Metevia, 2001; Demurie, Roeyers, Baeyens, & Sonuga-Barke, 2012; Wilson, Mitchell, Musser, Schmitt, & Nigg, 2011; points or time spent playing with a toy: Coghill et al., 2014; Lambek et al., 2010; Utsumi, Miranda, & Muszkat, 2016). Several factors could potentially account for these discrepancies in research with children and adolescents. Barkley et al. (2001) find that the reward value matters: children with ADHD have steeper delay discounting than controls when the delayed reward is $100, but no differences emerge between groups for a $1000 delayed reward. Costa Dias et al. (2013, 2015) find that age matters: group

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differences emerge in delay discounting (e.g., those with ADHD have steeper discounting of the delayed reward vs controls) only after age is accounted for. Lambek et al. (2010) finds that the absence of executive dysfunction matters, as discounting is similar between individuals with ADHD 1 executive dysfunction and healthy controls whereas those with ADHD and no executive dysfunction discount more. Finally, Scheres, Tontsch, et al. (2010) found that the subtype of ADHD matters, as those with ADHD-combined show steeper delay discounting than controls but those with ADHD-inattentive are no different from HC. Results are just as mixed among adults responding to hypothetical delays. Whereas several researchers find no differences in discounting by ADHD diagnosis (Crunelle, Veltman, van Emmerik-van Oortmerssen, Booij, & van den Brink, 2013; Mies, de Water, & Scheres 2016; Ortiz et al., 2015; Plichta et al., 2009; Wilbertz et al., 2012) or the presence of ADHD symptoms on self-report measures (Segala et al., 2015), others find those with ADHD (or symptoms of ADHD; Beauchaine, Ben-David, & Sela, 2017) discount the delayed rewards more steeply than controls (Dai, Harrow, Song, Rucklidge, & Grace, 2016; Hurst, Kepley, McCalla, & Livermore, 2011; Mostert et al., 2015; Onnink et al., 2015). Varying the maximum total gain and the reward magnitude do not vary findings (Scheres, Sumiya, & Thoeny, 2010), and wording options as gains versus losses does not affect performance (Mies et al., 2016). A diagnosis of cocaine dependence in addition to ADHD made the difference in Crunelle et al. (2013), as those with ADHD only did not differ from controls on a delay discounting task. Several studies directly compare the use of hypothetical and real rewards by having participants complete both tasks in the same study session. Scheres, Sumiya, et al. (2010) had adults complete four different delay discounting tasks with varying levels of rewards, delay periods, and real versus hypothetical outcomes. Few correlations emerge between tasks, indicating each measures different aspects of the construct; however, no differences are found when real versus hypothetical rewards are used. On the other hand, Hinvest and Anderson (2010) find that impulsive decisions decrease slightly when real versus hypothetical rewards are used. The remaining researchers assess potential differences in children with and without ADHD. Hypothetical discounting tasks mimic those previously discussed. Real discounting tasks take the form of (1) playing a game they brought to the session for a small amount of time now or a longer amount of time after a delay (Martinelli, Mostofsky, & Rosch, 2017; Rosch & Mostofsky, 2016) or (2) a small monetary reward now or a larger award after up to a 28-second delay (Paloyelis, Mehta, Faraone, Asherson, & Kuntsi, 2010). Children with ADHD have steeper delay discounting on the real discounting task but not the hypothetical task (Martinelli et al., 2017; Paloyelis et al., 2010; Rosch et al., 2016).

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A few reviews and meta-analyses exist examining the overall effects of a diagnosis of ADHD on delay discounting and/or choice impulsivity. Overall, these reviews find a moderate effect, in that individuals with ADHD decide more impulsively on these tasks (Patros et al., 2016; Pauli-Pott & Becker, 2015; Plichta & Scheres, 2014). These effect sizes do not differ based on the specific task used (Patros et al., 2016; Pauli-Pott & Becker, 2015), proportion of males (Pauli-Pott & Becker, 2015), or number of comorbid diagnoses (Pauli-Pott & Becker, 2015), but the effect size is larger when diagnosis of ADHD relies on a single source rather than multiple sources (Patros et al., 2016) and among younger children (Patros et al., 2016). In addition, Plichta and Scheres (2014) indicate that, overall, there is evidence of ventral striatal hyporesponsiveness in adolescents and adults with ADHD.

Reward responsiveness A related construct, several studies instead examine reward responsiveness/sensitivity among children and adults with ADHD. Many of these studies rely on protocols in which participants receive some type of reward (usually money or points) for a correct response/guess and, in some but not all cases, lose money/points for an incorrect response/ guess. The majority of studies utilizing such a study design result in significant between-group differences among children; however, a few researchers fail to show differences in reward-motivated performance among children with ADHD and similarly aged children without ADHD (Gatzke-Kopp et al., 2009; Groom et al., 2009; Holroyd, Baker, Kerns, & Muller, 2008; Marx, Pieper, Berger, Hassler, & Herpertz, 2011; Paloyelis et al., 2012). Others find children with a diagnosis of ADHD are slower to respond on rewarded trials than children without ADHD (Groen, Tucha, Wijers, & Althaus, 2013; Ma et al., 2016; Umemoto, Lukie, Kerns, Muller, & Holroyd, 2014; Wodka et al., 2007). Van Meel, Oosterlaan, Heslenfeld, and Sergeant (2005) find no ADHD-related differences in reaction times on a reward-based task, but children with ADHD self-report a higher performance level than that self-reported by children without ADHD. In terms of accuracy, those with ADHD are less accurate or have lower overall task performance compared to children without ADHD (Hammer et al., 2015; Luman et al., 2009; Ma et al., 2016; Wodka et al., 2007). Fewer studies exist for adults with ADHD. Two of those studies find no effect of ADHD on task performance. Stark et al. (2011) asked participants to rate their current symptoms of ADHD (no participant had a previous diagnosis) and found no correlation between self-rated current ADHD symptoms and performance on either reward anticipation or

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punishment avoidance trials. Wetterling et al. (2015), in their sample of adults with a diagnosis of ADHD, find no differences in performance on a rewarded forced-choice selection task. On the other hand, Thoma, Edel, Suchan, and Bellebaum (2015) find worse rewarded probabilistic selection performance among individuals with ADHD compared to controls. Across the studies of delay discounting and reward responsiveness, some participants were paid their winnings, a portion of their winnings, or winnings from a “random selection” of their trials. These monetary incentives are told to them in advance of the actual task. Of the 27 studies discussed here, 15 (55.55%) show differences on the task as a function of ADHD diagnosis. For those 25 studies without a task-related payout, 11 (44%) show performance differences based on ADHD diagnosis. These findings, coupled with the work by Scheres, Sumiya, et al. (2010) finding no differences in delay discounting rates between hypothetical payouts and real ones, indicate that the presence or absence of a real reward is likely not the only reason for such discrepant findings. Most studies utilize samples of children and adults with a mix of male and female participants and there was no consistent pattern of results when all-female or all-male samples were considered. Only three studies (Hurst et al., 2011; Wilbertz et al., 2012, 2013) rely on a retrospective diagnosis of childhood ADHD or the participants’ self-report of a prior diagnosis, so this could not be the culprit. However, only three studies (Furukawa et al., 2014; Ortiz et al., 2015; Wetterling et al., 2015) directly assess adult ADHD participants who continue to experience symptoms of ADHD at present. The other studies of adults tend to describe their participants as individuals with a diagnosis of ADHD, not a diagnosis of ADHD and continued symptom expression at present. It is possible that a lower level of ADHD symptoms (residual symptoms) could account for some of the differences in findings across studies of adults with ADHD. In addition, it is unclear in several studies whether their adult ADHD participants were diagnosed in childhood or were diagnosed later on (with a retrospective examination of childhood symptoms indicating they “likely” had it then). Coupled with concerns about symptom malingering (see upcoming section), it is important to have the most accurate assessment picture of ADHD symptoms prior to making solid conclusions. Finally, most studies examine participants with a diagnosis of ADHD-combined subtype or a mix of all three subtypes indiscriminately, although some split their results by specific subtype with varying results (e.g., Scheres, Tontsch, et al., 2010). It is unclear to what extent the specific subset of symptoms (inattentive vs hyperactive/ impulsive) could account for risky decision making in this population (see personality section below for discussion of impulsivity as a personality characteristic).

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What factors could be affecting risky decision making in attention-deficit/hyperactivity disorder? The influence of medication The majority of studies discussed thus far, with both child/ adolescent and adult samples, ask participants to discontinue use of psychostimulant medication prior to the testing session. A relatively small number of studies to date directly examine the effect of methylphenidate (MPH) on risky decision making in randomized placebocontrolled trials. Several researchers examine the influence of MPH on IGT performance. Individuals with ADHD treated with MPH show few improvements on the IGT compared to those given a placebo (Agay, Yechiam, Carmel, & Levkovitz, 2010). However, the researchers also administered a second version of the IGT they developed called the “foregone payoff” version. In this version, participants receive feedback about all four of the cards after making each selection. The researchers indicate that receiving information about all of the decks could serve as a distractor, as it could sway later selections toward the disadvantageous decks. With this version of the IGT, there is no effect of MPH on performance, but individuals with a diagnosis of ADHD make more disadvantageous decisions than healthy controls. However, in contrast to their first study, ADHD diagnosis is associated with riskier decisions on the original but not the foregone payoff version of the IGT (Agay, Yechiam, Carmel, & Levkovitz, 2014). Only one study to date examined the effect of MPH on GDT performance, finding those taking MPH place lower bets (i.e., are less risky) on the task than those taking placebo (DeVito et al., 2008). DeVito et al. (2009) utilize a different task, the information sampling task, in which participants are asked to determine which of two colors is dominant in a box. Participants can select from the box as many times as wanted before making a decision, similar to the Beads task (Phillips & Edwards, 1966). Individuals with ADHD administered a placebo are riskier than healthy controls and treatment with MPH does not improve decision making on this task. Several other medication trials examine the influence of MPH on delay discounting and reward responsiveness tasks. Shiels et al. (2009) had participants with ADHD complete a series of tasks, including assessment of delay discounting with both hypothetical and real rewards, with placebo and MPH administered in a random order. MPH administration has no effect of results of the hypothetical delay discounting task, but rates of delay discounting are lower in the MPH conditions versus placebo on the real reward task. On the other hand, two studies show minimal influence of MPH on task performance. Children with ADHD completed a set of tasks after administration of a placebo

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or three different dosages of MPH (Luman, Papanikolau, & Oosterlaan, 2015). They were then asked to estimate a time interval (1000 ms), with a cue indicating if their performance would be potentially rewarded or punished on a particular trial. Performance is worse among individuals with MPH (placebo) compared to controls. In addition, treatment with MPH does not improve accuracy on the task, but it does decrease response variability as the MPH dose increases. Finally, Rubia et al. (2009) had participants complete a reward-based variant of a continuous performance task, in which participants received a reward of one British pound per three correct responses. Individuals with ADHD given the placebo have more omission errors than both the control group and individuals with ADHD-administered MPH. The authors conclude that MPH enhances activation of the fronto-striato-cerebellar and parieto-temporal regions necessary for accurate performance on this task. Compared to the number of studies of risky decision making in ADHD, relatively few directly examine MPH effects in a placebocontrolled design. Those that have do not paint a consistent picture of potential positive effects on decision making processes. In many of the previously discussed studies, participants refrained from taking their psychostimulant medications prior to the testing day. Most completed a 24 48 hour (or more) washout period, so that no lingering effect of last MPH dose could affect performance. However, within a particular sample of participants, some with a diagnosis of ADHD were being treated with MPH, whereas others were not. It is impossible to tell from these results the extent to which consistent treatment with MPH might “correct” a difficulty individuals were having with decision making.

Comorbid diagnoses Examination of the effects of comorbid diagnoses is relatively new. Across multiple studies, all or some of the ADHD participants also meet criteria for a comorbid diagnosis, most commonly ODD or CD for children and substance use/dependence for adults. A secondary diagnosis of ADHD results in riskier decision making on the IGT among individuals with a history of methamphetamine abuse (Duarte, Woods, Rooney, Atkinson, & Grant, 2012) and cocaine dependence (Miguel et al., 2016), but not current cannabis abuse (Tamm et al., 2013). Abouzari et al. (2015, 2016) find that individuals with ADHD and gambling behaviors are riskier on a simplified version of the IGT. However, Hobson et al. (2011) find no differences on the IGT as a function of ADHD diagnosis, instead concluding that the presence of ODD or CD (in those with a diagnosis of ADHD) accounts for the variability in IGT

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performance. On the BART, Humphreys and Lee (2011) find that individuals with a diagnosis of ADHD 1 ODD are riskier than those with just ADHD, again pointing toward the influence of comorbid diagnoses on task performance. In some studies, ADHD appears to have an additive effect on decision making impairment, whereas in others, the comorbid diagnosis has the additive effect on performance. In contrast to the research on self-reported involvement in risky behaviors, it is unclear to what extent the diagnosis of ADHD itself exerts a consistent and negative influence on risky decision making in children, adolescents, and adults. On the delay discounting and reward responsiveness tasks, few samples have comorbid diagnostic groups. Three studies examine individuals with ADHD and autism spectrum disorder (ASD), two of which find no differences in performance or reward-linked activation between those with ADHD only and those with ADHD 1 ASD (Chantiluke et al., 2014; Van Hulst et al., 2017). A third study instead finds ADHD 1 ASD has worse reward-based task performance than individuals with ADHD only (Demurie et al., 2016). Participants in Barkley et al. (2001) were diagnosed with ADHD 1 ODD, so it is unclear whether their significant findings were driven primarily by the ADHD symptoms. Finally, substance dependence is also a comorbid diagnosis in two delay discounting studies. Segala et al. (2015) find self-reported ADHD symptoms among individuals with heroin dependence predicts performance on the IGT but not a delay discounting task, but again, all participants were dependent on heroin. By contrast, Crunelle et al. (2013) found their participants with ADHD and cocaine dependence had greater delay discounting than individuals with ADHD only.

Impulsivity as a personality characteristic As a central feature of ADHD is impulsivity, it is important to consider to what extent differences in risky decision making or related constructs may be due to the personality characteristic of impulsivity itself, rather than the diagnosis of ADHD. Multiple studies examine the effect of impulsivity on risky decision making, with inconsistent results. Responses on self-report measures of impulsivity, such as the ImpSS, I-7, BIS-11, and UPPS, are not correlated with or predictive of performance on the IGT (Bayard et al., 2011; Dom, De Wilde, Hulstijn, & Sabbe, 2006; Goudriaan, Grekin, & Sher, 2007; Grassi et al., 2015; Monterosso, Ehrman, Napier, O’Brien, & Childress, 2001; Moreno et al., 2012; Overman et al., 2004; Perales, Verdejo-Garcia, Moya, Lozano, & Perez-Garcia, 2009; Powers et al., 2013; Upton, Bishara, Ahn, & Stout, 2011; Yechiam, Stout, Busemeyer, Rock, & Finn, 2005), BART (Barnhart & Buelow, 2017a;

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Cheng, Ng, & Lee, 2012; Hopko et al., 2006; Hunt, Hopko, Bare, Lejuez, & Robinson, 2005; Reynolds, Ortengren, et al., 2006; Upton et al., 2011), GDT (Mueller, Schiebener, Stockigt, & Brand, 2017), a type of DDT (Barnhart & Buelow, 2017a; Dom et al., 2007; Monterosso et al., 2001; Moreno et al., 2012; Ortner, MacDonald, & Olmstead, 2003; Reynolds, Ortengren, et al., 2006; Reynolds, Penfold, & Patak, 2008), CGT (Monterosso et al., 2001), or other decision making task (Dinu-Biringer, Nees, Falquez, Berger, & Barnow, 2016; Grassi et al., 2015; Sasaki & Kanachi, 2005). On the other hand, greater self-reported impulsivity (Buelow & Suhr, 2013; Burdick et al., 2013; Christodoulou et al., 2006; Crone, Vendel, & van der Molen, 2003; Franken & Muris, 2005; Franken, van Strien, Nijs, & Muris, 2008; Kertzman, Kagan, Vainder, Lapidus, & Weizman, 2013; Ochoa et al., 2013; Sweitzer, Allen, & Kaut, 2008; Tomassini et al., 2012; Zeng et al., 2013; Zermatten, van der Linden, d’Acremont, Jermann, & Bechara, 2005) or reward responsiveness (Buelow & Suhr, 2013; Davis, Patte, Tweed, & Curtis, 2007; Franken & Muris, 2005; Suhr & Tsanadis, 2007; Van Honk, Hermans, Putman, Montagne, & Schulter, 2002) is associated with riskier decision making on the IGT in multiple studies. Relatively fewer studies, by comparison, find impulsivity and reward responsiveness associated with riskier decisions on the GDT (Bayard et al., 2011), CCT (Buelow, 2015; Penolazzi, Gremigni, & Russo, 2012), BART (Vigil-Colet, 2007), CGT (Franken et al., 2008; Kraplin et al., 2014), or other tasks (Leland & Paulus, 2005; Mann, 1973; McHugh & Wood, 2008; Mueller et al., 2017; Studer & Clark, 2011; Yando & Kagan, 1970). Lauriola, Panno, Levin, and Lejuez (2014) conducted a meta-analysis on the BART, finding an overall small effect for the relationship between impulsivity and task performance. Finally, other research suggests that the particular participant subgroup analyzed affects detection of significant relationships between self-reported impulsivity and risky decision making (Dowson et al., 2004; Garrido & Subira, 2013; Heyes et al., 2012). That said, it is possible that these inconsistent comparisons of selfreport measures of impulsivity with behavioral tasks could be due to significant differences in what these measures are intended to measure. Cyders and Coskunpinar (2011) find a small (r 5 0.097) relationship between self-report and behavioral measures in their meta-analysis, indicating that the exact specifications of the construct used in a particular study need to be elucidated moving forward. In addition, impulsivity itself predicts early substance use and diagnosis of SUD (Huddy et al., 2017; Molina & Pelham, 2014), and higher rates of alcohol and tobacco use are seen in individuals with subthreshold ADHD symptoms compared to controls (Malmberg et al., 2011). In a recent meta-analysis, a small but significant positive relationship was shown between impulsivity and involvement in risky sexual behaviors among adolescents (Dir, Coskunpinar, & Cyders, 2014). Taken together, there is support

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for a link between the personality characteristic of impulsivity and involvement in risk-taking behaviors and decisions. How does self-reported impulsivity correlate with ADHD? Various researchers ask participants with ADHD to self-report their ADHD symptoms as well as complete measures sensitive to aspects of impulsivity, such as behavioral inhibition/activation systems (BIS/BAS; Carver & White, 1994), the Barratt Impulsiveness Scale (BIS-11; Patton, Stanford, & Barratt, 1995), Sensitivity to Punishment Sensitivity to Reward (SPSRQ; Torrubia, Avila, Molto, & Caseras, 2001), and a quick delay questionnaire (Clare, Helps, & Sonuga-Barke, 2010). Presence and extent of ADHD symptoms are positively correlated with BIS but not BAS (Heym, Kantini, Checkley, & Cassaday, 2015), scores on a selfreport measure of impulsivity (Krause-Utz et al., 2016; Paloyelis et al., 2010), SPSRQ (Li, 2018; Thorell, Sjowall, Mies, & Scheres, 2017), and the quick delay questionnaire (Hsu, Benikos, & Sonuga-Barke, 2015). Thus, underlying impulsivity might be a contributing factor to risky decision making in ADHD.

Demographic factors Several demographic factors can affect performance on risky decision making tasks and the relationship between risky decision making and ADHD. Participant age appears to affect the consistency of findings, as, in general, riskier decisions are seen when children with ADHD are compared to their same-aged peers than when adults are compared to their same-aged peers (Groen et al., 2013). Multiple studies exhibit a male participant bias and there are known gender effects on at least some common behavioral decision making tasks. As previously described, variations within samples regarding current use and type of medication, current level of symptoms and ADHD subtype, and age of symptom onset could account for discrepancies in results between studies. These factors could also increase within-group variability in a study, leading to increased difficulty detecting the known small effects (e.g., Dekkers et al., 2016) in relatively small samples.

The influence of retrospective self-report, effort, and potential malingering Increasing attention is being paid to how individuals are diagnosed with ADHD and whether there are concerns about inaccuracies in retrospective self-report all the way up to the level of malingering. Malingering refers to the deliberate exaggeration and/or fabrication of symptoms for secondary gain (APA, 2013; Slick, Sherman, & Iverson, 1999). In the context

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of ADHD, malingering could be to attain accommodations or a prescription for a psychotropic medication (Williamson et al., 2014). Malingering or symptom exaggeration is common in evaluations of ADHD, with prevalence estimates ranging from 8% to 48% (Musso & Gouvier, 2014). College-student simulators can successfully mimic the symptoms of ADHD on self-report and behavioral measures (Fisher & Watkins, 2008; Tan, Slick, Strauss, & Hultsch, 2002; Young & Gross, 2011), but scores on self-report measures can also correlate with failures on symptom validity tests (Musso & Gouvier, 2014). Individuals malingering ADHD symptoms for research purposes (simulators) or clinical evaluation endorse self-reported symptoms equivalent to those with a confirmed ADHD diagnosis (Bryant et al., 2018; Cook et al., 2018; Marshall, Hoelzle, Heyerdahl, & Nelson, 2016). Malingering and symptom exaggeration is not always caught by clinical judgment (Faust, Guilmette, et al., 1988; Faust, Hart, Guilmette, & Arkes, 1988; Marshall et al., 2016), pointing toward the recommended use of symptom validity tests in evaluations (Heilbronner et al., 2009; Tucha, Sontag, Walitza, & Lange, 2009). In nearly all studies examined, no mention is made of assessing validity of self-report measures of ADHD symptoms. This unknown factor could contribute significant variability to the pattern of task performance on risky decision making tasks, making it difficult to truly assess decision making impairment in those with ADHD.

Performance on other executive function tasks Across multiple studies of risky decision making in ADHD, a consistent pattern emerges. Although other measures of executive functions are administered, very few researchers provide a correlation or assessment of the relationship between risky decision making and these other measures. Decision making is an executive function and is associated with functioning in the prefrontal cortex. The relationship between different executive functions allows us to better understand the functioning of the frontal lobe in ADHD and helps to better plan treatments for children and adults with the diagnosis.

Performance on other tasks when decision making was impaired Multiple studies find no significant differences between individuals with ADHD and controls on other measures of executive function when risky decision making was assessed in the same study. Specifically, despite impairments on various decision making measures, no significant differences are seen between groups on the Test of Variables of

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Attention (Agay et al., 2010, 2014), digit span and/or letter-number sequencing (Agay et al., 2010; Miguel et al., 2016), Trail Making Test (Matthies et al., 2012), the continuous performance test (CPT) (Miguel et al., 2016), the Wisconsin Card Sorting Task (WCST) (Miguel et al., 2016), the stop signal task (Segala et al., 2015), and the Tower of Hanoi (Miguel et al., 2016). On the other hand, multiple studies show both riskier performance among individuals with ADHD on decision making tasks and impairments on delayed match to sample (Coghill et al., 2014), measures of sustained attention (Drechsler et al., 2010), letternumber sequencing and spatial span (Sorensen et al., 2017; Toplak et al., 2005), the CPT (Malloy-Diniz et al., 2007), and the Stroop (Mantyla et al., 2012). In addition, faster responses are seen amongst ADHD individuals on tapping (Coghill et al., 2014) and stop signal tasks (DeVito et al., 2009).

Performance on other tasks when decision making was not impaired Among those finding no between-group differences on decision making tasks, some find differences on other cognitive tasks whereas others find no group differences. Researchers who find no group differences on the IGT, HDT, or CGT find those with ADHD are impaired compared to controls on spatial span (Antonini et al., 2015; McLean et al., 2004), Trail Making (Tamm et al., 2013; Winther Skogli et al., 2014, 2017), a card sorting task (Antonini et al., 2015), digit span backwards (Gonzalez-Gadea et al., 2013), letter-number sequencing (GonzalezGadea et al., 2013; Hovik et al., 2015; Winther Skogli et al., 2014, 2017), the Stroop (Hovik et al., 2015; Tamm et al., 2013; Winther Skogli et al., 2014, 2017), go/no-go (Hobson et al., 2011; McLean et al., 2004; Tamm et al., 2013), a CPT (Hobson et al., 2011), and a tower task (McLean et al., 2004). However, a number of studies find both no ADHD-based differences on a decision making task and no differences on some of these same measures (go/no-go, stop signal, card sort, trail making test, tower task; Geurts et al., 2006; Gonzalez-Gadea et al., 2013; Hobson et al., 2011; Weafer et al., 2011; Winther Skogli et al., 2014). Across these studies, authors report results of group comparisons of performance on the various tasks. What is lacking in their reports is an indication of potential correlations between task performance within their participants, making it difficult to assess the extent to which impairment on a decision making task is related to impairment (or lack of impairment) on other executive function tasks. Two studies, however, do provide task correlations. Nonsignificant correlations are found between the IGT and delay discounting (r 5 0.11) and spatial span

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(r 5 0.01) (Antonini et al., 2015), whereas small but significant correlations are seen between the IGT and failures to maintain set on the WCST (r 5 2 0.15 to 20.21, Ernst, Grant, et al., 2003).

Neuroimaging Most studies utilizing neuroimaging techniques examine rewardrelated neuroactivity among children, adolescents, and adults on delay discounting or reward-based tasks, with a few examining activation during formal decision making tasks. Among children and adolescents, individuals with a diagnosis of ADHD and better task performance (i.e., lower delay discounting) show activation in areas including the right inferior frontal cortex and premotor area (Chantiluke et al., 2014). In addition, greater delay discounting is associated with greater activation in the nucleus accumbens, ventromedial prefrontal cortex, and connections between the nucleus accumbens and prefrontal cortex (Costa Dias et al., 2013). It is likely that activation of the mesocorticolimbic reward pathway among individuals with a diagnosis of ADHD leads to steeper delay discounting on these tasks. Gatzke-Kopp et al. (2009) compare activation levels on rewarded and nonrewarded trials among children with and without a diagnosis of ADHD. Activation in the striatum is equivalent on reward and nonreward trials in those with ADHD, whereas healthy controls instead show activation in the anterior cingulate cortex during nonreward trials (but the striatum during reward trials). Others (Ma et al., 2016) find increased activation in the striatum during reward trials among individuals with ADHD compared to controls. Treatment with fluoxetine, compared to placebo, increases activation in frontal regions, including the right inferior frontal cortex, and decreases activation in the prefrontal cortex during a delay discounting task (Carlisi et al., 2016), indicating treatment improves dysfunctional activations in children with ADHD. Several studies examine activation to reward in adults with and without ADHD. Ernst, Kimes, et al. (2003) find group differences in activation in the right anterior cingulate cortex (ADHD adults have greater activation) and left hippocampal gyrus and insula (ADHD adults have less activation) during the IGT. On a different task (probabilistic gambling task), neural feedback among those with ADHD is dependent on the feedback condition and likely points to activity in the frontocentral and centroparietal circuits affecting decision making (Mesrobian et al., 2018). fMRI during the GDT indicates higher activation in the medial orbitofrontal cortex during reward in controls but not those with ADHD and that this dysfunctional mOFC activity is associated with performance on the GDT and a delay discounting task (Wilbertz et al., 2012).

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On delay discounting and other rewarded tasks, greater responses are seen in adults with ADHD in the orbitofrontal cortex (Furukawa et al., 2014; Wetterling et al., 2015) but lower responses in the ventral and dorsal striatum compared to controls (Furukawa et al., 2014; Plichta & Scheres, 2014).

Potential mechanisms Overall executive dysfunction One potential reason for riskier decision making across multiple studies is that decision making is an executive function and executive function deficits are common in ADHD. Decreased gray matter volume is seen in the prefrontal cortex among those with ADHD compared to their same-aged peers (Bralten et al., 2016; Seidman, Valera, & Bush, 2004; Valera, Faraone, Murray, & Seidman, 2007). In addition to these structural differences, functional differences are also seen, most commonly hypoactivity in the prefrontal cortex (Bush et al., 1999; Cubillo, Halari, Smith, Taylor, & Rubia, 2012; Dickstein, Bannon, Xavier Castellanos, & Milham, 2006; Tamm, Menon, Ringel, & Reiss, 2004; Zametkin et al., 1990). ADHD is often conceptualized as a difficulty with control and response inhibition (e.g., Schachar, Tannock, & Logan, 1993), which results in impairments across various executive function measures (e.g., Pennington & Ozonoff, 1996). These difficulties with planning, organization, inhibitory control, working memory, and other executive functions could translate into difficulties learning relative advantages and disadvantages of different choices and adapting to feedback on those decisions. In addition, some of the discrepancies noted across decision making tasks may be due to nuances in the task presentation itself. Some behavioral tasks tap into cold, Type II, or explicit decision making, whereas others tap into hot, Type I, or implicit decision making. Among those with ADHD, adults show impaired explicit decision making and children impaired implicit and explicit decision making (Groen et al., 2013). These findings map onto research with nondecision making tasks. Children with ADHD also show decreased performance on cold executive function tasks more generally (e.g., Antonini et al., 2015; Kopecky, Chang, Klorman, Thatcher, & Borgstedt, 2005; Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005; Romine et al., 2004; Weyandt & Willis, 1994; Wilkutt, Doyle, Nigg, Faraone, & Pennington, 2005), despite intact performance on hot executive function tasks (e.g., Antonini et al., 2015). Unfortunately, the relative dearth of examinations of decision making in addition to other executive functions—and the relationship between the

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measures—limits our understanding of how the underlying type of decision making ability assessed may vary based on other executive impairments.

Alterations in neural processing of rewards and learning from feedback Alterations in both structure and function of portions of the reward pathway, most notably the ventral striatum and its connections with the prefrontal cortex, could also account for risky decision making in those with ADHD. Decreased activity is evident in the ventral striatum among individuals with ADHD compared to their same-aged peers, both in general (Plichta & Scheres, 2014) and during measures of delay discounting (Edel et al., 2013; Hoogman et al., 2013; Kappel et al., 2015; Scheres et al., 2007). Coupled with these functional impairments is evidence of decreased ventral striatum volume in ADHD (Carmona et al., 2009). The previously noted hypofrontality (e.g., Zametkin et al., 1990) could interact with the decreased ventral striatum activation, leading to lowered resources needed during decision making tasks. Additional research points toward dysfunction in the dorsal anterior midcingulate cortex (or dorsal anterior cingulate cortex) in ADHD (Bush et al., 1999; Dickstein et al., 2006; Tamm et al., 2004), a structure associated with behavior adjustments following feedback (Bush, 2009; Bush et al., 2002; Williams et al., 2004). Disrupted connections between the dorsal anterior midcingulate cortex and other portions of the prefrontal cortex, as well as with the striatum (Morecraft & Tanji, 2009; Rolls, 2009), could account for difficulties learning to decide advantageously based on feedback during reward-based decision making tasks. These dysfunctions are also consistent with the dual-pathway model of ADHD (e.g., Sonuga-Barke, 2003, 2005; Sonuga-Barke, Sergeant, Nigg, & Willcutt, 2008), which states that ADHD symptoms occur due to both executive deficits associated with the mesocortical pathway and motivation-based impairments in the mesolimbic reward pathway. As a secondary hypothesis, individuals with a diagnosis of ADHD might experience a reward deficiency syndrome (Blum, Cull, Braverman, & Comings, 1996) that negatively affects decisions in which potential rewards may occur. Briefly, this syndrome is based on the cascade theory of reward (Blum & Kozlowski, 1990; Cloninger, Svrakic, & Przybeck, 1993; Stein & Belluzzi, 1986) in which the excitatory and inhibitory neurotransmitter effects “cascade” down. In particular, activation flows from the hypothalamus to the ventral tegmental area to the amygdala and hippocampus. In ADHD, dopamine activity is altered leading to inattention as well as hyperactivity due to the altered

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dopamine activity in the ventral tegmental area (Le Moal & Simon, 1991). If the above-described cascades of neurotransmitter activation/ inhibition are disrupted, impulsive behaviors, such as those seen in ADHD, can occur (Blum et al., 1996). This theory is consistent with others focused on the contributions of the mesocorticolimbic pathways to reward and motivation deficits in ADHD (McNab et al., 2009; Wise, 2002, 2004). In addition, functional neuroimaging indicates greater reward anticipation (von Rhein et al., 2015), altered orbitofrontal cortex responsivity to signals of reward (Strohle et al., 2008), and ventral striatum hyporeactivity to reward cues (especially future rewards; Carmona et al., 2011; Scheres et al., 2007). Combination of these theories and evidence indicate altered reward processing (including anticipation of future rewards) combines with impairments in executive functions, including those linked to feedback-based learning in dopaminergic pathways, to create riskier decisions in those with ADHD. Immediate rewards may be more recognizable (Sonuga-Barke, Taylor, Sembi, & Smith, 1992; Volkow et al., 2009, 2011), leading to discounting of future rewards. However, the continued development of the prefrontal cortex in adolescence and early adulthood may help explain some inconsistencies in results across ages.

Conclusions and future directions Although there is clear evidence of self-reported involvement in realworld risk-taking behaviors with potential negative consequences for health and well-being, this consistency does not translate to behavioral assessments of risk-taking and risky decision making. There does appear to be greater consistency of decision making impairments among younger versus older participants, but overall only small to moderate effects are noted. All theories of the mechanisms underlying these decision making deficits point to the dopaminergic mesocorticolimbic pathway affecting reward reactivity and learning from feedback. Several avenues for future exploration are examined.

Type of decision making task and comparison to other executive tasks Reliance on just one risky decision making task in a study can miss nuances in how reward-based deficits can affect reward-based decision making. As previously stated, individuals with ADHD may be more impaired on explicit than implicit gambling tasks (Groen et al., 2013). This finding could explain the inconsistent pattern of results when,

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for example, a study using just the GDT is compared to a study using just the BART. We know from factor analyses that not all decision making tasks assess the same components of decision making (e.g., Buelow & Blaine, 2015). To try to further understand the nature and extent of decision making difficulties in ADHD, future research should examine decision making not as a solitary construct, but rather in the context of overall executive functioning. Just assessing multiple executive functions in one study is not enough—potential relationships between those functions (including decision making) should be assessed. At this point, it is difficult to conceptualize the relationship between decision making and other executive functions in ADHD. Finally, researchers should utilize the newest analytic approaches to understand performance on the different tasks. For example, examining the IGT by individual decks points to a Deck B and Deck D (rather than Deck C and Deck D) preference among those with ADHD (Toplak et al., 2005). This focus on minimizing frequency of losses rather than maximizing long-term gains points toward the theorized focus on immediate versus distant outcomes in ADHD and could explain between-study differences in results on this task.

Participant-related factors Several participant-related factors exist as potential confounds as well as explanations for decision making impairments in ADHD. First, validity of symptom report should be considered in all studies moving forward. There is a high potential for malingering or symptom exaggeration in ADHD and very few studies to date assess for this in their participants. Second, adults with a diagnosis of ADHD should be split into two groups for analyses: those with continued current symptoms and those with the history of ADHD symptoms but without current symptoms (or symptoms currently below threshold). Combining these participants into one group could blur the relationship between current ADHD symptoms and current decision making impairments. Third, the potential influence of ADHD subtype and ADHD medication/treatment should be examined further. Obtaining large samples of participants with ADHD-inattentive, ADHD-hyperactive, and ADHD-combined is necessary to see the potential unique contributions of each set of symptoms to reward-based decision making. Many researchers request that participants refrain from medication use 24 48 hours prior to the study; however, this does not tell us if the medications themselves are acting as a positive influence on real-world decision making during the rest of the participant’s activities. Treatment-outcome studies or within-subject designs (on/off medications) would be of benefit to the field, but the

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concerns about practice effects and test retest reliability of the tasks limit our current ability to conduct this research at this time. Finally, the potential influence of comorbid diagnoses is unclear. There is some evidence that cooccurring conduct or ODD can lead to greater decision making impairments, but it is unclear if ADHD is the “additive component” or if ODD/CD is. As previously stated, it is unclear to what extent the diagnosis of ADHD itself exerts a consistent and negative influence on risky decision making in children, adolescents, and adults.

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10 Addictive behaviors: gambling and substances of abuse This chapter examines risky decision making among individuals experiencing difficulties with behavioral and substance-based addictions. Pathological gambling is the primary behavioral addiction that is diagnosable in the DSM-V, while other behaviors are currently being investigated for possible inclusion (e.g., problematic internet or video game use) or are included in other sections that were previously discussed (e.g., binge eating). Multiple substances of abuse can be used to meet criteria for a substance use disorder, including alcohol, caffeine, cannabis, nicotine, opioids, and stimulants (APA, 2013). Although the specific mechanisms may vary, the substance use and gambling disorders share some common symptoms, including tolerance, withdrawal, altered thinking patterns, continued involvement in the behavior despite known negative consequences, and craving or urge to engage in the behavior. In addition, the varying addictive behaviors have several commonalities, including disruption in the structure and function of portions of the brain’s reward pathway and a pattern of impulsive responses (Bolla et al., 2003; Bolla, Eldreth, Matochik, & Cadet, 2005; Goldstein & Volkow, 2002; Gowin, Mackey, & Paulus, 2013; Karim & Chaudhri, 2012; Leeman & Potenza, 2012). Delay discounting also seems to underlie the onset and maintenance of the difficulties, potentially increasing risk of other health-risk behaviors (Amlung et al., 2017; Bickel, Jarmolowicz, Mueller, Gatchalian, & McClure, 2012; Koffarnus & Kaplan, 2018; Mackillop et al., 2011). Substance dependence is also one of the more common DSM-V diagnostic categories, with 9.2% over age 12 meeting criteria for substance abuse or dependence within the past year (Hughes, Sathe, & Spagnola, 2009). High comorbidities are seen between the substance use disorders and most other classes of diagnosable disorders, including mood, anxiety, and sleep disorders. In addition, individuals frequently use and/or abuse more than one substance

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at a time or across the lifetime. This high rate of polysubstance use and abuse can make it difficult to differentiate the specific contributions of one substance to risky decision making and delay discounting. In the following sections, evidence of risk-taking behavior, risky decision making, and steeper delay discounting will be examined across the various addictive behaviors. Pathological gambling and the other behavioral addictions will be examined first, followed by the substance use disorders. As many of the studies examining individuals dependent on a substance actually utilize polysubstance using samples, the effects of polysubstance dependence and comorbid diagnoses will be examined at the end of this chapter.

Pathological gambling Gambling disorder, or pathological gambling, is associated with recurrent episodes of gambling that can lead to difficulties similar to those of the substance use disorders: increasing amounts of gambling and bets (i.e., tolerance), difficulties quitting, increased irritability (i.e., withdrawal), and a preoccupation with gambling (APA, 2013). Distorted thinking can also be seen, such as beliefs about personal control over gambles and a tendency to “chase losses,” or go all-in on a bet to quickly make up for previous losses. Prevalence rates can vary, with lifetime prevalence rates ranging from 0.4% to 1.6% in the overall adult population (APA, 2013; Shaffer, Hall, & Vander Bilt, 1999). Rates are higher in college students (5% meet criteria for gambling disorder, 9% fall in the subclinical range; Shaffer et al., 1999). Gambling can start early, with 39% of students in Grade 5, 80% of students in Grade 11, and 75% of college students reporting gambling in the past year (Barnes, Welte, Hoffman, & Tidwell, 2010; Turner, Macdonald, Bartoshuk, & Zangeneh, 2008). Gambling turns to pathological gambling in a smaller portion of individuals (15%; Wardle et al., 2007) and can take multiple years to develop (6 on average; Grant & Kim, 2001). Higher prevalence rates are seen in males versus females and high levels of comorbidity are seen with mood and anxiety disorders, alcohol use (73%), drug use (38%), suicidality, and substance use disorders (APA, 2013; Bray et al., 2014; Grant, Derbyshire, Leppink, & Chamberlain, 2014; Petry, Stinson, & Grant, 2005).

Delay discounting Most studies of delay discounting focus on samples of pathological or problem gamblers, with some instead studying individuals who engage

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in gambling behaviors. Across studies, there is significant evidence of steeper delay discounting in problem or pathological gamblers (AlbeinUrios, Martinez-Gonza´lez, Lozano, Clark, & Verdejo-Garcı´a, 2012; Alessi & Petry, 2003; Andrade & Petry, 2012; Dixon & Holton, 2009; Dixon, Marley, & Jacobs, 2003; Kraplin et al., 2014; Ledgerwood, Alessi, Phoenix, & Petry, 2009; MacKillop, Anderson, Castelda, Mattson, & Donovick, 2006; Miedl et al., 2015; Petry, 2001a; Wiehler & Peters, 2015) and those who engage in gambling behaviors without meeting full diagnostic criteria (Holt, Green, & Myerson, 2003; Hong, Zheng, & Li, 2015; Petry & Casarella, 1999). However, some do find no effect of gambling on delay discounting (Madden, Petry, & Johnson, 2009; Shead, Callan, & Hodgins, 2008). Reynolds (2006) theorizes that the reason for these mixed findings could be cross-study differences in participant age and recruitment methods (i.e., treatment vs nontreatment seeking), but others find no effect of age on discounting (Steward et al., 2017).

Risky decision making Just as we have seen with other categories of psychopathology, mixed findings are seen as a function of disordered gambling behaviors. Individuals with problem or pathological gambling decide riskier/less advantageously on the Balloon Analogue Risk Task (BART) (Ciccarelli, Malinconico, et al., 2016; Ledgerwood, Alessi, Phoenix, & Petry, 2009), Cambridge Gambling Task (CGT) (Lawrence, Luty, Bogdan, Sahakian, & Clark, 2009; Kraplin et al., 2014), Game of Dice Task (GDT) (Brandherr et al., 2005b; Labudda, Wolf, Markowitsch, & Brand, 2007), a risky gamble task (Ligneul, Sescousse, Barbalat, Domenech, & Dreher, 2013), and the Iowa Gambling Task (IGT) (Brevers, Koritzky, Bechara, & Noel, 2014; Brevers, Cleeremans, et al., 2013; Bottesi, Ghisi, Ouimet, Tira, & Sanavio, 2015; Cavedini, Riboldi, et al., 2002; Ciccarelli, Griffiths, et al., 2016; Ciccarelli, Griffiths, Nigro, & Cosenza, 2017; Forbush et al., 2008; Goudriaan, Oosterlaan, de Beurs, & van den Brink, 2005; Goudriaan, Oosterlaan, de Beurs, & van den Brink, 2006; Kertzman, Lidogoster, Aizer, Kotler, & Dannon, 2011; Kraplin et al., 2014; Ledgerwood et al., 2012; Linnet, Rojskjaer, Nygaard, & Maher, 2006; Lorains et al., 2014; MallorquiBague et al., 2016; Oberg, Christie, & Tata, 2011; Ochoa et al., 2013; Power, Goodyear, & Crockford, 2012; Roca, Torralva, Lopez, et al., 2008; Roca, Torralva, Meli, et al., 2008). In their reviews, Brevers, Bechara, Cleeremans, and Noel (2013) and Wiehler and Peters (2015) both point to impaired decision making on the IGT in those with problem gambling behaviors. These difficulties are not limited to just those with gambling-related difficulties, as those who engage in gambling behaviors (no diagnosis) also show impairments on the CGT (Grant, Chamberlain, Shreiber, Odlaug, & Kim, 2011),

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Georgia Gambling Task (Lakey, Goodie, Lance, Stinchfield, & Winters, 2007; Lakey, Goodie, & Campbell, 2007), and IGT (Hong, Zheng, & Li, 2015; Lakey, Goodie, Campbell, 2007; Lakey, Goodie, Lance, et al., 2007; Nigro, Ciccarelli, & Cosenza, 2018; Nigro & Cosenza, 2016). However, others find no effect (Goodie & Lakey, 2007; Miedl, Fehr, Meyer, & Herrmann, 2010; Tanabe et al., 2007) or even less risky/more risk-averse task performance (del Valle Vera, Pilatti, Garimaldi, & Pautassi, 2018; Giorgetta et al., 2014).

Neuroimaging Multiple studies examine results of decision making in conjunction with EEG or fMRI. Activation differences between gamblers (both those meeting and those not meeting full diagnostic criteria for a gambling disorder) and nongamblers are seen in the ventral striatum (Linnet, Moller, Peterson, Gjedde, & Doudet, 2010, 2011; Linnet, Peterson, Doudet, Gjedde, & Moller, 2010), prefrontal cortex (Miedl et al., 2010, 2015; Mohammadi et al., 2016; Power, Goodyear, & Crockford, 2012; Tanabe et al., 2007), and portions of the limbic system including the amygdala (Power et al., 2012). Activation of the reward pathway or lowered functioning in portions of the prefrontal cortex may underlie risky decision making among those with gambling difficulties. Although this pathway is sensitive to dopamine, dopamine function itself does not differ between gamblers and nongamblers (Clark, Stokes, et al., 2012; Linnet et al., 2010). However, activation in the ventral striatum is associated with worse decision making in pathological gamblers but improved decision making in controls (Linnet et al., 2010, 2011), indicating it is not just pathway activation but how it interacts with other cognitive and personality processes to affect decisions (e.g., Miedl et al., 2015). Other researchers specifically examine how responses to losses and gains may differ between gamblers and controls. Frequent gamblers and those with gambling-related difficulties show alterations in the neural response to rewards in the anterior cingulate (Miedl et al., 2014) and prefrontal cortex (de Ruiter et al., 2009) as well as altered responses to feedback more generally in the medial frontal cortex (Miedl et al., 2014; Oberg, Christie, & Tata, 2011). Still others point to impairments in the neural response to risk (de Ruiter et al., 2009; Kreussel et al., 2013; Oberg et al., 2011) and to uncertainty in decision making (Linnet et al., 2012).

Relationship with other executive functions Inconsistencies are seen in performance patterns across various measures of executive functions. When impairments are seen on decision making

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tasks, additional impairments are seen in planning, inhibitory control, and problem-solving (Brand, Kalbe, et al., 2005; Forbush et al., 2008; Hong et al., 2015; Kraplin et al., 2014; Ledgerwood et al., 2012; Ochoa et al., 2013; Roca, Torralva, Lopez, et al., 2008). Others instead find no impairments in these functions despite impaired decision making (Kertzman et al., 2011; Lawrence et al., 2009; Ledgerwood et al., 2012). Forbush et al. (2008) examine potential predictors of pathological gambling, finding that impaired decision making and personality characteristics such as impulsivity, but not other executive functions, can predict gambling difficulties. Coupled with impaired motor inhibition and cognitive flexibility (Odlaug, Chamberlain, Kim, Schreiber, & Grant, 2011), impaired executive functions could be both the predictor and the consequence of gambling behaviors.

Theories Several theories are put forth as to the reasons for impaired decision making and delay discounting in clinical and subclinical gambling difficulties. Impulsivity The personality characteristic of impulsivity may be one factor affecting decision making in gamblers. Individuals who gamble score higher on measures of impulsivity than those who do not gamble (Alessi & Petry, 2003; Bottesi et al., 2015; Ciccarelli, Malinconico, et al., 2016; Kraplin et al., 2014; Lawrence et al., 2009; Ledgerwood et al., 2009; ´ lvarez-Moya et al., 2011) and impulsivWiehler & Peters, 2015; but see A ity can also predict severity of gambling difficulties (MacLaren, Fugelsang, Harrigan, & Dixon, 2012). Someone high in impulsivity may be more prone to the initial engagement in gambling behaviors (Krueger, Schedlowski, & Meyer, 2005), due in part to a focus on immediate versus long-term outcomes (Ciccarelli, Malinconico, et al., 2016). Impulsivity is also associated with more severe gambling difficulties (MacKillop et al., 2014), pointing to at least some influence of impulsivity on the behavior and subsequent decision making impairments. Difficulties with learning from reward or loss and perception of risks Individuals who gamble may also have difficulties processing the level of risk associated with different decisions and feedback following a decision. Evidence suggests that individuals who gamble expect greater rewards and fewer risks or process less of a risk associated with a decision (Spurrier & Blaszczynski, 2014). These biases in risk perception can be due in part to cognitive distortions, such as belief in skill

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versus luck determining gambling outcomes, that can affect processing of different options in a decision (Cocker & Winstanley, 2015; Donati, Chiesi, & Primi, 2013). After a decision is made, however, individuals who gamble also experience alterations in feedback processing (Brand, Kalbe, et al., 2005; Goudriaan et al., 2005; Oberg, Christie, & Tata, 2011; Ulrich, Ambach, & Hewig, 2016). These changes in feedback processing fall on both sides of the reward/punishment spectrum. Individuals who gamble have both increased and impaired sensitivity to rewards and losses compared to those who do not gamble (Brevers, Koritzky, et al., 2014; de Ruiter et al., 2009; del Valle Vera et al., 2018; Gelskov, Madsen, Ramsoy, & Siebner, 2016; Oberg et al., 2011), which can lead to increased risk-taking behavior in subsequent decisions (Brunborg et al., 2010; Power et al., 2012). Difficulties processing feedback, altered perception of risks, and cognitive distortions indicating greater personal effect on gambles can combine to create a decision making situation in which individuals do not accurately use previous results to change future decision making to be more optimal. Differential focus on shortterm versus long-term outcomes, and the contribution of hot versus cool executive functions, can also play a role (e.g., Brevers, Bechara, et al., 2013). Urge to gamble and craving behaviors Although not as researched, craving may also play a role in gambling behaviors. Given the overlap between the behavioral and substance addictions, craving-related behaviors could occur in those who gamble just as they do in those who are dependent on various substances of abuse. There is some evidence that frequent gamblers experience an increased urge or craving to gamble following wins (Ashrafioun, Kostek, & Ziegelmeyer, 2013; Kushner et al., 2008). Craving for substances is known to activate the brain’s reward pathway, leading to increased involvement in behaviors to continue that activation (see later sections). This process may also be active in problem gamblers, in that continued risk-taking occurs to increase activation in the reward pathway and avoid gambling “withdrawal.” Gambling problem severity and type The severity and specific type of gambling difficulty (in-person, internet-based, etc.) may also play a role. Greater severity of gambling difficulties is associated with greater decision making impairments (Ligneul et al., 2013; Nigro et al., 2018), greater impulsivity (Mackillop et al., 2014), and greater reactivity to losses that can in turn lead to riskier real-world decision making (Ulrich et al., 2016). Impairments are seen in those exhibiting gambling difficulties without meeting diagnostic criteria (del Valle Vera et al., 2018; Grant et al., 2011), indicating that

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problem severity may not in fact matter with regard to cognitive impairments (Wiehler & Peters, 2015). It is also possible that those with a more severe gambling difficulty are also at greater risk of comorbid diagnoses such as substance use or depression, which could contribute to worsened decision making. Although some find that decision making impairments vary by subtype of gambling (Bonnaire, Bungener, & Varescon, 2009), others instead find decision making impairments independent of the gambling subtype (Chamberlain, Stochl, Redden, Odlaug, & Grant, 2017; Leppink, Blum, Chamberlain, & Grant, 2016). These differences by severity and type should continue to be investigated in future studies. State and participant variables As previously mentioned, gambling disorders typically present at higher rates in males than females. Accordingly, most studies of decision making and delay discounting focus on entirely or predominately male samples of participants. We currently have a better understanding of decision making and delay discounting in male gamblers than we do in female gamblers, as well as in younger (adolescent and young adult) versus older gamblers. State-dependent or situational variables can also affect decision making, such as the circumstances surrounding the decision itself (Dixon, Buono, & Belisle, 2016; Dixon & Holton, 2009; Dixon, Jacobs, & Sanders, 2006; Holt, Green, & Myerson, 2003). These statedependent factors could play a role in real-world decisions as well. As previously mentioned, some individuals with gambling disorders engage in a risky betting strategy in which they place a very high bet, all at once, in order to try to win back previously lost money. Factors such as the need for money or other incentives could constitute a stateor situation-dependent factor that affects decision making. Comorbid diagnoses The presence of comorbid diagnoses could also affect decision making among those with gambling difficulties. Greater decision making impairments and steeper delay discounting are seen among those with pathological gambling and a secondary diagnosis (ADHD, substance use; Abouzari et al., 2016, 2015; Aı¨te et al., 2014; Andrade, Alessi, & Petry, 2013; Chamberlain, Derbyshire, Leppink, & Grant, 2015; Ledgerwood et al., 2009; Petry, 2001a; Petry & Casarella, 1999). Others instead find no differences in task performance between those with gambling difficulties and those with gambling and a comorbid diagnosis (Alessi & Petry, 2003; Ciccarelli, Griffiths, et al., 2016; Goudriaan et al., 2005; Kraplin et al., 2014; Mallorqui-Bague et al., 2016; Stea, Hodgins, & Lambert, 2001).

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Treatment implications Several studies examined factors that could be targeted in treatment to improve outcomes for those seeking to stop problematic gambling behaviors. There is some evidence to suggest that impaired decision making and steeper discounting at the start of treatment (or baseline) can predict difficulties achieving gambling abstinence in the future or treatment dropout (Alvarez-Murcia et al., 2011; Petry, 2012). Others instead find no cognitive or personality predictors of successful treatment outcomes versus relapse to gambling (de Wilde, Goudriaan, Sabbe, Hulstijn, & Dom, 2013). Treatments that focus on increasing cognitive control (Moccia et al., 2017), correcting misperceptions of gambling-based normative behaviors (Celio & Lisman, 2014), changing cognitive distortions (Donati et al., 2018), increasing coping skills (Turner et al., 2008), and increasing mindfulness (de Lisle, Dowling, & Allen, 2012; Lakey, Campbell, Brown, & Goodie, 2007) can have beneficial effects on decreasing gambling behaviors.

Other behavioral addictions There are several additional behaviors that bear a number of commonalities with problematic gambling (e.g., Hollander & Allen, 2006). Some, such as hoarding disorder and binge eating, are currently listed in separate categories of the DSM-5 (obsessive compulsive and related disorders, eating disorders, respectively; APA, 2013). Compulsive buying, internet addiction, and problematic video gaming are other potentially addictive behaviors that share characteristics of gambling (Karim & Chaudhri, 2012). Individuals with hoarding disorder show impairments in decision making and steeper delay discounting than controls (Blom et al., 2011; Lawrence et al., 2006; but see Grisham, Norberg, Williams, Certoma, & Kadib, 2010; Rasmussen, Brown, Steketee, & Barlow, 2013; Tolin & Villavicencio, 2011) as well as greater impulsivity (Grisham, Brown, Savage, Steketee, & Barlow, 2007). These behavioral addictions are also associated with impaired prefrontal cortex functioning (MataixCols, Pertusa, & Snowdon, 2011). Compulsive buying, or pathological buying, is associated with greater impulsivity and riskier decision making (Billieux, Rochat, Rebetez, & van der Linden, 2008; Black, Shaw, McCormick, Bayless, & Allen, 2012; Derbyshire, Chamberlain, Odlaug, Schreiber, & Grant, 2014; Lejoyeux, Tassian, Solomon, & Ades, 1997; Nicolai & Moshagen, 2017; Trotzke, Starcke, Pedersen, Muller, & Brand, 2015; Voth et al., 2014). Those experiencing pathological buying also show greater activation in the brain’s reward pathway (nucleus

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accumbens) when viewing items to purchase, compared to controls (Raab, Elger, Neuner, & Weber, 2010). Internet gaming disorder is currently listed as a diagnosis for further exploration and possible inclusion in the DSM. Symptoms include recurrent internet and gaming use that can lead to distress and impairment, preoccupation with video games, withdrawal symptoms when away from games, and using games to escape factors such as negative mood (APA, 2013). More generalized than internet gaming, up to 9 million in the United States alone are at risk of experiencing difficulties with internet use (Block, 2007), as well as high rates of dependence on texting and similar technologies (Ferraro & Weatherly, 2016). Problematic internet use is associated with greater rates of impulsivity and delay discounting (Li, Tian, et al., 2016). Problematic internet and other video game play is similarly associated with greater impulsivity and delay discounting and impaired decision making (Bailey, West, & Kuffel, 2013; Beullens, Roe, & Van den Bulck, 2011; Fischer et al., 2009; Pawlikowski & Brand, 2011; but see Metcalf & Pammer, 2014), as well as difficulties with risk perception (Reynolds et al., 2015). Current rates of problematic video game play range from 7% to 9% across children, adolescents, and young adults (Bailey et al., 2013; Gentile, 2009; Gentile et al., 2011; Irvine et al., 2013), indicating additional research is warranted to determine how problematic gaming can affect cognition in this large subgroup of younger individuals.

Alcohol use disorder Alcohol use disorder is one of the most commonly diagnosed addictive disorders. Alcohol use disorders affect 4% 9% of the US population, with the specific rate depending in part on age at diagnosis (APA, 2013). Among adolescents, 36% report light and 38% heavy alcohol use (though not to the level of an alcohol use disorder; Field, Christiansen, Cole, & Goudie, 2007). Rates of alcohol use problems are higher in men than in women as well as in younger versus older adults (APA, 2013). Alcohol use, not just problematic use, is associated with involvement in other risktaking behaviors (e.g., Epstein, Botvin, & Spoth, 2003; Kelly et al., 2005). Recent alcohol consumption or acute intoxication is associated with increased sexual risk-taking (Bowers, Segrin, & Joyce, 2016; Cooper, 2002; Cue Davis, Hendershot, George, Norris, & Heiman, 2007; Davis et al., 2009, 2010, 2014; Johnson, Sweeney, et al., 2016; MacKillop et al., 2015; Parks et al., 2009; Townshend, Kambouropoulos, Griffin, Hunt, & Milani, 2014), gambling (Phillips & Ogeil, 2007), use of other substances (BrownRice et al., 2018; John, Meyer, Rumpf, & Hapke, 2003), and drinking and driving behaviors (Amlung et al., 2014; Bingham, Elliott, & Shope, 2007;

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Finken, Jacobs, & Laguna, 1998; Hopthrow et al., 2014). The decision to drink and drive is also associated with worse performance on risky decision making tasks (Bouchard, Brown, & Nadeau, 2012; Kasar, Gleichgerrcht, Keskinkillic, Tabo, & Manes, 2010), indicating a complex relationship between the alcohol use behavior itself and associated decision making impairments leading to additional risk-taking behaviors.

Risky decision making Although the majority of previous research studies support the notion of increased risky decision making among individuals with a history of alcohol use or overuse, some instead find no differences. Across tasks, including the IGT, GDT, BART, and Cups Task, individuals who currently use, abuse, or are dependent on alcohol, as well as those currently abstinent from alcohol use, make riskier decisions than nonalcohol controls (Ando´ et al., 2012; Bechara et al., 2001; Bo, Billieux, & Landro, 2016; Brand, Fujiwara, et al., 2005; Brevers, Bechara, et al., 2014; DeMartini et al., 2014; Dom, De Wilde, Hulstijn, van den Brink, & Sabbe, 2006; Dougherty et al., 2015; Fein, Klein, & Finn, 2004; Goudriaan, Grekin, & Sher, 2007, 2011; Gullo & Stieger, 2011; Johnson et al., 2008; Kim, Sohn, & Jeong, 2011; Korner, Schmidt, & Soyka, 2015; Kornreich et al., 2013; Le Berre et al., 2014; Mazas, Finn, & Steinmetz, 2000; Miranda, MacKillop, Meyerson, Justus, & Lovallo, 2009; Noel, Bechara, Dan, Hanak, & Verbanck, 2007; Salgado et al., 2009; Tomassini et al., 2012; Xiao, Bechara, et al., 2013; Xiao et al., 2009; Xie, Yuan, Meng, & Wang, 2018; Zorlu et al., 2013). There are, however, several findings suggesting no differences on the IGT, BART, Stoplight Task, or Wheel of Fortune as a function of alcohol use history (Cantrell, Finn, Rickert, & Lucas, 2008; Carbia, Cadaveira, Caamano-Isorna, Rodriguez Holguin, & Corral, 2017; Cservenka & Nagel, 2012; Erskine-Shaw, Monk, Qureshi, & Heim, 2017; Fein & McGillivray, 2007; Fein, McGillivray, & Finn, 2006; Gonzalez, Bechara, & Martin, 2007; Hildebrandt, Brokate, Hoffman, Kroger, & Eling, 2006; van der Plas et al., 2009; del Valle Vera, Pilatti, Garimaldi, & Pautussi, 2018). In fact, two studies found safer decision making on the BART among those with a history of alcohol use disorder (Ashenhurst et al., 2011, 2014). Campbell et al. (2013) refer to lowered pumps per balloon among long-term alcohol users as a suboptimal decision making strategy, as although they do not make more pumps per balloon, they also do not maximize chances of winning the most money on each balloon. A meta-analysis of performance on the IGT in alcohol use and gambling disorders indicated impaired IGT in alcohol use disorder (medium effect), but worse impairment in those with gambling disorders (Kovacs, Richman, Janka, Maraz, & Ando, 2017). A second meta-analysis

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indicated the IGT was one of the most sensitive measures to cognitive impairment in alcohol use disorders (Stephan et al., 2017). Several studies instead examine how risky decision making might be affected by acute alcohol intoxication and whether it could predict alcohol problems (concurrently and in the future). Acute alcohol administration typically increases risky decision making across tasks [Bidwell et al., 2013; Caneto, Pautassi, & Pilatti, 2018 (only in those with a family history of dependence); Corbin, Scott, Boyd, Menary, & Enders, 2015; George, Rogers, & Duka, 2005; Gilman et al., 2011; Lyvers, Mathieson, & Edwards, 2015; Rose, Jones, Clarke, & Christiansen, 2014; del Valle Vera et al., 2018], although findings are mixed on the BART in particular (no effect of alcohol intoxication; Euser, van Meel, Snelleman, & Franken, 2011; Heinz, de Wit, Lilje, & Kassel, 2013; Reynolds, Richards, et al., 2006). Length of alcohol use history can predict performance on decision making tasks (Bechara et al., 2001; Fein et al., 2004). In addition, risky decision making can predict current and future alcohol use problems above and beyond the contribution of impulsivity (Courtney et al., 2012; Fernie, Cole, Goudie, & Field, 2010; Fernie et al., 2013; Harvanko, Schreiber, & Grant, 2013; Skeel, Pilarski, Pytlak, & Neudecker, 2008). All in all, a strong relationship exists between alcohol use, alcohol use related problems, and riskier decisions across behavioral tasks.

Delay discounting and reward responsiveness If there is a relatively consistent pattern on risky decision making tasks in alcohol use, there is a much more consistent pattern when delay discounting tasks are examined. Individuals who currently use alcohol (Adams, Attwood, & Munafo, 2017; Bernhardt et al., 2017; Bjork, Hommer, Grant, & Danube, 2004; Bobova, Finn, Rickert, & Lucas, 2009; Field et al., 2007; Moody, Tegge, & Bickel, 2017; Petry, 2001b; Vuchinich & Simpson, 1998), abuse alcohol or are dependent on it (Bailey, Gerst, & Finn, 2018; Claus, Kiehl, & Hutchison, 2011; Dom, D’haene, Hulshijn, & Sabbe, 2006; Gerst, Gunn, & Finn, 2017; Gunn, Gerst, Lake, & Finn, 2018; Jarmolowicz, Bickel, & Gatchalian, 2013), or have a family history of alcohol dependence (Acheson, Vincent, Sorocco, & Lovallo, 2011) show steeper delay discounting than comparison non-alcohol-using participants (see Reynolds, 2006, for review). This steep delay discounting rate continues into abstinence (Petry, 2001b) and can be more pronounced for alcohol-based rewards than for monetary rewards (Adams et al., 2017; Jarmolowicz et al., 2013; Johnson, Sweeney, et al., 2016; Moody et al., 2017; Petry, 2001b; but see Field et al., 2007). The mixed findings are more apparent when the ability of delay discounting rate to predict future alcohol use problems or the effect of acute

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alcohol intoxication is examined. Acute administration of alcohol, compared to placebo, can have multiple effects on delay discounting: (1) alcohol consumption steepens delay discounting (i.e., even more emphasis on immediate rewards; Johnson, Sweeney, et al., 2016; Reed, Levin, & Evans, 2012); (2) alcohol lowers delay discounting (i.e., less emphasis on immediate rewards; Ortner, MacDonald, & Olmstead, 2003); or (3) alcohol has no effect on delay discounting (Adams et al., 2017; Bidwell et al., 2013; Richards, Zhang, Mitchell, & de Wit, 1999). Although there is evidence that delay discounting rate predicts concurrent alcohol use difficulties (Amlung & MacKillop, 2011; Dennhardt & Murphy, 2011) or even the development of an alcohol use disorder (Reynolds, 2006), others instead find that it cannot predict future alcohol use or alcohol-related problems (Bernhardt et al., 2017; Dennhardt, Yurasek, & Murphy, 2015; Fernie et al., 2010; Wang, Pandika, Chassin, Lee, & King, 2016).

Neuroimaging Neuroimaging results point to involvement of the prefrontal cortex and portions of the reward pathway in abnormal decision making and delay discounting in alcohol use disorders. Overactivity in the rewardbased ventromedial prefrontal cortex or striatum and underactivity in the cognitive control based dorsolateral prefrontal cortex are linked with both alcohol problem severity and riskier (more impulsive) decisions due at least in part to difficulties accurately processing feedback (Bogg et al., 2012; Claus et al., 2018; Claus & Hutchison, 2012; Cservenka, Jones, & Nagel, 2015; Dager et al., 2013; Gilman et al., 2011, 2014; Jones, Cservenka, & Nagel, 2016; Lim, Cservenka, & Ray, 2017; Oberline et al., 2015; Xiao, Bechara, et al., 2013). Chronic overuse of alcohol can lead to lowered gray matter volume in the reward pathway and frontal lobe (Bechara & Damasio, 2002; Le Berre et al., 2014; Wang et al., 2016), as well as impaired white matter integrity and connectivity in the frontal lobe (Fein & Chang, 2008; Wang et al., 2016; Zhu et al., 2015; Zorlu et al., 2013). These functional changes in activation in the anterior cingulate cortex, striatum, insula, and other portions of the reward pathway are also evident among those with a family history of alcohol use difficulties (Acheson, Robinson, Glahn, Lovallo, & Fox, 2009; Cservenka & Nagel, 2012; DeVito et al., 2013; Yarosh et al., 2014), pointing to a potential early risk factor in alcohol dependence.

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currently smokes and 22% are former smokers (APA, 2013). In terms of meeting criteria for nicotine dependence, 13% of US adults meet criteria in a given year, with similar rates in men and women (APA, 2013). Although these rates primarily focus on cigarettes, use of e-cigarettes has steadily gained prevalence in recent years (McMillen, Gottlieb, Shaefer, Winickoff, & Klein, 2015). A significant portion of regular smokers attempt to quit, but most (60%) relapse within 1 week of nicotine abstinence (APA, 2013).

Risky decision making Previous research focused primarily on the IGT and BART, with very mixed results. Both impairments (Businelle et al., 2009; Briggs et al., 2015; Ert, Yechiam, & Arshavsky, 2013; Rotherham-Fuller, Shoptaw, Berman, & London, 2004; Xiao et al., 2008) and a lack of impairments (Balevich, Wein, & Flory, 2013; Businelle et al., 2008; Harmsen, Bischof, Brooks, Hohagen, & Rumpf, 2006; Lejuez, Aklin, Jones, et al., 2003; Xiao, Koritzky, Johnson, & Bechara, 2013) are seen on the IGT as a function of current tobacco use status. A similar pattern is seen on the BART, as decision making is both impaired (Cavalca et al., 2013; Lejuez, Aklin, Jones, et al., 2003; Lejuez, Aklin, Zvolensky, et al., 2003; Lejuez et al., 2005) and not impaired (Dean, Sugar, Hellemann, & London, 2011; Galvan et al., 2013) in current smokers. Smoking actually improves performance on the IGT compared to nonsmokers (Yip, Sacco, George, & Potenza, 2009) and greater risk-taking on the BART is associated with lowered levels of nicotine dependence (Ryan, Mackillop, et al., 2013). Among nonsmokers, acute administration of nicotine improves performance on the Stroop but has no effect on the BART (Wignall & de Wit, 2011). Other risky decision making tasks are not typically assessed in this population.

Delay discounting and reward responsiveness A more consistent picture emerges when delay discounting is examined. Current smokers show a strong preference for smaller, more immediate rewards over more distant but larger rewards (Baker, Johnson, & Bickel, 2003; Baker et al., 2003; Bialaszek, Marcowski, & Cox, 2017; Bickel et al., 1999; Businelle et al., 2010; Fields, Collins, Leraas, & Reynolds, 2009; Fields, Leraas, et al., 2009; Friedel, DeHart, Madden, & Odum, 2014; Heyman & Gibb, 2006; Hofmeyr et al., 2017; Johnson, Bickel, & Baker, 2007; Kirby & Petry, 2004; Ku, Tucker, Laugesen, McKinlay, & Grace, 2017; Mitchell, 1999; Odum & Baumann, 2007; Ohmura, Takahashi, & Kitamura, 2005; Reynolds, 2004; Reynolds, 2006; Reynolds, Richards, Dassinger, et al., 2004; Reynolds, Richards, Horn, et al., 2004; Risky Decision Making in Psychological Disorders

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Reynolds, Patak, & Shroff, 2007; Reynolds, Petak, Shroff, Penfold, et al., 2007; Reynolds, Richards, Horn, & Karraker, 2004; Stillwell & Tunney, 2011; but see Balevich et al., 2013; Reynolds, Karraker, Horn, & Richards, 2003). Nearly all studies focus on traditional nicotine use (cigarettes), but a few examine differences between cigarette and e-cigarette smokers. Both no differences in delay discounting (Bialaszek et al., 2017) and less discounting in e-cigarette users (Chivers, Hand, Priest, & Higgins, 2016) are seen.

Neuroimaging Acute administration of nicotine activates the brain’s reward pathway (Kelley & Berridge, 2002; Kobiella et al., 2013; Pomerleau & Pomerleau, 1984) and is also correlated with greater feelings of craving when nicotine is not present (McClernon & Gilbert, 2004; Rose et al., 2003; Stein et al., 1998). Abstinent smokers are less likely to refrain from smoking when activation in the ventral striatum is affected (Wilson et al., 2014) and are less likely to smoke when the dorsolateral prefrontal cortex is activated (Amiaz, Levy, Vainiger, Grunhaus, & Zangen, 2009; Eichhammer et al., 2003; Johann et al., 2003; Wing, Bacher, Wu, Daskalakis, & George, 2012). Altering nicotine administration can affect functioning in the reward pathway as well as performance on risky decision making tasks in humans and animals (Galvan et al., 2013; Mitchell, Vokes, Blankenship, Simon, & Setlow, 2011).

Cannabis use disorder Cannabis dependence covers abuse and dependence on substances that act on delta-9-tetrahydrocannabinol (THC). Cannabis can be used for medicinal purposes or for recreation, but both can be associated with cannabis use disorder symptoms. One-year prevalence rates of cannabis use disorder range from 1.5% to 3.5% depending on age (APA, 2013), with increasing rates of cannabis use and disorder over time (Johnston, O’Malley, Bachman, & Schulenberg, 2012). Rates of use are also higher in younger ages, with 30% reporting first cannabis use prior to college and an additional 9% first using in their first year of college (Suerken et al., 2014). Cannabis use is associated with greater involvement in various risk-taking behaviors (Gilman, Calderon, Curran, & Evins, 2015; Metrik et al., 2016), although some suggest it is cannabis-related expectancies rather than the substance itself that affects risk-taking (Skalski, Gunn, Caswell, Maisto, & Metrik, 2017).

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Risky decision making The current research literature is quite mixed on the subject of risky decision making among current and former cannabis users. Although some studies find impairments across tasks among cannabis users (Becker, Collins, & Luciana, 2014; Crane, Schuster, & Gonzalez, 2013; Grant, Chamberlain, Schreiber, & Odlaug, 2012; Hanson, Thayer, & Tapert, 2014; Lamers, Bechara, Rizzo, & Ramaekers, 2006; Moreno et al., 2012; Sevy et al., 2007; Verdejo-Garcia et al., 2007; Wesley, Hanlon, & Porrino, 2011; Whitlow et al., 2004), others suggest no differences (Cousijn et al., 2012; Dougherty et al., 2013; Gonzalez et al., 2012; Ramaekers et al., 2006; Ross, Graziano, Pacheco-Colon, Coxe, & Gonzalez, 2016; Vadhan et al., 2007; Vaidya et al., 2012; Verdejo-Garcia et al., 2013). Some find that heavy marijuana use impairs decisions in women but not in men (Hefner & Starr, 2017). Worse performance on the IGT is associated with greater symptoms of cannabis dependence (Gonzalez et al., 2012) and with both greater THC concentration in a hair sample (Hermann et al., 2009) and self-reported cannabis use (Shannon, Mathias, Dougherty, & Liguri, 2010; Verdejo-Garcia et al., 2007). Impaired IGT performance is also the link between cannabis use and lifetime problems due to risky behaviors (Gonzalez, Schuster, Mermelstein, & Diviak, 2015; Hanson et al., 2014; Schuster, Crane, Mermelstein, & Gonzalez, 2012). There are also differences based on time since last cannabis use. Abstinence can impair decision making (Bolla et al., 2005; Whitlow et al., 2004), whereas acute administration can either impair (Lane, Cherek, Tcheremissine, Lieving, & Pietras, 2005) or enhance (Gunn, Skalski, & Metrik, 2017) decision making. There is also evidence that acute administration affects not performance but rather time to complete a decision making task (Vadhan et al., 2007).

Delay discounting and reward responsiveness Inconsistencies are also seen when delay discounting is assessed. Delay discounting is both correlated (Kollins, 2003; Mejia-Cruz, Green, Myerson, Morales-Chaine, & Nieto, 2016) and not correlated (Gonzalez et al., 2012; Johnson et al., 2010; McDonald, Schleifer, Richards, & de Wit, 2003) with cannabis use. Delay discounting may also predict frequency and severity of cannabis use disorder symptoms (Aston, Metrik, Amlung, Kahler, & MacKillop, 2016; Strickland, Lile, & Stoops, 2017). No differences in delay discounting rates are seen between current and former cannabis users (Johnson et al., 2010) nor as a function of acute cannabis administration (McDonald et al., 2003; Metrik et al., 2012).

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Neuroimaging Marijuana use affects the reward pathway. The receptors sensitive to cannabis (endocannabinoid) are located in the reward pathway, among others (Becker et al., 2014). Compared to healthy controls, cannabis users show greater activity in the ventromedial prefrontal cortex during risky decision making tasks (Vaidya et al., 2012). Abnormal activation in the reward pathway, including the orbitofrontal cortex and anterior cingulate, is associated with impairments in processing immediate rewards and negative feedback (Cousijn et al., 2012; De Bellis et al., 2013; Wesley et al., 2011).

Opioid-related disorders The DSM-V lists a diagnosis of opioid use disorder to account for individuals dependent on a number of different opioids, including prescription opiates and heroin and those maintained on agonists such as buprenorphine or methadone. In the remaining sections, research on individuals dependent on heroin and prescription opiates will be discussed collectively rather than separately. In some cases, opioid abuse and dependence start with a medical purpose for an opioid prescription. Among those prescribed oxycodone and other medications through hematology and oncology clinics, 11% display “aberrant” behaviors associated with addiction (Ehrentraut et al., 2014). Approximately 14% of high school students report using a prescribed medication for nonmedical purposes in the past year (Asbridge, Cartwright, & Langille, 2015). One-year prevalence rates of opioid dependence are 0.37%, with higher rates in men than women (APA, 2013). A diagnosis of opioid dependence is associated with a number of other risk-taking behaviors. Most notably, individuals who abuse or are dependent on opioids are also at higher risk of other substance use and dependence (Astals et al., 2008; Elhammady, Mobasher, & Moselhy, 2014; Joe & Simpson, 1995; Pulver, Davison, Parpia, Purkey, & Pickett, 2016), including on more than one opioid (Jones, 2013). Opioid use can also affect real-world risk-taking behaviors, including driving under the influence (Asbridge et al., 2015). Increased HIV-risk behaviors are very common, with 24% 76% of individuals meeting criteria for opioid use disorder engaging in unprotected sex and 9% 47% engaging in needle sharing (Abbott, Moore, Weller, & Delaney, 1998; Chaudhry et al., 2011; Crooks et al., 2015; Elhammady et al., 2014; Greene et al., 2017; Hall, Darke, Ross, & Wodak, 1993; Joe & Simpson, 1995; King et al., 1994; Lee, Shen, et al., 2011; Meade et al., 2014; Meade, McDonald, & Weiss, 2009; Mitchell, Kelly, Brown, O’Grady, & Schwartz, 2012; Odum,

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Madden, Badger, & Bickel, 2000; Subramaniam, Ives, Stitzer, & Dennis, 2010; but see McHugh et al., 2012). As we will see in greater detail, risky behaviors can also vary as a function of opioid satiety versus withdrawal (e.g., Kirshenbaum, Bickel, & Boynton, 2006).

Risky decision making Although the research literature is not as robust as it is for other substances of dependence, most results find impaired decision making across opioids. Risky decisions across tasks are seen as a function of opiate or heroin use/abuse/dependence as well as among those in methadone- or buprenorphine-maintenance (Biernacki, McLennan, Terrett, Labuschagne, & Rendell, 2016; Biernacki et al., 2018; Brand et al., 2008; Heyman & Dunn, 2002; Khodadadi, Dezfouli, Fakhari, & Ekhtiari, 2010; Lemenager et al., 2011; Li et al., 2013; Mintzer, Copersino, & Stitzer, 2005; Mintzer & Stitzer, 2002; Petry, Bickel, & Arnett, 1998; Rotherham-Fuller, Shoptaw, Berman, & London, 2004; Yan et al., 2014). Only a few studies find no differences in decision making compared to healthy controls (Ahn & Vassileva, 2016; Ersche et al., 2005a; Pirastu et al., 2006; Saleme et al., 2018; Zeng et al., 2013). These impairments may not be affected by the recency of opiate use, with some cognitive impairments continuing into remission (Biernacki et al., 2016; Li et al., 2013; Zhang et al., 2011). One study examined the acute administration of oxycodone to nonaddicted controls, finding no effect on the BART or a delay discounting task (Zacny & de Wit, 2009).

Delay discounting and reward responsiveness Steeper delay discounting of delayed rewards is seen across different opioids of dependence and even among those maintained on methadone or buprenorphine (Ahn & Vassileva, 2016; Cheng, Lu, Han, Gonzalez-Vallejo, & Sui, 2012; Giordano et al., 2002; Herrmann, Hand, Johnson, Badger, & Heil, 2014; Karakula et al., 2016; Kirby & Petry, 2004; Kirby, Petry, & Bickel, 1999; Li et al., 2013; Madden, Bickel, & Jacobs, 1999; Madden, Petry, Badger, & Bickel, 1997). Abstinence can lower delay discounting rates (Kirby & Petry, 2004), whereas others find no effect (Li et al., 2013). Steeper delay discounting in opioid dependence is also predictive of increased real-world risk-taking behaviors (Herrmann et al., 2014). When the specific reinforcer/reward type is examined, delay discounting is steeper for heroin and other opiates than for money (Giordano et al., 2002; Madden et al., 1997, 1999; Odum et al., 2000). Taken together, there is a strong relationship between opioid abuse and dependence and steep discounting of delayed rewards.

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Neuroimaging Relatively few studies examine risky decision making in conjunction with neuroimaging. Alterations in activation are seen in the orbitofrontal cortex and dorsolateral prefrontal cortex during decision making on the CGT (Ersche, Fletcher, et al., 2005; Ersche, Fletcher, et al., 2006). Others find that opioid abstinence is associated recovery of neurotransmitter function, with unfortunately no subsequent effect to improve decision making task performance (Shi et al., 2008; Yeh et al., 2012).

Stimulant use disorders The stimulant use disorders incorporate both legal (caffeine, psychostimulants such as those prescribed to treat ADHD or narcolepsy) and illegal (cocaine, methamphetamine, other amphetamines) substances. Caffeine is one of the most widely used psychoactive substances (James, 1991), with 73% 87% of adolescents and adults indicating regular caffeine use (Barone & Roberts, 1996; Branum, Rossen, & Schoendorf, 2014; Frary et al., 2005). Why? Stimulants can have a positive effect on some aspects of mood and cognition in the short term due to activation of the central nervous system (Barr & Markou, 2005; Barr, Markou, & Phillips, 2002), leading 7% 20% of college students to use psychostimulants without a prescription (Dietz et al., 2013; McCabe, Knight, Teter, & Wechsler, 2005; Teter, McCabe, Boyd, & Guthrie, 2003). Time to addiction can vary by the particular stimulant, as 16% of those who try cocaine will become dependent on it within 10 years (Wagner & Anthony, 2002), whereas time to caffeine dependence can occur much more quickly (APA, 2013). Use of stimulants is also associated with increased likelihood of involvement in other risky and addictive behaviors, including binge drinking (Harvanko, Derbyshire, Schreiber, & Grant, 2015; Kponee, Siegel, & Jernigan, 2014) and risky sex (Jarmolowicz, 2014; Johnson & Bruner, 2012; Koffarnus et al., 2016; but see Johnson et al., 2017) but not risky driving (Howland et al., 2010).

Risky decision making Risky decisions vary by task, particular stimulant, and time since last stimulant use. Acute administration of a stimulant does not affect performance on the BART (Franke et al., 2017) or IGT (Woicik et al., 2009) but does help normalize decisions on the IGT among individuals currently experiencing sleep deprivation or concurrent alcohol consumption (Killgore, Kamimori, & Balkin, 2011; Lalanne, Lutz, & Paille, 2017). Individuals currently dependent on or self-reporting regular use of

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caffeine, methamphetamine, cocaine, or other stimulants show impaired decisions compared to controls on the CGT, IGT, and other tasks (Balconi & Finocchiaro, 2015; Balconi, Finocchiaro, & Canavesio, 2014; Balconi, Finocchiaro, & Campanella, 2014; Bechara et al., 2001; Bickel, Yi, Landis, Hill, & Baxter, 2011; Fernandez-Serrano et al., 2011; Gonzalez, Bechara, & Martin, 2007; Grant & Chamberlain, 2018; Kirby & Petry, 2004; Kjome et al., 2010; Kluwe-Schiavon et al., 2016; Lane et al., 2010; Leland & Paulus, 2005; Leland et al., 2006; LoBue et al., 2014; Paulus et al, 2002; Stout et al., 2004; Vadhan et al., 2009; van der Plas et al., 2009; Verdejo-Garcia et al., 2007). Riskier decisions are also seen when compared to individuals dependent on alcohol (Gonzalez, Bechara, & Martin, 2007), cocaine (Simon et al., 2007), and opioids (Rogers et al., 1999). Others instead find no stimulant effect on risky decision making (Adinoff et al., 2003; Bolla et al., 2003; LoBue et al., 2014; Looby & Sant’Ana, 2018; Tanabe et al., 2009; Woicik et al., 2009), including after controlling for use of other substances of abuse (Harvanko et al., 2015; Jones & Lejuez, 2005; Killgore, Lipizzi, et al., 2007; Temple, Ziegler, Graczyk, & Crandall, 2017). Abstinence from a stimulant has varying effects on risky decision making. Early abstinence can have no effect (Cunha, Bechara, Guerra de Andrade, & Nicastri, 2010) or impair (Hulka et al., 2015) decisions, whereas longer term abstinence can instead improve decisions (Wang et al., 2013).

Delay discounting and reward responsiveness Steeper delay discounting (i.e., a greater preference for smaller immediate versus larger later rewards) is seen across stimulants. Although acute administration has mixed effects on delay discounting (e.g., Johnson et al., 2017; Reed & Evans, 2016; Temple, Ziegler, Graczyk, & Crandall, 2017), regular use or stimulant dependence is associated with a consistent pattern of steeper delay discounting compared to nonstimulant using controls (Albein-Urios et al., 2012; Bickel et al, 2011; Camchong et al., 2011; Coffey, Gudleski, Saladin, & Brady, 2003; Garcia-Rodriguez, Secades-Villa, Weidberg, & Yoon, 2013; Havranek et al., 2015; Hoffman et al., 2006; Hulka et al., 2014; Johnson, Bruner, & Johnson, 2015; Kirby & Petry, 2004; Monterosso et al., 2001). Occasional use (Reske, Stewart, Flagan, & Paulus, 2015) does not result in steep delay discounting, but abstinence also does not lower discount rate (Heil et al., 2006; Hulka et al., 2015). When the particular rewards are examined, delay discounting is steeper for cocaine than for money (Coffey et al., 2003) and for potential gains rather than potential losses (Johnson et al, 2015; Mejia-Cruz et al., 2016). There is also some evidence that treatment can lower delay discounting rate (Bickel et al., 2011).

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Neuroimaging Caffeine acts on multiple neurotransmitters, including dopamine (Ferre et al., 1997; Solinas et al., 2002). The stimulants in general activate the mesocorticolimbic (reward) pathway (Leshner & Koob, 1999), which can contribute to the development of an addiction. Riskier decisions across stimulants are seen among individuals with altered activation in decision making areas, including the orbitofrontal and dorsolateral prefrontal cortex, anterior cingulate, nucleus accumbens, striatum, and substantia nigra (Adinoff, Braud, Devous, & Harris, 2011; Berlingeri et al., 2017; Camchong et al, 2011; Gorini, Lucchiari, Russell-Edu, & Pravettoni, 2014; Lane et al., 2010; Monterosso et al., 2007; Oswald et al., 2015; Paulus et al., 2003; Payer et al., 2014; Reske et al., 2015; Tucker et al., 2004; Verdejo-Garcia et al., 2015; Volkow et al., 2001). Other neuroimaging results point to altered connections between various reward system structures that can contribute to difficulties evaluating future risks and benefits (Bischoff-Grethe et al., 2017), as well as responding to feedback from recent decisions (Reske et al., 2015). Altered reward system activations can also lead to aberrant responses to wins (hyperreactive) and losses (hyporeactive) (Balconi & Finocchiaro, 2015; Balconi et al., 2014; Koester et al., 2013) and are also predictive of relapse to cocaine use following treatment (Contreras-Rodriguez et al., 2015).

Ecstasy or MDMA use Research also suggests similar impairments in delay discounting and decision making among individuals who are current users of 3,4-methylenedioxymethamphetamine (MDMA) or ecstasy, classified in the DSM-V as an Other Hallucinogen Use Disorder (APA, 2013). Impairments are seen across measures of executive functions compared to healthy controls (Dafters et al., 1999; Fox et al., 2001, 2002; Halpern et al., 2010; Moeller et al., 2007; Quednow et al., 2007; Schifano et al., 1998; Weinborn, Woods, Nulsen, & Park, 2011), including on measures of risky decision making (Hopko et al., 2006; Moeller et al., 2007; Morgan et al., 2006; Roiser, Rogers, Cook, & Sahakian, 2006; Roiser, Rogers, & Sahakian, 2007; Schilt et al., 2009; but see Morgan, Impallomeni, Pirona, & Rogers, 2006; Pirona & Morgan, 2010). No differences are found on the IGT compared to users of other substances—all are impaired on the task (Hanson, Luciana, & Sullwold, 2008). But, in some cases, the addition of MDMA use leads to riskier decisions than among those with just the primary substance of abuse (Lamers, Bechara, Rizzo, & Ramaekers, 2006). These results show the importance of understanding the full accounting of substance use by

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individual participants in determining the presence of risky decision making in substance use disorders.

Theories of risky decision making across substances of dependence Rather than focus on the specific processes that might lead to risky decisions in each individual substance of dependence, given that many participants in the cited studies use more than one substance of use/ abuse, this next section compiles all the evidence across substances for or against each theory. Underlying each of these theories is the presence of alterations in the brain’s reward pathway that contribute to the development and maintenance of addiction and to the associated cognitive difficulties.

Impaired executive functions Common to all the chapters thus far, it is possible that risky decision making in the substance use disorders is due to overall impaired executive functions. Executive function impairments are frequently seen among the various substances of dependence (Baldacchino, Balfour, Passetti, Humphris, & Matthews, 2012). Correlations are seen between risky decision making task performance and performance on measures of problem-solving, inhibitory control, shifting, planning, working memory, and cognitive flexibility (Becker et al., 2014; Brand, Fujiwara, et al., 2005; Brand et al., 2008, 2009; Crean, Crane, & Mason, 2011; Field et al., 2007; Gerst et al., 2017; Gonzalez et al., 2012; Mintzer & Stitzer, 2002; Noel et al., 2007; Ramaekers, Kauert, et al., 2006; Stephan et al., 2017; Yip et al., 2009), with some variation depending on the particular addictive substance studied (Ersche, Clark, London, Robbins, & Sahakian, 2006; Ornstein et al., 2000). On the other hand, executive impairments can be seen in the absence of risky decision making (Hanson et al., 2014; Ramaekers, Kauert, et al., 2006; Wignall & de Wit, 2011) or no executive impairments can be seen at all (Bechara et al., 2001; Crane et al., 2013; McDonald et al., 2003; Verdejo-Garcia et al., 2013). But, executive impairments are sensitive to the recency of last substance administration as abstinence can lead to improved executive functions (Fein, Torres, Price, & Di Sclafani, 2006; Schreiner & Dunn, 2012) despite continued decision making impairments as previously described. Executive dysfunction can also interact with another theorized cause of risky decisions, such as difficulties assessing the likelihood of future risks and rewards, to affect risk-taking behavior (Kim et al., 2011; Weafer, Milich, & Fillmore, 2011).

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Thus there may not be just one causal pathway to increased risk-taking behavior and risky decisions in substance use disorders and impaired executive functions may be a contributing factor.

Altered processing of risks and rewards and difficulties learning from feedback Neuroimaging results across the substances of abuse indicate abnormal processing of risks and rewards underlies the development of a substance use disorder as well as the decision making deficits noted thus far. The dopaminergic reward pathway activates in the presence of stimulants, opiates, and nicotine, just to name a few, and this increased activation can lead to symptoms of withdrawal and substance craving if it is not maintained (e.g., Blum et al., 2000). Among individuals with nicotine, opioid, alcohol, cannabis, and stimulant use disorders, riskier decisions and steeper delay discounting are associated with greater salience of immediate versus distant rewards (Bo et al., 2016; Ert, Yechiam, & Arshavsky, 2013; Fridberg et al., 2010), lowered perception of risks or losses (Copeland, Kulesza, Patterson, & Terlecki, 2009; Fridberg et al., 2010; Gorini et al., 2014; Gullo & Stieger, 2011; Phillips & Ogeil, 2010), and increased reward responsivity (Bo, Billieux, Gjerdo, Eilertsen, & Landro, 2017; Johnson et al,. 2008; Khodadadi et al., 2010; Whitlow et al., 2004). Immediate rewards have a greater salience than larger but more distant rewards uncertain future consequences (i.e., myopia for the future) (Acuff et al., 2017; Bailey et al., 2018; Cantrell et al., 2008; Gonzalez et al., 2007; Reynolds, Patak, Shroff, 2007). This focus on immediate rewards could interact with diminished cognitive control and cue-induced craving to increase risky decisions regarding the substance of abuse (Bickel et al., 1999; Coffey et al, 2003; Friedel, DeHart, Madden, & Odum, 2014; Odum & Baumann, 2007). Add to that the difficulty substance users have using feedback to alter their decision making strategy on tasks (Brand et al., 2009; Kluwe-Schiavon et al, 2016; Paulus et al, 2002; Verdejo-Garcia et al., 2007; but see Stout et al., 2004) and we see impaired/risky decisions in the lab and real world.

Impulsivity Impulsivity as a personality characteristic can lead to the initial use of a substance, later substance abuse and dependence, and greater substance-related impairment. More impulsive or sensation-seeking individuals are more likely to first try a substance and at an earlier age (Clark, Robbins, Ersche, & Sahakian, 2006; Hamilton et al, 2014;

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Hommer, Bjork, & Gilman, 2011). Impulsivity also predicts continued substance use and/or substance dependence (Amlung et al., 2013; Balevich et al., 2013; Bernow et al., 2011; Bickel et al., 1999; Bickel & Marsch, 2001; Calvaca et al., 2013; Coggins, Murrelle, & Carchman, & Heidbreder, 2009; Cyders et al., 2010; Felton, Collado, Shadur, Lejuez, & MacPherson, 2015; Fields, Collins, et al., 2009; Gullo, Jackson, & Dawe, 2010; Johnson et al,. 2010; Lejuez, Aklin, Jones, et al., 2003; Levitt, Selman, & Richmond, 1991; LoBue et al., 2014; MacPherson, Magidson, et al., 2010; Moreno et al., 2012; Reed et al., 2012; Reynolds, 2006; Tomassini et al,. 2012; Taylor et al., 2016; Watson, Sweeney, & Louis, 2014; but see Wang et al., 2012). Impulsivity, per responses on selfreport measures, independently predicts performance on decision making and delay discounting tasks across substance use disorders (Balconi, Finocchiaro, & Canavesio, 2014; Balconi, Finocchiaro, & Campanella, 2014; Kirby et al., 1999; Korner et al., 2015; Lejuez et al., 2005; Noel et al., 2011; White, Lejuez, & de Wit, 2007). Others instead find the level of substance dependence, not impulsivity, predicts risky decision making (Dom, de Wilde, Hulstijn, & Sabbe, 2007; Goudriaan et al., 2007; Hermann et al., 2009; Kjome et al., 2010; Ku et al., 2017). Impulsivity may factor into the development of dependence (e.g., Joos et al., 2013) and may lead to involvement in additional risk-taking behaviors (e.g., Lalanne et al., 2017). Impulsivity may also interact with craving in response to substance cues, leading to greater likelihood of relapse and riskier decisions (e.g., Li et al., 2008; Volkow et al., 2007). It is difficult to disentangle impulsivity from substance use itself, given how tied together they are in the development and maintenance of substance use disorders.

Substance use expectancies, satiation, and cue-induced craving Beliefs about how a substance will affect behavior, cognition, and mood, as well as actual substance satiation level, may also drive the decision making impairments seen across substances. Greater risktaking behaviors and executive impairments are seen in the lab and real-world settings among individuals currently abstinent from the substance compared to those who recently used it (Ashare & Hawk, 2012; Briggs et al., 2015; Buelow & Suhr, 2014; Estle et al., 2007; Field et al., 2006; Havermans, Debaere, Smulders, Wiers, & Jansen, 2003; Odum & Rainaud, 2003; Powell, Dawkins, & Davis, 2002; Zack, Belsito, Scher, Eissenberg, & Corrigall, 2001; but see Addicott et al., 2012; Bickel et al., 1999) and among those who are considered heavier versus lighter alcohol drinkers and cannabis users (Ashenhorst et al., 2001; Hulka et al., 2014; Verdejo-Garcia et al., 2007; Vuchinich & Simpson, 1998).

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These satiation level-based differences may not only be due to the relative absence of the substance itself and subsequent effects on executive functions associated with the reward pathway, but may also be due at least in part to substance craving. Individuals dependent on a range of substances demonstrate increased craving in response to a substancerelated cue (Amlung & MacKillop, 2014; Robinson & Berridge, 2000), affecting cognitive task performance and perception of risks of one’s actions (Clay et al., 2018; Field et al., 2007; Lubman, Allen, Peters, & Deakin, 2007; Wright et al., 2013). We also know that craving can affect the decision to return to substance use during a quit attempt, indicating the strength of the relationship between craving and real-world decision making outcomes. Finally, beliefs about how the substance will affect oneself can also affect risk. Expecting positive outcomes and lowered negative outcomes can underlie the decision to use a substance currently and in the future (Goldberg, Halpern-Felsher, & Millstein, 2002; Lau-Barraco & Linden, 2014; Lienemann & Lamb, 2013). Positive views about substance use can also impact one’s evaluation of the risks and benefits associated with that substance’s use (Tsurugizawa et al., 2016), which can in turn affect the decision to continue using that substance. Coupled with known effects of substance use on delay discounting and executive functions such as inhibitory control (e.g., Lopez-Caneda et al., 2014), expectancies can result in riskier decisions being made while an individual is current using or abstinent from a substance of abuse.

Comorbidities and polysubstance use As previously mentioned, comorbidities are high within the substance use disorders and across the other categories of DSM-V psychopathology. Around 80% of those with a substance use disorder also use nicotine (Batel, Pessione, Maitre, & Rueff, 1995; Kalman et al., 2005), as do 67% of those with a primary diagnosis of schizophrenia or schizoaffective disorder (Dervaux et al., 2004). High comorbidity rates are seen between substance use disorders and anxiety and depression (Warden et al., 2016; Wilsey et al., 2008), as well as between different substances of dependence (Hershberger et al., 2004; Pahl, Brook, Morojele, & Brook, 2010). How does the presence of a comorbid diagnosis affect risky decision making task performance? Well, it depends. Polysubstance abuse/ dependence is associated with impaired decision making across tasks (Bornovalova et al., 2005; De Wilde et al., 2013; Grant, Contoreggi, & London, 2000; Hammers & Suhr, 2010; Moreno-Lopez et al., 2012; Verdejo-Garcia, Rivas-Perez, Vilar-Lopez, & Perez-Garcia, 2007).

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However, when a substance use disorder is paired with another diagnosis, both greater (Benaiges et al., 2013; Businelle et al., 2008; Crane et al., 2015; Crunelle et al., 2013; Dom et al., 2006; Duarte et al., 2012; GarciaRodriguez et al., 2013; Georgemiller et al., 2013; Gunn et al., 2018; Holmes et al., 2009; Krmpotich et al., 2015; Mata et al., 2008; Murray et al., 2015; Rotherham-Fuller et al., 2004; Segala et al., 2015; Taylor et al., 2016) and lesser (Businelle et al., 2010; Dean et al., 2011; Farris, Aston, Abrantes, & Zvolensky, 2017; Gerst et al., 2017; Hagen et al., 2016; Kornreich et al., 2013; Lamers et al., 2006; MacKillop & Tidey, 2011; Miranda et al., 2009; Moallem & Ray, 2012; Sevy et al., 2007) risky decisions and delay discounting are seen. The evidence points to the addition of substance use lowering task performance (Rotherham-Fuller et al., 2004), whereas others point to the comorbid anxiety or depression as the leading cause of impulsive/risky decisions (Lemenager et al., 2011). There is also some evidence that cannabis use can reverse decision making impairments among those with a primary schizophrenia diagnosis (Fischer et al., 2015; Yucel et al., 2012). However, a significant issue across studies of the various substance use disorders is the very small sample size. Additional research on more representative samples is needed to really tease apart the individual contributions of substance use versus another diagnosis on decision making.

Treatment implications Risky decision making, delay discounting, and their interaction with executive functions such as inhibitory control can collectively affect the success of a quit attempt. Successful long-term abstinence is associated with better (less risky) decision making and/or lowered delay discounting, or, conversely, relapses are associated with risky decision making and steep delay discounting (Aklin et al., 2012; Bernhardt et al., 2017; Bowden-Jones, McPhillips, Rogers, Hutton, & Joyce, 2005; Kovacs et al., 2017; Krishnan-Sarin et al., 2007; Loeber et al., 2009; McCarthy et al., 2016; Passetti et al., 2008; Prisciandaro et al., 2011; Rotherham-Fuller et al., 2004; Schepis et al., 2011; Sheffer et al., 2012; Stanger et al., 2012; Stevens et al., 2013, 2014; Verdejo-Garcia et al., 2012, 2014; Xiao et al., 2009; Xiao, Bechara, et al., 2013; Xiao, Koritzky, et al., 2013; Yoon et al., 2007; but see Adinoff et al., 2016; De Wilde et al., 2013; Harris et al., 2014; Heinz et al., 2013; Peters et al., 2013). Risky decision making and delay discounting can also improve as a function of treatment (Fecteau et al., 2014; Gorzelanczyk et al., 2014; Khodadadi et al., 2010; Zhang et al., 2011; but see Dallery & Raiff, 2007; de Wilde et al., 2013), but continued risky decision making in response to substancerelated cues predict relapse (Schepis et al., 2016; Wang et al., 2012).

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Treatments that focus on some of the factors affecting decision making, such as consideration of short-term versus long-term outcomes and reward values, can improve treatment (Alfonso, Caracuel, DelgadoPastor, & Verdejo-Garcı´a, 2011; Bates, Buckman, & Nguyen, 2013; Chiou & Wu, 2017; Dennhardt et al., 2015). Given the noted effects of substances of abuse on the reward pathway, treatments for other psychological disorders that do not consider the potential implications of continued substance use (such as alcohol and nicotine) may complicate successful treatments for those disorders.

Conclusions Risky decision making, steep delay discounting, and involvement in various health-risk behaviors are common across the addictive disorders. As a number of substances that can lead to addiction activate the brain’s mesocorticolimbic reward pathway, this result is not surprising and provides evidence that evaluation of long-term outcomes, reward and punishment sensitivity, impulsivity, and inhibitory control should all be incorporated into treatment programs to improve long-term abstinence. Several concerns emerged in a review of the current literature, however. A number of studies of risky decision making or delay discounting utilized very small sample sizes, often fewer than 20 participants per diagnostic group. As we have seen that there are high levels of comorbidities between psychiatric diagnoses and the behavioral and substance use addictions, these small samples make it very difficult to tease apart how decision making is affected by the use of just a single substance. It is also unclear to what extent samples were made up of individuals who acknowledged having difficulties with gambling or substance use, were seeking treatment, both, or neither. As we have also seen across the previous chapters, diagnostic rates across men and women can affect the gender breakdown of samples as well. Here, most studies employed predominantly or solely male participants, leaving our understanding of female risk-taking—most notably with problematic gambling behaviors—lacking.

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C H A P T E R

11 Schizophrenia and delusional disorders The updated DSM-5 reframed schizophrenia as “schizophrenia spectrum and other psychotic disorders” (APA, 2013). Across the schizophrenia spectrum disorders, which now include schizotypal personality disorder, delusional disorder, catatonia, brief psychotic disorder, schizophreniform disorder, schizophrenia, and schizoaffective disorder, several key features exist. Disorders on this spectrum might incorporate the presence of delusions—strongly held convictions that exist despite contrary evidence—and/or hallucinations, as well as disorganized speech, thoughts, and motor activity. In addition, individuals may experience one of a number of negative symptoms, including an absence of emotion, absence of speech, and absence of social characteristics (APA, 2013). Schizophrenia spectrum disorders vary in the specific set of symptoms experienced, as well as in the length of symptom presentation and potential negative effects on the individual’s level of psychosocial functioning. Individuals experiencing any of the previously noted schizophrenia spectrum symptoms for 1 day up to 1 month might be diagnosed with a brief psychotic disorder, whereas the diagnosis would be schizophreniform disorder if the symptoms last longer than 1 month but fewer than 6 months (APA, 2013). Symptoms that continue for 6 months or longer and cause functional impairments would instead increase the diagnosis to schizophrenia. Psychotic symptoms that wax and wane with mood symptoms would favor a diagnosis of schizoaffective disorder, whereas experiencing just delusions, and never meeting a diagnosis of schizophrenia, would point toward delusional disorder. Age of diagnosis is commonly in late adolescence and early adulthood (McGrath, Saha, Chant, & Welham, 2008); however, signs and symptoms of the disorders may be experienced for months or years prior to formal diagnosis. Given these difficulties in diagnosis, prevalence rates

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may underestimate the true prevalence of the disorders. Currently, prevalence rates range from 0.25% to 0.64% in the United States (Desai, Lawson, Barner, & Rascati, 2013; Kessler, Birnbaum, et al., 2005; Wu, Shi, Birnbaum, Hudson, & Kessler, 2006) and 0.33% 0.75% worldwide (Moreno-Kustner, Martin, & Pastor, 2018; Saha, Chant, Welham, & McGrath, 2005). The presence of schizophrenia spectrum disorders can lead to increased rates of disability and premature death (Olfson, Gerhard, Huang, Crystal, & Stroup, 2015; Palmer, Pankratz, & Bostwick, 2005; Simon et al., 2018). In the sections that follow, I examine the current literature on risktaking behaviors, risky decision making, and delay discounting among those with a current or past diagnosis of a schizophrenia spectrum disorder. As will become evident, the majority of these studies examine those with a diagnosis of schizophrenia or schizoaffective disorder, with few studies examining decision making impairments in those with other spectrum disorders. I will end by assessing the status of competing hypotheses regarding the underlying causes of decision making impairments and argue for changes in future research in this field.

The current literature: risk-taking behaviors Individuals with a diagnosis of schizophrenia or related disorder frequently engage in risk-taking behaviors with the potential to cause harm to health and well-being. Multiple studies show increased levels of impulsivity as a function of schizophrenia diagnosis (Fischer et al., 2015; Hutton et al., 2002; Kaladjian, Jeanningros, Azorin, Anton, & Mazzola-Pomietto, 2011; Knolle-Veentjer, Huth, Ferstl, Aldenhoff, & Hinze-Selch, 2008; Nolan, D’Angelo, & Hoptman, 2011), which may lead to increased involvement in these high-risk behaviors. Most studies focus on two primary areas of risk-taking: substance use/abuse and risky sexual practices. Previous research tends to focus on rates of alcohol, nicotine, and cannabis use in individuals at increased risk of developing a psychosis (e.g., positive family history) as well as those experiencing a first or subsequent psychotic episode. Use of cannabis increases risk of developing psychotic symptoms (Auther et al., 2015; Kuepper, van Os, Lieb, Wittchen, Hofler, & Henquet, 2011; Moore et al., 2007) and substance misuse more generally is associated with increased risk of relapse of psychotic symptoms (Hides, Dawe, Kavanagh, & Young, 2006; Kamali et al., 2006; Lewis, Tarrier, & Drake, 2005; Malla, Norman, BechardEvans, & Schmitz, 2008; Wade et al., 2006). One study found 70% of those recovering from a first episode of schizophrenia met criteria for a substance use disorder in the previous 12 months (Wade et al., 2006),

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whereas others show high rates of substance use/misuse rather than a diagnosable comorbid substance use disorder. Rates of cannabis use range from 20% to 38% among individuals reporting high level of psychotic symptoms (Auther et al., 2015; Faber et al., 2012; Green, Young, & Kavanagh, 2005; Westermeyer, 2006; Wobrock et al., 2013). Rates of alcohol use and alcohol use disorders are equally high (20% 46%; Auther et al., 2015; Koskinen, Lohonen, Koponen, Isohanni, & Miettunen, 2009). Finally, cocaine use rates can also be high (20% 50%; Regier et al., 1990). High rates of nicotine use are consistently seen in individuals with a diagnosis of schizophrenia and related disorders. Up to 67% of samples with a diagnosis of schizophrenia reported concurrent nicotine use (Dalack, Healy, & Meador-Woodruff, 1998; de Leon & Diaz, 2005; Dervaux et al., 2004; Hughes, Hatsukami, Mitchell, & Dahlgren, 1986; Lasser et al., 2000; McEvoy et al., 2005). Individuals with a diagnosis of schizophrenia who also use nicotine report greater severity of negative symptoms and experience higher rates of hospitalizations (Kotov, Guey, Bromet, & Schwartz, 2010; but also see Dervaux et al., 2004). Although individuals with schizophrenia want to quit smoking at similar rates to healthy controls, they also report lower self-efficacy to quit (Leas & McCabe, 2007). Taken together, high rates of co-occurring use of legal and illegal substances are frequently found among individuals with a schizophrenia spectrum disorder. It is unclear why individuals with a diagnosis of schizophrenia or related disorder exhibit high rates of substance use and abuse, but several theories exist. Qualitative studies show that substance use is “normalized” within the psychotic symptom community (Lobbana et al., 2010) and substance also occurs as a means of self-medication or to cope with positive/negative symptoms (Auther et al., 2015; Lobbana et al., 2010). Prior to formal diagnosis and treatment for symptoms, individuals might turn to various substances of abuse to ease some of the more impairing symptoms of psychosis. Later, after diagnosis and initiation of treatment, they might find the addictive qualities of the substance difficult to quit. We know that nicotine use improves symptoms and cognitive functions (Dalack et al., 1998; Mancuso, Warburton, Melen, Sherwood, & Tirelli, 1999; Warburton & Arnall., 1994), such as working memory impairments (Levin, Wilson, Rose, & McEvoy, 1996). These improvements might additionally lead to difficulties ceasing substance use/abuse. On the other hand, research also suggests that nicotine itself can increase impulsivity and sensation seeking (Bickel et al., 1999; Dervaux et al., 2004; Mitchell, 1999), which, independent of psychotic symptoms, leads to continued high levels of substance abuse. The second area frequently assessed is risky sexual behaviors. Higher rates of risky sexual behaviors, including inconsistent condom use and

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trading sex for some sort of gain, are seen among individuals with a diagnosis of schizophrenia or related psychotic disorder (Brown et al., 2010; Brown, Lubman, & Paxton, 2011; Cournos et al., 1994; Kalichman, Kelly, Johnson, & Bulto, 1994; Kelly et al., 1992; McKinnon, Cournos, Sugden, Guido, & Herman, 1996; Nyamali, Morakinyo, & Lawal, 2010). Higher rates of sexually transmitted diseases are also seen among individuals with psychotic symptoms compared to controls (Cournos et al., 1994; Grassi, Pavanati, Cardelli, Ferri, & Peron, 1999; Nyamali et al., 2010; Raja & Azzoni, 2003). In addition, higher rates of negative symptoms are associated with less HIV/AIDS risk behavior knowledge (Koen, Uys, Niehaus, & Emsley, 2007). Despite these high-risk behaviors, only 31% experiencing their first episode of psychosis expressed concerns about unsafe sexual practices (Shield, Fairbrother, & Obmann, 2005). These consistent high-risk sexual behaviors can lead to negative health outcomes for the involved individuals.

The current literature: risky decision making Multiple studies examine risky decision making on lab-based measures, with varying results. Most focus on Iowa Gambling Task (IGT) performance, with relatively fewer studies utilizing different tasks. The following sections summarize these findings by the specific measure used.

Balloon Analogue Risk Task Relatively few studies examine differences on the Balloon Analogue Risk Task (BART) as a function of diagnosis of a schizophrenia spectrum disorder. Across studies, participants with a diagnosis of schizophrenia or schizoaffective disorder show a safer, or more risk-averse, pattern of responding on the BART compared to similarly-aged healthy controls (Brown et al., 2015; Cheng, Tang, et al., 2012; Fischer et al., 2015; Reddy et al., 2014). This strategy is in contrast to participants with comorbid schizophrenia and cannabis dependence who are riskier on the BART (Fischer et al., 2015). This risk-averse decision making strategy on the BART is correlated with greater disorganized symptoms (Brown et al., 2015) but not overall level of impulsivity (Reddy et al., 2014).

Game of Dice Task Several studies examine performance on the Game of Dice Task (GDT) as a function of schizophrenia diagnosis. Riskier choices are seen

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among individuals with schizophrenia versus healthy controls (Fond et al., 2013; Pedersen, Goder, Tomczyk, & Ohrmann, 2017; Zhang, Tang, et al., 2015). Analysis of the individual options (1-, 2-, 3-, or 4-dice) indicate healthy controls preferr to choose 4-dice, whereas those with a diagnosis of schizophrenia prefer to choose the 1-die option (Pedersen et al., 2017; Zhang, Tang, et al., 2015). Riskier GDT performance is correlated with greater positive symptoms (Pedersen et al., 2017), greater perseverative errors on the Wisconsin Card Sorting Task (WCST), and lower Trail Making Test performance (Zhang, Li, et al., 2015). However, Lee et al. (2007) find no differences in GDT performance between those with schizophrenia and healthy controls. Of note, the sample size in Lee et al. (2007) is much smaller than the sample size in the remaining studies and this might help account for this contrary finding.

Iowa Gambling Task By far, the majority of the behavioral risky decision making task studies in schizophrenia spectrum disorders focus on the IGT. Significant variability exists in those findings. Multiple researchers find no significant differences in performance on the IGT as a function of schizophrenia or schizoaffective disorder diagnosis when compared to similarlyaged healthy controls (Beninger et al., 2003; Cavallaro et al., 2003; Choi et al., 2011; Evans, Bowman, & Turnbull, 2005; Matsuzawa, Shirayama, Niitsu, Hashimoto, & Iyo, 2015; Pedersen et al., 2017; Premkumar et al., 2010, 2015; Rodriguez-Sanchez et al., 2005; Sanchez-Torres et al., 2013; Shirayama et al., 2010; Wilder, Weinberger, & Goldberg, 1998). This lack of differences is seen despite a range of sample sizes (healthy controls: 15 140; schizophrenia: 12 110) and participant ages (20 30, Rodriguez-Sanchez et al., 2005; Shirayama et al., 2010; 30 40, Cavallaro et al., 2003; Evans et al., 2005; Matsuzawa et al., 2015; Premkumar et al., 2010; 40 50, Beninger et al., 2003; Brambilla et al., 2013). More males than females participate in most studies and there are known effects of gender on IGT performance (e.g., Bolla et al., 2004; Businelle et al., 2008; Davis, Patte, Tweed, & Curtis, 2007; Goudriaan, Grekin, & Sher, 2007; Reavis & Overman, 2001; van den Bos, Homberg, & de Visser, 2013). This lack of differences is not due to smoking status (Brambilla et al., 2013) or positive/negative symptoms (Evans et al., 2005; but see Matsuzawa et al., 2015; Rodriguez-Sanchez et al., 2005), but could be due to the particular type of medication used to treat schizophrenia symptoms. Beninger et al. (2003) find that those treated with typical antipsychotics show improvement across trials on the IGT, similar to that seen in healthy control participants, whereas those treated with atypical antipsychotics show no such improvement as the task

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progressed. Others find that this lack of differences in deck selections might be due to a focus on frequency of losses rather than long-term outcomes (i.e., selecting from Decks B and D over Decks A and C) in both controls and those with schizophrenia (Pedersen et al., 2017; Rodriguez-Sanchez et al., 2005; Wilder et al., 1998). That said, a number of studies find significantly impaired (i.e., riskier) performance among individuals diagnosed with a schizophrenia spectrum disorder compared to healthy controls (Bark, Dieckmann, Bogerts, & Northoff, 2005; Brambilla et al., 2013; Brown et al., 2015; Caletti et al., 2013; Cella, Dymond, Cooper, & Turnbull, 2012; Fond et al., 2013; Hori, Yoshimura, Katsuki, Atake, & Nakamura, 2014; Kester et al., 2006; Kim, Kang, & Lim, 2016; Kim et al., 2012; Lee et al., 2007; Martino, Bucay, Butman, & Allegri, 2007; Premkumar et al., 2008; Raffard et al., 2011; Ritter, Meador-Woodruff, & Dalack, 2004; Roca et al., 2014; Sevy et al., 2007; Shurman, Horan, & Nuechterlein, 2005; Stratta, Cella, Di Emidio, Collazzoni, & Rossi, 2015; Turnbull, Evans, Kemish, Park, & Bowman, 2006; Wasserman, Barry, Bradford, Delva, & Beninger, 2012; Wing, Rabin, Wass, & George, 2013; Yip, Sacco, George, & Potenza, 2009; Zhang, Tang, et al., 2015). These significant differences exist despite variations in sample sizes (healthy controls: 10 80; schizophrenia: 8 86) and ages (15 19, Kester et al., 2006; Zhang, Tang, et al., 2015; 20 29, Bark et al., 2005; Kim et al., 2012; Lee et al., 2007; Sevy et al., 2007; 30 39, Cella et al., 2012; Fond et al., 2013; Hori et al., 2014; Martino et al., 2007; Premkumar et al., 2008; Raffard et al., 2011; Roca et al., 2014; 40 49, Brown et al., 2015; Ritter et al., 2004; Wasserman et al., 2012). Again, more males than females participated in these studies, so it is unlikely that gender alone can account for these discrepancies in findings across studies. What could account for the risky decisions on the IGT among those with a diagnosis of schizophrenia or other schizophrenia spectrum disorder? Some of the factors “ruled out” by previous research include performance on other executive function tasks (Bark et al., 2005), global functioning (Caletti et al., 2013), age (Kester et al., 2006) and age of onset (Fond et al., 2013), illness duration (Fond et al., 2013), and level of insight into the disease process (Raffard et al., 2011). Positive and negative symptoms do not correlate with IGT performance (Ritter et al., 2004), but on a contingency-shifting variant of the IGT, those with greater negative symptoms perform worse than those with greater positive symptoms (Turnbull et al., 2006). Finally, although some indicate the type of medication treatment does not affect results (Boggs et al., 2012; Martino et al., 2007), others find that treatment with typical antipsychotics improves performance but treatment with atypical antipsychotics does not (Beninger et al., 2003; Wasserman et al., 2012). In a longitudinal study, individuals treated with ramelteon for 6 months

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showed improvement on the IGT over time (Shirayama, Takahashi, Suzuki, Tsuruoka, & Sato, 2014); however, no mention was made of known practice effects on the IGT (see Buelow & Barnhart, 2018) that could account for this improvement. There are differences between healthy controls and those with schizophrenia on variables such as substance abuse (Caletti et al., 2013), cannabis use (Sevy et al., 2007), and nicotine use (Wing et al., 2013; Yip et al., 2009), and these dualdiagnoses could contribute to some negative effects on IGT decision making. It is possible that these cross-study differences are due in part to how the IGT was analyzed. In a meta-analysis, Brown et al. (2015) found those with a diagnosis of schizophrenia select more from Decks A and B (the disadvantageous decks) than Decks C and D compared to healthy controls. Examining individual deck selections, those with a diagnosis of schizophrenia select more from Deck A and less from Deck D (Kester et al., 2006; Martino et al., 2007) and Deck C (Hori et al., 2014) compared to healthy controls. Others find that selections from Deck B are increased compared to Decks C (Shurman et al., 2005) and D (Ritter et al., 2004). Individuals with a diagnosis of schizophrenia might show the prominent Deck B phenomenon (Brown et al., 2015; Hori et al., 2014), in which participants focus on minimizing frequency of losses rather than maximizing long-term outcomes as their decision making strategy. As examination of individual decks is a relatively new analysis for the IGT, many initial studies of the IGT in schizophrenia do not report individual deck selections. Thus it is possible that the inconsistent findings could be due in part to this prominent Deck B phenomenon seen on some of the newer studies. A preferred focus on frequency of losses, or minimizing losses, could also help explain the different findings when the variant IGT is used (Kim et al., 2012). Several other comparisons are made between individuals with schizophrenia and individuals with related disorders (not on the schizophrenia spectrum). Those with a diagnosis of schizophrenia decide less advantageously on the IGT compared to individuals with cocaine dependence (Benaiges, Serra-Grabulosa, Prat, & Adan, 2013) and bipolar disorder (Brambilla et al., 2013). Individuals with schizophrenia select more from B but less from A compared to those with an autism spectrum disorder; however, both groups were riskier than healthy controls (Zhang, Tang, et al., 2015). No differences are seen between individuals with schizophrenia and individuals with obsessive compulsive disorder (Kazhungil et al., 2017; Whitney, Fastenau, Evans, & Lysaker, 2004) and bipolar disorder (Caletti et al., 2013). One study (Mata et al., 2008) found that those with schizophrenia actually decided more advantageously (selected more from Deck C than A and B) than individuals with comorbid schizophrenia and cannabis

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use. A history of suicide attempts leads to worse performance on the IGT than having no history of suicide attempts (Adan et al., 2017). Collectively, performance on the IGT among those with a diagnosis of schizophrenia varies in how their performance compared to individuals with related disorders.

Other risky decision making tasks Several researchers utilized other behavioral decision making tasks to assess potential impairments in decision making among those with a diagnosis of schizophrenia or another spectrum disorder. Performance on the Wheel of Fortune task is not associated with hallucination proneness (Jones, de-Wit, Fernyhough, & Meins, 2008). On the Cambridge/ Rogers Gamble Task, participants with schizophrenia have difficulty using potential losses in their decisions (Heerey, Robinson, McMahon, & Gold, 2007), whereas no differences are seen among individuals with schizotypal features (Li, Shi, et al., 2016) compared to controls. Healthy controls prefer risky to safe options on the risky gains task, but such a preference is not seen among those with schizophrenia (Cheng, Tang, et al., 2012). On rewarding tasks, in which participants determine the level of effort to expend on easy and hard trials with differing rewards, participants with schizophrenia are less willing to expend effort for higher incentives (Fervaha et al., 2013), instead preferring to expend greater effort for higher reward and higher likelihood trials (Fervaha et al., 2015). Again, results across different risky decision making tasks vary in whether or not those with a schizophrenia spectrum disorder are riskier than healthy controls.

Summary Collapsing across different risky decision making tasks, there is no consistent pattern of performance among individuals with a schizophrenia spectrum diagnosis compared to healthy controls or those with other psychiatric diagnoses. Several confounding factors could be affecting these results. The presence of comorbid diagnoses, type of antipsychotic treatment, and how the IGT is scored, in particular, could affect findings. Among those studies showing significant impairments on risky decision making tasks, one could argue that these impairments constitute an inability to learn to decide advantageously and to shift to a more advantageous strategy (Bark et al., 2005; Kim et al., 2016; Yilmaz, Simsek, & Gonul, 2012). Individuals may perseverate on a previously advantageous strategy despite additional evidence to the contrary (Turnbull et al., 2006). Thus individuals may still be learning from

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reinforcement and feedback (Heerey, Bell-Warren, & Gold, 2008), but that information may be out-of-date when it is being used on the next decision making trial. There does not appear to be a pattern of failure to recognize gains or losses (Premkumar et al., 2008), but examination of performance on delay discounting paradigms may shed more light on this topic.

The current literature: delay discounting and reward responsiveness Delay discounting tasks ask participants to decide between a smaller immediate reward and a larger but distant reward. Steep delay discounting is anticipated among individuals diagnosed with schizophrenia or a related disorder due to the tendency to decrease emphasis on losses in the decision making process (Heerey et al., 2008; Kester et al., 2006), and due to known decreases in goal-directed activities in the “real world” (Barch & Dowd, 2010; Reddy, Horan, & Green, 2015). In order to achieve these goals, individuals must set a plan to make incremental progress toward that large, distant goal. If, instead, individuals are focused on short-term, immediate gains, it would be difficult to maintain focus on this longer-term goal. In schizophrenia spectrum disorders, there is a tendency to focus on the immediate at the expense of the past or future (Salzinger, Portnov, Pisoni, & Feldman, 1970). The research to date on delay discounting is somewhat mixed. Most commonly, researchers examine individuals with a diagnosis of schizophrenia or schizoaffective disorder, rather than other disorders on the schizophrenia spectrum. Compared to healthy controls, individuals with a diagnosis of schizophrenia or schizoaffective disorder engage in steeper delay discounting (i.e., discount the distant reward to a greater extent and prefer the smaller, more immediate reward) (Ahn et al., 2011; Gold et al., 2013; Heerey et al., 2007; Heerey, Matveeva, & Gold, 2011; Weller et al., 2014; Yu, Lee, et al., 2017). Others find similar discounting rates among those with schizotypal features (Li, Shi, et al., 2016). This myopic focus on smaller, more immediate rewards contributes to difficulties with long-term planning, resulting in impulsive decision making. Yu et al. (2017) find these differences in delay discounting despite no between-group differences in risk aversion or loss aversion. However, several studies find no group differences in delay discounting (Weatherly, 2012a, 2012b), including after current smoking status is taken into consideration (MacKillop & Tidey, 2011; Wing et al., 2013; Wing, Moss, et al., 2012). Weller et al. (2014) and Yu et al. (2017) did take current smoking behaviors into consideration, yet still find steeper delay discounting among those with schizophrenia or

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schizoaffective disorder compared to controls. Thus stating that all differences in delay discounting is due to comorbid substance use might be an oversimplification of the relationship between these complex concepts. Horan et al. (2017) theorized that some of these between-study differences could be due to whether participants engaged in a hypothetical delay discounting task, where no rewards are disbursed, or an experimental delay discounting task in which real rewards are given out at the end of the task. They found that the group differences in delay discounting only emerge on the experimental task, as the schizophrenia and healthy control groups are not different on the hypothetical delay discounting task. It is possible that some of the decision making difficulties noted thus far could be due in part to a difficulty accurately assessing the monetary amount that could be won or lost on a task.

What factors could be affecting risky decision making in schizophrenia spectrum disorders? The influence of medication The varied finding of decision making deficits (or lack thereof) could be due in part to medication use at the time of examination. Most studies fail to control for medication status in their analyses, despite some findings of medication-specific influences on performance. The few that do examine the influence of medication point to a negative influence of atypical antipsychotics (Beninger et al., 2003; Wasserman et al., 2012; Yip et al., 2009) (see Alves & Rozenthal, 2006, for further discussion), or no effect of medication type on risky decision making (Boggs et al., 2012; Cavallaro et al., 2003; Martino et al., 2007; Shurman et al., 2005). Length of medication use is almost never examined as a particular covariate, which is concerning given potential negative cognitive effects of long-term antipsychotic use (Hulkko et al., 2017; Husa et al., 2017). At present, no conclusion can be reached about potential negative effects of antipsychotic medications on risky decision making task performance.

Current symptoms The specific nature of an individual’s current symptoms could also affect decision making task performance. Some previous research shows better executive functioning with current positive symptoms (Carey, Carey, & Simons, 2003; Herman, 2004), which could account for

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findings of no differences between individuals with a schizophrenia spectrum disorder and controls. In terms of negative symptoms, neuroimaging studies show increased negative symptoms correlate with the involvement of the prefrontal cortex in areas near the orbitofrontal cortex (Chemerinksi, Nopoulos, Crespo-Facorro, Andreasen, & Magnotta, 2002; Wolkin et al., 2003), which could lead to riskier decision making. Yet, few studies specifically compare those experiencing positive symptoms with those experiencing negative symptoms. Of those that did, mixed results emerge. Riskier decision making is associated with fewer disorganized symptoms (Brown et al., 2015), greater positive symptoms (Matsuzawa et al., 2015; Pedersen et al., 2017), and greater negative symptoms (Matsuzawa et al., 2015; Rodriguez-Sanchez et al., 2005). In one of the few direct comparisons of those high in positive symptoms to those high in negative symptoms, Turnbull et al. (2006) found riskier decision making among those with greater negative than positive symptoms. However, the vast majority of studies examining potential correlations between current positive/negative symptoms and performance on behavioral risky decision making tasks find no such correlations. Few also examine differences as a function of schizophrenia subtype, with a few notable exceptions. Individuals experiencing catatonia are worse on the IGT than individuals with paranoid schizophrenia (Bark et al., 2005), whereas no decision making deficits are seen on the Cambridge Gamble Task among those with schizotypal features (Li, Shi, et al., 2016). There is no consensus yet as to the influence of specific symptom sets on decision making, likely due to other confounding factors that could also affect decision making task performance.

Other diagnostic considerations Several other issues fall in the category of diagnostic considerations, including age of onset and comorbidity. Age of symptom onset and timing of the cognitive evaluation are confounded with length of medication use, as those in their first episode will likely have less of a medication history than those in a later episode. Cognitive deficits seen in prodromal schizophrenia (Corigliano et al., 2014) often remain to first episode (Haatveit et al., 2015; Zhang, Li, et al., 2015) and later into the disease process (Bergh et al., 2016; Haatveit et al., 2015). Functioning of the orbitofrontal cortex may diminish over time (Pantelis et al., 1999; Watkins et al., 2000), and given the links between risky decision making and the orbitofrontal cortex, we expect greater risky decision making in later than first-episode schizophrenia. Only one known study directly compared those earlier and later in the disease process, with no differences noted (Shurman et al., 2005). To fully tease apart age effects

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on risky decision making, additional studies comparing first- to later-episode schizophrenia are warranted. Comorbid diagnoses complicate our understanding of risky decision making in schizophrenia itself. Substance use disorders are the primary comorbid diagnosis, complicating decision making assessment as substance use/misuse itself has a long history of leading to impaired decision making. Co-occurring substance use disorders can increase schizophrenia symptoms (Bennett, Bellack, Brown, & DiClemente, 2009) and impair cognitive performance (Benaiges, Serra-Grabulosa, & Adam, 2013; Benaiges, Serra-Grabulosa, Prat, et al., 2013). That said, several studies point to cannabis use as a means to improve cognition in schizophrenia (Loberg, Jorgensen, & Hugdahl, 2003; McCleery, Addington, & Addington, 2006; Sevy et al., 2001), likely as cannabis helps “treat” the initial symptoms prior to formal diagnosis and treatment. In their metaanalysis, Yucel et al. (2012) found evidence that although individuals with schizophrenia, with and without cannabis use, perform lower on cognitive tasks compared to controls, those with cannabis use outperform those without cannabis use. Potvin et al. (2012) suggest that this comorbidity relationship is more complex, as it may depend on the individual’s age and the specific cognitive ability assessed. Many studies first exclude individuals with comorbid substance use disorder. Across other measures of executive function, performance does not differ between those with and without a substance use disorder (Addington & Addington, 1997; Cooper et al., 1999; Copersino et al., 2004; Sevy et al., 2007). Thus it may be that the presence of schizophrenia itself is the criterion needed to exhibit riskier decision making compared to healthy controls and the presence of a comorbid substance use disorder only finely tunes the specific impairment seen on a particular task.

Control group Most studies use age-matched participants without a mental health diagnosis as the comparison group. However, not all studies match on gender despite known gender effects on tasks such as the IGT. In addition, there are between-studies differences in control group performance that could account for the lack of between-group differences seen in some studies. The IGT creators put forth a minimum criterion to determine impaired decision making on the task. Individuals with a net score (total disadvantageous [Decks A 1 B] minus total advantageous [Decks C 1 D] selections) less than 10 were considered impaired on the task (Bechara et al., 2001). Implementing this criterion for the 22 previously cited studies that provided net scores, 10 studies have healthy control participants with a net score under 10, whereas the remaining

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12 studies have healthy control net scores over 10. Of those with a control net score under 10, six fail to find between-group differences, whereas four do. Of those with a control net score over 10, 10 find between-group differences, whereas 2 do not. Next, performance in the schizophrenia comparison groups is examined. Regardless of performance level in the healthy control participants, no study reports a schizophrenia group net score above 10. Taking away the individual study finding of significant or nonsignificant group differences on the IGT, the general trend is that participants with a diagnosis of schizophrenia or related disorder decide disadvantageously on this task compared to a baseline criterion. However, several caveats should be noted. Not all reviewed studies provide a total net score. The field has increasingly moved toward understanding how decision making evolves on the IGT across trials, and authors are increasingly providing information about individual deck selections across blocks of trials rather than relying on this overall net score. In addition, this cutoff criterion is a very basic indicator of impaired decision making on this task. A more fine-grained analysis can be conducted utilizing the normative data provided in the IGT manual (Bechara, 2007); however, a number of these studies were published prior to the standardized IGT. In addition, there are known concerns about variations in healthy control performance across studies (Steingroever, Wetzels, Horstmann, Neumann, & Wagenmakers, 2013). Newer modeling approaches can also be applied to examine specific facets of decision making on this and other tasks. Finally, the IGT is the only behavioral risky decision making task to date that has normative data associated with it. Although other tasks are increasingly being utilized in different patient populations, there remains no standardized way to assess level of decision making across samples.

Performance on other executive function tasks No consistent pattern emerges on correlations between risky decision making task performance and other executive function tasks in schizophrenia spectrum disorders. Consistent executive dysfunction is seen across tasks in schizophrenia (e.g., Addington & Addington, 2000; Cornblatt & Keilp, 1994; Heinrichs & Zakzanis, 1998; Kuperberg & Heckers, 2000; Reichenberg & Harvey, 2007; Toda & Abi-Darghem, 2007). Few studies directly examine correlations between decision making and other executive functions in schizophrenia. Correlations are seen between performance on the IGT and GDT and measures of planning (Tower of Hanoi; Cavallaro et al., 2003), fluency (Rodriguez-Sanchez et al., 2005), verbal memory (Heerey et al., 2007), working memory (Ahn et al., 2011;

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Li, Shi, et al., 2016; Zhang, Tang, et al., 2015), problem solving (WCST; Ritter et al., 2004; Rodriguez-Sanchez et al., 2005; Shurman et al., 2005; Wilder et al., 1998), and overall cognitive ability (Brown et al., 2015; Fond et al., 2013). In addition, riskier decision-makers on the IGT also show impaired performance on measures of problem solving (WCST), though a direct correlation between tasks is not always reported (Brambilla et al., 2013; Kim et al., 2012; Martino et al., 2007; Roca et al., 2014; Wing et al., 2013; Yip et al., 2009). Others find no relationship between risky decision making and other executive functions (Bark et al., 2005; Docx et al., 2015; Horan et al., 2017; Lee et al., 2007; Premkumar et al., 2010). These inconsistent findings are not due to scores on the decision making tasks, as there is a wide range of scores among both those studies that find correlations and those studies that do not. In terms of correlations between delay discounting and other executive functions, we find several inconsistencies. While some find no correlations (Docx et al., 2015; Horan et al., 2017), others find better performance on measures of verbal memory is associated with less severe delay discounting (i.e., less preference for immediate gains) (Heerey et al., 2007) and positive relationships between working memory and delay discounting (Ahn et al., 2011; Li, Shi, et al., 2016). Even after taking into consideration overall cognitive function, though, a diagnosis of schizophrenia still accounts for a significant portion of the variability in delay discounting (Ahn et al., 2011). Which came first? Do decision making impairments precede other symptoms of schizophrenia, or are the impairments secondary, occurring later in the process, to other cognitive impairments (e.g., Aukes et al., 2009; Glahn et al., 2003; Irani et al., 2012; Niendam et al., 2006; Wood et al., 2003)? Heerey et al. (2008, 2011) found evidence in favor of decision making deficits being secondary to other cognitive declines, whereas Ahn et al. (2011) found evidence to the contrary. If decision making impairments are secondary, we should see a pattern of decision making impairments being more extreme in later episodes rather than first-episode schizophrenia. However, the data to date is to the contrary. The overall scores on various decision making tasks do not show a pattern of impairment increasing as a function of first- versus later episode schizophrenia.

Neuroimaging Few of the risky decision making studies examined utilize neuroimaging in addition to neuropsychological assessment techniques. As some argue that the key difficulty in schizophrenia is dysfunction in the dorsolateral prefrontal cortex (Weinberger et al., 2001) while others

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argue that it is instead in the ventromedial prefrontal cortex (Saxena, Brody, Schwartz, & Baxter, 1998), inconsistencies are expected in the neuroimaging of decision making in schizophrenia. Some researchers find correlations between risky decision making task performance and orbitofrontal cortex (Premkumar et al., 2008) and anterior cingulate cortex (Szeszko et al., 2007) gray matter volume, whereas others find no correlation (Nakamura et al., 2008; Premkumar et al., 2010). Evidence of activation in the orbitofrontal cortex or other portions of the prefrontal cortex, or correlations of gray matter volume here, would show additional evidence of involvement of these regions in processing of rewards and punishments in decision making (Elliott, Newman, Longe, & Deakin, 2003; FitzGerald, Seymour, & Dolan, 2009; Knutson, Westdrop, Kaiser, & Hommer, 2000; O’Doherty, Kringelbach, Rolls, Hornak, & Andrews, 2001), as previous research shows links between performance on different behavioral decision making tasks and the frontostriatal circuits (Lee, Chan, et al., 2008; Lee, Chan, Leung, Fox, & Gao, 2009; Lee, Leung, Fox, Gao, & Chan, 2008; Li, Lu, D’Argembeau, Ng, & Bechara, 2010; Paulus, Rogalsky, Simmons, Feinstein, & Stein, 2003; Rao, Korczkowski, Pluta, Hoang, & Detre, 2008). Individuals with a diagnosis of schizophrenia demonstrate decreased orbitofrontal cortex volume (Davatzikos et al., 2005; Kumari et al., 2009; Schiffer et al., 2010) and hypoactivity in the region (Quintana et al., 2003), as well as disruption in the ventromedial prefrontal cortex (catatonia in particular; Northoff, 2002; Northoff et al., 2004), that could point to decreased cognitive flexibility during the decision making process. In addition, increased activation in the amygdala but decreased activation in the dorsomedial and dorsolateral prefrontal cortex are seen among those with schizophrenia on tasks evoking both emotional and cognitive components (Kring & Barch, 2014). Additional neuroimaging evidence is needed to examine potential markers of risky decision making in schizophrenia and related disorders.

Potential mechanisms Deficit in learning from feedback It is possible that a failure to learn from feedback drives decision making impairments in schizophrenia spectrum disorders. Failure to learn from feedback is seen on the WCST (Hellman, Kern, Neilson, & Green, 1998; Liu, Tam, Xie, & Zhao, 2002; Prentice, Gold, & Buchanan, 2007; Stratta, Daneluzzo, Bustini, Prosperini, & Rossi, 2000) and correlations exist between performance on the WCST and risky decision making tasks in those with schizophrenia. Disrupted associative

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learning (Barch, 2005) and impaired reversal learning (Waltz & Gold, 2007; Waltz et al., 2013) are also seen, providing further evidence of difficulties utilizing feedback to adapt to the most appropriate and advantageous strategy on future trials. Individuals with a diagnosis of schizophrenia also often jump to conclusions, making a decision without sufficient information (Garety & Freeman, 1999; Garety et al., 2005). Per Strauss, Waltz, and Gold (2014), rapid learning and decision making is guided by the orbitofrontal cortex, whereas more gradual learning is dictated by the basal ganglia. These differences are seen in schizophrenia, as rapid decision making may be more affected than gradual decision making from feedback.

Deficit in assessing the magnitude of rewards/losses and reward responsiveness It is also possible that risky decision making is due instead to a failure to accurately assess the relative magnitude of rewards and/or losses. Individuals with a schizophrenia spectrum disorder may fail to see the potential losses associated with a given decision or may downplay the magnitude of those potential losses in favor of the potential gains associated with the decision. Deficits in reward responsiveness are seen in schizophrenia (Rivas-Grajales et al., 2017; but see Strauss et al., 2014), especially when the rewards are unexpected (Kring & Barch, 2014). Decreased neural responses to reward are also seen (Waltz, Frank, Wiecki, & Gold, 2011), but this would argue instead for less risky decision making as individuals would not be reward-driven and focused on immediate gains. Individuals with schizophrenia may be better able to identify potential losses than rewards (Strauss et al., 2014), leading to difficulties mentally representing values of potential gains (Brown et al., 2013; Chandler et al., 2009; Gold et al., 2012; Gold, Waltz, Prentice, Morris, & Heerey, 2008; Heerey et al., 2011; but see Cohen & Minor, 2010 and Heerey et al., 2008). Others argue that it is not the representation of gains or losses that is impaired, but rather how that information is used to calculate expected values. Individuals with schizophrenia have greater difficulty judging which is the more favorable gamble (Brown et al., 2013) but may actually be more realistic in terms of judging low probability events than controls (Prentice, Gold, & Carpenter, 2005). Collectively, difficulties assessing the magnitude of rewards coupled with utilizing feedback on risks of losses/ gains from previous decisions could lead to riskier/more negative decision making in individuals with a schizophrenia spectrum disorder.

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Deficit in rewards guiding learning Risky decision making is driven by emotions, at least in part. Those emotions are often contingent on the reinforcing value of the stimulus (Rolls & Grabenhorst 2008). Emotion regulation is required to make the best possible decision (Bechara et al., 1994), or else decisions would be less rational and more impulsive (Patrick, Blair, & Maggs, 2008). Integrating multiple sources of information (e.g., cognitive and emotional) can lead to the best possible decision, one that minimizes potential losses to maximize potential rewards. Some research suggests that this process is intact in schizophrenia (Exner, Boucsein, Degner, & Irle, 2006; Waltz & Gold, 2007; Weickert et al., 2002; Takano et al., 2002), whereas others suggest a disruption in this process (Bigelow et al., 2006; Polgar et al., 2008). Individuals may be able to process the effect of an immediate reward but may not be able to utilize that information to improve long-term decision making outcomes (Barch & Dowd, 2010; Heerey et al., 2008). Abnormalities in reward-based learning, including as a response to feedback, are seen in schizophrenia (Heerey et al., 2008; Yilmaz et al., 2012). Additional research is needed to more fully assess this theory of decision making impairment in schizophrenia spectrum disorders.

Deficit in planning for the future Finally, it is possible that the impairments previously described are due at least in part to a difficulty in planning for the future. To do well on a number of behavioral risky decision making tasks, individuals must learn (through feedback) to avoid the higher immediate gain in favor of the long-term “slow” gain. Individuals with schizophrenia demonstrate a myopia for the future (Heerey et al., 2007), which can lead to difficulties anticipating future rewards from a decision (Kring & Barch, 2014). This myopia could interact with difficulties in learning from feedback, using rewards to guide learning, and assessing the magnitude of rewards/losses to produce decision making deficits.

Conclusions and future directions Although the specific mechanism underlying impairments is unclear, there does appear to be a general trend toward increased delay discounting and riskier decision making among individuals with a diagnosis of schizophrenia or schizoaffective disorder. No conclusions can be drawn for other schizophrenia spectrum diagnosis, given the relative

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dearth of research in the risky decision making realm. Several avenues for future exploration are examined.

Delusion proneness and levels of schizophrenia spectrum disorder symptoms The majority of the research to date focuses on individuals with a diagnosis of schizophrenia, with some adding individuals with schizoaffective disorder to create a composite group (schizophrenia spectrum) for comparison. However, the evidence for or against risky decision making in other disorders (schizophreniform, schizotypal personality disorder, delusional disorder, etc.) is severely lacking. New research that focuses on the presence or absence of decision making deficits and difficulties with delay discounting in these other disorders could shed light on whether the specific symptoms or length of illness affect these cognitions, as well as the specific mechanisms underlying known impairments. In addition, it would be beneficial to assess the extent of decision making difficulties among individuals without a diagnosable disorder but who exhibit higher-than-average levels of delusional thoughts. Delusion proneness is an individual-differences characteristic describing an individual’s tendency to experience delusional beliefs (Peters, Joseph, & Garety, 1999). Individuals can be described as hallucination prone (prone to vivid imagery; Aleman, Nieuwenstein, Bo¨cker, & de Haan, 2000; Chapman, Edell, & Chapman, 1980) and self-report measures are used to assess levels of delusion and hallucination proneness in the general population. Several studies show that individuals high in delusion proneness, as well as those with a diagnosis of schizophrenia or delusional disorder, tend to show a jumping-to-conclusions bias on tasks, in that they make a decision in the absence of sufficient information (Fine, Gardner, Craigie, & Gold, 2007; Garety & Freeman, 2013; Linney, Peters, & Ayton, 1998; Mortiz et al., 2016). The jumping-to-conclusions bias is typically assessed with the beads task (Phillips & Edwards, 1966). On it, participants see two jars with differing ratios of beads in them. For example, one jar might have 85% blue beads, whereas the other has just 15% blue beads (Garety, Hemsley, & Wessely, 1991; Huq, Garety, & Hemsely, 1988). The participant is tasked with determining which jar a series of beads are drawn from. They can make this decision after viewing as many or as few beads as they would like. Individuals high in delusion proneness, as well as individuals with schizophrenia, make a decision based on fewer beads— at times even after drawing just one bead—than those low in delusion proneness or without a diagnosed disorder (Dudley, John, Young, & Over, 1997; Moritz et al., 2016; Moritz & Woodward, 2005).

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Few have examined behavioral risky decision making task performance as a function of delusion or hallucination proneness. Cella, Dymond, and Cooper (2009) utilized the contingency-shifting variant of the IGT, finding that those high in delusion or hallucination proneness exhibited difficulty on Phase 2 (the shift) but not Phase 1 (learning) of the task. Utilizing the Wheel of Fortune task, no correlation was seen between performance and hallucination proneness (Jones et al., 2008). Finally, data from my lab suggests that individuals scoring higher on delusion proneness outperform those scoring lower on delusion proneness on the IGT but not the GDT (Runyon & Buelow, 2019). As these investigations are in their preliminary stages, it is too uncertain of a landscape to express a pattern of impairments in decision making as a function of delusion or hallucination proneness. But, this avenue is ripe for future exploration.

A focus on the development of normative data What is sorely missing in the literature to date, and a consistent theme in this text, is a set of normative data allowing for comparison of data across individual samples. The IGT has normative data, if the researcher utilizes the version of the task available through Psychological Assessment Resources (Bechara, 2007). However, nearly all other behavioral tasks are currently lacking in such a normative database. A number of the studies showing no significant differences between those with schizophrenia and healthy controls actually show impaired performance among those in the schizophrenia group compared to normative standards. Variations in the performance of healthy control participants appear to be the driving force behind these lack of differences within individual studies. Moving forward, research should focus on how groups of participants with schizophrenia spectrum disorders perform on tasks compared to a normative dataset, rather than focusing on the comparison to a within-study control group that might be biased or impaired on the task in some way. But, that would mean that these normative databases need to be created. Even if the normative database is years in the future, researchers could now begin to implement analyses of individual deck selections on the IGT (see Brown et al., 2015, for discussion of deck-level differences in schizophrenia) and the use of cognitive models to explain performance on various tasks.

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C H A P T E R

12 Conclusions and future directions Across the previous chapters, we examined evidence of risky decision making, steeper delay discounting, and involvement in realworld risk-taking behaviors across anxiety, mood, eating, sleep, impulsive, addictive, and delusional disorders. Although the evidence is not always consistent across or within samples, diagnoses, or tasks, there is a pattern of involvement in activities and decisions with potential negative consequences for the individual in the short term or in the long term. We also see evidence across disorders of both involvement of the frontal lobe and structures in the dopaminergic mesocorticolimbic pathway as well as impairments on measures of various executive functions. Both these characteristics point toward frontal lobe involvement in both the disorder itself and in decision making impairments. In this final chapter, we will revisit some of the common themes that emerged, starting first with the common theories of the causes of decision making impairments before moving into the possible reasons for the noted inconsistencies across studies. The remainder of the chapter is devoted to treatment implications and avenues for future exploration to solidify our understanding of risky decision making across psychological disorders.

Etiologies of risky decision making across disorders Impaired executive functions One of the earliest theories as to why decision making impairments are seen in psychological disorders is that they are due to overall executive function impairments. Across most if not all psychological disorders, individuals experience difficulties with planning, organization,

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initiation, maintaining set, shifting set, problem-solving, fluency, inhibitory control, or concept formation, among others. These executive impairments fluctuate according to the current level of symptoms or as a function of current treatment status, similar to the fluctuations we have seen in decision making impairments across patient samples. As decision making is linked with functioning of the dorsolateral (Type II/System II, cold) and ventromedial (Type I, System I, hot) prefrontal cortex and relies on other executive functions as part of the process, it makes sense that decision making is an executive function and thus should be impaired when performance on other executive tasks is impaired. But the current literature does not present such a consistent pattern. Even within the same diagnostic category, one study might show intact executive functions but impaired decision making, another study impaired executive functions but intact decision making, and so on. Remember that the Iowa Gambling Task (IGT) was originally created after patients with damage to the ventromedial prefrontal cortex and real-world decision making impairments performed in the average range or higher on clinical measures of executive functions (Bechara et al., 1994). It is likely that intact executive functions are just one of several components necessary for advantageous decision making, but further research is needed to investigate not just how other executive functions interact with performance on decision making tasks, but also how they relate to specific components of decision making that emerge from neuroeconomic modeling studies.

Type/System I versus Type/System II decision making The type of decision making assessed matters. Type I decision making relies more on affective, gut feelings (reward pathway, ventromedial prefrontal cortex) whereas Type II decision making is more deliberate and thought-out (dorsolateral prefrontal cortex). Tasks that lean more toward assessment of Type I decision making, such as the IGT, may show fewer correlations with other executive function tasks due to the involvement of reward-sensitive pathways. Tasks that instead lean more toward assessment of Type II decision making may be more correlated with executive tasks. It is also possible that state-dependent factors, such as increased stress in a lab setting or everyday situation, interact more with and negatively affect Type I than Type II decisions. Or, stress could interfere with the ability to reason and think through different options before arriving at an advantageous decision, instead pointing to impaired Type II decisions.

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Reward pathway activation or deficiency Making a selection that results in a win can activate the reward pathway. Taking a real-world risk can activate the reward pathway. Using an addictive substance, eating a nutrient-rich food, gambling, purchasing items, playing video games—these all activate the reward pathway during the behavior but lead to a reward deficiency during behavior abstinenc. Actions that activate the reward pathway lead to continued involvement in that behavior to maintain the “high,” or to avoid the potential “low” or withdrawal syndrome. Taking risks, betting on a high-stakes gamble, or choosing the gamble over the smaller sure thing can activate the pathway during a risky decision making task, leading to less advantageous outcomes. The reward pathway activation/deficiency theory is more tailored to the behavioral and substance addictions but could come into play in other psychological disorders as well. To the extent that rewards and losses are not experienced and responded to, activation of the reward pathway may be impaired. As will be seen in the cognitive modeling section, oversensitivity to gains or losses is a consistent factor affecting decision making task performance and may be tied to reward pathway activation or lack thereof.

Focus on immediate versus long-term outcomes The results of delay and probability discounting tasks point to a greater focus on immediate over delayed rewards that lead to risky real-world decisions. On laboratory tasks, this tendency results in choosing the smaller sooner rather than larger later option, or in a myopia for the future (e.g., Bechara et al., 1994). Being unable to shift focus to the long-term consequences of a decision results in a decision making strategy that maximizes immediate wins without acknowledging this may not be a sustainable decision making strategy. This same focus is seen across symptoms of eating (binge/purge or restrictive behaviors), impulsive, and addictive behavior disorders, indicating that this immediate/long-term focus may relate to both the disorder symptomatology and to risky decision making.

“State” versus “trait” factors Although not seen consistently across chapters, state and trait factors influence the level of risky decision making. State factor is used here to describe characteristics of the participant in-the-moment, whereas trait factor is used to describe the longer standing psychological diagnosis or diagnoses. State factors could include the current level of symptoms, stress, recency of medication use, or a lab-based manipulation to induce

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stress or another negative situation. There is evidence that state factors independently relate to risky decision making, as individuals without a diagnosed mood or anxiety disorder, for example, but experiencing a moderate to high level of current symptoms exhibit decision making impairments similar to those of individuals with mood/anxiety diagnoses. This pattern is most notable in the eating and delusional disorders chapters, as previous research indicates disordered eating behaviors (subclinical eating disorders) and delusion proneness (subclinical psychosis) can negatively affect decision making. Our burgeoning understanding of how decision making changes across the spectrum of symptomatology leads to a question of when risky decision making develops: before symptoms begin, in the early symptom stage, or later in the process.

Impaired feedback processing Finally, difficulties understanding, interpreting, and applying feedback to later decisions occur across disorders and decision making tasks. Although some tasks may not rely on feedback processing (e.g., Balloon Analogue Risk Task, Angling Risk Task), others do. Tasks such as the IGT, with an element of ambiguity at the start of the task, require participants utilize feedback about wins and losses in early decisions to adapt the decision making strategy in later decisions. Learning that is slowed down or deficient in some way will result in less advantageous decisions across the entirety of a task.

Current issues affecting understanding of risky decision making in psychological disorders Characteristics of the patient participants A number of concerns arose in the previous chapters that can be tied to participant characteristics. First, small sample sizes appear to be the norm, especially in the earlier risky decision making studies. Patient samples of 10 20 participants are not uncommon, but the field is trending toward significantly larger sample sizes in recent years. Given some of the to-be-described factors affecting decision making besides a psychiatric diagnosis, larger samples of participants are necessary for the field moving forward. Second, many studies utilize heterogeneous samples of patients. One sample of individuals with a diagnosis of major depressive disorder, for example, may comprise some participants with current mild symptoms, some with current severe symptoms, and some with residual symptoms. Attention-Deficit/Hyperactivity Disorder

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samples may collapse across inattentive, impulsive, and combined types. Yet symptom severity and type can affect risky decision making, indicating these groups of participants should all be examined separately. Third, demographic factors are not always adequately assessed. Participants may be all female or all male, yet this is not addressed as a potential study (or at minimum generalization) limitation. In disorders with a strong sex imbalance in diagnostic rates (gambling, eating disorders, etc.), such an imbalance in participant sex might make sense during project planning but will not provide information about the decision making difficulties of everyone.

Characteristics of the control participants Several of the abovementioned concerns with patient samples also apply to the control samples, including sample size and demographic factors. Control participants are not always free of all psychiatric, neurological, and medical diagnoses, indicating that they may have factors that negatively affect decision making task performance. Personality characteristics, most notably impulsivity and reward responsivity, affect decision making even among individuals with no diagnosed disorder, yet studies do not always assess these characteristics in their comparison/control participants. As was noted in the delusional disorders chapter, not all control participants do well on decision making tasks, which leads to an inaccurate conclusion that those with a delusional disorder do not have decision making difficulties.

Characteristics of the risky decision making tasks As is likely very evident by now, not all decision making tasks assess the same components of decision making and we are not entirely sure exactly what these components are on each of the decision making tasks. There is a tendency in the decision making literature to use performance on one task to determine the presence or absence of impairments, yet most other cognitive domains rely on impaired performance across multiple measures of the construct. The scoring approach for a particular task also varies across studies. For example, some researchers assess the average pumps per balloon on the Balloon Analogue Risk Task whereas others assess the number of explosions. On the IGT, multiple outcome variables were used over the past 20 years: total disadvantageous selections, total advantageous minus disadvantageous selections, total money earned, advantageous minus disadvantageous selections by blocks of trials (10-, 20-, 25-, and 50-card blocks), and most recently, selections from the individual decks. A consistent scoring

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approach should be used for each task to allow for more meaningful comparison of results across studies and across time.

The influence of comorbidities Arguably the largest issue affecting our understanding of risky decision making across disorders is that most disorders do not occur in a vacuum. Someone with bipolar disorder may also meet criteria for nicotine dependence. Someone with an alcohol use disorder may also have primary insomnia. Anxiety and depression are highly comorbid diagnoses, as are the various substance use disorders. In some cases, it is difficult to tease apart whether one particular diagnosis affects decision making as it occurs along with other comorbid conditions in the patient samples. Although some studies examine samples of patients with, for example, alcohol use disorders compared to alcohol use and cannabis use disorders, or schizophrenia with and without cannabis use disorder, other researchers provide data regarding the number of patients with comorbid diagnoses but fail to examine their influence on decision making. We know that some comorbidities have an additive effect, in that the presence of two diagnoses leads to even riskier decisions than the presence of either diagnosis alone. A better understanding of cognitive impairments in dually diagnosed individuals is warranted.

The influence of treatment Not given enough consideration in the literature thus far is the influence of psychological, pharmacological, and other treatments on decision making task performance. Some researchers directly examine samples of participants pre- and post-treatment, using/not using medications, or in the case of substance use disorders, currently using and currently abstaining from a substance, but others just provide statistics on the number of participants who received some type of treatment. Psychotropic medications can affect on cognition, including decision making. As we will see in the treatment implications section to come, impaired decision making may predict treatment outcomes and improved decision making can provide evidence of treatment effectiveness.

Treatment implications Risky decision making predicts treatment outcomes. Individuals who make riskier decisions during baseline (pre-treatment) evaluation undergo longer treatments and experience higher levels of symptoms at

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the end of treatment than those who make safer decisions during baseline. However, this line of research primarily appears in the substance use disorders literature, leaving our understanding of how pretreatment risky decision making complicates the treatment process in other disorders. Given the suggested etiologies for risky decision making across disorders, it makes sense that this impairment could lead to more intense, longer, and less effective psychological treatments. Assessing risky decision making prior to treatment initiation may help us devise new and improved treatment approaches targeting factors that negatively affect treatment (i.e., difficulties learning from feedback, myopic focus on immediate outcomes). There is also evidence to suggest treatment affects risky decision making itself. Individuals currently taking psychotropic medications as prescribed experience changes in decision making across tasks; however, when this medication is for methadone or buprenorphine maintenance purposes, the cognitive difficulties continue. We also see that repetitive transcranial magnetic stimulation to portions of the frontal lobe improves decision making in the short term. With additional research, we may develop a new neuromodulatory treatment targeting the functioning of the frontal lobe to affect cognition and psychiatric symptomatology. But to truly affect change in treatments and outcomes, several new avenues of research exploration are warranted.

Future directions for the field Assessment of ecological validity and task reliability Although there are correlations between performance on some risky decision making tasks and real-world risk-taking behaviors, this evidence is lacking for a number of newer measures. Moving forward, it is vitally important that researchers and clinicians alike understand precisely what elements of decision making a task assesses and how it might manifest in real-world situations. Ecological validity studies can help us obtain the knowledge of what a particular task assesses, in that seeing how risks play out in a nonlab setting can lead to testable hypotheses regarding the etiology of the behavior. As previously discussed, reliability is not consistently assessed across risky decision making tasks. Some tasks, such as the Adult Decision Making Competence, have known internal consistency and others, such as the Balloon Analogue Risk Task, have known split-half reliability. But test retest reliability is not frequently assessed across tasks. A complicating factor is the presence of practice effects. Common to nearly all the decision making tasks is the potential for performance at Time 1 to

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affect performance at Time 2. This practice effect is most frequently seen on tasks such as the IGT or Soochow Gambling Task, in which part of the task entails learning how the game works (so to speak). For the IGT in particular, practice effects are seen even 1 year after initial task administration. If these tasks are going to be used to predict treatment outcomes or to track change in treatment over time, then the full extent of potential practice effects or low test retest reliability should be known.

Development of parallel versions of tasks Related to the issue of practice effects is the lack of parallel versions of tasks. On the Columbia Card Task, the cold and hot versions of the tasks do not factor together (Buelow & Blaine, 2015), so they cannot be thought of as parallel versions. The IGT has an E-F-G-H version with varied reward/loss contingencies and magnitudes; however, there is also some evidence that participants do not perform equivalently on these two versions (e.g., Verdejo-Garcia et al., 2009), a requirement for parallel task versions. As we continue to assess what different tasks assess, such as through incorporating additional cognitive modeling techniques, we should also investigate ways to create parallel versions of these tasks. At minimum, tasks that assess the same decision making components should be correlated with one another and investigated for potential utility as intermethod- reliable measures in treatment-outcome research.

Examination of multiple measures in the same study As previously mentioned, most studies to date examine decision making using just one task in a particular sample. If the tasks do not all assess the exact same decision making components, then it is possible that one study finds impairments and another does not solely based on the decision making task used in those studies. To gain a fuller picture of the extent of decision making impairments and how they map on to other executive function impairments in a particular disorder, multiple measures of the construct are needed within each study.

Which came first: risky decision making or the psychological disorder As can be gleaned from the title of this volume, there is a tendency to focus on whether decision making is impaired among individuals who have a diagnosed disorder. This approach implies that risky decisions

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are a consequence of the disorder or its specific symptoms. It is possible that, instead, risky decisions lead to the development of the psychological disorder. There is extant evidence of risky decisions predating the development of substance use disorders, and even of being the primary driving force behind addictions, but this has not translated to other areas to the same degree. Gaining a better understanding of the pattern of decision making impairments over time in longitudinal studies can help us better understand how decision making difficulties complicate treatments.

Cognitive modeling techniques As has been the trend in recent years, the future of risky decision making research involves examination of the common components across tasks rather than a focus on individual task performance. These common components can give us a better understanding of what is working or not working well when decisions are being made and may also help inform treatment of psychological difficulties. Neuroeconomic and cognitive modeling approaches can tell us why decisions change, how learning occurs, and what matters more in decision making: gain sensitivity (Yechiam & Busemeyer, 2008), loss sensitivity (Bishara et al., 2009), or recent compared to more distant feedback (Pleskac, 2008). Models can even tell us that some individuals develop a process of staying on the same strategy after a win but change (shift) decisions after a loss (Lin, Lin, Song, Huang, & Chiu, 2016), even if overall performance is not impaired or does not differ between patient and control groups. Modeling research thus far lends some credence to the hypothesis that overreliance on or weighting of gains/rewards guides risky decisions on tasks (Khodadadi, Dezfouli, Fakhari, & Ekhtiari, 2010; Yechiam & Busemeyer, 2008). Models are already applied to some clinical populations and this trend should continue in the future. Even when no differences are seen in total task performance as a function of substance use, heavy users engage in greater risk-taking and make less consistent responses across trials (Fridberg et al., 2010; van Ravenzqaaij, Dutilh, & Wagenmakers, 2011). Two other elements of the decision making process examined with modeling techniques and that apply to psychological disorders are initial risk perception (Wallsten, Pleskac, & Lejuez, 2005) and learning (Prause & Lawyer, 2014). It is possible that some individuals with anxiety disorders, for example, experience a higher initial risk perception that may remain consistent across time, leading to differences in performance on the tasks. Relying on a total performance score also does not show us how (and if) participants learned on a task, or rather

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“what changed” as the task progressed. This learning may shift initial risk perception and lead to a more advantageous adjustment across later trials (e.g., Ahn et al., 2008; Byrne & Worthy, 2016; Rolison, Hanoch, & Wood, 2012). On the other hand, sticking with an initial perception of risk may reflect an insensitivity to feedback (Khodadadi et al., 2010). These decision making factors that emerged in previous modeling attempts map onto the common etiologies for decision making impairments across psychological disorders, pointing to the necessity of increased modeling research to move the field into the future.

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Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A “Aberrant” behaviors, 204 Abnormal reward processing in MDD, 124 Abstinent smokers, 202 Abstract reasoning, 68 Acute alcohol administration, 199 Acute lab-based sleep deprivation, 153 154 Acute sleep deprivation, 153 Acute stress, neuroimaging for, 108 109 Addictive behaviors, 189 190 alcohol use disorder, 197 200 behavioral addictions, 196 197 cannabis use disorder, 202 204 ecstasy, 208 209 nicotine or tobacco use disorder, 200 202 opioid-related disorders, 204 206 pathological gambling, 190 196 risky decision making theories across substances of dependence, 209 213 stimulant use disorders, 206 208 treatment implications, 213 214 ADHD. See Attention-deficit/hyperactivity disorder (ADHD) Adult decision making competence (ADMC), 15 16, 72, 73t, 241 242 internal consistency, 44 45 test retest reliability, 42 validity, 49 Age in risky decision making, 34 35 Alcohol use disorder, 197 200 delay discounting, 199 200 neuroimaging, 200 reward responsiveness, 199 200 risky decision making, 198 199 Alexithymia, 117 118 American Psychiatric Association (APA), 163 164, 215 Amygdala, 85t AN. See Anorexia nervosa (AN) Angling Risk Task (ART), 16 17, 35 37, 238

validity, 49 Anhedonia, 113, 117, 123 124 Anorexia nervosa (AN), 135 139, 143 144, 147 148 Anterior cingulate cortex, 85t Anxiety, 3, 97, 240 alterations in neural processing of rewards and learning from feedback, 111 cautiousness and indecisiveness, 110 executive dysfunction, 109 executive function task performance, 107 future directions, 111 112 generalized anxiety, 98 100 delay discounting and risky decision making, 99 100 risk-taking behavior, 99 impulsivity effects vs. behavioral inhibition, 110 111 neuroimaging Acute stress, 108 109 GAD, 107 108 OCD, 108 PTSD, 108 Social anxiety, 108 OCD, 103 105 delay discounting and risky decision making, 104 105 risk-taking behavior, 104 participant-related factors, 112 PTSD, 101 103 delay discounting and risky decision making, 102 103 risk-taking behavior, 102 social anxiety, 100 101 delay discounting and risky decision making, 101 risk-taking behavior, 100 101 state-dependent fluctuations, 109 111 state-dependent stress, 105 107 trait anxiety and nondiagnosable types, 105

391

392

Index

APA. See American Psychiatric Association (APA) Apathy, 65 ART. See Angling Risk Task (ART) ASD. See Autism spectrum disorder (ASD) “Asian disease” study, 5 6 Attention, 122 Attention-deficit/hyperactivity disorder (ADHD), 163 164, 206 alterations in neural processing of rewards, 185 186 decision making task and comparison to other executive tasks, 186 187 executive dysfunction, 184 185 executive function task performance, 181 183 performance on other tasks, 181 183 factors affecting risky decision making comorbid diagnoses, 177 178 demographic factors, 180 impulsivity as personality characteristic, 178 180 influence of medication, 176 177 influence of retrospective self-report, effort, and potential malingering, 180 181 future directions, 186 188 neuroimaging, 183 184 participant-related factors, 187 188 and risk-taking behaviors, 164 167 and risky decision making, 167 171 BART, 167 168 CGT, 168 delay discounting, 171 174 GDT, 168 IGT, 168 169 other tasks, 169 170 reward responsiveness, 174 175 summary, 170 171 samples, 239 Autism spectrum disorder (ASD), 178

B Balloon Analogue Risk Task (BART), 17 18, 35 37, 72, 73t, 99 100, 120, 238 ADHD, 167 168, 186 187 schizophrenia spectrum disorders, 218 sleep deprivation and risky decision making, 153 154 split-half reliability, 46 test retest reliability, 42

validity, 49 51 Barratt Impulsiveness Scale (BIS-11), 178 180 BART. See Balloon Analogue Risk Task (BART) BAS. See Behavioral activation system (BAS) Bayesian sequential risk-taking model, 37 BED. See Binge-eating disorder (BED) Behavioral activation system (BAS), 131 132, 147 hyperactivity in bipolar disorder, 131 132 Behavioral addictions, 196 197 Behavioral inhibition, 110 111 Behavioral inhibition system (BIS), 110 111, 131 Behavioral inhibition/activation systems (BIS/BAS), 180 Behavioral risky decision making task performance, 233 Behavioral task performance, 120 Beliefs, 211 212 Binge-eating disorder (BED), 135 137, 140, 143 144, 147 148 Bipolar disorders bipolar I disorder, 113, 115 116 bipolar II disorder, 113, 115 116 bipolar spectrum disorders, 113 delay discounting, and reward responsiveness, 124 mood and risk-taking behaviors, 115 116 mood and risky decision making, 119 121 BIS. See Behavioral inhibition system (BIS) BIS-11. See Barratt Impulsiveness Scale (BIS-11) BIS/BAS. See Behavioral inhibition/ activation systems (BIS/BAS) Blackjack Task, 18 BRET, 18 Card-Guessing Task, 19 CCT, 20 21 CGT, 18 19 Chicken game, 19 Choice Dilemmas Questionnaire, 19 20 Cups Task, 21 DEEP, 22 Delay and Probability Discounting Tasks, 21 22 Devil’s Task, 22

Index

Framing Spinner Task, 23 GDT, 23 IGT, 23 26 Mirror Drawing Risk-Taking Task, 26 Multioutcome Risky Decision Task, 26 27 Nonsymbolic Economic Decision Making Task, 27 Probabilistic Gambling Task, 27 Reyna and Ellis Risk Task, 27 28 Risk Propensity Task, 28 Risky Gains Task, 28 Sequential Investment Task, 28 29 Stoplight Game, 29 Two-Outcome Risky Decision Task, 29 validity, 51 Wheel of Fortune Task, 30 BMI. See Body mass index (BMI) BN. See Bulimia nervosa (BN) Body mass index (BMI), 135 137 Bomb Risk Elicitation Task (BRET), 18, 40, 51 52 Brain lateral view, 81f, 82 83 medial view, 82 83, 82f reward pathway, 80 83, 83f BRET. See Bomb Risk Elicitation Task (BRET) Brief psychotic disorder, 215 Bulimia nervosa (BN), 135 137, 139, 143 144, 147 148 Buprenorphine, 204

C Caffeine, 206, 208 Cake Gamble (Gambling) Task, 52 Cambridge Gambling Task (CGT), 18 19 ADHD, 168 split-half reliability, 46 suicidal ideation, 121 122 validity, 52 Cannabis use disorder, 202 204 delay discounting, 203 neuroimaging, 204 reward responsiveness, 203 risky decision making, 203 Card-Guessing Task, 19 CARE. See Cognitive Appraisal of Risky Events (CARE) Catatonia, 215 Cautiousness and anxiety, 110 CCT. See Columbia Card Task (CCT) CD. See Conduct disorder (CD)

393

Central route, 118 119 CGT. See Cambridge Gambling Task (CGT) Chicken game, 19 validity, 52 Children with ADHD, 184 185 Choice Dilemmas internal consistency, 45 reliability, 43 Choice Dilemmas Questionnaire, 19 20 validity, 52 Cocaine, 206 Cognitive abilities, 62 Cognitive Appraisal of Risky Events (CARE), 30 31 internal consistency, 45 test retest reliability, 42 validity, 52 53 Cognitive complaints, 149 Cognitive flexibility, 65 66 Cognitive modeling techniques, 242 243 Cold decision makings, 9 Cold pressor task, 105 106 Columbia Card Task (CCT), 20 21, 72, 73t, 242 test retest reliability, 42 validity, 53 Comorbid diagnoses, 195, 226 in ADHD, 177 178 Comorbidities, 212 213 for disordered eating behaviors, 145 146 Concept formation, 68 Concurrent validity, 48 Conduct disorder (CD), 169 Consistent executive dysfunction, 227 228 Construct validity, 48 49 Content validity, 47 48 Continuous performance test (CPT), 69, 71, 181 182 Convergent validity, 48 49 CPT. See Continuous performance test (CPT) Craving behaviors, 194 Criterion validity, 48 Cue-induced craving, 211 212 Cups Task, 21, 86 87 for test retest reliability, 42 validity, 53 Cyclothymic disorder, 113

D Daytime sleepiness, 151 152 Decision Styles Scale, 31 Decision making, 3 11, 39, 47 48, 61, 181

394 Decision making (Continued) and executive functions, 70 71 modeling on behavioral tasks, 35 37 neuroimaging lessons for, 88 90 neuroscience of, 80 88 DEEP Risk. See Dynamic Experiments for Estimating Preferences (DEEP) Risk Deficit in assessing magnitude of rewards/ losses and reward responsiveness, 230 in learning from feedback, 229 230 in planning for future, 231 in rewards guiding learning, 231 Delay and Probability Discounting Tasks, 21 22 Delay-discounting, 72, 73t, 88, 124 125, 189 190 ADHD, 171 174 alcohol use disorder, 199 200 cannabis use disorder, 203 disordered eating behaviors, 141 142 generalized anxiety, 99 100 nicotine or tobacco use disorder, 201 202 OCD, 104 105 opioid-related disorders, 205 pathological gambling, 190 191 PTSD, 102 103 schizophrenia spectrum disorders, 223 224 sleep-related disorders, 156 157 social anxiety, 101 stimulant use disorders, 207 for test retest reliability, 42 43 validity, 53 Delayed reward, 53 54, 70 71 Delta-9-tetrahydrocannabinol (THC), 202 Delusion proneness, 232 233 Delusional disorder, 215 Dementias, 70 Demographic factors on ADHD, 180 in test performance age, 34 35 sex, 35 Depression, 3, 113, 115, 117 119, 123 124, 131 132, 240 Depressive symptoms and disorders in mood, delay discounting, and reward responsiveness, 123 124 in mood and risk-taking behaviors, 114 115

Index

in mood and risky decision making, 117 119 Devil’s Task, 16 17, 22, 54, 86 87 Dietary restriction, 135 Dieting behavior, 136 137 Discounting of future rewards, 163 Disinhibition, 163 Disordered eating behaviors, 136 137, 140 141 alterations in neural processing of rewards and learning from feedback, 146 147 comorbidity, 145 146 delay discounting and reward responsiveness, 141 142 executive dysfunction, 144 145 executive function task performance, 142 143 future directions, 147 148 impulsivity, 147 neuroimaging, 143 144 risk-taking behaviors, 137 138 risky decision making AN, 138 139 binge-eating disorder, 140 BN, 139 comparisons between eating disorder diagnostic groups, 141 obesity, 139 140 Disruptive mood dysregulation disorder, 113 Divergent validity, 48 49 Dohmen Measure, 54 Dohmen Scale, 31 Domain-Specific Risk-Taking (DOSPERT), 57 internal consistency, 45 test retest reliability, 43 validity, 54 Domain-Specific Risk-Taking Scale, 31 Dopamine activity for ADHD, 185 186 Dorsolateral prefrontal cortex, 85t, 236 Dorsomedial prefrontal cortex, 85t DOSPERT. See Domain-Specific RiskTaking (DOSPERT) Dual-process models, 7 8 Dynamic Experiments for Estimating Preferences (DEEP) Risk, 22 Dysthymia, 113

E Eating disorders, 135 Ecological validity, 48, 241 242

Index

Ecstasy, 208 209 Emotions, 7 8, 128 EV model. See Expectancy valence model (EV model) Evaluation of Risk Scale, 31 32, 155 validity, 54 55 Everyday Risk Inventory, 32 internal consistency, 45 test retest reliability, 43 validity, 55 Executive dysfunction, 109 ADHD, 184 185 for disordered eating behaviors, 144 145 on disruptions of mood, 128 130 of sleep-related disorders, 158 159 Executive functions, 61 69 assessment, 63 69 abstract reasoning, 68 concept formation, 68 fluency or response/idea generation, 67 inhibition, 65 67 inhibitory control, 66 67 planning and organization, 63 65 problem-solving, 68 processing speed, 68 69 self-regulation and learning from feedback, 65 shifting or cognitive flexibility, 65 66 sustained attention or vigilance, 69 working memory, 69 decision making and, 70 71 correlations, 71 80 in disruptions of mood, 129 130 impairments in, 69 70 neuroimaging lessons for decision making processes, 88 90 neuroscience of decision making, 80 88 pathological gambling relationship with, 192 193 task performance on ADHD, 181 183 on anxiety, 107 on disordered eating behaviors, 142 143 on disruptions of mood, 125 schizophrenia spectrum disorders, 227 228 Expansive mood, 113 Expectancy valence model (EV model), 35 36 Extreme dieting behavior, 136 137

395

F Failure to plan ahead, 163 Fatigue, 150, 153, 156 Fluency, 67 “Foregone payoff” version, 176 Framing Spinner Task, 23 validity, 55 Frontal systems behavior scale (FrSBe), 65 Functional research, 84

G GAD. See Generalized anxiety disorder (GAD) Gambling disorder. See Pathological gambling Gambling Game Task, 26 Gambling problem severity and type, 194 195 Game of Dice Task (GDT), 23, 73t, 131 ADHD, 168, 186 187 schizophrenia spectrum disorders, 218 219 test retest reliability, 43 validity, 55 General Decision Making Style, 32 internal consistency, 45 test retest reliability, 43 validity, 55 General Risk Propensity Scale, 32, 55 Generalized anxiety, 98 100 delay discounting and risky decision making, 99 100 risk-taking behavior, 99 Generalized anxiety disorder (GAD), 97 99, 107 108

H Hallucinations, 222 proneness, 232 233 HDT. See Hungry Donkey Task (HDT) Herpes simplex encephalitis, 70 Hot decision makings, 9 Hungry Donkey Task (HDT), 25 26, 72 80, 73t, 169 validity, 55 Hydrocephalus, 70 Hyperactivity, 163 Hypersomnia, 151 152 Hypomania in mood, delay discounting, and reward responsiveness, 124

396

Index

Hypomania (Continued) in mood and risk-taking behaviors, 115 116 in mood and risky decision making, 119 121 Hypothetical discounting tasks, 173 Hypothetical rewards, 53 54

I IGT. See Iowa Gambling Task (IGT) Immediate outcomes, 237 Impaired executive functions, 209 210, 235 236 Impaired feedback processing, 238 Impairments in executive functions, 69 70 ImpSS, I-7, 178 179 Impulsivity, 11 12, 163, 192 193, 210 211 for disordered eating behaviors, 147 effects on anxiety, 110 111 pathological gambling, 193 as personality characteristic, 178 180 Inattention, 163 164, 185 186 Indecisiveness in anxiety, 110 Inflated self-esteem, 113 Inhibition, 62 63, 65 67 Inhibitory control, 66 67, 163 Insomnia, 149, 151 152, 156 Insula, 85t Intermethod reliability, 44 Internal consistency, 44 45 Internet gaming disorder, 197 Iowa Gambling Task (IGT), 3 4, 16 17, 23 26, 35 36, 41, 72 80, 73t, 88, 99 100, 117 118, 125, 131, 138 139, 218, 235 236, 241 242 ADHD, 168 169 schizophrenia spectrum disorders, 219 222 sleep deprivation and risky decision making, 153 154 split-half reliability, 46 suicidal ideation, 121 122 test retest reliability, 43 validity, 55 57 Irritability, 113

K Knife Switches Task. See Devil’s Task

L Learning from feedback, 65 Long-term outcomes, 237

M Major depressive disorder (MDD), 102 103, 113, 115, 117 Malingering, 180 181 Mania in mood, delay discounting, and reward responsiveness, 124 in mood and risk-taking behaviors, 115 116 in mood and risky decision making, 119 121 Marijuana use, 204 MDD. See Major depressive disorder (MDD) Measurement methods, 15 Blackjack Task, 18 Risk Propensity and Risk Attitude Measures, 30 34 risky decision making measures, 15 30 Medication influence on ADHD, 176 177 Meningioma, 10 11 Mental flexibility, 65 66 Methadone, 204 Methamphetamine, 206 3,4-Methylenedioxymethamphetamine (MDMA). See Ecstasy Methylphenidate (MPH), 165, 176 177 MID. See Monetary incentive delay task (MID) Mirror Drawing Risk-Taking Task, 26, 57 Monetary incentive delay task (MID), 171 172 Monetary incentives, 175 Mood delay discounting, and reward responsiveness depressive symptoms and disorders, 123 124 influence of suicidal ideation and behaviors, 124 125 mania, hypomania, and bipolar disorders, 124 fluctuations in, 113 and risk-taking behaviors depressive symptoms and disorders, 114 115 influence of suicidal ideation and behaviors, 116 117 mania, hypomania, and bipolar disorders, 115 116 and risky decision making conclusion, 122 123

Index

depressive symptoms and disorders, 117 119 influence of suicidal ideation and behaviors, 116 117 mania, hypomania, and bipolar disorders, 119 121 Mood disruptions alterations in neural processing of rewards and learning from feedback, 130 131 BAS hyperactivity in bipolar disorder, 131 132 executive dysfunction, 128 130 future directions, 133 134 mood, delay discounting, and reward responsiveness, 123 125 mood and risk-taking behaviors, 114 117 mood and risky decision making, 117 123 neuroimaging, 125 127 participant-related factors, 132 133 performance on other executive function tasks, 125 state-dependent mood, 127 128 MPH. See Methylphenidate (MPH) Multi-Outcome Risky Decision Task, 26 27, 57 Multiple forms reliability, 44

N Narcolepsy, 151 152, 156, 206 National Institutes of Health (NIH), 136 Negative mood, 113, 118 119, 128, 197 Neuroimaging ADHD, 183 184 alcohol use disorder, 200 anxiety acute stress, 108 109 GAD, 107 108 OCD, 108 PTSD, 108 social anxiety, 108 cannabis use disorder, 204 for disordered eating behaviors, 143 144 on disruptions of mood, 125 127 lessons for decision making processes, 88 90 nicotine or tobacco use disorder, 202 opioid-related disorders, 206 pathological gambling, 192

397

schizophrenia spectrum disorders, 228 229 of sleep-related disorders, 158 stimulant use disorders, 208 Neuroscience of decision making, 80 88 functional research, 84 structural research, 83 84 by task, 84 86 of risk, 9 11 of risky decision making, 9 10 Nicotine dependence, 200 201 Nicotine use disorder, 200 202 delay discounting, 201 202 neuroimaging, 202 reward responsiveness, 201 202 risky decision making, 201 NIH. See National Institutes of Health (NIH) Nonplanning, 163 Nonsymbolic Economic Decision Making Task, 27 Nucleus accumbens, 85t

O Obesity, 135 140, 143 144 Obsessive compulsive disorder (OCD), 97 98, 103 105 delay discounting and risky decision making, 104 105 neuroimaging, 108 risk-taking behavior, 104 OCD. See Obsessive compulsive disorder (OCD) ODD. See Oppositional defiant disorder (ODD) Opioid abstinence, 206 dependence diagnosis, 204 205 opioid-related disorders, 204 206 delay discounting, 205 neuroimaging, 206 reward responsiveness, 205 risky decision making, 205 Oppositional defiant disorder (ODD), 169 Orbitofrontal meningioma, 10 11 Overweight, 137 138

P Paced Auditory Serial Addition Test, 71 Panic disorder, 97 98 Parallel forms reliability, 44

398

Index

Parkinson’s disease, 65 Participant variables, 195 Participant-related factors ADHD, 187 188 in anxiety, 112 on disruptions of mood, 132 133 sleep-related disorders, 160 161 Passive Risk-Taking test retest reliability, 43 validity, 57 Passive risk-taking scale (PRT scale), 32 33 Pathological gambling, 189 196 delay discounting, 190 191 neuroimaging, 192 relationship with other executive functions, 192 193 risky decision making, 191 192 theories comorbid diagnoses, 195 difficulties with learning from reward or loss and perception of risks, 193 194 gambling problem severity and type, 194 195 impulsivity, 193 state and participant variables, 195 urge to gamble and craving behaviors, 194 treatment implications, 196 Peripheral route, 118 119 Perseveration, assessing, 65 66 Persistent depressive disorder, 113 Personality characteristic of impulsivity, 193 Phineas Gage, 80 82 Planning and organization, 63 65 Polysubstance abuse/dependence, 212 213 Polysubstance use, 212 213 Positive mood, 117 119, 128 Posttraumatic stress disorder (PTSD), 97 98, 101 103 delay discounting and risky decision making, 102 103 neuroimaging, 108 risk-taking behavior, 102 Predictive validity, 48 Premenstrual dysphoric disorder, 113 Probabilistic Gambling Task, 27 Problem-solving process, 68 Problematic internet use, 197 Prospect valence model (PV model), 35 36

PRT scale. See Passive risk-taking scale (PRT scale) Psychological Assessment Resources, 233 Psychostimulants, 206 PTSD. See Posttraumatic stress disorder (PTSD) PV model. See Prospect valence model (PV model)

R Random selection, 175 Reliability, 15, 39 47. See also Validity affecting factors, 58 59 conclusion, 46 47 intermethod, 44 internal consistency, 44 45 parallel or multiple forms, 44 split-half, 45 46 test retest, 40 43 Repetitive transcranial magnetic stimulation (rTMS), 120 Response/idea generation, 67 Responsivity to reward, 118 119 Reward pathway, 111, 236 activation or deficiency, 237 Reward responsiveness ADHD, 174 175 alcohol use disorder, 199 200 cannabis use disorder, 203 for disordered eating behaviors, 141 142 nicotine or tobacco use disorder, 201 202 opioid-related disorders, 205 schizophrenia spectrum disorders, 223 224 sleep-related disorders, 156 157 stimulant use disorders, 207 Reward seeking, 163 Reyna and Ellis Risk Task, 27 28 Rey Osterrieth complex, 63 65 Risk, 3 11 neuroscience of, 9 11 Risk Propensity and Risk Attitude Measures CARE, 30 31 Decision Styles Scale, 31 demographic factors in test performance, 34 35 Dohmen Scale, 31 Domain-Specific Risk-Taking Scale, 31 Evaluation of Risk Scale, 31 32 Everyday Risk Inventory, 32

Index

General Decision Making Style, 32 General Risk Propensity Scale, 32 modeling decision making on behavioral tasks, 35 37 PRT scale, 32 33 Risk Propensity Scale, 33 Risk-Taking Propensity, 33 Stimulating-Instrumental Risk Inventory, 33 34 Risk Propensity Scale, 33 Risk Propensity Task, 28 split-half reliability, 46 validity, 57 Risk-taking behaviors, 11 12, 163 ADHD and, 164 167 disordered eating behaviors and, 137 138 among individuals with generalized anxiety, 99 among individuals with OCD, 104 among individuals with PTSD, 102 among individuals with social anxiety, 100 101 schizophrenia spectrum disorders and, 216 218 Risk-taking propensity, 33, 86 87, 155 Risky decision making, 7, 15, 39, 83 84, 189 190, 242 243 ADHD and, 167 171 alcohol use disorder, 198 199 cannabis use disorder, 203 characteristics of control participants, 239 of patient participants, 238 239 of risky decision making tasks, 239 240 cognitive modeling techniques, 242 243 development of parallel versions of tasks, 242 and disordered eating behaviors AN, 138 139 binge-eating disorder, 140 BN, 139 comparisons between eating disorder diagnostic groups, 141 obesity, 139 140 ecological validity and task reliability assessment, 241 242 examination of multiple measures, 242 focus on immediate vs. long-term outcomes, 237 functional neuroimaging studies, 85t

399

generalized anxiety, 99 100 impaired executive functions, 235 236 impaired feedback processing, 238 influence of comorbidities, 240 influence of treatment, 240 measures ADMC, 15 16 ART, 15 16 BART, 17 18 neuroscience of, 9 10 nicotine or tobacco use disorder, 201 OCD, 104 105 opioid-related disorders, 205 pathological gambling, 191 192 PTSD, 102 103 reward pathway activation or deficiency, 237 schizophrenia spectrum disorders and, 218 223 social anxiety, 101 “state” vs. “trait” factors, 237 238 stimulant use disorders, 206 207 theories across substances of dependence, 209 213 altered processing of risks and rewards and difficulties, 210 comorbidities and polysubstance use, 212 213 impaired executive functions, 209 210 impulsivity, 210 211 substance use expectancies, satiation, and cue-induced craving, 211 212 treatment implications, 240 241 Type/System I vs. Type/System II decision making, 236 Risky driving behaviors, 165 Risky Gains Task, 28, 87 Risky sexual behaviors, 217 218 Rogers Gambling Task. See Cambridge Gambling Task (CGT) rTMS. See Repetitive transcranial magnetic stimulation (rTMS)

S SA. See Social anxiety (SA) Sadness, 113 Satiation, 211 212 Schizoaffective disorder, 215 Schizophrenia, 3, 215 Schizophrenia spectrum disorders, 215 216 affecting factors for risky decision making

400

Index

Schizophrenia spectrum disorders (Continued) control group, 226 227 current symptoms, 224 225 diagnostic considerations, 225 226 influence of medication, 224 deficit in assessing magnitude of rewards/ losses and reward responsiveness, 230 in learning from feedback, 229 230 in planning for future, 231 in rewards guiding learning, 231 delay discounting, and reward responsiveness, 223 224 delusion proneness, 232 233 executive function task performance, 227 228 focus on development of normative data, 233 future directions, 231 233 levels of schizophrenia spectrum disorder symptoms, 232 233 neuroimaging, 228 229 and risk-taking behaviors, 216 218 and risky decision making, 218 223 BART, 218 GDT, 218 219 IGT, 219 222 other tasks, 222 Schizophreniform disorder, 215 Schizotypal personality disorder, 215 Selective mutism, 97 98 Self-regulation from feedback, 65 Self-reported sleep loss, 153 154 Sensation seeking, 163 Separation anxiety disorder, 97 98 Sequential Investment Task, 28 29 Sex differences in risky decision making, 35 SGT. See Soochow Gambling Task (SGT) Shifting, 62 63, 65 66 Sleep deprivation, 149 risk-taking behaviors, 150 153 risky decision making, 153 156 BART, 154 155 IGT, 153 154 other tasks, 155 Sleep loss, 158 161 Sleep-related disorders, 149 alterations in neural processing of rewards and learning from feedback, 159 160

executive dysfunction, 158 159 future directions, 160 161 neuroimaging, 158 participant-related factors, 160 161 performance on other executive function tasks, 157 Sleep wake disorders, 149 Social anxiety (SA), 100 101 delay discounting and risky decision making, 101 disorder, 97 98 neuroimaging for, 108 risk-taking behavior, 100 101 Somatic marker hypothesis, 9 11 Soochow Gambling Task (SGT), 25, 35 36, 241 242 Specific phobia, 97 98 Split-half reliability, 45 46 State anxiety, 109 State factors, 237 238 State vs. trait mood, 127 128 State variables, 195 State-dependent anxiety, 97 fluctuations in anxiety, 109 111 mood on disruptions of mood, 127 128 stress, 97 of anxiety, 105 107 Stimulant use disorders, 206 208 delay discounting, 207 neuroimaging, 208 reward responsiveness, 207 risky decision making, 206 207 Stimulating-Instrumental Risk Inventory, 33 34 internal consistency, 45 validity, 57 Stoplight Game, 29 Stoplight Task, 58 Stress, 106 107 Striatum, 85t Structural research, 83 84 Substance, 216 217 abuse, 189 190 dependence, 189 190 expectancies, 211 212 Substance use disorder (SUD), 166 167 Suicidal ideation or behaviors, 129 in mood delay discounting, and reward responsiveness, 124 125 and risk-taking behaviors, 116 117

Index

and risky decision making, 116 117 Survey of Children’s Health, 163 164 Sustained attention, 69 Symbol Digit Modalities, 68 69

T Task psychometrics, 15 Task reliability assessment, 241 242 Test retest reliability, 40 43 THC. See Delta-9-tetrahydrocannabinol (THC) Tobacco use disorder. See Nicotine use disorder Trail Making Test, 65 66, 71 Trait anxiety, 105, 109 Trait factors, 237 238 Trier Social Stress test, 105 106 Two-Outcome Risky Decision Task, 29, 58 Type/System I decision making, 236 Type/System II decision making, 236

401

U Uncertainty, 3 11 “Under uncertainty” trials, 127 128 Unipolar depression, 121 122 Updating task, 62 63 UPPS, 178 179

V Validity, 15, 39, 47 58. See also Reliability affecting factors, 58 59 conclusion, 58 Ventromedial prefrontal cortex, 85t, 236 Vigilance, 69

W Wheel of Fortune Task, 30, 58 Wisconsin Card Sorting Task (WCST), 3 4, 66, 68, 181 182, 218 219, 227 230 Working memory, 69