Psychology of Gambling : New Research [1 ed.] 9781622573219, 9781621005032

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Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Psychology of Gambling : New Research, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Psychology of Gambling : New Research, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

PSYCHOLOGY RESEARCH PROGRESS

PSYCHOLOGY OF GAMBLING

Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

NEW RESEARCH

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PSYCHOLOGY RESEARCH PROGRESS Additional books in this series can be found on Nova‘s website under the Series tab.

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PSYCHOLOGY RESEARCH PROGRESS

PSYCHOLOGY OF GAMBLING NEW RESEARCH

ANDREA EUGENIO CAVANNA Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

EDITOR

New York Psychology of Gambling : New Research, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

Copyright © 2012 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers‘ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA

Psychology of gambling : new research / editor, Andrea Cavanna. p. cm. Includes index. ISBN:  (eBook) 1. Gambling--Psychological aspects. 2. Compulsive gambling. I. Cavanna, Andrea E. HV6710.P793 2012 616.85'841--dc23 2012021406

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CONTENTS Preface

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Chapter 1

vii Evidence for a Biological Component of Decision-Making in Gambling: A Critical Review Eleanor Crossley and Andrea E. Cavanna

Chapter 2

The Somatic Marker Hypothesis in Pathological Gambling David Polezzi, Elena Casiraghi and Giulio Vidotto

Chapter 3

Prevention Is Better Than Cure. Vulnerability Markers for Problem Gambling Neal Hinvest

1 9

23

Chapter 4

Pathological Gambling in Frontotemporal Dementia Abhinav Rastogi and Andrea E. Cavanna

43

Chapter 5

Decisional Processes Underpinning Online Gambling James G. Phillips and Rowan P. Ogeil

51

Chapter 6

Home-Cage Testing of Choice Behaviour: Proneness to Risk in a Gambling Task Walter Adriani, Francesca Zoratto and Giovanni Laviola

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Prominent Deck B Phenomenon: Are Decision-Makers Sensitive to Long-Term Outcome in the Iowa Gambling Task? Yao-Chu Chiu, Ching-Hung Lin and Jong-Tsun Huang

73

93

The Validity of an Integrated Cognitive Behavioural Model of Gambling Behaviour with A Chinese Sample Jasmine M. Y. Loo, Namrata Raylu and Tian P. S. Oei

119

The Role of Temperament and ‗Near-Miss‘ When Playing a Simulated Slot Machine: The Case of Betting Behavior Attila Körmendi and Győző Kurucz

139

Behavioral Treatment for Gambling Mariano Chóliz

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vi Chapter 11

Chapter 12

Contents Estimating the Effects of Casinos and of Lotteries on Crime: A Panel Data Set Approach Clifford F. Thies and Bogdan Daraban Noisy Verification Algorithms Protected against Gambling Vladimir B. Balakirsky and A. J. Han Vinck

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Index

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179 189 209

PREFACE At that point I ought to have gone away, but a strange sensation rose up in me, a sort of defiance of fate, and a desire to challenge it, to put out my tongue at it. I laid down the largest stake allowed-four thousand gulden-and lost it. Then, getting hot, I pulled out all I had left, staked it on the same number, and lost again, after which I walked away from the table as though I were stunned. I could not even grasp what had happened to me.

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Fyodor Dostoevsky, The Gambler Generally considered a social and/or recreational activity, in some cases gambling can become an addictive behavior. Pathological gambling is classified by the Diagnostic and Statistical Manual of Mental Disorders (DSM) as an impulse control disorder, characterized by failure to resist the impulse to gamble despite severe and devastating personal, family, or vocational consequences. The lifetime prevalence of pathological gambling in the adult population of North America has been estimated to be over 1%. Pathological gambling can also be associated with significantly specific behavioral problems and neuropsychiatric conditions, mainly affecting dopaminergic reward pathways. This book provides a 360-degree overview on the current psychological models for gambling behaviors, informed by both neurobiological and clinical observations. Chapter 1 critically reviews the evidence for a biological component of decision-making in gambling, with focus on the integrated and distinct roles of the orbitofrontal cortex and the ventromedial prefrontal cortex. This opening chapter also highlights the considerable implications of the decision-making psychological process in understanding daily human behaviors, addictions and pathological gambling. Chapter 2 reviews theoretical models aimed at explaining the mechanisms underlying pathological gambling, highlighting key aspects of cognition, behavior, and biological markers. Particular attention is paid to the somatic marker hypothesis, which focuses on the role of emotions in decision making, considering it part of cognitive process performed by a person when choosing between two or more options. Chapter 3 provides an overview on the markers that have been evidenced to increase vulnerability specifically for problematic gambling. Chapter 4 reviews the literature on the recent reports of late-onset pathological gambling in patients with frontotemporal dementia, suggesting that dysregulation of prefrontal cortex and mesolimbic pathways might be implicated in the pathophysiology of gambling behaviors associated with neurodegenerative processes. Chapter 5 presents a model of gamblers‘ decision making, with focus on the roles

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electronic inducements and decisional support play in sustaining gambling behavior. Chapter 6 describes a pilot study with a probabilistic-delivery task, aimed to identify a phenotype of risk proneness in animal models, adapted to home-cage testing. Chapter 7 presents the results of an empirical study which contains a complex and simple version of the Iowa Gambling Task to illustrate the prominent deck B phenomenon. Chapter 8 shows the validity of an integrated cognitive behavioural model of gambling behaviour with a Chinese sample. Chapter 9 examines the effect of increased near-miss ratio on betting behavior, and the moderating effect of temperament factors. Chapter 10 presents a therapeutic protocol for gambling addiction based on the principles of motivation and learning. The final two chapters adopt slightly different approaches to the problem of gambling. Chapter 11 adopts a panel data set approach to estimating the effects of casinos and lotteries on crime, whilst Chapter 12 shows that the problem of constructing the verification algorithms designed to accept or to reject identity of a user on the basis of his biometric measurements can be viewed as a problem of specifying the rules of a game with a gambler. The diverse contents of this comprehensive volume highlight the need for a multidisciplinary approach to the neurobiological and psychological mechanisms of gambling behaviors which can inform treatment strategies for pathological gamblers.

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In: Psychology of Gambling: New Research Editor: Andrea Eugenio Cavanna

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Chapter 1

EVIDENCE FOR A BIOLOGICAL COMPONENT OF DECISION-MAKING IN GAMBLING: A CRITICAL REVIEW Eleanor Crossley1 and Andrea E. Cavanna1,2, 1

Department of Neuropsychiatry, BSMHFT and University of Birmingham, UK 2 Institute of Neurology, University College London, UK

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ABSTRACT Decision-making in gambling involves evaluation of the likely consequences of one‘s actions and a consideration of the desires for immediate and long-term gratification. These processes require intact brain mechanisms which have been investigated through the use of cognitive tests and neuroimaging studies, primarily functional magnetic resonance imaging studies. Recently, contrary to the ‗emotion versus cognition‘ hypothesis, it has been suggested that the cognitive and emotional components of decision-making are incorporated through cortex-subcortex interactions and this interplay will be discussed and critically reviewed, using the exemplars of decisionmaking under ambiguity and regret. This review explores the involvement of specific brain regions and the neurotransmitters which regulate these areas, primarily focusing on the integrated and distinct roles of the orbitofrontal cortex and the ventromedial prefrontal cortex. The mediatory roles of other important brain regions, such as the amygdala and the cingulate cortex, will also be examined. This review highlights the considerable implications of the decision-making psychological process in understanding daily human behaviours, addictions and pathological gambling.

Keywords: Decision-making; gambling; orbitofrontal cortex; ventromedial prefrontal cortex; functional Magnetic Resonance Imaging (fMRI)



Correspondence: Dr Andrea E. Cavanna, MD PhD - Department of Neuropsychiatry, The Barberry National Centre for Mental Health, 25 Vincent Drive, Birmingham B152FG, United Kingdom. Email: [email protected].

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INTRODUCTION Gambling can be defined as the act of placing an item of value at risk based on hopes of gaining something of greater value, with the outcome determined by chance [1, 2]. The decision-making process involved in gambling requires evaluation of the potential beneficial and negative consequences of given response options and a balance between immediate gratification and the long term consequences is sought [3]. It has been recognised that the ability to make advantageous choices and avoid risky behaviours is dependent on intact mechanisms of decision-making [4]. Although there is recognised overlap between decision-making and impulsivity, recent studies have branded these two processes as cognitively and anatomically distinct and this chapter will therefore focus on the neural bases of decision-making process in gambling, which are largely restricted to functional Magnetic Resonance Imaging (fMRI) studies [4, 5]. Studies suggest 80-90% of individuals in developed countries report lifetime participation in gambling, suggesting that an appreciation of the underlying neuropsychological processes has important implications for understanding everyday human decision-making behaviour and also disorders of addiction and pathological gambling [1, 2]. Several brain areas have been implicated in the decision-making process through the use of cognitive tests and neuroimaging findings which will be discussed and critically reviewed.

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ORBITOFRONTAL CORTEX AND VENTROMEDIAL PREFRONTAL CORTEX: INTEGRATED ROLES? The orbitofrontal cortex(OFC) encompasses the ventral surface of the prefrontal cortex in the brain and its medial portion therefore overlaps with the ventromedial prefrontal cortex(vmPFC), which lies along the inferior part of the frontal lobe‘s medial wall [6]. Unsurprisingly due to their close anatomical relations in the prefrontal cortex(PFC), these structures have many integrated functions which have well-established associations with decision-making behaviour. It has been demonstrated that both these structures have the ability to process and represent excitatory and inhibitory sensory input from other brain regions and fMRI data reflect activation of the OFC and vmPFC during simulated gambling [6, 7, 8]. It is thought that these areas enable evaluation of the future consequences of chosen actions, thus mediating the control of motivated or impulsive behaviours via neuronal output to the premotor-motor cortex, cerebellum and other motor areas in the primary motivation circuitry [6]. This prefrontal cortical processing is part of a distributed network of parallel neuronal connections between different brain areas allowing individuals to make predictions and risk estimates about future events based on environmental cues which guide the decision-making process [6, 7, 9]. This theory is supported by evidence from patients with selective vmPFC and OFC lesions who strongly discount or even neglect the future consequences of their decisions due to a suggested lack of processing [6]. Although the sensitivity of the Iowa Gambling Task to lesions in these areas is suboptimal, several anecdotal reports describe poor or risky decision-making in vmPFC- and OFC-lesioned patients [6]. Similarly poor decisionmaking was attributed to vmPFC damage as a result of a penetrating head injury in the

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historical case of Phineas Gage [9]. Although neuropsychological testing on lesions in these areas remains an ‗imperfect science‘ as the associated severe memory problems are often not accounted for, these prefrontal cortical regions‘ alleged central role in information processing is fundamental to understanding action-oriented decision-making [6].

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ORBITOFRONTAL CORTEX AND VENTROMEDIAL PREFRONTAL CORTEX: DISTINCT ROLES? In addition to their seemingly combined role in overall risk-taking behavioural processing, research has identified distinct OFC and vmPFC functions. Importantly, a recent study emphasised the strong influence emotions can have in guiding human decision-making, largely through OFC and amygdala activity [10]. It is thought that the OFC‘s representation of stimuli and the amygdala‘s review of these valuations can act either cooperatively or antagonistically [11]. Regardless of the nature of this relationship, the amygdala input is considered critical for normal OFC functioning [6]. The amygdala can also mediate an anticipatory arousal and emotional response to potential rewards and link stimuli to innate behaviours, such as autonomic responses [6, 11]. The decision variable of regret is an emotion associated with a choice which had a poor outcome. An fMRI study demonstrated that in addition to processing future consequences of actions, the OFC also mediates and incorporates previous regrettable experiences into the decision-making process to prevent further suboptimal choices [10]. Thus healthy individuals tend to avoid gambles likely to result in regret [10]. However, patients with OFC lesions and therefore diminished OFC activity were incapable of learning from previous regret-inducing decisions so did not experience anticipated regret or even appreciate their liability for their choices‘ outcomes [10]. Furthermore, the fMRI results of the regret gambling task demonstrated a positive correlation between anticipated regret and activity in the medial OFC region, supporting the established fundamental role of OFC activity in emotion-based decision-making [10]. Additionally the OFC‘s lateral region has shown increased activation when accumulating supplementary information to help suppress previously rewarded responses [7]. This response inhibition to acquired urges is a contributory role of the OFC and Go/NoGo tasks have confirmed lesions in the OFC prevent effective response inhibition [6]. However, researchers have questioned the validity of the Go/NoGo instrument in assessing OFC function as it is allegedly a more accurate measure of functioning in more laterally situated structures [6]. Interestingly, one MRI study demonstrated how the developmental changes which occur during adolescence in the prefrontal cortex(PFC) – encompassing both OFC and vmPFC regions - enable inhibition of acquired urges, such as the desire to gamble [9]. This study showed that during adolescence the relative size of cortical regions increases whilst the grey matter decreases – these changes were most prominent in the PFC [9]. Therefore it appears full inhibitory capacity of the OFC is not achieved until adulthood, providing a plausible biological explanation for the high rates of gambling in adolescents [12]. Heightened OFC activity was also apparent in fMRI studies during anticipation and rising expectations of risky gambles and even when individuals watched gambling videos [8, 13].

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This OFC activity produces emotional signals or somatic states which have recently been shown to increase skin conductance response activity [4]. One study used the neuropsychological Game of Dice test to examine the association between decision-making and neuroendocrine responses which had been implicated in the somatic marker hypothesis [3, 4]. Despite the authors stating the task did not induce sufficient stress to invoke neuroendocrine responses, the study showed raised salivary alpha-amylase(sAA) concentrations in patients with less severe decision-making deficits, suggesting the biasing positive effect the somatic marker autonomic signals (sAA) – which are integrated with other information by the OFC - may have had on decision-making behaviour [3]. Therefore, contrary to the ‗emotion versus cognition‘ hypothesis, it is now suspected that the cognitive and emotional components of decision-making are integrated through cortex-subcortex interactions between the OFC and amygdala [10]. In concordance with previous research suggesting gamblers disconnect from their rational beliefs during gambling, a recent study demonstrated that lottery gamblers fail to use rationality and expected value to guide their decision-making and indeed those with superior mathematical knowledge have more flawed assessments of gambling risks [14, 15]. Furthermore, another study stated that PFCcompromised individuals may be less aware of the damaging consequences of ‗gut feelings‘ that are represented in the PFC and associated emotional and memory systems [9]. This is consistent with the hypothesis of the interplay between emotional and cognitive components determining the decision-making process. An fMRI study showed that lateral OFC activation is also heightened by decision-making under ambiguity [16]. The study identified reduced adversity to ambiguity in OFC-lesioned patients which suggests the aversive processes are controlled by the OFC [16]. Furthermore, a study using the Iowa Gambling Task to test decision-making ability in initially ambiguous situations showed patients with OFC lesions demonstrated a preference for immediate reward at the risk of greater net losses, indicating impaired decision-making under ambiguity in the absence of an intact OFC [17]. In contrast, healthy controls learn to sacrifice short-term benefits for long-term gains [3]. However, it is speculated that this involvement is purely emotional and motivational, with the risk-evaluation occurring in lateral prefrontal and parietal cortices [16]. vmPFC activity has important roles in normal decision-making and studies have therefore demonstrated that damage to this region can result in disadvantageous social and personal decision-making [4, 8]. A recent study indicated patients with vmPFC lesions are more likely to make suboptimal bets and partake in more risky gambling, although the small sample size questioned the generalisability of these results [18]. Equally, excessive vmPFC activity has been shown to cause hypersensitivity to gambling cues and therefore have similar negative consequences [3]. It has been proposed that, in addition to activity changes, the alterations in decision-making as a result of the vmPFC could be partly due to changes in the blood oxygen level-dependent signal which are particularly prominent on the left side [7]. Therefore, although one study claimed more lateral OFC areas may be more influential in risky decisionmaking related to risk, there is substantial evidence to suggest the vmPFC has some role in risk-determination in decision-making [6]. In addition to processing and risk-determination, the vmPFC can work in collaboration with the hippocampus to retain information in a readily accessible manner to allow time for consideration which presumably would minimise risky decision-making [9].

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INVOLVEMENT OF OTHER BRAIN REGIONS The brain‘s response to uncertainty is prerequisite to gaining a deeper understanding of decision-making and studies investigating uncertainty have identified other brain regions‘ involvement in the decision-making process [16]. In 2010, two studies indicated the insula‘s involvement in responding to uncertain or ambiguous situations [3, 7]. This was supported by fMRI data showing increased insula activation with increasing uncertainty and risk, resulting in risk-averse decision-making [6, 16]. Similarly, one study illustrated individuals with insular damage are unable to adjust their gambling with changes in the odds, highlighting the consequences of ambiguity in the absence of a functioning insula [7]. Conversely, the same study demonstrated insular activation can contribute to conscious urges which influence the decision-making process and seemingly negate any uncertainty [7]. Thus, although evidence suggests the insula has some involvement in decision-making under ambiguity, the precise nature of this influence is unclear in the literature. It has also been proposed that the posterior cingulate cortex(PCC) may be involved in ambiguous decision-making [16]. Activation in the PCC has been demonstrated during ambiguous decisions, either in terms of amount or time, and the magnitude of PCC activation is dependent upon the allure of the choices [19, 20, 21]. These findings support previous indications of the PCC‘s role in subjectively evaluating external information and consequently guiding behaviour [22, 23]. However, critics have argued that the activation detected may simply be increased arousal levels due to the apparent risky or ambiguous choice, as opposed to inherent activation of the PCC [24]. Research has also shown the anterior cingulate cortex(ACC) is influential in choosing an option when rewards are conflicting [3]. This seems most noticeable in action-oriented decision-making when, following OFC information processing, the ACC appears focussed on selecting the appropriate actions to achieve the desired outcome. Thus, a couple of studies have found the dorsal ACC, in particular, responsible for action selection when an individual is faced with contradictory options [3, 6]. Moreover, it seems the emotional components of decision-making also affect the action selection process in the ACC as one study demonstrated increased activity in this region with enhanced regret [10].

THE ROLE OF NEUROTRANSMITTERS In addition to the reviewed neuroimaging studies investigating the roles of different brain regions in decision-making, some research has focused on the role of neurotransmitters in modulating neural activity and synaptic functioning in these brain regions. The mesocorticolimbic dopamine system has been frequently associated with decision-making in gambling [25]. Simulated gambling tasks and brain imaging data have demonstrated that the dopamine neurotransmitter enters and activates the nucleus accumbens(NAc) - a specific brain region in the ventral striatum - following excitatory signals from the cortex and other brain regions [9, 25, 26]. It is thought that this dopamine release into the NAc influences emotional processing but it is unclear whether the NAc, in addition to the well-established dopamine reward system, should be considered at least partially responsible for the excitement observed in risk-taking which is likely to influence decision-making and gambling

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behaviour [9]. Interestingly, one study showed the vmPFC‘s dependence on dopamine projections from other limbic brain regions for information processing and integration and suggested that disruptions in dopamine transmission could underlie impaired vmPFC functioning [27]. However, dopamine agonists, often used to treat patients with Parkinson‘s disease, have been shown to enhance risky decision-making [16]. Similarly, another study demonstrated that noradrenaline levels, in addition to vmPFC functioning, could influence participation in risk-taking gambling as measures of extraversion and desire for gambling were positively correlated with urinary, plasma and CSF levels of noradrenaline [8]. There is therefore mounting evidence that precortical function is receptive to dopaminergic, noradrenergic and other hormonal influences [7].

CONCLUSION

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Unfortunately research into the decision-making psychological process in gambling is scarse and most of the literature involves studies with limited generalisability due to small sample sizes, a distinct lack of female participants or patients with the psychiatric comorbidities. The vast majority of studies are fMRI studies with cross-sectional protocols. Thus further investigations using different imaging and cognitive tests as longitudinal studies, especially around the period of adolescent neural developmental changes, may improve the current knowledge base. Although the OFC and vmPFC have consistently been shown to influence the decision-making process in gambling, it seems likely that decision-making is strongly influenced by other brain areas, such as the amygdala, and alterations in specific neurotransmitter pathways. Further research should combine pharmacological and neuroimaging techniques with large study sample sizes to achieve a deeper understanding of the neurobiological mechanisms underlying decision-making in gambling.

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Zald D, Andreotti C. Neuropsychological assessment of the orbital and ventromedial prefrontal cortex. Neuropsychologia 2010; 48(12): 3377-3391. Potenza MN. The neurobiology of pathological gambling and drug addiction: an overview and new findings. Phil. Trans. Royal Soc. B 2008; 363: 3181-3189. Williams WA, Potenza MN. The neurobiology of impulse control disorders. Rev. Bras. Psiquitr. 2008; 30(1): S24-30. Chambers RA, Potenza MN. Neurodevelopment, impulsivity, and adolescent gambling. J. Gambl. Stud. 2003; 19(1): 53-84. Coricelli G, Dolan RJ, Sirigu A. Brain, emotion and decision making: the paradigmatic example of regret. Trends Cogn. Sci. 2007; 11(6): 258-265. Murray EA, Wise SP. Interactions between orbital prefrontal cortex and amygdala: advanced cognition, learned responses and instinctive behaviors. Curr. Opin. Neurobiol. 2010; 20: 212-220. Shaffer HJ, Korn DA. Estimating prevalence of adolescent gambling disorders: a quantitative synthesis and guide toward standard gambling nomenclature. J. Gambl. Stud. 1996; 12: 193-214. Breiter HC. Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron 2001; 30: 619-639. Sevigny S, Ladoucer R. Gamblers‘ irrational thinking about chance events: The ‗double switching‘ concept. Int. Gambl. Stud. 2003; 3: 149-161. Pelletier MF, Ladoucer R. The effect of knowledge of mathematics on gambling behaviours and erroneous perceptions. Int. J. Psychol. 2007; 42: 134-140. Platt ML, Huettel SA. Risky business: the neuroeconomics of decision making under uncertainty. Nat .Neurosci. 2008; 11(4): 398-403. Messer S. Reflection-impulsivity: a review. Psychol. Bull. 1976; 83: 1026-52. Clark L, Bechara H, Demasio H, Aitken MR, Sahakian BJ, Robbins TW. Differential effects of insular and ventromedial prefrontal cortex lesions on risk decision-making. Brain 2008; 131: 1311-1322. Smith K, Dickhaut J, McCabe K, Pardo JV. Neuronal substrates for choice under ambiguity, risk, gains, and losses. Manag. Sci. 2002; 48: 711-718. Kable JW, Glimcher PW. The neural correlates of subjective value during intratemporal choice. Nat. Neurosci. 2007; 10: 1625-1633. Small DM, Zatorre RJ, Dagher A, Evans AC, Jones-Gotman M. Changes in brain activity related to eating chocolate: from pleasure to aversion. Brain 2001; 124: 17201733. Olson CR, Musil SY, Goldberg ME. Single neurons in posterior cingulate cortex of behaving macaque: eye movement signals. J. Neurophysiol. 1996; 76: 3285-3300. McCoy AN, Crowley JC, Haghighian G, Dean HL, Platt ML. Saccade reward signals in posterior cingulated cortex. Neuron 2003; 40: 1031-1040. Meyer G, Hauffa BP, Schedlowski M, Pawlak C, Stadler MA, Exton MS. Casino gambling increases heart rate and salivary cortisol in regular gamblers. Biol. Psychiatry 2000; 48: 948-53. Fattore L, Melis M, Fadda P, Pistis M, Fratta W. The endocannabinoid system and nondrug rewarding behaviours. Experimental Neurology 2010; 224: 23-36.

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[26] Grant JE, Brewer JA, Potenza MN. The Neurobiology of Substance and Behavioral Addictions. CNS Spectr. 2006; 11(12): 924-930. [27] van Holst RJ, van den Brink W, Veltman DJ, Goudriaan AE. Brain imaging studies in pathological gambling. Curr. Psychiatry Rep. 2010; 12: 418-425.

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Chapter 2

THE SOMATIC MARKER HYPOTHESIS IN PATHOLOGICAL GAMBLING David Polezzi, Elena Casiraghi and Giulio Vidotto Department of General Psychology, University of Padova, Italy

ABSTRACT

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Gambling is generally a social and/or recreational activity, although in a few cases, it becomes an addictive behavior. Pathological gambling is characterized by a loss of control over this activity and by continued gambling despite its negative effect on daily life (personal, familiar, financial, professional, and legal). Several theoretical models tried to explain the mechanisms underlying pathological gambling, stressing aspects of this population such as cognition, behavior, or biological markers. However, essentially, all of the models acknowledge that the interaction of these variables plays a crucial role in the etiology of the disease. The present chapter will briefly review evidences these models. For instance, genetic studies have reported that pathological gamblers are significantly more likely to possess the dopamine D2A1 allele receptor gene compared to healthy controls. Behavioral theorists have observed that intermittent reinforcements, such as those delivered by a slot machine, lead to particularly fast acquisition and are very resistant to extinction, even in the absence of reinforcement over many trials. Moreover, pathological gamblers reported cognitive bias such as the illusion of control, which refers to the belief that one can control, or somehow predict, events governed by chance. In recent years, a novel hypothesis has been suggested. The somatic marker hypothesis focuses on the role of emotions in decision making, considering it part of cognitive process performed by a person when choosing between two or more options. The somatic marker hypothesis posits that emotion-related bodily signals assist cognitive decision making. Abnormal decision making has been reported in patients with brain lesions who showed impairmence in the emotional process. A considerable number of studies reported variations of phisiological indexes in pathological gamblers as well as in healthy population while performing gambling tasks. In the present chapter, these data will be discussed in light of the somatic marker hypothesis in order to clarify whether this theoretical model can be a plausible explanation of pathological gambling behaviour. 

Corresponding author: Dr David Polezzi, Department of General Psychology, University of Padova, via Venezia 8, 35131 Padova, Italy, Email: [email protected].

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David Polezzi, Elena Casiraghi and Giulio Vidotto

Keywords: Gambling; somatic marker hypothesis; decision making; emotion; dopamine; reinforcement; biological marker; cognition

Pathological gambling is a psychological disease that is still not completely understood. The numbers of casinos, on-line games, and other types of gambling are increasing the availability of these games and, consequently, the number of gamblers. Pathological gambling is thus becoming an emerging sanitary problem. People with this disease continue to gamble despite the negative consequences it produces in their lives (financial, personal, and legal). The research interest in this pathology is, at the same time, increasing in order to improve comprehension and enhance therapy efficacy. The present chapter describes the disease and illustrates the etiological models proposed to explain the phenomenon with a special focus on the somatic marker hypothesis, which has been recently proposed to explain normal and pathological decision making. Finally, we present several studies that investigated somatic reactions in pathological gamblers and healthy controls to give an overview of the evidence in favor or in contrasts with this hypothesis.

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PATHOLOGICAL GAMBLING: DESCRIPTION Gambling refers to wagering money or something of material value on an event with an unpredictable outcome that may result in a more beneficial gain or in losing the stake. The outcomes are governed by a mixture of skill and, for the most part, chance. An estimated 70% - 90% of people gambled at sometime in their lives. Moreover, people have gambled since ancient times and in different cultures (Raylu and Oei, 2002). Hence, gambling could be intended as a basic aspect of life in which people face uncertainty and risk on purpose. Although gambling is generally a social and/or recreational pursuit, in few cases, it becomes a problematic behavior and therefore requires clinical attention. Pathological gambling was formally recognized by the American Psychiatric Association since the third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSMIII) (APA, 1980), and it is currently included in DSM-IV-TR, listed under ―impulse-control disorders‖ (see Table 1 for criteria) (APA, 2000). Pathological gambling is characterized by a loss of control over this activity and by continuous play despite negative consequences in several aspects of daily life (personal, familiar, financial, professional, and legal). Even though repeated losses severely affected their financial assets, pathological players are unable to resist urges to gamble. This situation often causes debts, legal problems, and relational conflicts and has a negative impact on the physical and mental health of all persons involved (Raylu and Oei, 2002). It should be noted that existing literature on pathological gambling results mostly from research with Caucasian male samples and some caution should be exercised in generalizing data to female or other cultural groups (Raylu and Oei, 2002). Prevalence rates of pathological gambling over the general population in several countries have been estimated at 1-2 %, while lifetime prevalence rates have ranged from 0.1 % to 5.1 % (Raylu and Oei, 2002). Prevalence rates have been found to be higher among children and adolescents (4.4–7.4 %), general psychiatric patients (6.7-12 %), and among

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family members of persons who had gambling problems (Raylu and Oei, 2002). Children are reportedly more likely to develop pathological gambling when parents suffer from pathological gambling, and the probability rises when pathological gambling also occurs in grandparents (Blaszczynski and Nower, 2002). Therefore, a family history of pathological gambling is considered a predisposing risk factor. This vulnerability results from both genetic transmission and environmental factors. For instance, positive attitudes of relatives toward gambling and exposure to gambling may encourage the engagement in gambling behavior from an early age (Sharpe, 2002). Gambling is either banned or controlled by law. Nowadays, in Western countries, legal forms of gambling are rising, both in amount and quality, and, at the same time, the prevalence of pathological gambling seems to be increasing. Countries or localities with many available gambling opportunities also have the highest number of pathological gamblers. A combination of processes presumably contributes to this correlation: greater accessibility, increased social acceptance, and attraction of individuals with gambling problems from other localities (Sharpe, 2002). Furthermore, the spread of legalized gambling includes new forms of gambling activities that are defined as more ―difficult‖ (e.g., on-line gambling, slot machines). In fact, each type of gambling (casino, sports betting, lotteries, slot machines) has revealed the potential to become problematic depending on its structural characteristics (e.g., skill vs. luck, continuity vs. discontinuity, frequency, and immediacy of payout) (Raylu and Oei, 2002). Table 1. DSM-IV-TR Criteria for Pathological Gambling

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A. Persistent and recurrent maladaptive gambling behavior are indicated by five (or more) of the following: (1) Preoccupation with gambling (e.g., preoccupied with reliving past gambling experiences, handicapping or planning the next venture, or thinking of ways to get money with which to gamble) (2) A need to gamble with increasing amounts of money in order to achieve the desired excitement (3) Repeated unsuccessful efforts to control, cut back, or stop gambling (4) Restlessness or irritability when attempting to cut down or stop gambling (5) The use of gambling as a way of escaping problems or of relieving a dysphoric mood (e.g., feelings of helplessness, guilt, anxiety, and depression) (6) Often returning another day to get even after losing money gambling, (―chasing‖ one‘s losses) (7) Lying to family members, therapists, or others to conceal the extent of involvement with gambling (8) Commitment to illegal acts such as forgery, fraud, theft, or embezzlement to finance gambling (9) Jeopardizing or losing a significant relationship, job, or educational or career opportunity because of gambling (10) Relying on others to provide money to relieve a desperate financial situation caused by gambling B. The gambling behavior is not better accounted for by a manic episode.

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David Polezzi, Elena Casiraghi and Giulio Vidotto

Some forms of gambling are considered to carry greater potential risk than others, such as slot machines, and usually include small bets, high stakes, rapid event frequency, and short payout intervals (i.e., give regular small payouts), which induces continued gambling (Griffiths, 1999). Most pathological gamblers in treatment state that slot machines are the leading type of gambling in which they are engaged (Raylu and Oei, 2002). On-line gambling is also a new and highly addictive form of gambling; in fact, it is easily accessible, ensures anonymity, and has rapid event frequency and a short wager-payout cycle time (Raylu and Oei, 2002). Although pathological gambling is classified as a disorder of impulse control, the diagnostic criteria overlap in part the criteria for substance dependence; therefore, DSM-V is proposing a reclassification alongside other addictive behaviors (Mitzner, Whelan, and Meyers, 2010). For example, pathological gamblers need to gamble with increased amounts of money as substance addicts need progressively higher doses of drugs, a symptom called tolerance (Raylu and Oei, 2002). Also, craving (i.e., incoercible urge to engage in the behavior) and withdrawal (i.e., being restless or irritable when attempting to stop gambling or drug use) are symptoms shared by the two populations (Raylu and Oei, 2002). There is one suggestion that, like substance use behaviors, gambling behaviors can be distinguished between abuse (problem gambling) and dependence (pathological gambling), yet the debate is still open (Raylu and Oei, 2002). Research indicates that pathological gambling resembles substance-based addictions not only in its clinical presentation, but also in its comorbidity, association with personality factors, cerebral systems, and neurotransmitter involvement, genetic transmission, and treatment options (Petry, 2006; Potenza, 2006). For example, as explained later on, abnormalities in the dopaminergic mesocortical limbic system, which is implicated in rewarding and reinforcing behaviors, reportedly contributes to drug- and gambling-related behaviors (Goudriaan, Oosterlaan, de Beurs, and Van den Brink, 2004). Furthermore, there are high rates of comorbidity between pathological gambling and other addictions (Potenza, 2006); hence, a common vulnerability has been suggested for these disorders (van Holst, van den Brink, Veltman, and Goudriaan, 2010). Pathological gamblers also show different characteristics regarding the type of gambling activity in which they are engaged, as there are different profiles of substance addiction with the drug they assume (Raylu and Oei, 2002). In order to clarify whether pathological gambling would be better classified as an impulse control or addictive disorder, future studies should compare pathological gambling groups with impulse control disorder and addiction groups (Goudriaan, Oosterlaan, de Beurs, and Van den Brink, 2004). In addition to the high co-occurrence between pathological gambling and alcohol/drug addiction, research indicates comorbidity of other Axis I disorders among pathological gamblers, most frequently anxiety, depression, and suicidal ideation or attempts (Raylu and Oei, 2002). Furthermore, there is evidence of comorbidity between pathological gambling and adult ADHD, antisocial personality disorder (Goudriaan, Oosterlaan, de Beurs, and Van den Brink, 2004) and obsessive-compulsive spectrum disorder (Raylu and Oei, 2002). Significantly higher rates of childhood ADHD have been found in pathological gamblers compared to normal controls, suggesting ADHD may act as a risk factor for developing pathological gambling (Raylu and Oei, 2002). However, Blaszczynski (Blaszczynski and

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Nower, 2002) has argued that only the most severe subgroup of pathological gamblers suffer from a biologically-based impulsivist psychopathology, such as ADHD or antisocial personality disorder. The inclusion of various and well-defined control groups of patients in gambling studies is needed to assess the specificity of findings and the nature of potential links between pathological gambling and other psychiatric disturbances or personality traits (van Holst, van den Brink, Veltman, and Goudriaan, 2010). Evidence suggests that pathological gamblers form a heterogeneous population. Although they show the same behavioral symptoms, different etiological factors could be of primary importance in each case. Moreover, different motives to gamble are likely to be reflected in the choice of a specific type of game (Goudriaan, Oosterlaan, de Beurs, and Van den Brink, 2004). This diversity has lead researchers to classify pathological gamblers into different subgroups. For instance, Blaszczynski (Blaszczynski and Nower, 2002), in his pathways model of pathological gambling, subdivided pathological gamblers into three subtypes that present similar symptoms but different vulnerabilities, etiological factors, demographic features, and, thus, treatment approaches. The three subgroups lie on a continuum of severity. On the less severe end, (1) ―behaviorally conditioned‖ gamblers are characterized by an absence of any specific premorbid feature of psychopathology or display minimal levels of psychopathology and gamble because of behavioral conditioning, distorted cognition, or poor decision making rather than impaired control. Instead, (2) ―emotionally vulnerable‖ gamblers have premorbid emotional problems, such as anxiety and/or depression, in addition to conditioning and poor cognitive abilities. Lastly, (3) ―antisocial/impulsivist‖ gamblers possess both the former psychosocial- and biologically-based vulnerabilities and the feature of impulsivity. When impulsivity is present, the disorder is more severe, given that impulsivity in gamblers correlates comorbidity with personality disorders and low compliance in treatment (Blaszczynski and Nower, 2002). Otherwise, Sharpe (Sharpe, 2002) described two subgroups of gamblers with different triggers for the behavior and preferred type of game. Individuals with feelings of boredom who gamble to seek a higher level of arousal belong to the first group belong and are usually horse race and casino gamblers. Instead, the other subgroup consists of gamblers who seek a decrease in arousal because of stressful situations or dysphoric moods from which they try to escape, and they generally choose electronic gambling. A similar distinction based on arousal also exists among alcohol dependents (Goudriaan, Oosterlaan, de Beurs, and Van den Brink, 2004). Pathological gambling is certainly a complex and multidimensional phenomenon. The literature indicates that social, psychological, and biological factors are implicated in the development and maintenance gambling problems. Several theoretical models tried to explain the mechanisms underlying pathological gambling, including behavioral, cognitive, and psychobiological approaches. Each model emphasizes different aspects of this population, such as cognition, behavior, or biological markers. However, essentially all the models acknowledge that the interaction of these variables plays a crucial role in the etiology of the disease. In the present chapter, evidence in favor of these models will be briefly reviewed.

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David Polezzi, Elena Casiraghi and Giulio Vidotto

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A POSSIBLE EXPLANATION BY A GENETIC POINT OF VIEW Genetic studies have reported that pathological gamblers are significantly more likely than healthy controls to possess the dopamine D2A1 allele receptor gene, which has been associated with reduced D2 receptor density (Blaszczynski and Nower, 2002). Allelic variants of other dopamine receptor genes (DRD1, DRD3, and DRD4) have also been observed in pathological gamblers (Goudriaan, Oosterlaan, de Beurs, and Van den Brink, 2004). According to this finding, neuroimaging studies have reported abnormal brain activation in dopaminergic pathways in pathological gamblers (Goudriaan, Oosterlaan, de Beurs, and Van den Brink, 2004). This genetic component has been found in pathological gambling as well as in substance addiction, alcoholism, and in impulse control disorders (e.g., ADHD, antisocial personality disorder) (Blaszczynski and Nower, 2002). Moreover, it is more prevalent in pathological gamblers with comorbid alcohol or drug dependence than in those without comorbidity (Goudriaan, Oosterlaan, de Beurs, and Van den Brink, 2004). Hence, it should not be considered a specific risk factor for pathological gambling, but rather a shared biological vulnerability for impulsivity and addictions (Raylu and Oei, 2002). The lower availability of dopaminergic receptors results in a lack of activation in the neurotransmitter circuits. Since the dopaminergic mesocortical limbic system (nucleus accumbens, amygdala, limbic system, and ventromedial prefrontal cortex) underlie reward sensitivity, this dysregulation is reflected in diminished neurobiological reward responsiveness. These findings are consistent with a ―reward deficiency syndrome,‖ leading individuals to seek intense pleasure-generating activities (e.g., gambling, drugs, eat, sex) that increase the release of dopamine in order to compensate for its scarcity and thus reach and maintain a homeostatic level of dopamine. In order to experience the same rewarding effect, pathological gamblers need to gamble for a longer period and have larger rewards than people with higher dopamine receptor density (van Holst, van den Brink, Veltman, and Goudriaan, 2010). Moreover, repeated gambling behavior results in a progressive reduction of reward sensitivity due to adaptation; therefore, pathological gamblers would be more prone to continue gambling (van Holst, van den Brink, Veltman, and Goudriaan, 2010). Impulsivity, craving, and withdrawal might all be explained by the ―reward deficiency syndrome.‖

CONDITIONING AND PATHOLOGICAL GAMBLING Since the 1950s, behavioral theorists suggested that classical and operant conditioning could encourage participation in gambling activities. Currently, the role of conditioning is recognized by all theoretical accounts of pathological gambling and is believed to influence all gamblers. First, operant conditioning is established through fixed and variable ratio schedules of reinforcement (i.e., economic gains, sensory stimulation, and, in gambling subcultures, even social rewards) that produce a favorable change in the arousal state (Raylu and Oei, 2002). In

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pathological gambling, this is likely to be similar to the ‗high‘ experienced by drug users (van Holst, van den Brink, Veltman, and Goudriaan, 2010). There are evident similarities between variable schedules of reinforcements and the payout arranged in many types of gambling (Petry, 2005). Occasional small wins, such as those delivered by a slot machine, have demonstrated to act as positive reinforcement for gamblers, producing excitement and feelings of success. In particular, a pattern of early wins is a strong positive reinforcement available to gamblers (Sharpe, 2002). In fact, gamblers who reported big or even small early wins in their gambling history are more likely to continue playing (Raylu and Oei, 2002; Sharpe, 2002). Intermittent reinforcements lead to particularly fast acquisition and are very resistant to extinction, even in the absence of reinforcement over many trials (Sharpe, 2002). There are other structural positive reinforcements in gambling activities, such as visual and auditory effects, which are typical of slot machines (Raylu and Oei, 2002). Furthermore, negative reinforcement is produced when gambling works as ―selfmedication.‖ That is, when it relieves dysphoric feelings, such as depression and/or anxiety, or it allows an escape from life problems and distress through dissociation and narrowing the focus of attention (Blaszczynski and Nower, 2002). Moreover, McConaghy argued that, after habitual patterns of gambling have been established, attempts to resist the urges to play provoke an aversive state (Raylu and Oei, 2002). The behavioral completion mechanism leads to carrying out habitual behavior in order to reduce this negative state of arousal, thus operating as a negative reinforcement. Secondly, due to repeated gambling, classical conditioning occurs when a range of environmental stimuli associated with the gambling begin to elicit the same arousal (van Holst, van den Brink, Veltman, and Goudriaan, 2010). Behavioral conditioning increases the probability of continued gambling, but does not explain why only a part of gamblers lose control (Blaszczynski and Nower, 2002). It seems likely that physiological and/or psychological individual vulnerabilities, in association with conditioning effects, foster the development of pathological gambling, for instance, a lack of self-regulation, poor coping and problem-solving skills or an altered reward and punishment sensitivity. In fact, the reinforcing effects are mediated by the dopaminergic reward cerebral system, which has shown a deficit in pathological gamblers. The low neurobiological reward sensitivity of pathological gamblers increases the need for immediate and easily accessible rewarding activities, and thus the effect of reinforcements in gambling is enhanced (van Holst, van den Brink, Veltman, and Goudriaan, 2010).

DISFUNCTIONAL BELIEFS IN PATHOLOGICAL GAMBLERS Cognitive biases related to personal skill and the probability of winning in gambling have been seen as important factors in the development and maintenance of pathological gambling (Blaszczynski and Nower, 2002). In order to investigate the beliefs of gamblers, some authors asked them to verbalize all their thoughts during a gambling session, a technique called the thinking aloud method. They have found a high percentage of gambling-related irrational verbalizations in both regular and occasional gamblers and in a range of different games

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David Polezzi, Elena Casiraghi and Giulio Vidotto

(Raylu and Oei, 2002). Nonetheless, the percentage of faulty beliefs was greater in regular versus non-regular gamblers (Raylu and Oei, 2002). Toneatto (1997) presented a list of typical gambling-related cognitive distorsions. For instance, the illusion of control over luck, which refers to the belief that one can directly or indirectly influences the outcome of an event governed by chance (it is evident in superstitious beliefs). The illusion of control has been linked to predictions of long-term success and thus fosters involvement in gambling activities (Sharpe, 2002). The following are other cognition disorders shown by pathological gamblers: the idea that they can make predictions regarding the outcome, selective memory for wins, and the so-called gambler‘s fallacy, namely the belief that a series of losses is a signal that a win is imminent. All the above mentioned irrational thoughts lead to overestimating the probability of winning. Evidence suggests that some gambling activities, such as cards, sports betting, and slot machines, are associated with higher levels of irrational thinking compared to others because they require or seem to require more skills (Raylu and Oei, 2002). In fact, the more a gambler is involved in the gambling process (i.e., has the possibility to choice and take an active part), the more it creates an illusion of control. Near misses are one outcome provided by many types of games in which the winning sequence is missed by only one symbol. Pathological gamblers reportedly respond to near misses as wins rather than losses, suggesting that near misses foster the development of the irrational thought that a win is close (Sharpe, 2002). Therefore, also some gaming characteristics could exaggerate the confidence of one‘s chances of winning. Cognitive therapy focused on modifying erroneous perceptions and has shown efficacy in improving control over gambling behavior (Raylu and Oei, 2002).

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THE INTEGRATION OF THE MODELS All of the above models provide information on the mechanisms underlying pathological gambling and are supported by evidence. However, they do not have to be considered mutually exclusive. First, they can be easily integrated (for example, pathological gambling can be the product of a reinforcement but this does not exclude that the person can have disfunctional beliefs about this behavior). Second, assuming that pathological gamblers do not form a homogeneus population, but distinct subtypes of gamblers exist, it would be the result of different etiological factors and yet display similar phenomenological features (Blaszczynski and Nower, 2002). An integrated perspective will provide the most useful guide for the study of pathological gambling. Taking this into account, some authors provided more comprehensive speculative explanatory models of pathological gambling that have not yet been systematically tested; nevertheless, they seem to be a valid guide for future research. One is the pathways model of pathological gambling by Blaszczynski (Blaszczynski and Nower, 2002) already presented above. Similarly, Sharpe (Sharpe, 2002) developed a biopsychosocial cognitive behavioral model of pathological gambling that acknowledges the involvement of biological, psychological, and environmental risk factors. Sharpe‘s model points to the interacting role of behavioral conditioning and cognitive distortions in promoting engagement and persistence in gambling. In addition, once habitual patterns of gambling behavior have been established, the

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presence of life problems increases the urge to use gambling as a coping mechanism. Thus, gambling behavior is further reinforced and becomes self-perpetuating. In fact, Sharpe considers the presence of poor coping skills (i.e., response inhibition, self-control, problemsolving skills, ability to challenge cognitions and delay reinforcement, and control over autonomic arousal) to be a crucial factor in the development and maintenance of pathological gambling. Moreover, negative consequences of pathological gambling increase stress and weaken coping skills, creating a vicious circle. This process is similar in current etiological models of addictions (Goudriaan, Oosterlaan, de Beurs, and Van den Brink, 2004).

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THE SOMATIC MARKER HYPOTHESIS The above exposed etiological models are largely known in the field of pathological gambling research. However, decision-making research has suggested a recent and influential view that includes emotions in decision-making, considering them part of the process performed by a person when choosing between two or more options; this is called the somatic marker hypothesis (Bechara and Damasio, 2005). The somatic marker hypothesis posits that emotion-related body signals assist cognitive decision making (Bechara and Damasio, 2005; Bechara, Damasio, Tranel, and Damasio, 2005). This theoretical model moves from the idea that knowledge and reasoning alone are usually not sufficient to make advantageous decisions. This concept is in contrast with the traditional point of view, in which emotion has been considered a disturbance to rational decisions. Instead, in Bechara and Damasio (2005)‘s model, emotion can be either beneficial or disruptive depending on whether it is integrated into the task or unrelated to it, respectively. Emotion is defined as a collection of changes in body and brain states triggered by brain structures such as amygdala, insula, ventro-medial prefrontal cortex and the brainstem (Bechara and Damasio, 2005; Damasio, 2005). Several empirical studies support the somatic marker hypothesis (Bechara and Damasio, 2005; Bechara, Damasio, Tranel, and Damasio, 2005; Damasio, 2005; Bechara, Damasio, Tranel, and Damasio, 1997; Bechara, Damasio, Damasio, and Lee, 1999). Most of these studies employed the Iowa Gambling Task, an experimental paradigm in which people are required to choose cards from four decks: Two advantageous and two disadvantageous. While performing this task, a group of healthy participants showed an increase in skin conductance levels before selecting cards from disadvantageous decks. Moreover, in the final part of the task, participants selected more cards from advantageous decks. These data indicate that the increase of skin conductance associated with disadvantageous decks provided a sort of ―alarm signal‖ that induced a behavioral shift toward advantageous decks (Bechara, Damasio, Tranel, and Damasio, 1997). In addition, patients with ventro-medial prefrontal lesions showed no modulation of skin conductance prior to card selection and chose more cards from the disadvantageous decks (Bechara, Damasio, Tranel, and Damasio, 1997). Further studies with the same paradigm compared patients with amygdala lesions with a group of healthy controls and found that patients failed to generate any skin conductance reactions prior to cards selection and, again, selected more cards from disadvantageous decks (Bechara, Damasio, Damasio, and Lee, 1999; Brand, Grabenhorst, Starcke, Vandekerckhove, and Markowitsch, 2007). This evidence would suggest that when brain structure is involved in emotion, damaged patients can no longer register is the pain of losing money (Bechara and Damasio,

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David Polezzi, Elena Casiraghi and Giulio Vidotto

2005). The somatic marker is, however, a still debated hypothesis (see Bechara, Damasio, Tranel, and Damasio, 2005; Maia and McClelland, 2004; Maia and McClelland, 2005 for a discussion about knowledge of advantageous strategies in healthy people while playing the Iowa Gambling Task). An interesting critique to the idea of somatic markers as body signals that help in decisions has been moved by Tomb and colleagues (Tomb, Hauser, Deldin, and Caramazza, 2002), who replicated the above exposed findings in healthy participants using a modified version of the Iowa Gambling Task. In the classical version of the Iowa Gambling Task, disadvantageous decks are have the largest reward and penalties and are thus the riskiest decks. Conversely, in Tomb et al.‘s (2002) task, advantageous decks had larger rewards and penalties with respect to other decks. Healthy participants performing this task showed a preference for advantageous decks but also an increase in skin conductance prior to card selection from those decks. These data could appear in contrast with the idea of somatic markers, although Damasio, Bechara, and Damasio (2002) suggested that two explanations, compatible with somatic marker hypothesis, can account for these data. First, the somatic marker is not necessarily a negative signal preceding bad outcomes; it can be positive by helping to endorse an option. Second, given that in the modified version advantageous decks have the highest risk, the skin conductance increases before choosing from advantageous decks can be an alarm signal due to large penalties. As already outlined above, to date, there is no agreement about the somatic marker hypothesis, but the contribution of emotions to decision-making processes is a relevant issue in need of exploration.

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SOMATIC REACTIONS IN PATHOLOGICAL GAMBLERS In order to review evidence that could support the somatic marker hypothesis as an etiological model of the pathological gambling, we present several studies that investigated somatic reactions in this population. The relationship between autonomic responses and decision making in pathological gamblers was initially explored by Dickerson, Hincy, and Fabre (1987), who investigated somatic reactions using self-report measurements. The authors interviewed a large sample of gamblers who usually bet on racing and asked them to complete the Sensation Seeking Scale as well as an anxiety questionnaire. Some of the participants showed out-of-control betting behaviors. Surprisingly, Sensation Seeking Scale scores were not significantly higher than the normal population. However a subscale of the test, Boredom Susceptibility, was correlated with arousal levels claimed by participants after the bet but prior to racing outcomes. These findings would suggest an association between somatic feelings and decision making, but it is important to underline that what people states about their sensation and feelings is not always mirrored by what they really feel. People can be either unaware of their somatic reactions or they can over/underestimate them (see Dickerson and Adcock, 1987) for similar findings. Carrol and Huxley (1994) measured diastolic and systolic blood pressure in dependent and non-dependent gamblers at rest and before, during, and after slot machine play. Subjects were given £5 for this purpose. From a cognitive point of view, the dependent gamblers expected to win more than the non-dependent gamblers. Blood pressure showed an increase in play before and during gambling and dropping after. The dependent group had a lower

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diastolic blood pressure throughout the measurements, and during the resting, the difference became more pronounced. No group differences were found in systolic blood pressure. In short, diastolic blood pressure discriminated dependent and non-dependent gamblers, suggesting that dependents gamblers had a lower baseline arousal level. Sharpe and colleagues (Sharpe, Tarrier, Schotte, and Spence, 1995) investigated the role of arousal in gamblers (high and low frequency social gamblers and pathological gamblers). Five conditions were employed in order to determine under which conditions gamblingrelated cues were related to increased autonomic arousal, as measured by skin conductance level, heart rate, and frontalis electromyography (EMG). The five conditions were a neutral task, a videotaped poker machine gambling scenario presented with and without distraction, a personally relevant ―win‖ situation, and a videotaped horse race. Comparisons between responses for the videotaped poker machine gambling stimuli versus a horse-racing video task only demonstrated differences for the pathological gamblers and only for skin conductance levels, while there were no differences between these tasks on heart rate and electromyography. When personally relevant situations were presented and compared to a neutral task, differences were observed in all three groups. However, the nature of these differences varied between the groups and the indices of arousal. For pathological gamblers, increases were evident in all three measures. Increases were also observed for the healthy groups in comparison to the neutral task, but only in heart rate and skin conductance levels. For skin conductance levels, the pathological gamblers group became significantly more aroused than the control groups, but no differences were observed between the high and low frequency gamblers. The above exposed study suggested that somatic reactions of pathological gamblers are different from the normal population. The somatic marker hypothesis indicate this reactions is the cause of inappropriate decision making, while Sharpe et al. (1995) only showed an association between somatic reactions and pathological gambling, without exploring the nature of this association. In an ecological study, Griffiths (1993) measured the heart rate of a group of 30 adolescent male gamblers while playing fruit machine gambling. The study was designed to test heart rate differences between regular (N=15) and non-regular (N=15) fruit machine gamblers and the differences against the players‘ baseline rates. Results showed that there were no heart rate differences between regular and non-regular gamblers, although during gambling, both groups‘ heart rates increased by approximately 22 beats per minute. However, non-regular gamblers‘ heart rates did not decrease significantly after gambling, whereas regular gamblers did. The authors interpreted these data to mean that regular gamblers developed a sort of tolerance for gambling (presumably due to habituation). These data would fit with the somatic marker hypothesis, which shows different somatic reactions depending on the frequency of gambling. However, the same data could be also interpreted to show that the different somatic reactions are the consequence and not the cause of the regular gambling. A further study with good ecological validity (Meyer, Hauffa, Schedlowski, Pawlak, Stadler, and Exton, 2000) investigated the secretion of a stress hormone (cortisol) in a casino environment. Ten male gamblers participated in both an experimental and control session. In the experimental session, gamblers played a game of blackjack using their own money. Gamblers played cards in the same setting during the control condition; however, the game was played for accumulation of points rather than money. Heart rate and endocrine parameters were recorded at baseline, 30 min, and 60 min following the commencement of each session, and again at completion of the game.

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David Polezzi, Elena Casiraghi and Giulio Vidotto

Heart rate increased significantly from baseline to 30 min in the experimental session and remained elevated for the remainder of the recording period. Salivary cortisol was raised at 30 min and further elevated at 60 min during gambling, and then it returned to control levels following completion of the game. The authors interpreted the data to indicate that the increase of cortisol during gambling may contribute the development of gambling addiction. Goudriaan, Oosterlaan, de Beurs, and van der Brink (2006) recently explored physiological reactions associated with gambling in normal and pathological populations, testing the somatic marker hypothesis (Bechara and Damasio, 2005; Bechara, Damasio, Tranel, and Damasio, 1997). A large sample of 46 pathological gamblers was recruited, and for each one, comorbility with other mental diseases and drug assumption was excluded. A group of 47 healthy volunteers served as the control. The task consisted in the classical version of the Iowa Gambling Task, in which people have to learn to pick up more cards from advantageous decks with respect to the other decks. While people performed the task, skin conductance responses as well as electrocardiogram were recorded. Behavioral data showed that although the healthy volunteers started selecting cards from all the decks, in the end, they picked up more cards from advantageous decks. In contrast, pathological gamblers choose cards from all the decks, from the beginning till the end, without showing a preference for one or more decks. Skin conductance responses were consistent with previous findings, showing increased skin conductance levels prior to cards selections from disadvantageous decks for healthy controls. Differently, skin conductance reactions of pathological gamblers were analogous for selection from advantageous and disadvantageous decks. Electocardiogram also revealed different body reactions. Deceleration of heart rate is more pronounced in correspondence of selection from disadvantageous decks compared to that associated with advantageous decks in normal controls. For pathological gamblers, the opposite pattern applied. Moreover, a direct comparison of hearth rate after win and losses showed that it decreased after a win and increased after losses in healthy controls, while no differences were found in pathological gamblers, who experienced increased hearth rate in both cases. Interestingly, self-report measures of arousal did not differ between the two groups. The above exposed data are in line with the somatic marker hypothesis predictions. This study showed, once more, that there is a relationship between body reactions and decision making.

CONCLUSION The somatic marker hypothesis found much evidence that can support it, in pathological gamblers as well as in normal population. However, the hypothesis is still debated given that the data also fit with different interpretations, and, to date, no clear conclusions can be drawn. In particular, clinicians and researchers seem to agree that pathological gamblers are a heterogeneous population and the etiology probably do not rely on a unique factor. The presence of different or abnormal physiological responses cannot exclude the presence of cognitive bias, the effect of operant conditioning, or a genetic predisposition for the disease. However, the increasing focus on the physiological reactions associated with gambling can provide new insight for comprehension and treatment of pathological gambling.

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REFERENCES APA. (1980). Diagnostic and Statistical Manual of Mental Disorders, 3rd Edition. Washington DC: American Psychiatric Association. APA. (2000). Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Washington, DC: American Psychiatric Association. Bechara, A., and Damasio, A. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52, 336-372. Bechara, A., Damasio, H., Damasio, A., and Lee, G. (1999). Different contributions of the human amygdala and ventromedial prefrontal cortex to decsion-making. Journal of Neuroscience, 19, 5473-5481. Bechara, A., Damasio, H., Tranel, D., and Damasio, A. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275, 1293-1295. Bechara, A., Damasio, H., Tranel, D., and Damasio, A. (2005). The Iowa Gamgling Task and the somatic marker hypothesis: some questions and answers. TRENDS in Cognitive Sciences, 9, 159-162. Blaszczynski, A., and Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction, 487-499. Brand, M., Grabenhorst, F., Starcke, K., Vandekerckhove, M., and Markowitsch, H. (2007). Role of the amygdala in decisions under ambiguity and decisions under risk: Evidence from patients with Urbach-Wiethe disease. Neuropychologia, 45, 1305-1317. Carroll, D., and Huxley, J. (1994). Cognitive, dispositional, and psychophysiological correlates of dependent slot machine in young people. Journal of Applied Social Psychology, 24, 1070-1083. Damasio, A. (2005). L'errore di Cartesio. Milano: Adelphi. Damasio, H., Bechara, A., and Damasio, A. (2002). Reply to: Do somatic markers mediate decisions on the gambing task? Nature Neuroscience, 5, 1104. Dickerson, M., and Adcock, S. (1987). Mood, arousal and cognitions in persistent gambling: preliminary investigation of a theoretical model. Journal of Gambling Behavior, 3, 3-15. Dickerson, M., Hincy, J., and Fabre, J. (1987). Chasing, arousal and sensation seeking in offcourse gamblers. British Journal of Addiction, 82, 673-680. Goudriaan, A. E., Oosterlaan, J., de Beurs, E., and Van den Brink, W. (2004). Pathological gambling: a comprehensive review of biobehavioral findings. Neuroscience and Biobehavioral Reviews, 123-141. Goudriaan, A., Oosterlaan, J., de Beurs, E., and van der Brink, W. (2006). Psychophysiological determinants and concomitants of deficient decision making in pathological gamblers. Drug and Alcohol Dependence, 84, 231-239. Grant, J. E., and Potenza, M. N. (2004). Pathological gambling:a clinical guide to treatment. APPI. Griffiths, M. (1993). Tolerance in gambling: An objective measure using the psychophysiological analysis of male fruit machine gamblers. Addictive Behaviors, 18, 365-372. Maia, T., and McClelland, J. (2004). A reexamination of the evidence for the somatic marker hypothesis: What participants really know in the Iowa Gambling Task. Proceedings of the National Academy of Sciences, 101, 16075-16080.

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Maia, T., and McClelland, J. (2005). The somatic marker hypothesis: still many questions but no answers. TRENDS in Cognitive Sciences, 9, 162-164. Meyer, G., Hauffa, B., Schedlowski, M., Pawlak, C., Stadler, M., and Exton, M. (2000). Casino gambling increases heart rate and salivary cortisol in regular gamblers. Biological Psychiatry, 48, 948-953. Mitzner, G. B., Whelan, J. P., and Meyers, A. W. (2010). Comments from the Trenches: Proposed Changes to the DSM-V Classification of pathological Gambling. Journal of Gambling Studies . Petry, N. M. (2006). Should the scope of addictive behaviours be broadened to include pathological gambling? Addictions, 152-160. Raylu, N., and Oei, T. P. (2002). Pathological gambling: a comprehensive review. Clinical psychology review, 1009-1061. Sharpe, L. (2002). a riformulated cognitive-behavioural model of pathological gambling. A biopsychosocial perspective. Clinical psychology review, 1-25. Sharpe, L., Tarrier, N., Schotte, D., and Spence, S. (1995). The role of autonomic arousal in problem gambling. Addiction, 90, 1529-1540. Tomb, I., Hauser, M., Deldin, P., and Caramazza, A. (2002). Do somatic markers mediate decisions on the gambling task? Nature Neuroscience, 5, 1103-1104. van Holst, R. J., van den Brink, W., Veltman, D. J., and Goudriaan, A. E. (2010). Why gamblers fail to win: a review of cognitive and neuroimaging findings in pathological gambling. Neuroscience and Biobehavioral Reviews, 87-107. Zack, M., and Poulos, C. X. (2007). A D2 Antagonist Enhances the Rewarding and Priming Effects of a Gambling Episode in Pathological Gamblers. Neuropsychopharmacology, 1678-1686.

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Chapter 3

PREVENTION IS BETTER THAN CURE. VULNERABILITY MARKERS FOR PROBLEM GAMBLING Neal Hinvest Department of Psychology, University of Bath, UK

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ABSTRACT In recent years, interest has increasingly turned to potential vulnerability markers for addiction. These vulnerability markers are psychological, environmental and biological factors that predispose an individual to be at greater risk of developing an addiction compared to a person who does not show such markers. Investigations into these markers are of great value as they can be used to create a form of assessment that will measure risk for the development of addictions, or related problematic behaviours, later in life. Thus, vulnerable individuals can be identified and, if desired, given further education in order to help them identify risk factors in themselves or others, such as family members. Knowledge of vulnerability markers will thus potentially significantly decrease the prevalence of a wide range of addiction disorders. This chapter will review the research on vulnerability markers for problematic gambling. Problematic gambling can carry devastating impacts upon not only the gambler‘s life, but their peers in addition to the wider community. Research in the last few years has put forward a number of vulnerability markers for pathological gambling. Furthermore, research from our own lab has shown that individuals showing sub-syndromal levels of addictive gambling behaviour also express these vulnerability markers, strengthening the hypothesis that these markers may predispose an individual to move along the continuum from nongambler to problematic or pathological gambler. It is hoped by the author that by providing a review of vulnerability markers, future research can help create a simple assessment that can ascertain risk of developing an addiction, in this case, pathological gambling, so that individuals at risk can be identified thus decreasing the prevalence of such disorders and burden of such disorders upon the community, government spending and public health services.

Keywords: Vulnerability markers; Addiction; Problematic gambling; Public health Psychology of Gambling : New Research, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

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INTRODUCTION Problem gambling commonly leads to severe negative effects not only on the gambler but on their family and peers, not to mention the economic expense due to criminal activity, lost productivity and other related factors. It is fair to say that problematic gambling affects us all. With this in mind, research into the factors underlying problematic gambling is of great importance as it will allow the severity of its associated impacts to lessen. One important area of research is the identification of ―vulnerability markers‖ for problem gambling. Vulnerability markers are factors in an individual that are thought to increase the susceptibility of developing a particular psychiatric issue. Research into vulnerability markers is incredibly important as they allow the development of assessments which will identify individuals at risk of developing a gambling addiction but, critically, before such addiction develops. If so wished, these individuals (and, perhaps, their family and peers) can then obtain education to increase their awareness of the development of problematic gambling with the intention of minimising, or even negating, the chances of developing a gambling problem. This research field, therefore, is concerned with halting the spread of problematic gambling. Such an approach falls in line with the core objectives of some health services, such as the National Health Service within the United Kingdom, of which one of their aims is to shift from a curative to preventative healthcare system (NHS, 2009), the outcomes of which will be significantly increased quality of life within the population and decreased spending on areas of public health. This chapter will provide a review of the markers that have been evidenced to increase vulnerability specifically for problematic gambling. Unfortunately, a complete list of vulnerability markers cannot be given at the current time as significantly more research is required. The chapter will identify factors for which causality can be determined (i.e. the factor predates and predicts problem gambling). Factors for which causality cannot be determined will not be included unless there is some suggestive evidence that they may be potential vulnerability markers. This makes this review separate from other important reviews (e.g. Johansson et al., 2009) which have identified risk factors and have not separated out those factors which can be used in an early, pre-morbid, assessment. The aim of this review is to provide a single reference source with which individuals can begin to craft assessments for the early detection of problematic gambling. This review can be split into three sections. The first section will focus on vulnerability markers which have a valid and robust underlying evidence base and, as such, seriously warrant inclusion in any assessment for problematic gambling. The second section will detail those markers which have been identified as potential vulnerability markers. Markers described in this section have suggestive, yet little, evidence. The third section will cover potential biological and physiological markers. Although the focus of this chapter is to permit individuals to create brief assessments that can be carried out in the field, such as the psychiatrist‘s office, knowledge of biological markers is important as it will allow researchers and healthcare providers to assess individuals using other platforms, such as neuroimaging. Although such techniques are significantly more expensive than a field assessment, they are another method by which susceptible individuals can be screened.

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IMPORTANT VULNERABILITY MARKERS The vulnerability markers described within this chapter are those which have a strong base of evidence supporting their role as a vulnerability marker for problematic gambling. Unfortunately, there has been relatively less research investigating vulnerability markers for problematic gambling compared to research investigating markers for substance use disorders. Therefore, only a small number of markers can be designated as strong contenders for inclusion in a pre-morbid assessment. For the rest of this review ―problem gamblers‖ will be used to refer to both pathological gamblers and gamblers who are not diagnosed as pathological but still express problems with their gambling.

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Impulsivity Impulsive behaviour has been linked to a wide range of addictions (Verdejo-Garcίa et al., 2008). Over the years, within the research literature, impulsivity has received a variety of definitions. The generally accepted current definition within the literature terms impulsivity as a trait that is comprised of several behaviours, namely a tendency to act without forethought, poor control over one‘s responses and a focus on immediate gratification (Evenden, 1999). Heightened levels of each of these sub-behaviours in addition to higher general levels of impulsivity (compared to gambling-naïve controls) has been linked with problematic gambling in several retrospective studies (Blaszczynski et al., 1997; Clarke, 2004; Hinvest et al., 2011; King et al., 2010; Steel and Blaszczynski, 1998), however, such studies cannot determine whether impulsivity pre-dates the onset of gambling or is a consequence of gambling. Fortunately, there have been several longitudinal studies that have sampled impulsivity within childhood or adolescence and subsequently measured gambling behaviour at a later date. These studies have typically measured general levels of impulsive behaviour using widely accepted self-report measures of impulsivity. Levels of problematic gambling were typically measured using the South Oaks Gambling Screen (SOGS), a well-accepted measure of problem gambling severity (Lesieur and Blume, 1987). Dussault et al (2011) sampled 1004 males from Montreal, Canada. A composite rating of impulsivity was determined at 14 years of age via self-reports, teachers‘ reports and mothers‘ reports. Problem gambling severity was measured at 17 years of age. Impulsivity at 14 years was found to be a positively associated with problem gambling severity at age 17. Vitaro et al. (1997) sampled 754 French-speaking Caucasian boys from Montreal. Impulsivity was measured by self-report and teachers‘ ratings at 13 years of age and problem gambling severity at age 17 years. Increases in either rating of impulsivity were associated with increased problem gambling severity. Interestingly a continuum was borne out in which non-gamblers had the lowest impulsivity scores, individuals who gambled recreationally but not at problematic levels showed the next highest scores, gamblers with low level problems showed the next highest and high problem gamblers expressed the highest scores, thus providing evidence that childhood levels of impulsivity highly influence the development of the severity (if any) of problematic gambling behaviour later in life. This inference was further confirmed by a study measuring teacherrated levels of impulsivity in 163 children (again, in Montreal) with a mean age of 5.5 years

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and then self-reports of gambling involvement (using the authors‘ own measurement scale) at a mean age of 11.5 years (Pagani et al., 2009). Impulsivity ratings at the first testing session were positively correlated with gambling involvement at the second testing session and each 1-unit increase in impulsivity ratings was associated with a 25% increase in later self-reported involvement in gambling, even when holding constant potential child and family-related confounds including sex, level of maternal education, family dysfunction, familial involvement in gambling and state ratings of depression. A criticism that can be levelled at these studies is that they only investigate gambling behaviour within childhood or adolescence. These stages of development, notably adolescence, are marked by a significant increase in risk-taking behaviour (Casey et al., 2008) and therefore risky behaviours such as gambling may be inflated within such samples and subsequently decrease as the individual matures. To combat this criticism, a recent prospective study measured gambling behaviour in adults rather than at a child or adolescent stage. This study measured impulsivity at age 7 years via a psychiatrist‘s interview (individuals were classified as either ―impulsive‖ or ―non-impulsive‖) and then measured problem gambling severity at a mean age of 39 years (Shenassa et al., 2012). The 958 participants were resident within Boston, Massachusetts or Providence, Rhode Island. Individuals who were classified as impulsive at age 7 were 3.09 times more likely to report signs of problem gambling in adulthood compared to the non-impulsive group. These results suggest that childhood levels of impulsivity predict the development of problem gambling not just in childhood or adolescence but also in adulthood. However, there are some provisos that need to be made clear before any assessment includes impulsivity as a vulnerability marker. Problem gambling is highly co-morbid with other addictions (Grant and Kim, 2003; Lorains et al., 2011; Petry et al., 2005) and thus care must be taken to ensure that impulsivity can be determined to be a marker for problematic gambling rather than a marker for another addiction. Several studies have shown that the pattern of impulsive behaviour seen in problematic gambling is comparable to that seen in substance and alcohol use disorders (Lawrence et al., 2009aandb: Verdejo-Garcίa et al., 2008). Details pertaining to comorbid diagnoses are not given in the Pagani et al. (2009), Vitaro et al. (1997) or Dussault et al. (2011) articles. In one study measuring self-rated impulsivity in 939 individuals (464 women) of 18 years of age and problem gambling severity at 21 years, impulsivity was initially found to be a significant predictor of problem gambling severity (Slutske et al., 2005). However over half of the individuals determined by SOGS scores to be potential problem gamblers (N=52) expressed a comorbid alcohol, cannabis or nicotine use disorder (as determined by interview using the DSM-III). This left a sample of 21 ―pure‖ problem gamblers in which impulsivity was no longer a significant predictive factor. However it can be argued that the pure sample was no longer representative of the wider population of problem gamblers as there is a high degree of comorbid substance/alcohol use in this population. Further muddying of the waters has occurred from the suggestion that impulsivity is a predictor of problem gambling severity but only in individuals from particular socioeconomic backgrounds. The Vitaro et al. (1997), Dussault et al. (2011) and Pagani et al. (2009) studies recruited individuals from a low socioeconomic background, however there was no comparable group from a high socioeconomic background so inferences as to the effect of this factor could not be determined. In contrast, a recent study by Auger et al. (2010) split participants into a high or low socioeconomic background group. Level of socioeconomic

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background was determined by two variables; mother/father education level (completed university/did not complete university) and a measure of area material deprivation in the area in which the participant resided. Impulsivity was rated by self-report measure at approximately 15-16 years of age with level of gambling behaviour self-rated via the authors‘ own criteria at a mean age of 20.3 years. Impulsivity was a significant predictor of involvement in gambling but only in the low socioeconomic group. There were limitations to this study, most importantly the use of only two variables to measure socioeconomic status. Furthermore, the Auger et al. study is the only one to investigate whether impulsivity is a vulnerability marker only for low socioeconomic groups, therefore replication of these results is required before any decision as to its efficacy as a factor within any assessment can be made. A further item to be taken into consideration is the particular effect of the behaviours of impulsivity that are predictive of problem gambling. As previously mentioned, impulsivity can be fractionated into further components. Although an early assessment for problem gambling could include a general assessment of impulsivity, if the particular predictive power of each component was known then this would likely lead to a more sensitive assessment. Although problem gambling has been associated with increases in each component of impulsivity, there has, to the author‘s best knowledge, only been one study of a prospective design which has assessed the predictive power of impulsivity components. This study involved a sample of 154 males from an economically deprived area in Montreal (Vitaro et al., 1999). Ratings of general levels of impulsivity were collected at 12-14 years of age via self-reports and teachers‘ rating. A behavioural task that assessed delay-of-gratification (the opposite of immediate gratification) was completed by the participants at 13 years of age and a further task assessing the tendency to persist in responses that are maladaptive (response perseveration) was given at 14 years. Ratings of problem gambling severity were collected at 17 years of age. Self-report measures of impulsivity and decreased response perseveration positively predicted problem gambling, however, teachers‘ rating and delay-of-gratification scores were not significant predictors. This study highlights the need for studies which investigate the effect of impulsivity components on the future development of problem gambling. In summary, the evidence for impulsivity being a pre-morbid vulnerability marker for problem gambling is strong, and thus is the suggestion that it should be included within any early assessment. However, as has been shown, more research is necessary to ascertain the predictive power of this factor within different populations.

Familial Functioning Poorer levels of familial functioning have repeatedly been positively associated with problem gambling severity. In this case, ―familial functioning‖ relates to a variety of factors including lower parental supervision, perceived unsupportive parenting, perceived fragile bonds with parents and siblings and family conflict (McComb and Sabiston, 2010). Perhaps unsurprisingly, parental involvement in gambling is associated with an offspring‘s engagement in gambling (King et al., 2010) and development of a gambling problem (Black et al., 2006; Casey et al., 2011; Dannon et al., 2006; Delfabbro and Thrupp, 2003; Grant and Kim, 2002; Grant et al., 2010; Langhinrichsen-Rohling et al., 2004; Magoon and Ingersoll,

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2006; McComb and Sabiston, 2010; Shead et al., 2010). Further evidence comes from a longitudinal study by Vitaro et al. (2001) in which parental supervision reported by 717 males aged 13-14 years (scored by the author‘s own two-item measure) predicted gambling problems at age 16-17 years. However, the association does not simply apply to parental gambling. Problem gamblers also commonly report a higher incidence of parents and siblings having a drug or alcohol use problem compared to non-problem gamblers and non-gamblers (Black et al., 2003; Black et al., 2006; Hodgins et al., 2010; Schreiber et al., 2009). The heritability of problem gambling from parent to offspring has repeatedly been estimated to be approximately 50% (Eisen et al., 1998; Eisen et al., 2001; Lobo and Kennedy, 2009; Shah et al., 2005; Slutske et al., 2010a) and there have been a number of studies that have explored the particular molecular genetic factors involved in this high rate of heritability (Petry, 2007). These studies have commonly reported that variants of genes associated with serotonin and dopamine functioning are associated with problem gambling severity (Comings et al., 1996; Comings et al., 1999; Comings et al., 2001; Ibañez et al., 2000; Perez de Castro et al., 1997; Perez de Castro et al., 2002). The research identified above thus points to relatively poorer familial functioning as being a vulnerability marker for problem gambling, however, the impact of such a factor on the calculation of risk of developing a gambling addiction is unclear. Longitudinal studies in children and adolescents have found evidence for familial factors being predictors of problem gambling, however, impulsivity has been found to predict problem gambling above and beyond familial factors (Pagani et al., 2009; Pagani et al., 2010). Any assessment must take this into account when balancing the impact of familial factors on the calculation of risk for developing a gambling problem. Furthermore, as studies investigating familial factors typically depend on the participant‘s memory and perceptions, they may be prone to error. In a study which obtained reports on parental gambling from discordant co-twins, of which one had a gambling problem, the gambler typically reported significantly more parental involvement in gambling compared to the non-gambling twin (Slutske et al., 2010b). Therefore, any individual providing an assessment in which the responder must provide historical accounts of familial functioning must be aware that such accounts may be inflated or deflated depending on the responder‘s current level of involvement in gambling. Care must especially be taken with individuals who do not currently report a gambling problem but who may be at risk as they may provide biased, tempered, accounts of family gambling behaviour. Finally, one study has provided evidence that familial factors may be confounded by cultural factors and thus any assessment must be sensitive to cultural influences (Ellenbogen et al., 2007).

POTENTIAL VULNERABILITY MARKERS This section details several factors for which the evidence for them being a vulnerability marker (i.e. a causal factor) is lacking but have been identified as risk markers that may have a causal influence on problem gambling. It is hoped that this section will be used as a guide for future research that aims to identify vulnerability markers for problem gambling.

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Negative Emotional States Problem gambling has been associated with the prevalence of negative emotional states including depression, anxiety and heightened stress. Although it is sensible to assume that problem gambling can lead to negative emotional states, there is a question regarding whether such emotional states may also lead to the its development. Although there is evidence associating problem gambling with these three negative emotional states, the literature investigating causal effects of these states on the development of problem gambling is scarce. Large and small-scale retrospective surveys have provided evidence that problem gambling is commonly co-morbid with depression, anxiety and the occurrence of stressful life events (Afifi et al., 2010; Boughton and Falenchuk, 2007; Chou and Afifi, 2011; Hopley et al., 2010; Moodie and Finnigan, 2006; Rodda et al., 2004; Rømer Thomsen et al., 2009). One very recent study investigating gender perceptions of stressful life events and their association with gambling cravings found that in both men and women pathological gamblers, heightened levels of stress were positively associated with the urge to gamble (Tschibelu and Elman, 2011). Although this study highlights the link between stress and the felt need to gamble following development of a gambling problem, it does not provide any evidence to suggest that stress is a pre-cursor to the development of gambling. A study in older children and adolescents (11-20 years of age) found that those meeting criteria for problem gamblers reported more major stressful life events within their lifetime, but not more minor stressful life events, compared to non-problem (social) gamblers and nongamblers (Bergevin et al., 2006). The degree to which a stressful life event was ―major‖ or ―minor‖ depended on the rating of the participant themselves. Interestingly, there was a difference in the way that problem gamblers coped with negative events compared to social and non-gamblers. Problem gamblers were more likely to use coping strategies that involved the avoidance of the event, rather than reflecting and acting upon it (further evidence for this coping style being expressed by problem gamblers has been found by Gupta et al., 2004). Furthermore, male problem gamblers were more prone to react in an emotional way (e.g. becoming angry/shouting) compared to female problem gamblers. The results of this study may be used to suggest that individuals presenting different styles of coping with negative life events may be at risk of developing a gambling problem, however the study cannot distinguish whether this is a pre-morbid factor or whether the gambling problem itself affects an individual‘s coping style. Coping style and the occurrence of major negative life events, however, remain potential vulnerability markers and thus further research investigating these is required. Anxiety and depression have been linked to the occurrence of problem gambling (Boughton and Falenchuk, 2007; Lee et al., 2011). In a study of 14,934 individuals aged 1864 years, those reporting the occurrence of a mood (depressive/manic episode) or anxiety disorder in the 12 months previous to the study were 1.7 times more likely to be classed as severe problem gamblers (el-Guebaly et al., 2006), however, causality cannot be determined by this study. In a study of 33 pathological gamblers (31 males) and 42 non-gamblers, pathological gambling was found to be associated with a variety of personality traits including high neuroticism, low conscientiousness, low cautiousness, high extraversion, high adventurousness low cheerfulness and lower self-esteem (Kaare et al., 2009). Pathological gamblers were also more likely than non-gamblers to report having experienced frequent episodes of anxiety and depression in the month preceding the study. It

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is interesting to note that two studies have put forward evidence to suggest that pathological gambling shares a similar genetic origin to both depression (Potenza et al., 2005) and Generalized Anxiety Disorder (Giddens et al., 2011), thus providing a potential explanation as to their high co-occurrence. The question as to whether anxiety precedes problem gambling was addressed by a study by Vitaro and Wanner (2011) which recruited 1125 children. Teacher‘s ratings of anxiety and impulsivity were taken at ages 6-8 years while gambling problem severity and onset of gambling were assessed at 8 and 10 years respectively. Impulsivity was positively associated with later problem gambling. Anxiety was a predictor of later gambling severity but, perhaps surprisingly, there was an inverse relationship, i.e. lower anxiety was associated with later higher gambling problem severity. Although there are some issues such as the age of the sample, in which risk-taking behaviours are common (Casey et al., 2008) and the lack of testing within an adult population, it shows that the issue as to whether anxiety is a vulnerability marker for problem gambling is not clear-cut. Nevertheless, the literature shows that the effects of anxiety, depression and stress on problem gambling require further investigation as their inclusion within an early assessment may be important.

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Co-Morbid Substance and Alcohol Use A commonly-found scenario (Weich et al., 2011) is that a problem gambler will also present another addiction, typically to drugs, alcohol or nicotine (Afifi et al., 2010; Black et al., 2003; Boughton and Falenchuk, 2007; Chou and Afifi, 2011; Dannon et al., 2006; elGuebaly et al., 2006; Grant and Kim, 2003; Grant et al., 2006; Grant et al., 2010; Lorains et al., 2011; Martens et al., 2009; McGrath and Barrett, 2009; Petry et al., 2005; Rodda et al., 2004; Rush et al., 2008; Winslow et al., 2010). A similar pattern is seen in individuals who express a substance or alcohol use disorder in that problem gambling is often comorbid (Wareham and Potenza, 2010). Such findings support the theory that all addictions stem from a single syndrome (Shaffer et al., 2004). From this theory it can be hypothesised that particular individuals are at risk of developing a multitude of addictions but that the development of a particular addiction depends on other factors, for example, availability (one cannot become a problem gambler if one has no access to any means of gambling). Comorbid addictions do not just end with the more ―established‖ addictions (i.e. drugs, alcohol and nicotine) and problem gamblers may also show addictive tendencies towards other stimuli, such as the television, internet, video-games and even chocolate (Greenberg et al., 1999). Therefore, it would be prudent to include the presence of comorbid disorders, especially addiction disorders, within an assessment for problem gambling. However, it must be borne in mind that problem gambling may lead to the development of other addictions and thus if an individual is classed as ―at risk‖ of developing problem gambling, it would be wise to treat that individual as being at potential risk of developing a further addiction or multiple addictions, and thus any education provided would be amiss if it did not cover a range of psychiatric issues.

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Level of Education Shorter time in education has been linked to increases in problem gambling severity. In a sample of 809 gambling adults from Alberta, Canada, lower levels of education prior to testing were associated with an increase in problem gambling severity (i.e. from a low problem group to a high problem group) over a 14-month period (Currie et al., 2012). Furthermore, within a sample of 1675 North American adults, having an educational level of High School or higher was associated with a decrease in the prevalence of problem and pathological gambling (Scherrer et al., 2007). Therefore, level of educational attainment may be a useful marker to include within an early assessment. However, inclusion of this marker would not be of use within a school-aged individual. A question also remains as to whether this marker simply reflects other markers such as socioeconomic status, intelligence or familial functioning. Therefore, more research is required to pull apart such factors so as to create an assessment that does not artificially inflate calculates of risk.

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Gender Studies from several countries have reported that males are more likely to engage in gambling and develop gambling problems compared to females (Adlaf and Ialomiteanu, 2000; Bakken et al., 2009; Currie et al., 2011; Greenberg et al., 1999; Guo et al., 2011; RuizOlivares et al., 2010; Villella et al., 2011) although this discrepancy in rates appears to be growing smaller (Fong et al., 2005) with some large scale studies finding no differences in the percentages of male and female problem gamblers within the population (el-Guebaly et al., 2006). There are gender differences in preferences for game types with females reporting preference for lottery games, bingo, slot machines and scratch cards while males report preferences for ―face-to-face‖ games such as card games (Boughton and Falenchuk, 2007; Potenza et al., 2001; Tavares et al., 2003). Furthermore, it is becoming increasingly apparent that males and females often differ in their motivations to gamble and the consequences that they report as a result. Males commonly report comorbid issues with alcohol or drugs whereas females tend to report higher rates of depression (Desai et al., 2005; Ellenbogen et al., 2007; Fong et al., 2005; Ibáñez et al., 2003; Tavares et al., 2003; Vitaro et al., 2011) and females report that a common motivation to gamble is to escape from negative feelings (Sacco et al., 2011). Females also report that engagement in gambling is usually more for social reasons rather than monetary gain, the latter of which is a common motivation reported by males (Fong et al., 2005). Females also report that social factors, such as parental and peer influences, have a greater effect on their gambling involvement compared to males (Chalmers and Willoughby, 2006). Lastly, in a study measuring cortisol response, an indicator of physiological arousal, in male and female gamblers before and after betting on a horse race, males expressed a greater cortisol response compared to females at every time point sampled (Franco et al., 2010) therefore suggesting that the activity of gambling itself may affect males and females in very different ways, although it must be noted that this study only focused on one type of gambling. This comparative increase in arousal during gambling in males may be linked to the tendency for males to engage in more emotional-focused (i.e. highly arousing) styles of coping with gambling problems compared to females (Bergevin et al., 2006).

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These studies highlight the need for gender to be taken into account when calculating the impact of various factors on the risk of developing a gambling problem. For example, the occurrence of depression in females may need to be more influential in the calculation of risk compared to males, in whom occurrence of a co-morbid addiction potentially needs to be relatively more influential. More research is needed into gender differences in order to make any assessment more precise and tailored to the specific needs of each gender.

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Sensation-Seeking Sensation-seeking is the tendency to seek out and engage in exciting, risky, experiences. With this in mind it may be a sensible hypothesis that problem gamblers would show elevated levels of such a characteristic due to their repeated engagement in risky events. Some studies have provided evidence to suggest that general levels of sensation-seeking are higher in problem and pathological gamblers compared to non-gambling controls (Bonnaire et al., 2007; Coventry and Constable, 1999; Cyders and Smith, 2008; Sáez-Abad and BertolίnGuillén, 2008). Some of the more recent studies have investigated scores on the sub-scales of sensation-seeking rather than using general scores. Studies that have measured scores on subscales have found that problem gamblers, in comparison with non-gamblers, show elevated scores on boredom susceptibility, disinhibition and the need for more intense stimuli but not thrill and adventure seeking and experience seeking (Blaszcynski et al., 1990; Fortune and Goodie, 2010; Myseth et al., 2009; Nower et al., 2004). However, the argument for sensation-seeking to be included in an early assessment for problem gambling is by no means clear. In a recent meta-analysis of 44 studies investigating personality traits of problem gamblers vs. controls, sensation-seeking was not found to be a significant predictor of problem gambling severity (MacLaren et al., 2011). The argument is further clouded by claims that levels of sensation-seeking may differ according to the games that the gambler prefers and plays the most. Coventry and Brown (1993) recruited a sample of 79 gamblers approached in a betting shop. The authors found that gamblers reporting preferences for casino or race-track betting expressed higher levels of sensation-seeking compared to a sample of the general population of Glasgow, Scotland, while gamblers who did not play such games expressed lower levels to the comparison group. Further work is required to determine whether a measure of sensation-seeking would be required in an early assessment for problem gambling. It may be the case that within individuals at risk of developing a gambling problem, sensation-seeking may, in part, influence the type of game preferred and thus may not be of use for a simple early assessment.

BIOLOGICAL MARKERS Although the main focus of this review is to identify markers that should/could be incorporated into a simple, easy-to-administer, early assessment for individuals at risk of developing problem gambling, investigating the biological functioning of an individual is a further, albeit significantly more expensive, method of assessment. Therefore, this chapter will review the literature on biological risk factors associated with problem gambling.

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A repeated finding within the neuroimaging literature is that problem gamblers show dysfunction of the neural system involved in reward processing (Clark, 2010). Two studies have shown that problem gamblers exhibit increased activity within striatal areas involved in reward processing when playing blackjack (Hollander et al., 2005; Meidl et al., 2010). However, no comparison group was included in these studies and they therefore cannot ascertain whether such activity is dysfunctional. Reuter et al. (2005) found that problem gamblers expressed reduced function of the ventral striatum and the ventromedial prefrontal cortex compared to non-gambling controls during a guessing game that lead to a win or loss. Furthermore, the reduced function within problem gamblers was inversely correlated with scores of gambling severity. The task used in the Reuter et al. study was slightly modified in a subsequent study, which found similar results, i.e. decreased function within striatal structures compared to controls (de Greck et al., 2010). These findings can be linked to another study which found that baseline levels of activity within midbrain structures involved in reward processing, as measured by positron emission tomography, was significantly lower in pathological gamblers compared to non-gambling controls (Pallanti et al., 2010). Reduced function has also been measured within the frontal cortex in pathological gamblers compared to controls. Dysfunction within this region has been linked to the tendency of pathological gamblers to engage in impaired decision-making strategies that are risk-seeking and myopic (Cavedini et al., 2002; Kalechstein et al., 2007). Dysfunction within the ventral prefrontal cortex has also been linked to impaired behavioural control in pathological gamblers compared to non-gambling controls (de Ruiter et al., 2009; Potenza et al., 2003). An interesting biological marker within problem gamblers is the neural response to nearmisses while gambling. Non-problem gamblers have been found to show neural activity associated with a near-miss similar to that seen for a win, albeit at a lower level (Clark et al., 2009); however, in problem gamblers this response is significantly greater as evidenced by two studies that have used simplified slot machine tasks to assess response to wins, losses and near-misses. In these tasks, near-misses were presented as two of the windows in the slotmachine having the same item and the third showing the item one space away from forming a win (three items in a row). In the first study, in pathological gamblers the experience of a near-miss was associated with increased activity in midbrain regions associated with winning compared to non-pathological gamblers (Habib and Dixon, 2010). In the second study, regular gamblers who individually varied in gambling severity (no problems to probable pathological gamblers) were recruited. In this group, activity within midbrain areas associated with reward processing was positively correlated with scores of gambling severity (Chase and Clark, 2010). Thus problem gamblers process near-misses as comparable to wins at a neural level compared to non-problem gamblers which may underlie the tendency to continue play as experience of near-misses, which entail loss, carry rewarding properties instead of being aversive. Further biological markers are associated with cue-induced procedures, in which gamblers and non-gamblers are presented with gambling-related stimuli. Psychophysiological assessment of problem gamblers during cue-induced tasks have shown that problem gamblers express higher heart rate during exposure to gambling cues compared to non-gambling controls (Blanchard et al., 2000). Presentation of videos of gambling scenarios or descriptions of gambling scenarios has been associated with differential functioning of the frontal and temporal cortices, amygdala and posterior regions of the brain associated with visual processing (Balodis et al., 2011; Crockford et al., 2005; Goudriaan et al., 2010; Potenza et al.,

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2003). However, although such techniques could be used to identify current problem gambling, they would probably be of no use as an early assessment as biased response to gambling cues would hypothetically occur over time due to repeated connections between the gambling cues and hedonic experiences. As mentioned in a previous chapter problem gambling is highly heritable and studies are beginning to identify genetic factors that underlie this heritability. These studies have commonly found that variants of particular genes underlying dopaminergic and serotonergic functioning are linked to problem gambling. This research can be linked to pharmacological research which has found evidence for dysfunction of not only ascending dopaminergic and serotonergic function, but also noradrenergic function, within pathological gamblers compared to controls (Bergh et al., 1997; Moreno et al., 1991; Nordin and Sjӧ din, 2006; Pallanti et al., 2006; Pallanti et al., 2010). Furthermore, treatment approaches have identified that selective serotonergic reuptake inhibitors (SSRIs) can be widely used to decrease the severity of problem gambling (De Caria et al., 1996; Hollander et al., 2000; Saiz-Ruiz et al., 2005). This chapter has identified a number of biological markers of problem gambling. However, care must be taken as some of the literature cannot be used to infer causality. Problem gambling does not include a pharmacological component and thus any findings from a ―pure‖ sample are not confounded by such effects. However, as stated earlier, a question then arises as to how representative the findings are to the wider population of problem gamblers, in which comorbidity rates are relatively high. Further research combining cognitive neuroscience and pharmacological methodologies in a longitudinal framework, although expensive relative to behavioural studies, are necessary if biological vulnerability markers are to be identified.

CONCLUSION The aim of this review was to identify vulnerability markers for pathological gambling which could be used as an early assessment for identifying individuals at risk of developing a gambling problem. Through the literature a number of markers were identified, some of which were evidenced by valid, robust, findings and some for which the evidence is currently lacking but deserve possible inclusion within an assessment. It is hoped that researchers and treatment providers will be able to begin creating early assessments for the development of problem gambling. Unfortunately, it would be extremely difficult to create a perfect assessment at this time as the literature for some markers is sparse. However, this review has highlighted where research into vulnerability markers needs to be directed. The lack of evidence however should by no means delay attempts to create early assessments as these assessments are designed to not only decrease public health spending but, more importantly, dramatically increase the quality of life in individuals, and family/peers of individuals, who would have gone on to develop a gambling problem, which is a very worthy task.

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Ruiz-Olivares, R., Lucena, V., Pino, M.J. and Herruzo, J. (2010). Analysis of behaviour related to use of the internet, mobile telephones, compulsive shopping and gambling among university students. Addiciones, 22, 301-309. Rush, B.R., Bassani, D.G., Urbanoski, K.A. and Castel, S. (2008). Influence of co-occuring mental and substance use disorders on the prevalence of problem gambling in Canada. Addiction, 103, 1847-1856. Sacco, P., Torres, L.R., Cunningham-Williams, R.M., Woods, C. and Unick, G.J. (2011). Differential item functioning of pathological gambling criteria: an examination of gender, race/ethnicity, and age. Journal of Gambling Studies, 27, 317-330. Sáez-Abad, C. and Bertolίn-Guillén, J. (2008). Personality traits and disorders in pathological gamblers versus normal controls. Journal of Addictive Diseases, 27, 33-40. Saiz-Ruiz, J., Blanco, C., Ibañez, A., Masramon, X., Gόmez, M.M., Madrigal, M. and Dίez, T. (2005). Sertraline treatment of pathological gambling: a pilot study. Journal of Clinical Psychiatry, 66, 28-33. Scherrer, J.F., Slutske, W.S., Xian, H., Waterman, B., Shah, K.R., Volberg, R. and Eisen, S.A. (2007). Factors associated with pathological gambling at 10-year follow-up in a national sample of middle-aged men. Addiction, 102, 970-978. Schreiber, L., Odlaug, B.L., Kim, S.W. and Grant, J.E. (2009). Characteristics of pathological gamblers with a problem gambling parent. American Journal of Addiction, 18, 462-469. Shanassa, E.D., Paradis, A.D., Solan, S.L., Wilhelm, C.S. and Buka, S.L. (2012). Childhood impulsive behaviour and problem gambling by adulthood: A 30-year prospective community-based study. Addiction, 107, 160-168. Shah, K.R., Eisen, S.A., Xian, H. and Potenza, M.N. (2005). Genetic studies of pathological gambling: a review of methodology and analyses of data from the Vietnam era twin registry. Journal of Gambling Studies, 21, 179-203. Shead, N.W., Derevensky, J.L. and Gupta, R. (2010). Risk and protective factors associated with youth problem gambling. International Journal of Adolescent Medical Health, 22, 39-58. Slutske, W.S., Caspi, A, Moffitt, T. and Poulton, R. (2005). Personality and problem gambling: A prospective study of a birth cohort of young adults. Archives of General Psychiatry, 62, 769-775. Slutske, W.S., Zhu, G., Meier, M. and Martin, N.G. (2010a). Genetic and environmental influences on disordered gambling in men and women. Archives of General Psychiatry, 67, 624-630. Slutske, W.S., Piasecki, T.M., Ellingson, J.M. and Martin, N.G. (2010b). The family history method in disordered gambling research: a comparison of reports obtained from discordant twin pairs. Twin Research in Human Genetics, 13, 340-346. Steel, Z. and Blaszczynski, A. (1998). Impulsivity, personality disorders and pathological gambling severity. Addiction, 93, 895-905. Tavares, H., Martins, S.S., Lobo, D.S., Silveira, C.M., Gentil, V. and Hodgins, D.C. (2003). Factors at play in faster progression for female pathological gamblers: an exploratory analysis. Journal of Clinical Psychiatry, 64, 433-438. Tschibelu, E. and Elman, I. (2011). Gender differences in psychosocial stress and its relationship to gambling urges in individuals with pathological gambling. Journal of Addictive Diseases, 30, 81-87.

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Chapter 4

PATHOLOGICAL GAMBLING IN FRONTOTEMPORAL DEMENTIA Abhinav Rastogi1 and Andrea E. Cavanna1,2 1

Department of Neuropsychiatry, BSMHFT and University of Birmingham, UK 2 Institute of Neurology, University College London, UK

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ABSTRACT Pathological gambling (PG) is an impulse control disorder characterized by the irresistible urge to gamble, potentially leading to severe personal and social consequences. This disorder has been reported in a few neurological conditions, mainly affecting the dopamine reward pathways. The present chapter reviews the literature on the recent reports of late-onset PG in patients with frontotemporal dementia (FTD). It is suggested that dysregulation of prefrontal cortex and mesolimbic pathways might be implicated in the pathophysiology of gambling behaviors associated with neurodegenerative processes. Specifically, these case reports show that FTD can present with a wide range of behavioural symptoms and should be considered in the differential diagnosis of late-onset PG.

Keywords: Pathological gambling; Frontotemporal dementia; Dopamine; Reward

INTRODUCTION Pathological gambling (PG) is classified by the Diagnostic and Statistical Manual of Mental Disorders-Fourth edition, text revision (DSM-IV, 2000) as an impulse control disorder, characterized by failure to resist the impulse to gamble despite severe and devastating personal, family, or vocational consequences. The lifetime prevalence of PG in 

Correspondence: Dr Andrea E. Cavanna, MD PhD, Department of Neuropsychiatry, The Barberry National Centre for Mental Health, 25 Vincent Drive, Birmingham B152FG, United Kingdom, Email: [email protected]

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adults in North America has been estimated to be 1.6%. (Shaffer et al., 1999). PG is also associated with significantly high comorbidity with other psychiatric disorders. The rates of comorbid alcohol use disorder and drug use disorder have been estimated to be as high as 73.2% and 38.1% respectively (Petry et al., 2005). Apart from high comorbid prevalence rates, PG and drug use disorders share a number of phenomenological characteristics including craving, tolerance, repeated attempts to cut back and continued behaviours in spite of harmful effects. The mesolimbic reward-system is thought to play a pivotal role in the development and maintenance of drug addiction, and several lines of evidence converge towards the hypothesis that drug-addicted subjects have a deficient reward-system and that drug intake is an attempt to compensate for this deficit (Potenza, 2001). It has been suggested that dopamine release in the accumbens is required for the drug high and for the initiation of addiction but that repeated use of a drug causes gradual recruitment of the prefrontal cortex and its glutamatergic efferents to the accumbens (Kalivas and Volkow, 2005). It has been speculated that PG might also be related to disruptions in mesolimbic dopaminergic pathways (Blum, 1996). In a controlled study of 12 pathological gamblers, Reuter et al noted decreased activation of the ventral striatum and decreased ventromedial pre-frontal cortex activation thereby favoring the view that PG is a non-substance-related addiction (Reuter et al., 2005). Recent research demonstrates that PG could be overrepresented in a number of neurological conditions, including Parkinson disease (Voon et al., 2006) and restless legs syndrome (Tippmann-Peikert et al., 2007), possibly due to dysregulation of dopaminergic reward pathways following neurodegenerative or iatrogenic processes (Grosset et al., 2006). A recent study demonstrated an association between PG and frontal lobe dysfunctions in nondemented patients with Parkinson Disease thereby asserting that low scores on the frontal assessment battery (FAB) indicates patients with Parkinson disease at high risk for PG (Santangelo et al., 2009). Moreover, it has been suggested that PG in Parkinson Disease is associated with frontal dysfunction even in the absence of memory dysfunction, which is in contrast with other impulse control disorders such as hypersexuality that appear to be associated both with frontal and memory dysfunction (Vitale et al., 2011). Frontotemporal dementia (FTD) is a progressive condition characterized by selective degeneration of the frontal and anterior temporal lobes, resulting in profound alterations in behaviour and social conduct, in the context of relative preservation of perception, spatial skills, praxis, and memory (Neary et al., 1998). It has been pointed out that behavioural criteria could be inadequate for the early diagnosis of FTD, as they tend to overlook a number of psychiatric features. In fact, it has been reported that the first symptom of FTD may be depression, anxiety, or other psychiatric features not included in the DSM-IV-TR classification scheme for this disorder (Mendez and Perryman, 2002, Rosen et al., 2002). Patients with FTD may present with a wide spectrum of psychiatric and impulse control disorders including anxiety, depression, hyperorality, changed dietary habits, hoarding, disinhibition, agitation and self-neglect. Late onset alcoholism or sudden increase in alcohol intake may also be a part of the behavioural spectrum. In a retrospective study of 19 neuropathologically verified cases with FTD, two patients, previously teetotallers, started to abuse alcohol, whereas several patients with hitherto modest social drinking habits increased their alcohol consumption (Passant et al., 2005). In the same study, the authors noted that one of their patients developed PG however further details were not provided. Early recognition of behavioural symptoms has significant clinical implications especially as presence of minimal

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behavioural impairment, especially in lack of cognitive symptoms, may confer a higher risk of dementia as compared to minimal cognitive impairment and this is more likely to be a prodromal phase of FTD (Taragano et al., 2009). A recent meta-analysis identified seven significant pathological clusters including several frontomedian regions (anterior medial frontal cortex, pregenual anterior cingulate cortex, nomenclature according to Vogt (2005), subcallosal/septal area, gyrus rectus), medial thalamus, left superior frontal sulcus, and right anterior insula (Schroeter et al., 2008). In a study of 29 patients with FTD, ventromedial frontopolar cortex was identified as the single area that was affected in each and every patient (Salmon et al., 2003). Medial orbitofrontal cortex is related to learning, prediction and decision making for emotional/reward related behaviours (Kringelbach, 2005). Similar neural co-relates in PG may lead to the speculation that there may be association between FTD and PG. However, to the best of our knowledge, in the literature there are only three cases of PG as prominent feature of FTD. Lo Coco and Nacci (2004) described a 49-year old male, without pre-existing medical and psychiatric history, presenting with PG as an initial symptom of FTD. This was accompanied with progressive decline in patient‘s social conduct, disinhibition and distractibility thereby suggesting that FTD could be considered in the differential diagnosis of a new-onset gambling behaviour in adults if there are changes of personality and other more ―typical‖ features of FTD. According to the authors, this case supported the idea that an abnormal functioning of the orbitofrontal cortex might be implicated in the pathophysiology of gambling behaviour (Lo Coco and Nacci, 2004). Nakaaki et al. (2007) described a 54-year old man affected by FTD presenting hoarding and dietary changes as initial symptoms. Gradually the patient developed marked PG and sexual disinhibition shortly after. The patient was deemed to have no insight into his illness. Brain magnetic resonance imaging examination showed bilateral mild frontal lobe atrophy, with no other abnormal findings. A brain 99mTc-ethylcysteinate dimer single-photon emission computed tomography (99mTc-ECD-SPECT) examination showed bilateral hypoperfusion in the frontal lobes. The authors reported significant hypometabolism in the left inferior frontal region, bilateral orbitofrontal and medial frontal regions of the cerebral cortex and also in the left cingulate gyri and the left insula. The performances of the patient on the Wechsler Adult Intelligence Scale, Revised and the Mini-Mental State Examination were normal. Patient‘s visuospatial functions and performances on attention and executive function tasks were reported to be within the normal range. However marked impairment was noted on the Iowa Gambling Task (IGT) where the patient persistently kept on choosing the disadvantageous card decks. The authors suggested that the IGT might be a useful tool for assessing decision-making cognition in patients with frontal variant FTD (fv-FTD), even during the early stages of the disease (Nakaaki et al., 2007). Manes et al (2007) described the case of a 69 years old lady who presented with PG as the only initial presentation symptom. The behavioral deficits were not accounted for by a medical disorder, substance-induced condition or use of any medications associated with PG, such as dopamine agonists. On the first assessment, the patient‘s cognitive performance was entirely normal even on executive tasks, with the exception of IGT. The patient met the DSM-iv diagnostic criteria for PG. A diagnosis of FTD was established according to Lund– Manchester criteria. Progressive frontal lobe atrophy was detected on MRI scans taken at initial presentation and after 2 years. Marked hypoperfusion of the frontal and temporal lobes was detected by hexamethylpropyleneamine oxime single photon emission Ct (HmPao-

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Abhinav Rastogi and Andrea E. Cavanna

sPeCt). These findings supported a diagnosis of behavior variant Frontotemporal dementia (bv-FTD). On the neuropsychiatric inventory the patient‘s profile was characteristic of bvFTD, with high scores in the domains of apathy, disinhibition and stereotyped behaviors. The authors initiated treatment with paroxetine for impulsive behavior and carbamazepine to stabilize mood. Authors reported that compulsivity and impulsivity were reduced with treatment. In addition to the drug therapy, authors implemented psychoeducation, behavioral intervention, and assistance to the caregivers as part of a non-pharmacological treatment program. Over the next 2 years, the patient‘s executive symptoms progressed, and her behavioral changes increasingly worsened, with severe frontal lobe dysfunction, perseveration, echolalia, and poor emotional and cognitive awareness (Manes et al., 2010). These case reports provide the evidence that PG can be an initial behavioural manifestation in FTD. In two cases (Lo Coco 2004 and Manes 2010) PG was the initial presenting symptom whereas in another case (Nakaaki 2007) PG emerged after other behavioural symptoms. It has been pointed out that the initial behavioural manifestations of FTD vary depending on the regions that are involved early in the disease, as revealed by functional neuroimaging investigations (Weder et al., 2007). Converging evidence suggests that frontal and temporal regions may contribute independently to the development of impulse dyscontrol and compulsive behaviours (Grant et al., 2006). Prefrontal and temporal regions are differentially associated with apathy and disinhibition, the two main behavioral symptoms in FTD (Zamboni et al., 2008). The severity of apathy correlated with atrophy in the right dorsolateral prefrontal cortex and the severity of disinhibition correlated with atrophy in the right nucleus accumbens, right superior temporal sulcus, and right mediotemporal limbic structures (Zamboni et al., 2008). In particular, a recent study showed that temporal atrophy in the context of FTD could be associated with complex compulsive behaviours (Rosso et al., 2001). A possible mechanism might be that atrophy disrupts frontotemporal-limbicsubcortical circuitries involved in the suppression of compulsive thoughts and behaviours in healthy subjects; specifically, temporolimbic structures, including the amygdala, might be involved in the inhibition of compulsive impulses, leading to expression of highly rewarding compulsive behaviours when selectively damaged (Rosso et al., 2001). In a study that examined the neural substrates by which appetitive incentive value influences prospective goal selection, using positron emission tomographic neuroimaging Arana et al (2003) showed that orbitofrontal cortex and amygdala are differentially involved in reward behaviours. In this study, sated subjects were presented with restaurant menus constructed from individual food preference ratings and half of them were also asked to make a choice. Both amygdala and medial orbitofrontal cortex were activated on incentive valuation. Medial orbitofrontal cortex was selectively activated when incentive value informed goal selection(Arana et al., 2003). Substance misuse, PG and FTD seem to have overlapping neural correlates characterised by disruption of orbitofrontal cortex and mesolimbic pathways. The exact neural basis of PG in FTD remains unascertained. Progressive degeneration of the anterior regions of medial frontal structures characterising the early stages of the bv-FTD has been associated with the theory of mind deficits in patients with FTD thereby suggesting the importance of using theory of mind tests during the diagnostic process of bv-FTD (Adenzato et al., 2010). In a study of a group of patients with early/mild fvFTD and matched normal controls on the IGT for affective decision-making, and the ―reading the mind in the eyes‖ and faux pas tests of ToM, the authors noted that the performance on ToM tasks didn‘t co-relate with IGT and suggested that

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whilst similar prefrontal circuitry is implicated in ToM and decision making tasks, these cognitive domains may be independent (Torralva et al., 2007). In two of the case reports of PG in FTD described above, the cognitive examination including executive functioning were normal (Manes et al., 2010, Nakaaki et al., 2007). Standard frontal and executive cognitive tests or ToM tests may therefore not be adequate to assess varied behavioral symptoms such as PG that may present in the initial stages of FTD. The IGT (Bechara et al., 1994) requires the subjects to make choices from four decks of cards, labeled A–D. The subjects are told that the goal of the task is to maximize profit on the loan of play money, they are free to switch from any deck to another at any time, and as often as wished but they are not told ahead of time how many card selections must be made; the task is stopped after a series of 100 card selections. Each choice results in the subject either winning or losing money in form of penalties. Decks A and B results in high rewards but over time they result in overall loss due to occasional high penalties. Deck C and D result in small rewards but result in net gain over time as they also have smaller penalties. Healthy controls typically sample from the four decks and realize that the decks fall into two categories. Healthy subjects are likely to develop a safe strategy and will consistently choose cards from decks C or D, realizing that even though the rewards are smaller they are more likely to win over time due to smaller penalties. However, patients with PG or FTD may persistently choose Deck A and B due to the high rewards associated with the decks and may overlook the consequences of high penalties associated with these decks. In both of the case reports of PG in FTD (Manes et al., 2010, Nakaaki et al., 2007), the patient had significantly reduced performance on IGT thereby asserting the usefulness of IGT as an adjunct tool in patients presenting with PG. At present, there is no disease modifying treatment for FTD. Manes et al noted that the compulsivity and impulsivity was reduced in their patient with PG in FTD after commencing paroxetine (Manes et al., 2010). However the authors did not comment if it indeed led to an improvement in patient‘s PG. Conventionally there has been considerable interest in selective serotonin reuptake inhibitors (SSRI) in symptomatic treatment of behavioral manifestations of FTD as well as PG. The results from recent researches however have been less promising. A multicenter, randomized, placebo-controlled, double blind study on PG found no significant difference between paroxetine and placebo (Grant et al., 2003). Similarly a double blind randomised control trial of paroxetine in FTD showed no improvement in behavioural symptoms. Moreover the authors of this study noted that chronic course of paroxetine may selectively impair paired associates learning, reversal learning and delayed pattern recognition (Deakin et al., 2004). A recent double blind, placebo-controlled study investigated the effects of a single dose of methylphenidate upon a range of different cognitive processes including those assessing prefrontal cortex integrity in patients with FTD. The patients showed decreased risk taking behavior without any significant effects on other aspects of cognitive function, including working memory, attentional set shifting, and reversal learning (Rahman and Clark, 2006). The authors of this study hypothesized that methylphenidate may ameliorate reward-based deficits in fvFTD by stimulating dopaminergic transmission in the orbitofrontal fronto-striatal circuitry. The current literature on psychotherapeutics for behavioural manifestations of FTD is limited and medications should be chosen only when absolutely necessary and based on needs of individual cases.

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In conclusion, these case reports provide additional evidence that FTD (especially fvFTD) should be considered in the differential diagnosis of late-onset PG, and raise the possibility of broadening the behavioural criteria for FTD toward psychiatric symptoms in the early phase of the disease. Additional research is needed to further clarify the relationship between behavioural symptoms and regional brain involvement in patients with FTD.

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frontotemporal lobar degeneration (FTLD) presenting with pathologic gambling and hoarding as the initial symptoms. Cogn. Behav. Neurol., 20, 121-5. Neary, D., Snowden, J. S., Gustafson, L., Passant, U., Stuss, D., Black, S., Freedman, M., Kertesz, A., Robert, P. H., Albert, M., Boone, K., Miller, B. L., Cummings, J. and Benson, D. F. 1998. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology, 51, 1546-54. Passant, U., Elfgren, C., Englund, E. and Gustafson, L. 2005. Psychiatric symptoms and their psychosocial consequences in frontotemporal dementia. Alzheimer Dis. Assoc. Disord, 19 Suppl 1, S15-8. Petry, N. M., Stinson, F. S. and Grant, B. F. 2005. Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. J. Clin. Psychiatry, 66, 564-74. Potenza, M. N. 2001. The neurobiology of pathological gambling. Semin. Clin. Neuropsychiatry, 6, 217-26. Rahman, S. R., T. W.; Hodges, J. R.; Mehta, M. A,; Nestor, P.J,; and Clark, L. S., B.J. 2006. Methylphenidate (‗Ritalin‘) can Ameliorate Abnormal Risk-Taking Behavior in the Frontal Variant of Frontotemporal Dementia. Neuropsychopharmacology, 31, 651-658. Reuter, J., Raedler, T., Rose, M., Hand, I., Glascher, J. and Buchel, C. 2005. Pathological gambling is linked to reduced activation of the mesolimbic reward system. Nat. Neurosci., 8, 147-8. Rosen, H. J., Hartikainen, K. M., Jagust, W., Kramer, J. H., Reed, B. R., Cummings, J. L., Boone, K., Ellis, W., Miller, C. and Miller, B. L. 2002. Utility of clinical criteria in differentiating frontotemporal lobar degeneration (FTLD) from AD. Neurology, 58, 1608-15. Rosso, S. M., Roks, G., Stevens, M., De Koning, I., Tanghe, H. L. J., Kamphorst, W., Ravid, R., Niermeijer, M. F. and Van Swieten, J. C. 2001. Complex compulsive behaviour in the temporal variant of frontotemporal dementia. J. Neurol, 248, 965-70. Salmon, E., Garraux, G., Delbeuck, X., Collette, F., Kalbe, E., Zuendorf, G., Perani, D., Fazio, F. and Herholz, K. 2003. Predominant ventromedial frontopolar metabolic impairment in frontotemporal dementia. Neuroimage, 20, 435-40. Santangelo, G., Vitale, C., Trojano, L., Verde, F., Grossi, D. and Barone, P. 2009. Cognitive dysfunctions and pathological gambling in patients with Parkinson's disease. Mov. Disord, 24, 899-905. Schroeter, M. L., Raczka, K., Neumann, J. and Von Cramon, D. Y. 2008. Neural networks in frontotemporal dementia--a meta-analysis. Neurobiol. Aging, 29, 418-26. Shaffer, H. J., Hall, M. N. And Vander Bilt, J. 1999. Estimating the prevalence of disordered gambling behavior in the United States and Canada: a research synthesis. Am. J. Public Health, 89, 1369-76. Taragano, F. E., Allegri, R. F., Krupitzki, H., Sarasola, D. R., Serrano, C. M., Lon, L. And Lyketsos, C. G. 2009. Mild behavioral impairment and risk of dementia: a prospective cohort study of 358 patients. J. Clin. Psychiatry, 70, 584-92. Tippmann-Peikert, M., Park, J. G., Boeve, B. F., Shepard, J. W. and Silber, M. H. 2007. Pathologic gambling in patients with restless legs syndrome treated with dopaminergic agonists. Neurology, 68, 301-3.

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Torralva, T., Kipps, C. M., Hodges, J. R., Clark, L., Bekinschtein, T., Roca, M., Calcagno, M. L. and Manes, F. 2007. The relationship between affective decision-making and theory of mind in the frontal variant of fronto-temporal dementia. Neuropsychologia, 45, 342-9. Vitale, C., Santangelo, G., Trojano, L., Verde, F., Rocco, M., GROSSI, D. and BARONE, P. 2011. Comparative neuropsychological profile of pathological gambling, hypersexuality, and compulsive eating in Parkinson's disease. Mov. Disord, 26, 830-6. Voon, V., Hassan, K., Zurowski, M., Duff-Canning, S., De Souza, M., Fox, S., Lang, A. E. And Miyasaki, J. 2006. Prospective prevalence of pathologic gambling and medication association in Parkinson disease. Neurology, 66, 1750-2. Weder, N. D., Aziz, R., Wilkins, K. and Tampi, R. R. 2007. Frontotemporal dementias: a review. Ann. Gen. Psychiatry, 6, 15. Zamboni, G., Huey, E. D., Krueger, F., Nichelli, P. F. and GRAFMAN, J. 2008. Apathy and disinhibition in frontotemporal dementia: Insights into their neural correlates. Neurology, 71, 736-42.

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Chapter 5

DECISIONAL PROCESSES UNDERPINNING ONLINE GAMBLING James G. Phillips and Rowan P. Ogeil School of Psychology and Psychiatry, Monash University, Australia

ABSTRACT

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The use of electronic devices (computers, mobile phones) to deliver gambling services has led to an unprecedented potential for the industry to monitor (consumer loyalty) and support (odds tabulation) gambling behaviours. Drawing on our previous work with decision aids, the present chapter develops theory to better understand how these enhanced capabilities can influence decisions during gambling. Although (Pascallian) probability theory was developed to quantify odds during gambling, it only really applies within a casino where actual sample sets are known. In the real world outside a casino, statements of probability are regressive, supplying information as to the relative frequency of past events. As such, forecasts of the odds for real life events (Baconian probability) are only as accurate as the underlying model upon which they are based, and such models can be regularly updated to be protected from disconfirmation. Gamblers, as intuitive scientists, apply their understanding of the odds of real life events to relatively trivial chance events, and then allocate funds to these outcomes. Gamblers have a more cognitively complex appreciation of the odds of winning, taking into account not just the posted odds, but also the duration of the gambling experience, their systems for overcoming these odds, and any information that supports these systems. The ability to prolong a session (whether by extra funds or inducements) is felt to increase the chances of winning (gamblers fallacy). Decisional support can also contribute to the systems gamblers employ to beat the odds, encouraging further gambling. Although the long term (Pascallian) expectation is for gamblers to lose, for a gambler each win confirms a (Baconian) belief that the odds can be beaten, whereas abstinence guarantees that no further wins are possible (entrapment). Indeed our studies of decisional style indicate that gamblers‘ decisions are less likely to be influenced by long term outcomes. Based upon our work with decision aids, an evidential model of gamblers‘ decision 

Correspondence: Dr James Phillips, School of Psychology and Psychiatry, Monash University, Australia, Email: [email protected].

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James G. Phillips and Rowan P. Ogeil making is presented, in which we outline the roles electronic inducements and decisional support play in sustaining gambling behaviour.

Keywords: Online gambling; Decisional process; Baconian probability; Pascallian probability; Gamblers fallacy; Entrapment; Decision aids

The capacity to transmit information and process financial transactions on a variety of computing devices such as workplace computers, digital televisions or mobile phones now means that it is becoming increasingly difficult to prevent people from accessing gambling via the Internet (Parke and Griffiths, 2004), and there are indications that a higher proportion of internet gamblers are problem gamblers (Griffiths and Barnes, 2008; Griffiths, Wardle, Orford, Sproston, and Erens, 2008; Wood and Williams, 2007; 2009). However; the capacity to gamble remotely has been present in some form since the advent of telecommunications. At issue are actually the more tangible improvements in the capability of telecommunications to make gambling personally accessible (at one‘s finger tips 24 hours a day, 7 days a week) (Griffiths, 2003; Griffiths, Parke, Wood, and Parke, 2006) and the ability to offer a complete package comprising all the necessary elements of the gambling experience (odds, cash and outcomes online) (Griffiths, 2003; 2007; Griffiths and Barnes, 2008). To inform providers and regulators the present chapter reviews factors underpinning gambling behaviour, thereby identifying some of the essential forms of information that need to be conveyed to conduct both offline gambling and online gambling.

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PROBABILITY AND GAMBLING Classic probability theory evolved in the 1800s from the work of Pascal, Fermat and others to address the specific circumstances faced by gamblers of that period (such as Pascal‘s acquaintance Chevalier de Méré). Indeed Pascal was involved in the development of the roulette wheel. Classic probability theory (herein called Pascallian probability) considers the likelihood of a specific event or combination of independent events (the sample) relative to all possible events (the population) (Cohen, 1979). Pascallian probability theory is typically used to explain why people, particularly gamblers are deluded (e.g. Tversky and Kahneman, 1974). In an effort to understand gambling behaviour the present chapter will outline some of the real-world limitations of Pascallian probability theory (Cohen, 1979), to better understand some of the delusions of gamblers and how electronic developments might influence these delusions. The following sections address some of the limitations associated with assumptions made by Pascallian probability theory and indicate that as these assumptions are special cases that only apply within the confines of a Casino it is not surprising that the behaviour of gamblers (especially those of lower education) are influenced by their understanding of real world processes and thus depart from mathematically recommended strategies (Edwards and von Winterfeldt, 1986). This is particularly likely as Pascallian probability only really applies in Casinos and gamblers are likely to bet on other less mathematically tractable propositions such as sports and racing as well (Turner, Fritz and Mackenzie, 2003).

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THE POPULATION OF EVENTS To determine the likelihood of a specific event or a combination of events, Pascallian probability theory requires that all possible events be known (Cohen, 1979). Outside of Casinos this is unlikely. For instance, in real life events that are frankly unimaginable can sometimes occur (Taleb, 2007). In the northern hemisphere, prior to the discovery of Australia, one such unthinkable event was the existence of a ―black swan‖, another was the existence of an egg laying mammal. Hence, naturalists from the northern hemisphere were amazed at the existence of such creatures in the southern hemisphere. Indeed such unforeseen events are sometimes now called ―black swans‖ (Taleb, 2007). Pascallian probability theory requires that all possible events be known, and thus strictly only really applies to the sorts of discrete events occurring in Casinos (Taleb, 2007). Indeed the very strong belief that probability theory applies outside Casinos (as is taught in our school systems) is sometimes called a ―ludic fallacy‖ (Taleb, 2007). In fact, even within Casinos specific efforts have to be made by regulatory bodies and dealing procedures to ensure that only these specified events are allowed to occur. This can be illustrated by consideration of the Australian game called ―Two Up‖ (Solonsch, 1991), where these untoward events are actually formalised. In ―Two Up‖ two coins are thrown by a patron (called ―the Spinner‖) who attempts to spin a nominated outcome, such as both coins landing heads up (or both coins landing tails up). Croupiers (called ―Boxers‖) are required to ensure that the patron tosses the coins appropriately, that is one coin is placed head up and the other coin is placed tail up on a piece of wood (called ―the kip‖) and the coins are thrown up with a twist of the wrist such that the coins spin and travel a required distance in the air. If the coins do not spin or go high enough in the air, the croupiers are required to catch the coins and declare ―no spin‖, or failing that step on the coins to ensure that an outcome is not discerned by patrons (thereby generating dispute). Similarly, ―no spin‖ is declared if coins land on their side (this can happen if coins land near a wall). If the spin is deemed valid, and the coins land on the ground, bets are paid on two outcomes, namely two coins both showing ―heads‖ or two coins both showing ―tails‖. Bets are not paid if the two coins show odds (that is one ―head‖ and one ―tail‖). These odd outcomes that do not enter into normal betting are still counted, and if too many of these occur, the Casino wins all bets. This is how the Casino maintains its‘ edge over patrons, but it is mentioned because it explicitly illustrates how the population of possible outcomes associated with the use of two coins to throw 2 heads or 2 tails is managed. Other games of chance within Casinos also have a variety of disallowed outcomes to ensure that the ―laws‖ of chance apply. For instance, in roulette a ball spins around a wheel with 37 numbers (0 to 36), with the wheel itself spinning in the opposite direction. Bets can be placed on a variety of ranges of outcomes. Disallowed events are for the wheel to stop spinning, presumably because the outcome may thus become more predictable (Thorp, 1982). Nevertheless there are other events that are disallowed. ―No spin‖ is declared if the ball misbehaves. For instance, the ball may be spun out of the wheel. The ball may balance perfectly on the rim of the wheel, or balance on the edge of the divider between two numbers. These events are relatively infrequent, but they definitely do occur within Casinos as we have seen them ourselves. In fact dealing procedures actually specify that wheels are not be spun too fast so as to reduce the likelihood that the ball balances on the rim of the wheel. To our

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knowledge no Casino allows bets on these unusual events, indeed, it is only because Casinos and regulatory bodies monitor and control these disallowed events that fair and sensible calculation of probabilities can occur (see Blaszczynski, Ladouceur, Nower, and Shaffer, 2008). However, in these days of ―betting exchanges‖ it is potentially possible for people to take up bets on unusual occurrences. Nevertheless, the actual odds of these disallowed events are far more difficult to determine. As will be explained later, in such cases it is no longer possible to calculate the odds based upon the total population of events, instead one would either determine odds on the basis of what bettors are willing to pay, or upon the basis of past frequency of such events as is done when wagering on sports or racing using betting exchanges, bookies or totalisator systems.

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INDEPENDENCE OF EVENTS Pascallian probability requires that events be independent when probabilities are calculated. In the game of Two Up, the Spinner volunteers to bet on his ability to spin up 3 events in a row, namely 3 pairs of heads, or 3 pairs of tails (the successful performance of this feat pays 7.5 to one). Other gamblers bet on the outcome of individual spins. In the delusion described as ―gamblers fallacy‖ it is assumed that after a series of heads, that the ―laws of probability‖ will require that a tail will be spun. Hence some gamblers are more likely to bet on tails if the spinner has been successfully throwing heads. However Pascallian probability theory insists that this is a delusion called gamblers‘ fallacy. Coins do not come equipped with onboard memories and brakes that will record and stop a coin if it is breaking the law of probability (Levez, 2006). Nevertheless, gamblers will sometimes act as if this is the case. Hence the prior history of outcomes is of interest to gamblers. Pascallian probability theory only gives a likelihood that a specific event will occur. Indeed the proportions of times that a specific event as predicted by probability theory over a number of repeated trials will only be correct at asymptote when the number of trials approaches infinity (Eisler, 1992). This means that there are short term departures from what might be expected according to probability theory, and these short term departures appear to be of more interest to gamblers (Lopes, 1981) (e.g. a series of heads in two up). Research has indicated that people‘s perceptions of randomness differ in quantitative ways from that occurring from random number generators (Wagenaar, 1972). When people are asked to generate a random series of values (e.g. heads or tails), people supply more alternations and fewer runs than would be expected by chance (Wagenaar, 1972). It seems likely that people‘s perceptions are specifically adapted to detect patterns even when no such pattern exists (Gilovich, Vallone, and Tversky, 1985). But most of us would find it unnerving if faced with 6 heads thrown in a row at Two-up, or if the same number (e.g. 19) was spun in 3 out of 4 spins of a roulette wheel. Indeed even Pascallian probability gives these events low likelihoods (i.e. 6 heads p=.015625 or 19 spun in 3 out 4 spins p=.0000768). Nevertheless such conjunctions of events can occur and are quite memorable (Brown and Kulik, 1977) and tend to be noticed over other events, potentially serving as evidence supporting a belief that a wheel is biased or that a hand is ―hot‖ and more likely to throw more winning shots in basketball (Gilovich, Vallone, and Tversky, 1985).

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Apart from the memorability of unusual events, there is even some mathematical basis for thinking that short term departures from randomness could occur. When gambling online, instead of the computer cycling randomly without replacement through a matrix/list of all possible outcomes (thereby ensuring fixed odds), an algorithm is used to supply random numbers (Eisler, 1992). These algorithms are functions that have been determined by regulatory bodies to provide a specified distribution (e.g. uniform/flat) as the number of events sampled approaches asymptote. Randomisers typically need a starting value (a seed) to work. However, as these randomisers tend to be mathematical functions, inputting the same number will always return the same value, hence after initialising the randomiser, the randomiser tends to use the previous number to ensure that the function continues in a random fashion. Indeed a person knowing the randomising function and the initial seed would potentially be able to predict every outcome (Kelsey, Schneier, Wagner, and Hall, 1998; Thorp, 1982; Turney and Horbay, 2004), hence this information is kept under very strict control. If there is a human tendency to detect patterns, there is also a tendency for some simple systems to organise themselves into patterns (Gleick, 1987). According to chaos theory, iterations of a simple system may have a number of properties that are less desirable. Repeated iterations can sometimes reveal ―attractors‖, that is parts of simple systems that tend to ―stick‖ and return the same values (Gleick, 1987). For such reasons multiple randomisers may be used. Nevertheless, chaos theory also notes that repeated iterations of several simple systems can sometimes lead to ―resonance‖ where the systems tend to bounce the same values back and forth (Gleick, 1987). The existence of these attractors and resonances offer some real world justification for gamblers‘ interest in short term outcomes, bias and cycles (Eisler, 1992) Indeed laypersons (Wagenaar, 1972), gamblers (Gilovich, Vallone, and Tversky, 1985) and even Casino staff (Vinson, 1987) perceive ―random‖ events to be less random than they would expect, such that gamblers are predisposed to respond to short term outcomes. Although Casinos initially sought to monitor outcomes as a means of detecting anomalies of dealing procedure or problems with randomising devices, such as biases in roulette wheels, they sometimes make such information available to punters as a ―form guide‖. We mention this point because such information appears to influence gambling behaviour. Within most Casino games the relative frequencies merely refer to previous behaviour, although they could otherwise be discerned by gamblers as evidence of bias in randomisers. Even if prior outcome history merely contributes to gamblers‘ delusions, there are games of chance where even Pascallian probability theory would agree that past history is of more definite use to gamblers. In the offline game of Blackjack, cards can be dealt without replacement from a box called a ―shoe‖. Higher proportions of 10, J, Q, K remaining in the shoe tend to favour the player (Thorp, 1966), hence some players count the cards that have been played, and increase their wagers when they judge the proportions are in their favour. Although Casinos normally always have a margin in their favour, when the proportions of cards are right in Blackjack, it is possible for the odds to be slightly in the players‘ favour. Hence Casinos have tended to ban card counters (Lehman, 1984). Even so, counting cards can be a difficult proposition from 6 deck shoes, but that does not stop people from trying. In fact one of the early wearable computers was actually developed to assist players count cards (http://www.blackjackforumonline.com/ content/taftint.html). In the offline game of Blackjack cards may sometimes be continuously shuffled, and in online games of Blackjack, cards may sometimes be dealt from an infinite deck of cards (i.e.

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generated from a randomiser). This elicits complaints from players (http://wizardofodds.com/ askthewizard/blackjack-shuffling.html and http://wizardofodds.com/askthewizard/blackjackonline.html) as it invalidates card counting strategies. Indeed it illustrates how gamblers are keen to utilise strategies to ―overcome‖ the odds. Not only are failures of independence of interest to gamblers, they can be an important component of the gambling activity. Playing a game when the odds are stacked against you is for fools, and most gamblers have a variety of systems that they believe will help them overcome the odds (Wagenaar, 1988; Walker, 1992a). Thus a variety of forms of information such as ―prior form‖ and the current odds can be crucial to the gambling activity. In particular gamblers tend to seek situations where their opinion as to the odds of winning are better than the currently posted returns (Ceci and Liker, 1986). It is actually immaterial as to where these delusions actually come from, but a belief in a failure of independence and methods of overcoming the odds appear common to most forms of serious gambling (Walker, 1992a). Indeed one of the more serious delusions associated with gambling is that involvement and persistence pays off, when Pascallian probability clearly indicates persistence will lead to a loss as the Casino‘s slight margin asserts itself (Wagenaar, 1988). Although Pascallian probability theory would suggest the difference between no chance and an infinitesimal change is negligible, there is a common belief that one has to be ―in it to win it‖ and that a bet has demonstrably improved one‘s chances of winning. Indeed promotions and inducements are a feature of Casino marketing both offline (Gullo, 2002) and online (Zangeneh, Griffiths and Parke, 2008; http://www.casinomeister.com/ online_casino_spam.php), and inducements to commence gambling tend to encourage the belief that involvement can lead to wins at gambling. John Cohen (1972) has found evidence for the belief that persistence can lead to wins in sequential lotteries. Participants were asked to choose which sequential lottery they would prefer. The choices were mathematically equivalent, being a ½ chance followed by a 1/10 chance, or a 1/10 chance followed a ½ chance. Although mathematically identical according to Pascallian probability, participants were more likely to choose the easy chance followed by the long shot than the long shot followed by the easy chance (Cohen and Hansel, 1959; Cohen and Chesnick, 1970). Although the overall Pascallian probability for these two sequential gambles is the same, the early likely loss from the long shot seems to be felt by people as being more likely to rule out the chance of an eventual win. People feel they have to stay in the game to win it. Walker (1992a) calls this need to continue involvement as entrapment, and it is a particular problem where players make habitual bets. It seems likely that gamblers‘ behaviour is influenced by the prior real-world experience, as outside Casinos skill and persistence can normally lead to successful outcomes (Welford, 1968). It is mathematically unlikely that persistence will lead to long term wins when gambling (Turney and Horbay, 2004), but indications that others are winning (Rockloff and Dyer, 2007) tend to support the belief that persistence pays off. In the eCommerce sector a number of decisional support systems are being developed for the purposes of retaining customers (Phillips, Ogeil, and Blaszczynski, in press b) and complimentary (Gullo, 2002) and consumer loyalty schemes are likely to support the belief that persistence pays off. The game of poker further formalises a belief in persistence. In the simplest version of poker, where just 5 cards are dealt to players, the outcome of the game has been ruled to be simply chance. Nevertheless, a number of versions of poker exist (Levez, 2006) with wild and shared cards and multiple rounds of bidding. In these more complicated versions of poker,

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although education as to the odds and strategies can improve play (Dedonno and Detterman, 2008), the random dealing of cards still means there is an appreciable chance element to outcomes. As there is uncertainty as to whether one‘s hand is good, players can raise their bets or fold and surrender their bets. Hence persistence, can lead to wins in this specific game, either by: 1) staying in the game and meeting opposing players‘ bets and having a better hand; 2) bluffing, making large bets and encouraging other players to fold. Offline forms of poker allow players to scrutinise each other for ―tells‖, non-verbal signs that they are bluffing. This is an element of the skill associated with poker (Dedonno and Detterman, 2008), as the identity and prior history of player behaviour may offer cues as to whether a player is bluffing (Levez, 2006). However online forms of poker may lack these cues (Levez, 2006) and this means that gamblers are less able to exercise this ―skill‖ and thus be at a disadvantage. Decreasing fidelity may impair the gambling experience, interfering with gamblers‘ ability to conduct their own evaluation of odds during poker. Indeed, the effect of decreasing fidelity may extend to other games. For instance, decreased fidelity has been observed to reduce wagering in simulated roulette (Goh, Phillips, and Blaszczysnki, 2011).

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PROBABILITY The classic concept of Pascallian probability considers an event (or combination of events) as a function of the total number of possible outcomes. It operates quite nicely when applied to the discrete sets of events associated with dice, coins, and roulette wheels, but less so when considering more complex events such as the outcome of a horse race or sporting event. For these more complex events, bookies and tabulator agencies determine the odds of winning on the basis of what people are prepared to pay. For instance, if one were to bet on whether a ball would fly out of the roulette wheel, then if other gamblers bet less money on the ball being spun out of the wheel, and gamblers bet more money is on the ball staying within the wheel, then a winning bet on a flying ball would pay greater dividends. In other words the probability of a specific outcome may merely be a statement of belief or a description of the market of bets, rather than the sort of ―lawful‖ relationship described by Pascallian probability theory. Alternatively, if records are kept and a ball had been found on average to spin out of the wheel on 1 in 300 spins in the past, then a flying ball would pay something less than 1/300 to ensure the Casino had an edge on patrons. However, the likelihood of a flying ball is not that simple. Some dealers do it more often than others. Whether due to inexperience, or a difficulty flicking a ball of the wrong size, in the non-preferred hand, some dealers are more likely to flick a ball out of the wheel than others. Thus it is pointed out that certain events occur, but may be difficult to predict their likelihood, instead one can make a best guess based on the relative frequency of events so far. In real life the determination of the odds of certain events can be difficult. The best estimators of such events can be past history (i.e. relative frequency). For instance the arithmetic mean is that value that minimises deviations about it, and is a reasonable bet in the absence of better knowledge. But where there are other events (e.g. gender of the dealer) that

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occur in conjunction with the event of interest (e.g. flying balls) it may be possible to use other techniques (e.g. regression) to predict the likelihood of a ball flying out of the roulette wheel (see http://graphics.cs.columbia.edu/courses/mobwear/resources/thorp-iswc98.pdf). Techniques such as regression use least squares techniques to choose parameters to minimise deviations. In other words they seek to produce the best explanation of a data set, but are sensitive to outliers, and capitalise on chance as they are prone to overfit a set of numbers (Cohen, 1983). Thus an explanation that applies to one set of data may not apply as well to other data sets (i.e. the model is prone to shrinkage) (Cohen, 1983). This means that even if a couple of specific female roulette dealers were more likely to flick the ball out of the wheel last week, the same might not apply the following week. Hence any additional factors that might be used to improve the ability to predict events such as flying balls tend to apply to past history and are only as good as their capacity to correctly infer the underlying factors causing the event. Taleb (2007) warns people to regard all attempts at forecast and predictive algorithms with suspicion. The success of such techniques are based upon past history, and are only as good as the forecaster‘s conceptual model, and when wrong, forecasters tend to maintain the illusion of competence by updating their models (Taleb, 2007). Cohen (1979) makes the distinction between Pascallian probability as applicable in Casinos, and Baconian probability. Pascallian probability properly applies to circumscribed events within Casinos whereas Baconian probability refers to more complex real world events. Baconian probability reflects our understanding of the processes and relevant factors (Cohen, 1979), and applies to real world processes such as the outcomes of horse races and the probability that bridges will fall down. Baconian probability invokes our knowledge of causal theory to predict a likely outcome, and is most formally manifested as the scientific method. Indeed, gambling propositions usually require decisions involving Baconian probability because gamblers are encouraged to predict an outcome to make money. In contrast, Pascallian probability is a theory of the future distribution of outcomes at the end of time, and this is not typically how gambling propositions are couched (except perhaps Martingale systems). When a gambler considers whether a horse will win a race, the gambler needs to consider a variety of factors such as the ―class‖ of the horse and its competition, the conditions of the track, the health of the horse, the draw, the jockey and any strategies required by the owner (Levez, 2006). Hence the likelihood of a specific horse winning improves if one knows it is the favourite, operating at its preferred distance, and has travelled a long way to compete (Levez, 2006). The ability to properly determine the odds of a horse winning is called handicapping, and some gamblers may be better at this task than others (Ceci and Liker, 1987). When an engineer considers whether a bridge will fall down, the engineer applies a knowledge of geometry, physics and statistics to determine the confidence intervals associated with components, that is how many similar components would fail if placed under similar stresses. The engineer sets tolerances that are sufficiently conservative that most bridges do not fall down (Cope, 2004; Walpole and Myers, 1972). But the bridge is still only as good as the underlying conceptual model and the materials used. Sometimes engineers overlook factors such as resonance and bridges collapse (e.g. Tacoma Narrows Bridge). Engineers and gamblers differ in the adequacy of their conceptual models. The conceptual models developed to account for physical systems are far better developed, whereas those developed to account for betting on horses are less well developed. Indeed

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complex systems are notoriously difficult to understand (Bamber and van Santen, 1985), and the additional variability associated with more complex systems may mean that there may be little appreciable benefit from the use of the system. For instance the use of card counting only confers a slight benefit to the player (about a 1% advantage depending upon the system), whereas engineers can build bridges with a far greater certainty that their efforts will be successful (e.g. better than 99.99%). Indeed, the suggestion that there is no skill in gambling is erroneous (see Dedonno and Detterman, 2008). It is more realistic to say that any elements of skill are such that they confer small and only marginal benefit (i.e. are of low efficacy) compared with what is possible in other real world activities such as engineering.

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DECISION PROCESSES In the previous sections we have outlined some of the areas where Pascallian probability fails to account for behaviour in the real world. Pascallian probability does not necessarily apply to real world experience (Cohen, 1979). However, as people are more familiar with the sorts of Baconian probability that operates in the real world, it should not be surprising that gambling behaviour (particularly in the less educated) departs from the sorts of strategies recommended by Pascallian probability. In a series of experiments we have considered the decision processes associated with gambling. It seems the decision to gamble, although deluded, has elements in common with other real life decision making tasks (Coventry, 2002). We have previously suggested that gamblers are intuitive scientists and that their decisions evolve along evidential lines, accumulating support to approach and avoid, with magnitude of wagers reflecting a balance of evidence (Phillips and Amrhein, 1989). More recently the information and evidence gamblers process have become more important within online environments where information may be transmitted in a variety of forms (e.g. advertising, spam) to deliberately influence these decisions (Hrywna, Delnevo, and Lewis, 2007; Phillips, Ogeil, and Blaszczynski, in press a). For our laboratory paradigm we have chosen the game of Blackjack. Unlike many other games of chance, Blackjack is a game where there are strategies that are not deluded (i.e. meaningful to Pascallian probability) and can influence outcomes, and it is possible to manipulate factors within the online environment to determine how decisions may evolve. This has become more important in recent years with the development of online gambling, with both providers (Braverman and Shaffer, 2010; http://www.casinomeister.com/ online_casino_spam.php) and regulators seeking to influence player behaviour (Reid, 2005). In our laboratory paradigm participants are required to play a computerised game of Blackjack. Information relevant to the decision process is variously supplied in terms of advice that serves to minimise player losses (e.g. the Basic system), or advice that confers an advantage to players (i.e. card counting) (Thorp, 1966). In such tasks it is possible to address the effect of variables such as time pressure and risk, and monitor compliance with the decision aid, and measure the influence of the manipulations upon consumer confidence in terms of wagering behaviour. Such studies indicate that the provision of decisional support increases willingness to wager. For instance, in a game of computerised blackjack, Chau, Phillips and Von Baggo (2000) considered the effect of online decisional support upon players' wagering. They

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observed a tendency for players to wager more in the presence of decisional support. Subsequent studies also found that wagering can significantly increase with decisional support. For instance an unpublished study by O'Hare, Phillips, and Moss (2005) observed higher levels of wagering in the presence of decisional support (Basic). This was also demonstrated by Phillips and Ogeil (2007). Philips and Ogeil (2007) found players were more likely to bet more in the presence of decisional support (Basic) in a higher stakes condition than the low stakes condition. Gamblers have a variety of strategies (deluded or not) whereby they seek to overcome the odds (Walker, 1992a), but it may take time to perform the calculations underlying these strategies (Rosecrance, 1988). Hence it should not be surprising that the time available to make decisions may influence gamblers (Phillips and Amrhein, 1989). An unpublished study by Lok (2008) further demonstrated that it takes time for people to process decisional support..

(a)

(b) Figures 1a and 1b. Effect of advice and time pressure (response deadline) upon number of Basic errors (in 35 hands of Blackjack) and the time to select cards.

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Lok (2008) used Basic advice that minimised loss, studying the behaviour of 24 students within the laboratory on our simulated Blackjack task. The data suggested that the processing of the decisional support requires time and may possibly impose an additional processing load on players. During the computerised Blackjack task, Lok (2008) exposed participants to decisional support, but imposed time pressure in the form of a deadline of one, three, or seven seconds for participants‘ responses. The influence of the aid increased with greater exposure time, but only up to a point. Most of the effect of the decision aid upon the reduction of Basic errors had occurred by the three second deadline. Apparently participants need time to appreciate and utilise information online, but continued exposure does not guarantee further influence, possibly because Basic is only a strategy that minimises loss, and participants may prefer to exercise their own personal strategies (see Phillips and Amrhein, 1989). The decrease in error as a function of time is similar to that seen in other evidential decision processes where accuracy increases with the square root of the number of observations (Swets et al., 1959). Phillips, Laughlin, Ogeil, and Blaszczynski (2010) manipulated the sorts of information supplied to gamblers. Sometimes players were only given Basic advice when Hits would be recommended by Basic. At other times players were only given advice when players would be advised to Stand according to Basic. Phillips, Laughlin, Ogeil, and Blaszczynski (2010) demonstrated that such online messages could influence players‘ behaviour in riskier or more conservative directions. This indicates that information in the form of online messaging can be employed to bias player behaviour, and is comparable with other decisions that are subject to bias and shifts in criterion (Swets, Tanner, and Birdsall, 1961). O‘Hare, Phillips and Moss (2009) used the Blackjack paradigm to address consumer response to advice. They monitored education levels and logical reasoning ability. In this study Basic advice was supplied that simply served to minimise loss. Compliance with this advice was associated with logical reasoning ability. Less well educated individuals were more confident when decisional support was available. Better educated individuals used the decision aid to avoid errors. However, people are not unreasoning consumers of decisional support. Phillips and Amrhein (1989) examined wagering when players controlled their own cards, in comparison to decisional support that played for them (a simple "never bust" algorithm). Players bet more when they could control their own cards and use their own strategies. In addition players wagered significantly less on an algorithm when they were losing. Such data suggest that players prefer their own personalised strategies, and are more prepared to abandon advice in the face of losses. When providing online advice to participants, Chau, Phillips and Von Baggo (2000) found participants that were provided with online advice initially felt the outcome of their gambling was less likely to be due to luck, but were less optimistic as to the outcomes of the next set of hands to be played, and these players were less likely to comply with the decision aid as the session progressed. Considerable effort has been devoted commercially to the development of decision aids (see Phillips, Ogeil, and Blaszczynski, in press b) but the use of decision aids appears to vary as a function of the perceived reliability of the aid (Parasuraman and Riley, 1997) or the transparency of its' operation (McSherry, 2005). For instance Phillips, Laughlin, Ogeil, and Blaszczynski (2011) were concerned that people might do the opposite to that recommended by good advice when risk was low. In addition, O‘Hare, Phillips, and Moss (2009) suggested

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that some groups of individuals appeared to test or argue with advice rather that use it appropriately to enhance their performance.

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EVIDENCE Gambling is a heterogeneous activity (Blaszczynski and Nower, 2002; Shaffer et al, 2004) and attempts to explain gambling as the result of a number of cognitive illusions (e.g. Tversky and Kahneman, 1974) runs the risk of engaging in nominal fallacies (Coventry, 2002). Naming a delusion does not mean that we understand it. However, irrespective of the actual delusions themselves, there are indications that behaviours can be understood in terms of the operations of some form of evidential decision process. Gamblers can be considered to be ―intuitive scientists‖ that weigh the evidence that the odds can be overcome (Coventry, 2002; Phillips and Amrhein, 1989). It seems time, personal control, the balance of evidence and fidelity of the experience can increase wagering behaviour. While more work is required there are already some indications that problem gamblers have a reduced criteria to action (Phillips and Ogeil, 2011). Phillips and Ogeil (2011) examined the decisional styles of a cohort of 464 young adults. Applying a previously validated and recognised model of decision making (Mann, Burnett, Radford, and Ford, 1997), Phillips and Ogeil (2011) measured tendencies towards vigilance, procrastination, buckpassing, hypervigilance as well decisional self esteem. Young adults at risk of gambling problems had lower decisional self esteem and higher hypervigilance (panic) scores, indicating that their thought processes were pressured or impulsive, implying that problem gamblers could be more susceptible to inducements. There is evidence to support this, with Phillips, Ogeil, and Blaszczynski (in press a) finding that problem gamblers report more problems with spam, and Wood, Griffiths, and Parke (2007) reporting that 11.4% of their sample first started to play poker online after receiving a spam e-mail or pop-up link. Other data suggests that problem gamblers differ in their criterion for cessation of gambling, and are less likely to stop before they do themselves harm (Brown, Rodda, and Phillips, 2004; Currie et al., 2006; Grifftiths and Whitty, 2010). Brown, Rodda and Phillips (2004) approached people before and after leaving a gaming venue. Problem gamblers were more likely to report bigger losses, and were more likely to leave in distress. Hence it seems that evidential models of impulse control (e.g. Brebner, 1998) that process information until a criterion is reached for initiation or cessation of behaviour could be profitably employed to consider the influence of online messaging and limit setting behaviours (Broda et al., 2008; Nelson et al., 2008), particularly as jurisdictions are considering implementing precommitment strategies that will force gamblers to select a level at which they will stop play (Ariyabuddhiphongs, 2010, Delfabbro, 2008; Nower and Blaszczynski, 2010; Schellinck and Schrans, 2007). Although there are a number of accounts of gambling behaviour (Blaszczynski and Nower, 2002) suggesting that problem gamblers may be emotionally vulnerable, antisocial or have problems with impulse control, a cognitive account invoking the evidence that gamblers process is now becoming more important as it addresses the sorts of information now being used by providers and regulators to influence gambling behaviour and is relevant to some of

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the interventions contemplated (e.g. restricting advertising, precommittment). In the following section we outline other studies seeking to influence gambling behaviour online.

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ONLINE INFLUENCE Gambling researchers have long been interested in gamblers‘ thoughts and motivations to gamble. Economically, the expected value of gambling is negative, such that accumulating debt is inevitable over a large number of trials (Clark, 2010). Therefore, a gambler‘s perceived chance of a win and/or their expected payout is likely to influence their decision to participate in the game (Corney and Cummings, 1985). Researchers have studied gamblers‘ irrational beliefs (Langer, 1975; Walker, 1992b; Delfabbro and Winefield, 2000) and also considered consumer warnings designed within a regulatory framework (to make gamblers‘ aware of their chances of winning) (Monaghan and Blaszczynski, 2007) or from a decisional bias framework (to influence how players bet within a session) (e.g. Phillips, Laughlin, Ogeil, and Blaszczynski, 2011). Behaviour within gambling environments can be influenced by the provision of cues (e.g. visual, aural) which encourage people to bet or continue playing in the belief that they will eventually win (Corney and Cummings, 1985; Rockloff and Dyer, 2007). For instance, Rockloff and Dyer (2007) observed that cues indicating that other players were winning encouraged gambling, and Reid (1986) noted that cues that a player had just missed out on a prize (near miss) also encouraged further gambling. Indeed Corney and Cummings (1985) argued that within gambling environments, recency, particularly when it is associated with either a positive (e.g. big win) or negative outcome (e.g. big loss) is an important factor in determining gambling continuation. For example displays showing the amount of money paid out by a slot machine or the amount of the next jackpot as well as the provision of previously spun numbers in a game of roulette may heighten gamblers‘ perceptions that they are about to win. Some forms of information contribute to gamblers‘ involvement, and has prompted investigation into the decision making of gamblers. Huber and Kühberger (1996) attempted to train people‘s decision making using a decision tree. They required participants to make decisions under high or low risk using four different tasks: three were non-gambling (involved investment or resourcing) and one was a gambling task, where participants had to choose 1 of 4 gambles then spin a wheel to determine if they ‗won‘ or ‗lost‘ an amount of money. The investment and resourcing tasks were more obviously Pascallian in nature, dealing with future distribution of outcomes, whereas the gambling task was more obviously Baconian in that it addressed immediate outcomes. The decision tree was useful for the three non-gambling tasks, but not for the gambling task. Indeed, participants felt the decision tree was trivial and boring for the gambling task. This indicates that people feel that some types of formal analysis are less compatible with gambling tasks. For such tasks people prefer to control rather than passively analyse alternatives (Goodie et al., 2007; Phillips and Amrhein, 1989). Other researchers have used more complex gambling paradigms which manipulate emotion or risk during gambling tasks. Nygren (1998) manipulated mood and risk aversion in participants prior to them undertaking a gambling task. Nygren (1998) demonstrated that positive induced mood enhanced people‘s sensitivity to a loss under conditions of ‗high risk‘,

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and that under conditions of ‗low risk‘ those with a positive induced mood were more likely to take risks. This suggests a moderating role for mood, when people are making decisions involving a gamble and that these may vary depending on the level of perceived risk. In fact we have observed that intoxication reduces players‘ ability to process advice when they need it most (Phillips and Ogeil, 2010). Hence, mood and mental status appears to influence response to risk. Researchers and regulators have questioned whether gamblers are informed consumers and have considered initiatives to educate and promote informed choice in gambling contexts (Productivity Commission, 1999; Monaghan and Blaszczynski, 2007). While Hing (2004) found that in clubs there were high levels of recognition of signs related to the risks associated with problem gambling (86%) or on house policy and available counselling services (70%) by patrons, only 17% of players reduced the duration and 19% reduced the amount of money they spent after viewing such signs (Hing, 2004). Hence there is a discrepancy between recognition of the risks and changes in measurable behaviours linked to problem gambling. The degree of influence of advice may be related to the format by which it is delivered. For example, González-Vallejo et al. (1994) found that the format in which information was presented (either verbal, numerical) by an ‗expert‘ affected students‘ performance on a gambling task. Outcomes were correlated more closely with payoffs when the probabilities were expressed verbally by the expert, as opposed to numerically. Ladouceur and Sévigny (2003) considered whether warning messages or messages about taking a break could affect gambling behaviour on a video lottery terminal. Thirty participants (8 women, 22 men) with a mean age of 38.3 years were assigned to one of 3 groups: Message group – received 5 different messages, each lasting 7sec; a break group (message ‗break‘ was presented after each block of 15 trials) or a non-interruption group who played their video lottery terminal as normal without any message. They found that participants in the message group (136.2) and break group (137.8) played significantly less trials (max 200) than the noninterrupted group (197.2). Interruptions and warnings can curb gambling sessions. Monaghan and Blaszczynski, (2007) demonstrated both ‗free‘ and ‗cued‘ recall of gambling messages were recalled better when the message was dynamic (scrolling across the screen on an electronic gaming machine) as opposed to static (stationary on the screen). Specifically the dynamic presentation of the gambling-related messages were recalled on 83% of occasions compared with 15.6% during the static condition. These studies demonstrate that the format of any consumer warnings given during gambling sessions influences their recall and potential to change behaviour. To be effective any consumer warning need to engage cognitive, emotional, and motivational processes of the gambler, such that they will consider either the duration and/or money spent during a session (Monaghan and Blaszczynski, 2009). One limitation of Monaghan and Blaszczynski‘s (2007) study is that the content of message they provided was generic and they did not consider whether the messages were recalled by those people most at risk of developing a gambling problem. Content more relevant to the individual gambling, that encourages them to reflect on either the time or money they had spent, may be more effective in identifying problem gamblers (Delfabbro, 2008) or modifying gambling behaviour (Monaghan and Blaszczynski, 2010). In a follow–up experiment, Monaghan and Blaszczynski (2010) studied regular electronic gaming machine players (n=127, who played at least once every 2 weeks with n=19 classified

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as problem gamblers by the Canadian Problem Gambling Index (CPGI). Similar to their 2007 result, Monaghan and Blaszczynski (2010) found that warning signs were more often accurately recalled after dynamic compared to static mode of presentation. In terms of behavioural indices, the dynamic messages were also more likely to influence participants‘ thoughts and behaviours within sessions. While there was no difference between the recall of informative (generic) versus self-appraisal (personal) messages, the self appraisal ones were more likely to affect subsequent behaviour and encourage people to take a break thereby reducing players‘ session length). Interestingly, the problem gambler group were less likely to report that warning signs affected their thoughts and behaviour compared with non problem gamblers, however they were encouraged to take more breaks. This finding may mean that those who need the warning labels most (at higher risk of problem gambling) do not use them. To date, there are few studies that have considered the effect of warning messages as interventions to modify gambling behaviour. Steenbergh et al. (2004) sought to measure the impact of a simple warning message on both gambling knowledge (how likely a player is to lose in the long run) and subsequent gambling behaviour within a laboratory setting. There were three conditions in the study: (a) Control (watched a history of gambling in the US video - neutral); (b) Warning (computer Audio Visual message on the odds of winning at roulette and risks associated with gambling (economic, social, legal) + history of gambling video); (c) Warning + Intervention (warning + multimedia presentation on limit setting and belief modifications). The 2 groups exposed to the warning message displayed greater knowledge about losing in the long run and the risks associated with gambling. For example, they were more than 3 times more likely than those in the control condition to nominate that in the long run they would lose, and that for every $100 they bet, they could expect to get back $94.80. In terms of gambling behaviour (time and money limits set prior to play), those in the intervention group set limits 100% of the time prior to the gambling task, while only 35% of warning only group and only 24.3% of controls set limits. However on other behavioural measures (time spent gambling, average relative risk and the total wagered in the session) there were no differences between groups. Providing information on gambling odds and risks can modify subsequent knowledge of likelihood of winning and modify some elements of behaviour (but not all). This suggests that the type of information provided may be used to bias a gambler‘s subsequent behaviour in more conservative directions (see Phillips, Laughlin, Ogeil, and Blaszczynski, 2011).

CONCLUSION Regulation is essential to the conduct of gambling as it ensures fair and meaningful odds, although gamblers are predisposed to respond to short term fluctuations in the odds, and commonly utilise strategies (deluded or not) to overcome the odds. The fidelity of the gambling experience is important to the exercise of these strategies, as is the prior history of outcomes. Inducements and consumer loyalty programs may be important in the initiation and maintenance of gambling as they supply evidence that people can win from gambling. It seems that gamblers act as if they accumulate information according to their belief systems indicating the likelihood that they can beat the odds, but this balance of evidence may be

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altered online by inducements and warnings. A variety of attempts are being made to control online gambling, and an evidential model may assist in understanding the nature and the mode of the delivery of this information to players.

ACKNOWLEDGMENTS The authors would like to acknowledge their intellectual debt to Associate Professor Michael Walker (deceased) and funding support from Gambling Research Australia (Tender No. 119/06).

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Monaghan, S., and Blaszczynski, A. (2007). Recall of electronic gaming machine signs: a static versus a dynamic mode of presentation. Journal of Gambling Issues, 20, 253-268. Nelson, S.E., LaPlante, D.A., Peller, A.J., Schumann, A., LaBrie, R.A., and Shaffer, H.J. (2008). Real limits in the virtual world: Self-limiting behavior of internet gamblers. Journal of Gambling Studies, 24, 463-477. Nower, L., and Blaszczynski, A. (2010). Gambling motivations, money-limiting strategies, and precommitment preferences of problem versus non-problem gamblers. Journal of Gambling Studies, 26(3), 361-372. Nygren, T.E. (1998). Reacting to perceived High- and Low-Risk Win–Lose opportunities in a risky decision-making task: Is it framing or affect or both? Motivation and Emotion, 22, 73-98. O‘Hare, M.A., Phillips, J., and Moss, S. (2005). The effects of time-stress, risk level and deductive logic ability on decision making in dynamic environments. Paper presented at the 32nd conference of the Australasian Experimental Psychology Society, Melbourne, April 2005. O‘Hare, M., Phillips, J.G., and Moss, S. (2009). The effect of contextual and personal factors on the use of recommenders in e-Markets. The Ergonomics Open Journal, 2, 207-216. Parasuraman, R., and Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39, 230–253. Parke, A., and Griffiths, M.D. (2004). Why internet gambling prohibition will ultimately fail. Gambling Law Review, 8(5), 295-299. Phillips, J. G. and Amrhein, P. C. (1989). Factors influencing wagering in simulated Blackjack. Journal of Gambling Behavior, 5(2), 99-111. Phillips, J.G., Laughlin, A.L., Ogeil, R.P., and Blaszczynski, A. (2011). Effects of directional decisional support upon risk taking online. The Ergonomics Open Journal, 4, 47-54. Phillips, J.G., and Ogeil, R.P. (2007). Alcohol consumption and computer blackjack. Journal of General Psychology, 134(3), 333-353. Phillips, J.G. and Ogeil, R.P. (2010). Alcohol influences the use of decisional support. Psychopharmacology, 208(4), 603-611. Phillips, J.G. and Ogeil, R.P. (2011). Decisional styles and risk of problem drinking or gambling. Personality and Individual Differences, 51(4), 521-526. Phillips, J.G., Ogeil, R.P. and Blaszczynski, A. (in press a). Electronic interests and behaviours associated with gambling problems. International Journal of Mental Health and Addiction. Phillips, J.G., Ogeil, R.P., and Blaszczynski, A. (in press b). Human factors determining consumer response to recommenders. In F. Columbus (Ed.), Advertising: Types, Trends and Controversies. New York: Nova Science Publishers. Productivity Commission (1999). Australia‘s gambling industries: Inquiry report (vol. 2). Author: Melbourne, VIC. Available from: www.pc.gov.au Reid, J. (2005). Player information and on-screen messages - using EGMs as a communication channel. Paper presented at the 15th annual conference of the National Association for Gambling Studies, 10-12th November, 2005, Alice Springs. Reid, R.L. (1986). The psychology of the near miss. Journal of Gambling Behavior, 2(1), 3239. Rockloff, M.J., and Dyer, V. (2007). An experiment on the social facilitation of gambling behavior. Journal of Gambling Studies, 23(1), 1-12.

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Rosecrance, J. (1988). Professional horse race gambling: Working without a safety net. In W.R. Eadington (Ed.), Gambling research: Proceedings of the seventh international conference on gambling and risk taking (pp. 11-37). Bureau of Business and Economic Research: University of Nevada-Reno. Schellinck, T., and Schrans, T. (2007). VLT Player Tracking System. Report for the Nova Scotia Gaming Corporation Responsible Gaming Research Device Project. Shaffer, H.J., LaPlante, D.A., LaBrie, R.A., Kidman, R.C., Donato, A.N., and Stanton, M.V. (2004). Toward a syndrome model of addiction: Multiple expressions, common etiology. Harvard Review of Psychiatry, 12, 367-374. Solonsch, M. (1991). The complete guide to Australian gambling. North Brighton, Victoria: Wrightbooks. Steenbergh, T.A., Whelan, J.P., Meyers, A.W., May, R.K., and Floyd, K. (2004). Impact of warning and brief intervention messages on knowledge of gambling risk, irrational beliefs and behaviour. International Gambling Studies, 4, 3-16. Swets, J.A., Shipley, E.F., Sewall, S.A., Green, D.M., McKey, M.J., and Wasserman, A.G. (1959). XXIII. Signal detection by human observers. [Accessed 12th October 2011 from http://dspace.mit.edu/ bitstream/handle/1721.1/52240/RLE_QPR_052_XXIII.pdf?sequence=1} Swets, J.A., Tanner, W.P., and Birdsall, T.G. (1961). Decision processes in perception. Psychological Review, 68(5), 301-340. Taleb, N.N. (2007). The black swan: the impact of the highly improbable. London: Penguin Books. Thorp, E. (1966). Beat the dealer. Vintage Books: New York. Thorp, E. (1982). Physical prediction of roulette. Computer Sports Systems. Turner, N., and Horbay, R. (2004). How do slot machines and other electronic gambling machines actually work? Journal of Gambling Issues, 11. Turner, N., Fritz, B, and Mackenzie, B. (2003). How to gamble: Information and misinformation in books and other media on gambling. Journal of Gambling Issues, 9 ,doi: 10.4309/jgi.2003.9.13 Tversky, A., and Kahneman, D. (1974). Judgement and uncertainty: heuristics and biases. Science, 185, 1124-1131. Vinson, B. (1987). Las Vegas behind the tables. Gollehon: Grand Rapids Michigan. Wagenaar, W.A. (1972). Generation of random sequences by human subjects: a critical survey of the literature. Psychological Bulletin, 77(1), 65–72. Wagenaar, W. A. (1988). Paradoxes of gambling behavior. London: Lawrence Erlbaum Associates. Walker, M.B. (1992a). The psychology of gambling. New York: Pergamon Press. Walker, M.B. (1992b). Irrational thinking among slot machine players. Journal of Gambling Studies, 8, 245-261. Walpole, R.E., and Myers, R.H. (1972). Probability and statistics for engineers and scientists. New York: Macmillan Publishing. Welford, A.T. (1968). Fundamentals of skill. London: Methuen. Wood, R., and Williams, R. (2007). Problem gambling on the Internet: Implications for Internet gambling policy in North America. New Media and Society, 9(3), 520-542.

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Wood, R., and Williams, R. (2009). Internet gambling: Prevalence, patterns, problems, and policy options. Final Report prepared for the Ontario Problem Gambling Research Centre, Guelph, Ontario, Canada. Wood, R. T. A., Griffiths, M. D., and Parke, J. (2007). Acquisition, development, and maintenance of online poker playing in a student sample. Cyberpsychology and Behavior, 10, 354–361. Zangeneh, M., Griffiths, M., and Parke, J. (2008). The marketing of gambling. In M. Zangeneh, A. Blaszczynski and N. Turner (Eds.). In pursuit of winning (pp.135-153). New York: Springer.

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Chapter 6

HOME-CAGE TESTING OF CHOICE BEHAVIOUR: PRONENESS TO RISK IN A GAMBLING TASK Walter Adriani, Francesca Zoratto and Giovanni Laviola Section of Behavioural Neurosciences, Department of Cell Biology and Neuroscience, Istituto Superiore di Sanità, Roma, Italy

ABSTRACT

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Pathological gambling (PG) is a mental health concern and is essentially an impulsecontrol disorder, associated with risk-taking and sensation-seeking traits. Psycho-genetic studies have revealed a role for the dopamine transporter (DAT). In animal models, many operant-behaviour tasks do test the lack of self-control abilities and / or impulsive decision-making. Among these, the probabilistic-delivery (PD) task is based on choice between a ―Large and Luck-Linked‖ (LLL) or a ―Small and Sure‖ (SS) reward. During testing, the level of probability (―p‖) governing successful (or omitted) LLL delivery is decreased progressively, thus implementing a ―binge‖ but rarefied event. Using a PD task in rats, we demonstrated previously the emergence of distinct sub-populations: ―riskprone‖ rats maintained a significant attraction for LLL, while ―conservative‖ ones developed a clear preference for SS. Here, we describe a pilot study with a PD task, aimed to identify a phenotype of risk proneness in animal models, adapted to home-cage testing. Indeed, recent literature proposes that testing animals in their home cage avoids the distress of being handled daily, moved to a novel apparatus in a separate room, and given a limited time-window for data collection. Thus, our testing apparatus consisted of home-cage operant panels, placed in the rats‘ home cage. Animals were trained to feed by operating this panel through nosepoking. Daily sessions were run for 12h (8.00 pm to 8.00 am) during the dark phase of the cycle. The whole protocol lasted ten days in total. Training phase (three sessions at p = 50%) allowed subjects to reach a significant preference for LLL, then ―p‖ was decreased progressively until and beyond the indifferent point, i.e. when LLL becomes a sub-optimal choice. As expected, rats showed only a slight reduction in their LLL preference, but did not show any significant change of strategy and never developed a clear preference for SS, even beyond the indifferent 

Email: [email protected].

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Walter Adriani, Francesca Zoratto and Giovanni Laviola point. This is relevant for theories of behavioural economics, since they could have gained more food with less work had they chosen to shift towards a SS choice strategy. Present pilot experiment shows in principle that it is possible to measure gambling behaviour in a home-cage setting, and future experiments are directed at further validating this approach.

Keywords: Impulse-control disorders; probabilistic-delivery task; home-cage testing

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INTRODUCTION Pathological gambling (PG) is rapidly emerging as a mental health concern. PG is a chronic, progressive disorder with a prevalence of 1-4% among western civilizations, and is essentially an impulse-control disorder, very frequently comorbid with attention deficit hyperactivity disorder (ADHD) [1]. Comorbidity to other impulse-control disorders is common, especially compulsive buying and compulsive sexual behaviour [2]. PG may be conceptualized not only as an addictive disorder, but also as part of the obsessive-compulsive spectrum [3]: in fact, more than half of PG people have an obsessive-compulsive, avoidant / antisocial, schizotypal / paranoid personality, as well as substance abuse / dependence and bipolar disorders [4]. The aetiology of PG is multi-factorial and cognitive models of gambling argue that irrational beliefs and erroneous perceptions may play a key role. Indeed some authors underline that expectancies of winning, illusions of control, and subsequent entrapment do contribute to the development and the maintenance of gambling patterns [5]. There is obviously a relationship with risk-taking and sensation-seeking traits as revealed by psychometric tests [6]. Forebrain dopamine (DA) and serotonin (5-HT) play a central role in psychomotor control, being involved in subcortical processing of behavioural patterns, and in cortical processing of inhibitory control, respectively [7,8]. Psycho-genetic studies have revealed that, among genes involved in altered DAergic neuro-transmission within reward systems, the most significant for PG is the dopamine transporter (DAT) [9,10]. Animal models are crucial for studying behavioural endophenotypes and the underlying neurobiology. Many operant-behaviour tasks have been developed in order to test the lack of self-control abilities and / or impulsive decision-making [11], which may play an important role for PG and many other psychiatric disorders, such as ADHD [12,13]. In the intoleranceto-delay (ID) task [14], subjects may choose between a Large and Late-coming (LL) versus a Small and Soon-coming (SS) reward. Impulsive subjects are detected by their intolerance to forced waiting for the large reward [14,15]. In the probabilistic-delivery (PD) task [16,17], subjects may choose between either a ―Large and Luck-Linked‖ (LLL) or a ―Small and Sure‖ (SS) reward. We have shown recently that lab rodents are not only tolerant to this probabilistic delivery, but are also sub-optimally attracted by this random uncertainty [18,19]. Indeed, if the random food-delivery omission is marked by the same cue (a light flash) normally accompanying successful food delivery, this cue may act as a secondary reinforcer. As such, like in second-order schedules, this conditioned stimulus may sustain nosepoking at the LLL hole even though this implies a decreased overall foraging in the long term. Risk proneness may thus be sustained by the cue-induced expectation for random but eventual delivery of the large reward [18,19]. The traits of impulsivity (as evidenced by quick SS

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shifting in ID task) and risk proneness (as evidenced by sustained nosepoking for LLL in PD task) are correlated [20]. The question is open about how is it possible to delineate objectively the convenience or not of a strategy. In a setting where the SS hole visit gives ―1‖ pellet and LLL hole visit gives ―5‖ pellets with a probability of ―p‖ (decreasing from 100% to 10%) there are the following possibilities. In each trial, the animal has the potentiality to obtain maximally ―5‖ pellets but this will never happen for sure. If he decides to go to the SS hole, which animals do sometimes even for p = 100%, he will receive ―1‖ pellet (―got‖) and there will be a loss of ―4‖ pellets (―lost‖) compared to the theoretic maximum. If he decides to go to the LLL hole, at least in all cases when p < 100%, he will either receive ―5‖ pellets (―got‖) if he is lucky, or he will receive nothing at all if he‘s unlucky. Therefore, compared to the theoretic maximum, there will be a gain of pellets (―got‖ = 5·p) and also a loss of pellets (―lost‖ = 5·(1 – p)). It is evident that the number of ―got‖ pellets will never reach the ―theoretic maximum‖ and the number of ―lost‖ pellets will never reach zero. In these conditions, the better strategy is the one which allows to maximize the number of ―got‖ pellets and minimize the number of ―lost‖ pellets. We propose therefore that a strategy may be scored by the proportion of the two, namely, by the proportion between the number of ―lost‖ pellets and ―got‖ pellets. We call ―ghost ratio‖ the proportion between ―lost‖ / ―got‖ pellets, so that a strategy is good if it leads to a relatively low ―ghost ratio‖ vice versa a strategy is bad if it leads to a relatively high ―ghost ratio‖. Classically, the performance of lab rats on the ID and PD tasks is investigated by placing the individual animals in operant chambers for a short daily session [14-17,20]. Thus, differences in laboratory environments and human interventions, e.g. handling and transport to a novel testing room, may compromise the reliability and reproducibility of behavioural data across laboratories [21,22]. Recently, much effort has been devoted to the development of new and refined methods for behavioural phenotyping, especially designed for testing animals directly in their home-cage. Indeed, as human intervention, handling and transport are minimised, behaviour can be recorded and tested continuously in undisturbed conditions [23-25]. Different types of automated home-cage systems have been developed, but these systems are currently not able to carry out such operant procedures (Phenotyper®) or are far too complex for carrying out relatively simple operant tests (Intellicage®). For this reason, a new low-cost computer-controlled operant panel [26] was developed, which can be placed inside the home-cage, enabling rodents to operate it 24 hours / day. Using this new home-cage operant panel (HOP), distress caused by handling and by transportation to novel testing cages in a different room is prevented, as well as their possible influence on rats‘ performance. We have run a pilot experiment with an ID-protocol [26], and we report here results of a pilot experiment using this panel in a PD-protocol.

METHODS Experimental protocols were approved by Animal Survey Board on behalf of Italian Ministry of Health, and are in close agreement with the European Directive (86 / 609 / EEC) and Italian Law. All efforts were made to minimize animal suffering, to reduce the number of animals used, and to use alternatives to in vivo testing.

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Subjects Twelve adult male Wistar rats (Harlan, Italy) were housed in pairs within Makrolon III cages, kept in an air-conditioned room (temperature 21±1°C, relative humidity 60±10%), on a 12h standard light-dark cycle (lights on at 8.00 am). Prior to the experiments animals were housed in pairs, but from the start of the protocol animals were singly housed in novel Makrolon III cages containing the panel. Water was available ad libitum, whereas food (Altromin-R, A. Rieper S.p.A., Vandoies, Italy) was available ad libitum until the start of the experimental protocol. Rats had no previous experience in operant tasks, and were left undisturbed for two months prior to the present pilot.

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Apparatus The testing apparatus consisted of home-cage operant panels (―HOP‖, PRS Italia, Roma, Italy), one for each of the subjects, placed in the Makrolon III home-cage with sawdust bedding. These panels contain two nosepoking holes, hole lights, a chamber light, a feeder device, a food-magazine where pellets (F0021 Dustless Precision Pellet 45 mg, BioServe, Frenchtown, USA) are delivered, and a magazine light. These panels were connected through an interface to a PC, where a software (―Sk020‖, PRS Italia, Roma, Italy) controlled and recorded all events. Nosepoking in one of the two holes of the panel resulted in the delivery of 5 pellets (large reward), whereas nosepoking in the other hole resulted in only 1 pellet (small reward). Largereward delivery was randomly released or omitted, according to a percentage of set probability (p = releases / demands·100). After nosepoking and before food delivery, the hole light was turned on for 1s. Following food delivery the magazine light was turned on for 30s, during which nosepoking was recorded, but was without scheduled consequences (timeout). The magazine light was then turned off, the chamber light was turned on, and the system was ready for the next trial.

Protocol for Gambling Task After animals were placed in the cages containing the panel, which occupied one fourth of the total living area, the 2-day adaptation period started. After 24h of access to both standard (Altromin-R) and precision (BioServe) pellets, standard (Altromin-R) food was removed while animals only had access to precision (BioServe) pellets from the panel for 24h. Then, 12h of food deprivation followed in order to increase their motivation to work for food delivery [26]. During the subsequent training and testing phases, animals had only access to precision (BioServe) pellets by operating the panel. Each daily session was run for 12h (8.00 pm to 8.00 am), during the dark phase of the cycle. Data were automatically divided into 1h intervals, which were then collapsed into 3h bins and further into 6h bins. The end of the

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session was indicated by switching off all panel lights. Training lasted three sessions at p = 50%. During the testing phase, level of ―p‖ was decreased progressively, thus implementing the rarefaction of large-reward delivery. The small-reward delivery was unchanged. Hence, animals had a choice between a ―Large and Luck-Linked‖ (LLL) and a ―Small and Sure‖ (SS) reward. The level of ―p‖ was fixed for daily sessions and was changed every other day: 33%, 25%, 20%, 17%, 14% [18]. The testing phase lasted ten days in total. We underline here that the present protocol employed only free-choice trials and a totally random sequence of lucky versus unlucky trials. Usually, the use of a pseudo-random sequence of reinforced versus non-reinforced trials helps to minimize the discrepancy between the scheduled and the really experienced probability of reinforcement. This is of course needed when experiments are run in short sessions (up to 1h), and is not necessary in our case, as day-long sessions allow rats to experience all possible stochastic fluctuations of probability. Also, many forced-choice trials are often imposed to animals, to provide a similar exposure to the two alternatives before a single free-choice one. In our hands, LLL preference is never 100% as a residual value of SS visits is always expressed, and the baseline balance between LLL and SS during the training phase is also similar across subjects. Thus, animals have full awareness of the SS alternative, although few subjects reach levels of SS experience comparable to protocols with forced-choice trials. Presently, our aim was to leave full freedom to subjects and to study inter-individual differences, possibly induced by the task, instead of trying to reduce them.

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Analysis of Data in Terms of “Odds” Odds are defined as the ―mean number of omitted large-reward delivery (because of the ―unlucky‖ event of food-delivery omissions in the PD task) before a successful delivery (i.e. the ―lucky‖ event of successful food delivery in the PD task)‖. Each experimental subject, when nosepoking for LLL, will always face a given ―odds‖ value. The relation between level of probability ―p‖ and ―odds‖ value is mathematical: odds = (1/p)–1 or p = 1 / (odds+1). For instance, during the training period, a p = 50% corresponds to odds = 1 because there will be, as an average, one omitted LLL reward before each successful LLL delivery. Odds are useful for detecting what the ―optimal‖ strategy should be, since the whole task is characterized by an ―indifferent point‖ by definition at odds = 4 (p = 20%, see Conclusion). Two ranges of values exist: indeed, the optimal benefit unequivocally loads onto the LLL option before the indifferent point (i.e. odds < 4), whilst it unequivocally loads onto the SS option for values beyond the indifferent point (odds > 4). Choice behaviour of rats around and beyond this point is worth to be explored, for the purpose of identifying individuals with a ―risk-prone‖ phenotype versus those with a more ―conservative‖ strategy (see e.g. [20]). In Figure 1, the ―p‖ values selected to be set during the testing phase (i.e. 33%, 25%, 20%, 17%, 14%) were turned into the corresponding ―odds‖ and used as X-axis points. During the course of sessions at each set ―p‖ level, each rat displays many preference values, as choice varies during day.

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Figure 1. Range of variations for LLL% preference as a function of ―set‖ odds against reward. Choice behaviour in rats (N = 3) tested with the probabilistic-delivery (PD) protocol, shown during daily homecage sessions. Y-axis data represent the choice (%) range for the larger reward, as demanded by rats‘ nosepoking. X-axis data represent the ―set‖ odds against reward, calculated from set ―p‖ levels. The coloured areas denote the range of variations for each individual rat. Asterisks denote a remarkable spread (p < .05) between individual #1 (light), or #11 (dark), when compared to median (rat #6, halftone). These comparisons were drawn, at each specific ―set‖ odds level, separately within maximal (i.e. ―risk-prone‖, black triangles) and within minimal (i.e. ―conservative‖, white triangles) values.

For Figure 1, we decided to plot both the maximal and the minimal value of percent LLL preference, shown at each of the set ―p‖ levels. Each subject is thus represented by two curves, representing the intra-individual spread in their percent LLL preference (see Figure 1). As for Figure 2, the ―p‖ values actually experienced by individual rats during the testing phase were considered (i.e. (successful LLL / total LLL) · 100): their corresponding ―odds‖ values have been calculated and used as X-axis points. A logarithmic fit was also performed. Specifically, for each experimental rat, the slope and intercept of the probabilistic-delivery curve were calculated using Microsoft Excel functions, with Log(odds+1) as X-axis and percent LLL choice as Y-axis values. The resulting best-fit curve, which is linear if odds are expressed on Log scale, is superimposed to empirical observations (see Figure 2).

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Figure 2. LLL% preference and its Log fit as a function of actually experienced odds against reward. Choice behaviour in rats (N = 3) tested with the probabilistic-delivery (PD) protocol, shown during daily home-cage sessions. Y-axis data represent the choice (%) for the larger reward, as in Figure 1. Xaxis data represent the ―experienced‖ odds against reward. Such value was calculated from the percentage of larger reward successfully delivered, i.e. the real levels of ―p‖ actually experienced by rats. The coloured areas denote the difference between observed values (closed circles) and their Log fit (open circles). Asterisks denote a remarkable spread (p < .05) between individual #1 (light), or #11 (dark), when compared to median (rat #6, half-tone).

Analysis of Data in Terms of “Ghost Ratio” As a matter of fact, a fixed payoff with a ghost ratio of 4 is reached by an ideal strategy consisting of selecting the SS option only. However, the subject may be still attracted from the LLL option, giving him a ―rare but binge‖ reward, which may well generate a sort of temptation to gamble. Conversely, a variable payoff (with an increasing maximal ghost ratio) is reached by an ideal strategy consisting of selecting the LLL option only. However, subjects will never shown neither the 100% SS nor the 100% LLL strategy, as each individual will develop a certain strategy consisting of a given proportion of LLL and SS choices. The ghost ratio in these conditions is related to the ―p‖ level by simple formulas:

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Walter Adriani, Francesca Zoratto and Giovanni Laviola LOST pellets = 5·(LLL%)·(1–p)+4·(SS%) and GOT pellets = 5·(LLL%)·p+(SS%).

We may set the formulas in algebraic terms (i.e. LLL% = y; p = x; LOST / GOT = g) so that: g = (–5xy+y+4) / (5xy–y+1) and y = (4–g) / (g+1)·1 / (5x–1). It is easy to confirm that, when LLL% = 0 and SS% = 100, there are LOST = 4 per each GOT = 1, thus, a ghost ratio of 4. Also, when LLL% = 100 and SS% = 0, there are LOST = 5·(1–p) per each GOT = 5·p. Calculating such ghost ratio, i.e. 5·(1–p) / 5·p, we discover it to be (1 / p) – 1, a value already defined as ―odds‖. This is not surprising, since the lost pellets depend entirely on omitted deliveries before a successful one, at least ideally within a 100% LLL choice strategy. Consequently, the real ghost ratio of each subject, showing an individual choice strategy between SS and LLL, will always vary between 4 and (1 / p) – 1. Interestingly, for ―p‖ values beyond the indifferent point, single subject‘s strategies can be quantified by a number, ―g‖, varying between a minimum that (like the indifferent point) depends on reward sizes at LLL and SS versus a maximum that is the p-dependent ―odds‖ in that testing session.

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Data Analysis Criterion for exclusion was LLL preference lower than 50% at the end of the p = 50% training phase, and one rat was indeed excluded from analyses. As reported earlier [20], nearly half of the animals shift quickly to SS (i.e. they display a ―conservative‖ strategy), whilst half of them show nearly no shift despite a decreased LLL probability (i.e. they display a ―risk prone‖ strategy). Since the median slope value [20] allows to discriminate between the ―conservative‖ and the ―risk prone‖ rat subgroups, the 11 subjects were ordered and numbered on the basis of their slope value (see above). Thus, rats from #1 to #5 belong to the risk-averse subgroup while rats from #7 to #11 belong to the risk-prone subgroup, with #6 being the median one. We sought to represent the ―borders‖ of the two sub-populations generated by the task. Therefore, we display and discuss here the individual performance of three selected individuals, namely the most ―conservative‖ (rat #1), the most ―risk prone‖ (rat #11), as well as the median (rat #6) subject. These three individuals were analyzed by split-plot ANOVA. The design had a 3-level ―individual‖ (factor denoting the three rats) x ―odds‖ (set for or experienced during each session, a repeated-measure factor). ANOVA yielded a significant main effect for individual, F(2,20) = 10.6, p < .001, as expected. Multiple comparisons were performed with Tukey HSD. Specifically, using the Mean Square of the Residual (Individual x Odds) Error Term, the Minimal Statistical Difference (MSD) between individuals was calculated. This value turned out to be 14.95, and hence all points whose distance (between the Y coordinates) exceeded this value were considered as significantly different in the post-hoc planned comparisons. These comparisons were drawn within the maximal (―risk-prone‖) and, separately, within the minimal (―conservative‖) LLL (%) choice values, at each ―set odds‖ level, and at corresponding points of ―experienced odds‖ curves.

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RESULTS When the level of ―p‖ was decreased progressively (Table 1), rats slightly reduced their LLL preference, but did not show any significant change of strategy. Specifically, despite being tested far beyond the indifferent point of p = 20%, they never developed a clear preference for SS. This is relevant for behavioural economics, since they could have earned more food with less work had they developed a SS strategy [18,19]. There are two possible ways of showing these results. One way is to plot the LLL preference as a function of the imposed ―p‖ value, whilst another way is to plot the same LLL preference as a function of the experienced ―p‖ value. It is indeed important to underline that the ―p‖ level, which is set for a given daily session, governs the generation of random values. Thus, the actual ratio between successful versus omitted food delivery does not always coincide with the imposed ―p‖ value but tends to approximate this level according to stochastic laws. Naturally occurring fluctuations in the generation of random values may produce, transiently, some periods with excessive success in food release and some other periods with excessive omissions. Thus, differences in preference across individuals may depend on these stochastic fluctuations in LLL rarefaction rather than on individual coping with uncertainty. Thus, it is crucial to compare individual performance at similar levels of experienced ―p‖. Table 1. Results from the three selected individuals in the gambling pilot

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Set ―p‖ levels Rat #1 Choice LLL% Experienced ―p‖ Ghost ratio Rat #11 Choice LLL% Experienced ―p‖ Ghost ratio Rat #6 Choice LLL% Experienced ―p‖ Ghost ratio

50% 33% 33% 25% 25% 20% 20% 17% before before before before before indiff. indiff. after

17% after

14% after

14% after

93.64 46.93 1.21

92.34 32.12 2.21

94.84 36.05 1.84

79.74 25.97 3.04

86.73 23.98 3.26

85.71 74.12 75.00 23.44 20.26 20.83 3.36 3.95 3.85

81.72 61.16 43.45 24.42 17.06 11.64 3.24 4.49 5.12

94.67 52.94 0.95

96.07 33.11 2.07

97.49 31.96 2.16

97.57 27.76 2.63

98.48 24.46 3.09

98.67 99.74 100.00 99.47 98.97 99.30 18.33 17.85 24.69 21.77 13.99 13.48 4.45 4.60 3.05 3.59 6.12 6.39

88.48 48.20 1.22

94.57 32.06 2.18

98.59 37.86 1.66

96.47 23.81 3.22

96.46 27.52 2.67

96.34 84.85 76.19 21.52 25.00 25.00 3.66 3.12 3.20

92.91 59.51 65.30 17.80 13.91 11.38 4.57 5.11 5.96

Choice behaviour in rats (N = 3) tested with the probabilistic-delivery (PD) protocol, shown during daily home-cage sessions. Data represent (first line) the choice (%) for the larger reward as actually demanded by rats‘ nosepoking; (second line) the percentage of larger reward successfully delivered, i.e. the diverse levels of ―p‖ actually experienced by rats; (third line) the ―ghost ratio‖, a novel parameter proposed to denote individual strategies (see Methods). The ―experienced‖ and the ―set‖ levels of ―p‖ differ slightly due to stochastic factors.

Imposed “P” Value For each set ―p‖ level (i.e. the probability value that was imposed during daily sessions), the behaviour of each individual animal did show a certain range of variation. In Figure 1 we

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decided to plot, at each of the set ―p‖ levels, the maximal and the minimal value of observed LLL preferences. In this way, it is evident that each individual is denoted by a range of LLL preferences that were generated by each degree of uncertainty. In some cases, denoted by an asterisk on Figure 1, there is a remarkable distance between these two values. Closer inspection of Figure 1 suggests a spread between maximal and minimal LLL preference, which is indeed remarkable just around the indifferent point of odds = 4. While it is clear that the foraging payoff is identical with either LLL or SS selection at this point, it is also quite obvious that rats could not be aware of this task feature. The fact that their preference profile shows a wide spread may perhaps suggest that they are actually in a phase of transition, whereby they probe what the outcome of their favoured strategy might be. This evidence of a transient outcome probing and feedback adjustment in their choice behaviour can well be considered an index of flexibility. As for results at other odds values, individual performance may then be identified. In fact, rat #6 shows very high LLL preference (more than 90%) until odds < 4, then a remarkable spread at odds 4 and 5, and finally a shift towards SS thereafter. Interestingly, the spread between maximal and minimal LLL preference is low at this point, and LLL preference is maintained but at lower levels than before (around 60-65%). This profile was expected based on previous work [27] suggesting a ―smooth‖ coping strategy for foraging uncertainty. In other words, whilst still preferring to seek for the larger reinforcer, this rat apparently dilutes the non-rewarded periods by occasional SS selection (indeed, one third of times). This profile may be interpreted as the more appropriate strategy, at least from an economical viewpoint [27]. In contrast, rat #1 shows a quite different profile. First, compared to rat #6, this subject displays as a whole a lower LLL preference: the ―conservative‖ value was significantly lower for odds = 3, with choice around 80% LLL versus 20% SS. This means that this subject was more likely to nosepoke at the SS hole (despite a much smaller reward). In other words, rat #1 displays a slight reduction in LLL preference much earlier than rat #6 (i.e. already at odds 3). It can be hypothesized that this animal addresses his attention towards the SS reward as a part of the coping strategy against accumulating LLL omissions. Second, the shift towards SS at odds > 4 was considerably high, and the ―conservative‖ value was significantly lower for odds = 6, when compared to rat #6. This subject was apparently showing the more ―conservative‖ overall strategy, consisting of a proneness to SS shifting [20]. Finally, rat #11 shows a very high LLL preference (always more than 95%) along the whole testing period, never displaying any shift towards SS. The ―conservative‖ and ―risk prone‖ values were significantly higher, when compared to rat #6, from odds = 5 and at odds = 6, respectively. This perseverating profile may be interpreted as the more risk-prone strategy (i.e. nearly no shift), as LLL selection beyond the indifferent point is largely suboptimal from the economical viewpoint.

Experienced “P” Value The X-axis of Figure 2 contains the actual ―p‖ values that animals were experiencing - as calculated by the ratio between successful delivery and total requests (followed by either successful or unsuccessful delivery). The behaviour of each individual animal did show a

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certain profile of adaptation to these changing contingencies. From Figure 2, it is evident that each subject denoted a completely individual coping strategy in response to varying degrees of uncertainty. Indeed, individual performances may be identified. Interestingly, rat #6 shows very high LLL preference (more than 90%) until odds < 4, with a transient decrease around odds = 3. This is suggestive of a certain degree of intolerance generated by LLL rarefaction, triggering a sort of SS probing. It is worth to underline that such SS probing quickly recovered, suggesting that rat #6 came back to his previous preference for LLL when rarefaction of LLL was quite mild compared to its size. Then, a clear shift towards SS was only observed well after the indifferent point, for odds > 5, though a SS preference never developed (see above). This profile may be interpreted as the more appropriate strategy shift, at least from an economical viewpoint. In contrast, rat #1 shows a rather different profile. First, compared to rat #6, this subject displays as a whole lower LLL preference for 2 < odds < 4: values of choice fluctuate around a range of 85-75% LLL versus 15-25% SS. These figures mean that this subject was less likely to nosepoke at the LLL hole compared to the other rats, despite the attractiveness of a larger reward. Interestingly, around odds = 4, rat #1 displays a significantly and remarkably lower LLL preference, when compared to rat #6 (see Figure 2). Since both nosepoking behaviours lead to identical foraging, this difference is possibly suggestive of a certain degree of uncertainty-aversion in this subject. Then, when rat #1 shifts towards SS, he reaches a clear and strong SS preference, if compared to rat #6. Accordingly, the slope value of the logarithmic best fit is characterized by the highest steepness (–87.93 in rat #1 versus –49.27 in rat #6). The rats #2 to #5, in the risk-averse subgroup, showed an intermediate steepness (– 77.34; –61.82; –61.12; –58.74 respectively). All of these subjects were showing a risk-averse profile [20]. Again, rat #11 shows a very high LLL preference (always more than 95%) along the whole testing period, and displays a slightly positive value (+7.75) for slope. Namely, this subject develops a progressive increase of preference for LLL with its progressive rarefaction. All the other rats of the risk-prone subgroup (i.e. #7 to #10) showed an intermediate steepness (–45.69; –33.05; –28.80; –6.31 respectively). As a consequence, instead of a SS shift, all rats from #7 to #11 displayed a significant and remarkable LLL preference around odds = 6, when compared to the median (rat #6, see Figure 2). Although a bias due to ―perseveration‖ cannot be completely excluded, this profile may be taken as evidence for a risk-prone, or at least risk-indifferent, overall strategy [20]. In fact, the LLL selection becomes largely sub-optimal beyond the indifferent point, at least from the economical viewpoint, yet this kind of subject displayed no feedback adjustment of choice behaviour.

Analysis of Data in Terms of “Ghost Ratio” At the indifferent point (p = 20%) the ghost ratio is always 4. If the subject decides to choose the SS hole, he will receive ―1‖ pellet (―got‖ one) and there will be a loss of ―4‖ pellets (―lost‖ ones). The decision to ―loose‖ the ―4‖ pellets is part of the strategy, as the remaining ―1‖ pellet comes for sure. So, the ghost ratio is 4 in that this is the proportion between a quantity of food the subject decides not to receive over the quantity he decides to receive. In contrast, when a subject chooses for LLL at p = 20%, the overall gain is the same.

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It is easy to understand that each successful delivery of ―5‖ pellets will be followed - in average - by 4 omitted deliveries (also termed ―odds‖) of the 5 pellets, because of a 80% probability of omitted LLL delivery. In other words, there will be twenty pellets that we define ―lost‖ solely because they are not delivered, and this happens in 80% of trials, compared to 5 pellets which come altogether when the subject is ―lucky‖, and this happens 20% of trials. Therefore, there will be a loss of twenty pellets whereby the gain is five pellets, again leading to a ghost ratio of 4 compared to a theoretic maximum. It is evident that the subject can do nothing to change this ―ghost ratio‖ and this fits with the definition of indifferent point, i.e. the point where the payoff is the same whichever choice is made by the subject. After the indifferent point (for p < 20%), the ghost ratio is always greater than 4. Now, the payoff of SS options is unchanged, i.e. 1 pellet (―got‖) and 4 pellets (―lost‖) per each trial. As a matter of fact, the payoff of LLL options is much lower than this. In fact, choosing for LLL at p < 20% implies an increase of ―odds‖ (i.e. increasing probability of omissions before each success). Thus, as it is easy to understand, each successful delivery of 5 pellets happening at p < 20% will be followed in average by more than 4 omitted deliveries of 5 pellets, because this is the probability of omitted LLL delivery. In other words, there will be more than twenty pellets that are not delivered (defined ―lost‖) per each delivery of five pellets, which come altogether when the subject is ―lucky‖ (but this happens less than 20% of trials).

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Circadian Features of the Preference Shift The dynamics of SS shifting, shown by rats #6 and #1, was further explored. Indeed, as it is clearly evident from results described above, these two rats abandoned a significant LLL preference only on the last two days, when imposed ―p‖ value was 14% and experienced ―p‖ value was 13.5% on average. It was thus interesting to ascertain whether the loss of LLL preference was somewhat stable along the daily sessions or rather developed during its course. Indeed, rats expressed an average of 412±38 effective nosepokes (trials), which were unequally distributed along the daily session: 28±15 trials during 1st 3h bin; 85±16 trials during 2nd 3h bin; 99±15 trials during 3rd 3h bin; 199±40 trials during 4th 3h bin. To this aim, daily sessions were subdivided into two asymmetric halves: the first three 3h bins were collapsed to represent the time span to express the first 50% of all choices (around 9h) as opposed to the last 3h bin which represents the time span to express the remaining 50% of choices (around 3h). These two halves were then averaged across the last two days of testing. Interestingly, a robust preference for LLL was typical of sessions‘ first half (80.9±4.8 for rat #6; 78.5±8.5 for rat #1). Conversely, this preference was clearly abandoned during the sessions‘ second half, when the diverging individual strategies also developed. Indeed, while rat #6 was choosing almost at a chance level, a clear-cut preference for SS was found for the rat #1 (47.8±12.6 versus 26.9±4.2, respectively). This piece of data demonstrates that, when faced with a strong rarefaction in the successful delivery, all rats including the most conservative ones still try to get the ―binge‖ LLL reward and show no urge to get food reward and no aversion for its uncertainty. This was true at least for first half of their trials, which were diluted along 9h, i.e. more than half of available session time. Then, the reaction to a very low success rate only emerges later on, when subjects concentrate a great quantity of SS-

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directed choices during the last three available hours of the session. However, the extent of the uncertainty-induced drive towards a more conservative feeding strategy varies considerably among individuals.

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CONCLUSION We recently proposed that ID and PD tasks could be combined and used together, to evaluate specific aspects of self-control capacities in lab rodents [18,19]. Data generated in these two protocols can be compared by assuming that seconds of delay and percentages of probability are ―equivalent‖ values when they produce a similar level of rarefaction in actual delivery of larger reward (either a Luck-Linked or Late-coming one, termed LL / L). This equivalence can be calculated by referring both delay and probability values to the corresponding ―odds against reinforcing‖, a concept dealing with the average number of large rewards earned per time unit in both protocols. Odds are defined as the ―mean number of omitted large-reward delivery (because of the delay constraint in ID tasks, because of ―unlucky‖ events in PD tasks) before a successful delivery (the end of delay interval in ID tasks, the ―lucky‖ event in PD tasks)‖. Another way to look at odds is to consider that the experimental subject, when nosepoking for LL / L, will always face a given odds value. Now, note that a subject could rather receive the corresponding amount of the smaller reward (either a soon-coming or for-sure one, termed SS / S), instead of facing a procrastination of foraging, had he nosepoked for SS / S instead of LL / L. For the PD task, the relation between probability ―p‖ and odds value is mathematical (see Methods). For instance, a p = 50% clearly corresponds to odds = 1 because there will be, as an average, one omitted LLL reward before each successful LLL delivery. Accordingly, that subject could rather receive one SS food reward, instead of facing a procrastination of foraging. Clearly, the decision between the two alternatives is the result of a balance between their ―optimal‖ and ―affective‖ convenience [19]. The former refers to the objective gain (or loss) in foraging, which could be estimated via cortical executive functions, whilst the latter describes the subjective reactions (attraction versus avoidance drives) sub-cortically triggered by obstacles ruling against foraging, like delay and / or uncertainty of food. The process of decision-making thus comprises an emotional / motivational conflict [19], generated when these two instances (i.e. ―optimal‖ versus ―affective‖ convenience) load onto opposite devices. Intriguingly, they load inversely in either task (i.e. delay intolerance generates a suboptimal SS shift for ID tasks, while big / uncertain rewards may cause suboptimal LLL preferences for PD tasks at very low ―p‖ levels). The odds value helps to easily detect what the ―optimal‖ strategy should be, since both tasks are characterized by a given ―indifferent point‖ when the overall long-term payoff would be the same, irrespective of what decision is taken or what choice strategy is adopted. This point is dependent on relative sizes of the two alternative reinforcements. If SS is 1 pellet and LL / L is 5 pellets, then the ―indifferent point‖ is at odds = 4 by definition. For both tasks, two ranges of values exist: before the indifferent point (i.e. odds < 4), the optimal benefit unequivocally loads onto LL or LLL option, whilst of course the optimal benefit unequivocally loads onto the SS option for values beyond the indifferent point (odds > 4). It is noteworthy that the ―affective‖ benefit, namely the challenge for animals to choose under

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the pressure of a sub-optimal inner drive, displays an inverted location in these two tasks. For the ID task, the affective benefit comes from avoidance of delays, and loads onto the SS option before the indifferent point (i.e. for odds < 4, meaning at delay < 100s for timeout = 15s). All of our studies so far were indeed exploiting a range of delays from 0 to 100s (see e.g. [28,29]). Conversely, for the PD task, a temptation to gamble could be generated as an affective factor loading onto the LLL option, at least beyond the indifferent point (odds > 4, meaning at p < 20%). Our studies so far explored indeed a range of ―p‖ from 20% to 10% and below (see e.g. [20,27]).

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Table 2. Ideal curves to separate “conservative” from “risk-prone” subjects

Odds value

P value (%)

LLL% denoting a conservative choice Ghost ratio = 5

LLL% denoting a risk-prone choice Ghost ratio = 6

12.0

7.7

< 27.03

> 46.33

11.5

8.0

< 27.78

> 47.62

10.1

9.0

< 30.30

> 51.95

9.0

10.0

< 33.33

> 57.14

8.1

11.0

< 37.04

> 63.49

7.3

12.0

< 41.67

> 71.43

7.0

12.5

< 44.44

> 76.19

6.7

13.0

< 47.62

> 81.63

6.1

14.0

< 55.56

> 95.24

5.7

15.0

< 66.67

> 100.00

5.5

15.5

< 74.07

> 100.00

5.3

16.0

< 83.33

> 100.00

4.9

17.0

< 100.00

> 100.00

4.6

18.0

< 100.00

> 100.00

4.0

20.0

indifferent point

indifferent point

By using the formulas which define ―ghost ratio‖ values, it has been possible to calculate the specific levels of subjective LLL% preference that imply a ―ghost ratio‖ of 5 (third column) or 6 (fourth column). This table may be useful to quickly estimate the range of ―ghost ratio‖ values of any individual in any testing session, given his LLL% preference and his experienced ―p‖. Choice is between either 1 pellet for sure or 5 pellets linked to probability ―p‖.

In a recent work [20], we demonstrated that rats facing a progressive rarefaction of successful LLL delivery might show two opposite reactions. Indeed, nearly half of animals shifted their preference to SS, the small reinforcer scheduled to come for sure. This strategy reveals an aversion for excessively uncertain reinforcement. However, the rarefaction of successful LLL delivery did not induce any shift from LLL to SS in nearly half of those animals [20] as well as in the present pilot. Such a finding may be interpreted in several ways: either ―perseverance‖ or ―lack of feedback value adjustment‖ could be proposed, and both

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interpretations would rely on a balance between dorsal and ventral striatum, respectively [3032]. However, in recent studies we have drawn direct comparisons between the present PD task and the more classical delay-based ID task for impulsivity [14,15]. Noteworthy, despite being LLL-bound on the PD task for gambling, rats did not show LL perseveration in the ID task for impulsivity, as they were able to devaluate LL when its delayed delivery generated an aversion to it [18,20]. Thus, we rather proposed that LLL-bound rats were less able to consider the risks (i.e. a reduced overall payoff) that are associated with a large-reward rarefaction. In other words, PD tasks may elicit and detect either conservative risk avoidance or risk proneness. Comparison with the ID task run in the very same subject, thus, may allow to evaluate if delay intolerance is associated with any probabilistic strategy, i.e. with either aversion or attraction originated from the ―bigger / rarer‖ prizes. As for ―ghost ratios‖, we have set the formulas in algebraic terms (see Methods) so that:

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y = (4–g) / (g+1)·1 / (5x–1) and g = (–5xy+y+4) / (5xy–y+1) (where: LLL% = y; p = x; LOST / GOT = g). In this way, for each value of ―p‖ (the ―x‖ in the formula), there is a fixed relationship between LLL% (the ―y‖ in the formula), and the ghost ratio (the ―g‖ in the formula). Hence, a given ghost ratio can be attained by one specific level of LLL% choice preference for each value of ―p‖, and vice versa a given level of LLL% choice preference at each value of ―p‖ can also be expressed by its specific ghost ratio. The first more evident implication is that we can use this number, ―g‖, to denote the payoff in each subject‘s choice strategy. The second less evident implication is that we can find a value for this number, ―g‖, as a tool to separate the choice strategies between ―good‖ or ―bad‖ payoff. We postulate that a ―good‖ strategy is when the ghost ratio is less than 5, while a ―bad‖ (or risky) strategy is when the ghost ratio is more than 6. Based on this postulate, it is possible to calculate two ideal curves, expressing the specific LLL% value leading to exactly a ghost ratio of 5 and of 6 (see Table 2). This curve may be used to separate ―conservative‖ animals with a ―good‖ strategy from ―risk-prone‖ subjects with a ―bad‖ strategy. All LLL% values below those in the ideal curve for a ghost ratio of 5 are really conservative while, on the contrary, all LLL% values above those in the ideal curve for a ghost ratio of 6 are really denoting a risky strategy.

Advantages and Disadvantages of Home-Cage Testing Compared to studies carried out by means of the classical Skinner box setting for operant-behaviour testing, a couple of advantages in favour of the present home-cage testing panel and protocol can be distinguished. First of all, some potentially biasing factors for subjects‘ performance in the conventional protocol are clearly diminished. These include stressful experiences caused by: 1) extensive human handling, 2) removal and transport from the home-cage and 3) exposure to a novel test apparatus. Secondly, prolonged availability of the panels in the home-cage allows 12h- or even 24h-long training sessions, which can shorten the duration of the training period and of the whole schedule [14-17]. A short training and testing period is especially desirable for testing developmental phases, like in adolescent

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rodents (see e.g. [28,33]). Another advantage of the home-cage operant panel (HOP), compared to other currently available home-cage systems, is its simplicity in use and relative low costs. The HOPs can be placed in regular Makrolon III cages, allow housing of the animals in a common animal room, without any special requirement - except for cables connecting the panels to the interface and to the controlling computer (see [26]). Disadvantages of the HOP can be found as well. In the current setting, rats must be housed individually throughout the entire protocol duration. Social isolation stress is known to influence the reward system [34,35] and decision-making as well. Another important difference between the home-cage versus the traditional setting is the possibility of running longer or even day-long sessions, with rats being allowed to get all their daily meal with no feeding restriction. This is a key point in that using traditional time-limited sessions plus constant food-restriction conditions allows animals to be tested under the same level of motivation for feeding, but requires extra post-session feeding. Conversely, 24h-long sessions imply that rats‘ motivational level will obviously vary, as a consequence of their progressive satiation and of the circadian rhythm, but feeding occurs only through operantbehaviour. This notion points to the well-established differences between ―closed‖ and ―open‖ economies in rodents, in terms of behavioural adaptation [36]. There is evidence that different levels of hunger are a major factor in the behavioural differences observed between open and closed economies [37]. It could be argued that, whilst the time-limited sessions urge rats to maximize food collected and thus may generate a drive to take (or to avoid) risks, they have a lot of time available to buffer for the unlucky events in day-long sessions: this may allow better coping with omitted food delivery and encourage LLL selection.

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Implications for Preclinical Neuro-Psychiatry Models Classically, discounting hypotheses predicts that impulsivity (high degree of temporal, delay-induced discounting) should be associated with avoidance for uncertainty (high degree of probabilistic, uncertainty-induced discounting). This because each omission of delivery in a PD task implies a temporal rarefaction for any next successful delivery, so that uncertainty and delay may be somewhat similar [11,38]. If this is indeed the case, the same individual subject would show a similar strategy (either a shift to SS / S, or a maintained LL / L selection) in both ID and PD task, with these two possible strategies denoting the endophenotype of (im)patience, respectively [39]. It is questionable then to what extent these two tasks could be used for preclinical modelling in the field of ADHD, PG and similar disorders. Indeed, psychiatry rather suggest the opposite association [40], namely that a true ―self-control‖ deficit consists of both delay intolerance and risk proneness [41]. Research supports the key role of impulsivity in mediating the severity of gambling behaviour [42], which is thus viewed as an impulse-control pathology [43]. Also, PG is often found to be associated with the ―impulsive‖ subtype of ADHD, as reported by several clinicalepidemiology studies [44-46]. According to Madden and colleagues [47], there is an inverse relationship between delayand probability-induced discounting, in that ―aversion‖ for delayed rewards comes along with greater ―attractive‖ value attributed to unpredictable rewards. Similarly, impulsive and / or gambling behaviours likely result from a reduced self-control ability, rather than from a simplistic value-discounting function (see [48]). Accordingly, we have proposed recently that

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the joint use of both ID and PD tasks may allow to isolate those animals, which show opposite strategy in either ID and PD tasks. Specifically, individual subjects who display a shift to SS in the ID task may maintain LLL selection in the PD task (see [19]). It is noteworthy that some specific categories of animals, namely adolescent rats (see [18]) or rats with increased expression of accumbal dopamine transporter (DAT, see [20]) showed both: 1) spontaneous aversion for a large reward, when its delivery was delayed, and 2) enhanced tendency to choose for (or to gamble over) a similarly large reward, when its delivery was uncertain and rarefied. It is worth to underline that these two categories of rats are, at the same time, less prone to afford delays and increasingly attracted by uncertain-but-big reinforcers. As a matter of fact, the same ―risk prone‖ subjects were also the most ―impulsive‖ ones. A close association between traits of impulsivity and risk proneness, found both in the adolescent [18] and in the DAT over-expressing rats [20], when compared to controls, could denote perhaps the endophenotype of (in)temperance. This profile may hence be taken as evidence of rats demonstrating a reduction of selfcontrol capacity. Namely, due to an unbalance within the ―limbic loop‖ serving a prefrontocortical to accumbal motivational feedback, it could be proposed that these rats do not sustain the value of a delayed prize and do not discount the attractiveness of a highly uncertain one [30,31]. Since both processes would require a certain effort against an innate tendency, they could be comprised under the label of ―self-control‖. In both cases, a certain degree of inhibitory control would be useful to adopt the most fruitful strategy [7,8]. Such a notion would fit with recent work on the role of brain reward circuitry in PG, ADHD, as well as in other neuro-psychiatric disorders like substance abuse [41,49]. Further investigation is clearly needed, with a more tight comparison of the two tasks in the home-cage setting.

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Relevance and Future Perspective The present study was aimed to explore the possibility of testing risk proneness in the home-cage, using an operant-behaviour panel. The present data show in principle that it is possible to detect risk-prone versus conservative individuals based on their profile of choice within a PD operant-behaviour task. Specifically, stable preference for rare yet binge reinforcement may unveil a tendency to gambling in the animal model, or at least an insensitivity to reward uncertainty and rarefaction. Furthermore, presentation of data as a function of both imposed and experienced uncertainty (i.e. ―odds‖ at each level of ―p‖) allows a deeper investigation of the individual coping strategy in response to binge-but-rare reinforcement. More in general, we propose that classical and innovative two-choice operant tasks can well be adapted to a home-cage setting. Future experiments are needed and will be directed at further validating this kind of approach.

ACKNOWLEDGMENTS Research performed along the lines of the ―ADHD-sythe‖ young-investigator project (to WA): ―under-40 call‖ of Italian Ministry of Health (year 2008, assigned). Other supporting sources by the ERARE-EuroRETT Network ERAR / 6 (to GL). The authors wish to thank the

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European Mind and Metabolism Association (EMMA) and guest graduate students (like S. Koot, The Netherlands; E. Romano, Italy; E.M. Marco Lopez, Spain) for their invaluable help.

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[4] [5] [6] [7]

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[8]

[9]

[10]

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Sood, ED; Pallanti, S; Hollander, E. Diagnosis and treatment of pathologic gambling. Curr. Psychiatry Rep., 2003 5, 9-15. Black, DW; Moyer, T. Clinical features and psychiatric comorbidity of subjects with pathological gambling behavior. Psychiatr. Serv., 1998 49, 1434-1439. Lowengrub, K; Iancu, I; Aizer, A; Kotler, M; Dannon, PN. Pharmacotherapy of the pathological gambling: review of new treatment modalities. Expert Rev. Neurother., 2006 6, 1845-1851. Hollander, E; Sood, E; Pallanti, S; Baldini-Rossi, N; Baker, B. Pharmacological treatments of pathological gambling. J. Gambl. Stud., 2005 21, 99-110. Joukhador, J; Maccallum, F; Blaszczynski, A. Differences in cognitive distortions between problem and social gamblers. Psychol. Rep., 2003 92, 1203-1214. Powell, J; Hardoon, K; Derevensky, JL; Gupta, R. Gambling and risk-taking behavior among university students. Subst. Use Misuse, 1999 34, 1167-1184. Ragozzino, ME. Acetylcholine actions in the dorsomedial striatum support the flexible shifting of response patterns. Neurobiol. Learn Mem., 2003 80, 257-267. Ridderinkhof, KR; van den Wildenberg, WPM; Segalowitz, SJ; Carter, CS. Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn., 2004 56, 129-140. Comings, DE; Gade-Andavolu, R; Gonzalez, N; Wu, S; Muhleman, D; Chen, C; Koh, P; Farwell, K; Blake, H; Dietz, G; MacMurray, JP; Lesieur, HR; Rugle, LJ; Rosenthal, RJ. The additive effect of neurotransmitter genes in pathological gambling. Clin. Genet., 2001 60, 107-116. Reuter, J; Raedler, T; Rose, M; Hand, I; Gläscher, J; Büchel, C. Pathological gambling is linked to reduced activation of mesolimbic reward system. Nat. Neurosci., 2005 8, 147-148. Evenden, JL. Varieties of impulsivity. Psychopharmacology (Berl), 1999 146, 348-361. Bechara, A. The role of emotion in decision-making: evidence from neurological patients with orbitofrontal damage. Brain Cogn., 2004 55, 30-40. Strubbe, JH; Woods, SC. The timing of meals. Psychol. Rev., 2004 111, 128-141. Evenden, JL; Ryan, CN. The pharmacology of impulsive behaviour in rats VI: the effects of ethanol and selective serotonergic drugs on response choice with varying delays of reinforcement. Psychopharmacology (Berl), 1999 146, 413-421. Evenden, JL; Ryan, CN. The pharmacology of impulsive behaviour in rats: the effects of drugs on response choice with varying delays of reinforcement. Psychopharmacology (Berl), 1996 128, 161-170.

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[16] Mobini, S; Chiang, TJ; Al-Ruwaitea, AS; Ho, MY; Bradshaw, CM; Szabadi, E. Effect of central 5-hydroxy-tryptamine (5-HT) depletion on inter-temporal choice: a quantitative analysis. Psychopharmacology (Berl), 2000 149, 313-318. [17] Mobini, S; Body, S; Ho, MY; Bradshaw, CM; Szabadi, E; Deakin, JF; Anderson, IM. Effects of lesions of the orbitofrontal cortex on sensitivity to delayed and probabilistic reinforcement. Psychopharmacology (Berl), 2002 160, 290-298. [18] Adriani, W; Laviola, G. Delay aversion but preference for binge and rare rewards in two choice tasks: Implications for measuring of self-control parameters. BMC Neurosci., 2006 7, 52. [19] Adriani, W; Laviola, G. Animal models and mechanisms of impulsivity in adolescence. In: Palomo T, Beninger R, Archer T, Kostrezwa R editors. Cerebro y Mente (Volume 9). Madrid: Editorial CYM; 2009; 385-434. [20] Adriani, W; Boyer, F; Gioiosa, L; Macrì, S; Dreyer, JL; Laviola, G. Increased impulsive behavior and risk proneness following lentivirus-mediated DAT overexpression in rats‘ nucleus accumbens. Neuroscience, 2009 159, 47-58. [21] Crabbe, JC; Wahlsten, D; Dudek, BC. Genetics of mouse behavior: interactions with laboratory environment. Science, 1999 284, 1670-1672. [22] Wahlsten, D; Metten, P; Phillips, TJ; Boehm, SL 2nd; Burkhart-Kasch, S; Dorow, J; Doerksen, S; Downing, C; Fogarty, J; Rodd-Henricks, K; Hen, R; McKinnon, CS; Merrill, CM; Nolte, C; Schalomon, M; Schlumbohm, JP; Sibert, JR; Wenger, CD; Dudek, BC; Crabbe, JC. Different data from different labs: lessons from studies of gene-environment interaction. J. Neurobiol., 2003 54, 283-311. [23] De Visser, L. Home sweet home: home-cage testing for behavioural phenotyping of mice. PhD thesis. Utrecht University, Neuroscience and Pharmacology Department; 2008. [24] De Visser, L; Van den Bos, R; Kuurman, PWW; Kas, MJH; Spruijt, BM. Novel approach to the behavioural characterization of inbred mice: automated home-cage observations. Genes Brain Behav., 2006 5, 458-466. [25] Knapska, E; Walasek, G; Nikolaev, E; Neuhäusser-Wespy, F; Lipp, HP; Kaczmarek, L; Werka, T. Differential involvement of the central amygdala in appetitive versus aversive learning. Learn Mem., 2006 13, 192-200. [26] Koot, S; Adriani, W; Saso, L; van den Bos, R; Laviola, G. Home-cage testing of delaydiscounting. Behav Res. Methods, 2009 41, 1169-1176. [27] Adriani, W; Leo, D; Greco, D; Rea, M; Di Porzio, U; Laviola, G; Perrone-Capano, C. Methylphenidate administration to adolescent rats determines plastic changes on reward-related behavior and striatal gene expression. Neuropsychopharmacology, 2006 31, 1946-1956. [28] Adriani, W; Rea, M; Baviera, M; Invernizzi, W; Carli, M; Ghirardi, O; Caprioli, A; Laviola, G. Acetyl-L-carnitine reduces impulsive behaviour in adolescent rats. Psychopharmacology (Berl), 2004 176, 296-304. [29] Adriani, W; Canese, R; Podo, F; Laviola, G. [1H]-MRS-detectable metabolic brain changes and reduced impulsive behavior in adult rats exposed to methylphenidate during adolescence. Neurotoxicol. Teratol., 2007 29, 116-125. [30] Cardinal, RN; Winstanley, CA; Robbins, TW; Everitt, BJ. Limbic corticostriatal systems and delayed reinforcement. Ann. N. Y. Acad. Sci., 2004 1021, 33-50.

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[31] Christakou, A; Robbins, TW; Everitt, BJ. Prefrontal cortico-ventral striatal interactions involved in affective modulation of attentional performance: implications for corticostriatal circuit function. J. Neurosci., 2004 24, 773-780. [32] Salamone, JD; Correa, M. Motivational views of reinforcement: implications for understanding the behavioral functions of nucleus accumbens dopamine. Behav. Brain Res., 2002 137, 3-25. [33] Adriani, W; Laviola, G. Elevated levels of impulsivity and reduced place conditioning with d-amphetamine: two behavioral features of adolescence in mice. Behav. Neurosci., 2003 117, 695-703. [34] Hall, FS; Humby, T; Wilkinson, LS; Robbins, TW. The effects of isolation-rearing on sucrose consumption in rats. Physiol. Behav., 1997 62, 291-297. [35] Van den Berg, CL; Van Ree, JM; Spruijt, BM. Morphine attenuates the effects of juvenile isolation in rats. Neuropharmacology, 2000 39, 969-976. [36] Timberlake, W; Peden, BF. On the distinction between open and closed economies. J. Exp. Anal. Behav., 1987 48, 35-60. [37] Posadas-Sánchez, D; Killeen, PR. Does satiation close the open economy? Learn Behav., 2005 33, 387-398. [38] Ho, MY; Mobini, S; Chiang, TJ; Bradshaw, CM; Szabadi, E. Theory and method in the quantitative analysis of ―impulsive choice‖ behavior: implications for psychopharmacology. Psychopharmacology (Berl), 1999 146, 362-372. [39] Takahashi, T. Queuing theory under competitive social foraging may explain a mathematical equivalence of delay and probability in impulsive decision-making. Med. Hypotheses, 2006 67, 276-9. [40] Takahashi, T; Ikeda, K; Hasegawa, T. A hyperbolic decay of subjective probability of obtaining delayed rewards. Behav. Brain Funct., 2007 3, 52. [41] Chambers, RA; Potenza, MN. Neurodevelopment, impulsivity, and adolescent gambling. J. Gambl. Stud., 2003 19, 53-84. [42] Steel, Z; Blaszczynski, A. Impulsivity, personality disorders and pathological gambling severity. Addiction, 1998 93, 895-905. [43] Blaszczynski, A; Steel, Z; McConaghy, N. Impulsivity in pathological gambling: the antisocial impulsivist. Addiction, 1997 92, 75-87. [44] Petry, NM. Pathological gamblers, with and without substance use disorders, discount delayed rewards at high rates. J. Abnorm. Psychol., 2001, 110:482-487. [45] Specker, SM; Carlson, GA; Christenson, GA; Marcotte, M. Impulse control disorders and attention deficit disorder in pathological gamblers. Ann. Clin. Psychiatry, 1995 7, 175-179. [46] Rodriguez-Jimenez, R; Avila, C; Jimenez-Arriero, MA; Ponce, G; Monasor, R; Jimenez, M; Aragües, M; Hoenicka, J; Rubio, G; Palomo, T. Impulsivity and sustained attention in pathological gamblers: influence of childhood ADHD history. J. Gambl. Stud., 2006 22, 451-461. [47] Madden, GJ; Ewan, EE; Lagorio, CH. Toward an animal model of gambling: delay discounting and the allure of unpredictable outcomes. J. Gambl. Stud., 2007 23, 63-83. [48] Monterosso, J; Ainslie, G. Beyond discounting possible experimental model of impulse control. Psychopharmacology (Berl), 1999 146, 339-347. [49] Chau, DT; Roth, RM; Green, AI. The neural circuitry of reward and its relevance to psychiatric disorders. Curr. Psychiatry Rep., 2004 6, 391-399.

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Chapter 7

PROMINENT DECK B PHENOMENON: ARE DECISION-MAKERS SENSITIVE TO LONG-TERM OUTCOME IN THE IOWA GAMBLING TASK? Yao-Chu Chiu1, Ching-Hung Lin1,2,3,4,5 and Jong-Tsun Huang6# 1

Department of Psychology, Soochow University, Taipei, Taiwan Brain Research Center, National Yang-Ming University, Taipei, Taiwan 3 Laboratory of Integrated Brain Research, Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan 4 Biomedical Engineering R and D Center, China Medical University Hospital, Taichung, Taiwan 5 Biomedical Electronics Translational Research Center, National Chiao Tung University, Hsinchu, Taiwan 6 Graduate Institute of Neural and Cognitive Sciences, China Medical University, Taichung, Taiwan

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2

ABSTRACT Background: Damasio and Bechara et al. suggested somatic marker hypothesis (SMH) that under uncertainty, the intact emotion system can facilitate the rational decision in the long run. Most healthy decision-makers have the foresighted choice pattern. Namely, healthy subjects prefer advantageous decks to disadvantageous decks after the second block (about trials 21–40) in the Iowa Gambling Task (IGT) (Damasio, *Corresponding authors: Ching-Hung Lin and Jong-Tsun Huang Biomedical Electronics Translational Research Center, National Chiao Tung University. EIC501, 1001 Univ. Rd., Hsinchu 30010, Hsinchu, Taiwan. Tel: 886-922810746; 886-3-5712121 ext. 59450, Fax: 886-3-5731723, E-mail: [email protected] # Graduate Institute of Neural and Cognitive Sciences, China Medical University. No.91 Hsueh-Shih Rd., Taichung 40402, Taiwan. Tel: 886-939223034; 886-4-22057153, Fax: 886-4-22060248, E-mail: jongtsun@mail. cmu.edu.tw

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1994; Bechara et al., 1999). Conversely, the patients with ventromedial prefrontal lesions possess the myopic decision pattern. However, an increasing number of studies have identified the prominent deck B (PDB) phenomenon, indicating that normal decisionmakers have myopic choice behavior in the IGT. Normal subjects have difficulty avoiding disadvantageous deck B, even when near game end. Nevertheless, most IGTrelated studies, including those of the Iowa group, subtracted bad decks from good decks (the two-category format) when presenting experimental data. Consequently, the incidence of the PDB phenomenon was not accurately assessed. Here we provided an empirical study which contains a complex and simple version of IGT to illustrate the PDB phenomenon. Methods: Two computer versions of IGT (concurrent (cIGT) vs. net-value (nIGT) are utilized to determine whether healthy decision-makers start shifting their preference from bad decks to good decks after the second block (about trials 21–40). In total, 48 gender-balanced subjects participated in this experiment. Each subject played only the cIGT or the nIGT. In total, 300 trials for each IGT version were utilized to identify the extended preferences of subjects. Results: A few studies presented data acquired using the four-deck format, providing clear experimental results for each deck that verify that the PDB phenomenon exists. Additionally, empirical results obtained by this study indicate that the two-category format identified a turning point for preferences during the second block (about trials 21– 40) in both IGT versions. However, the PDB phenomenon existed in most stages of both IGT versions; namely, most healthy decision-makers avoided bad deck A, and had difficultly avoiding bad deck B. Conclusions: The present research indicates that the PDB phenomenon probably exists in most IGT related studies. Different presentation methods (two categories vs. four decks) can result in entirely different explanations of IGT decision behavior. Empirical data demonstrate that in both IGT versions, the PDB phenomenon existed, (Lin et al., 2007), which is counter to the basic IGT assumption that decks are chosen based on longterm outcome, even under an extended number of trials. This empirical finding indicates that players have a myopic view of long-term outcome in the IGT.

Keywords: Somatic marker hypothesis; Prominent deck B phenomenon; Iowa gambling task; Ventromedial prefrontal cortex; Gain loss frequency

INTRODUCTION Damasio et al. (Damasio, 1994; Damasio, Tranel, and Damasio, 1991) applied the somatic marker hypothesis (SMH) to describe how emotional optimism guides decisionmaking under uncertainty. The SMH states that emotion plays a supportive role in real-life decision-making (Damasio, 1994); the conventional view suggests that emotion adversely affects rational decision-making (Ariely, 2010; Baumeister, 2003; Berridge, 2003; Finger, 1994; LeDoux, 1998). Therefore, lesions on emotion circuitry (e.g., damage to the ventromedial prefrontal cortex (VMPFC) and amygdala) cause decision-makers to make irrational choices in real life (Damasio, Tranel, and Damasio, 1990; Eslinger and Damasio, 1985). The SMH counters the general perception of the role of emotion in decision-making (Ariely, 2010; Baumeister, 2003; Berridge, 2003; Panksepp, 2003; Taleb, 2004). To test the SMH, a task simulating real-life decision-making, the Iowa gambling task (IGT), was designed by the Iowa Group (Bechara, Damasio, Damasio, and Anderson, 1994).

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Bechara et al. (Bechara, et al., 1994) demonstrated that normal decision-makers can use emotion in the IGT to infer long-term outcome. The IGT has garnered considerable attention from affective neuroscience researchers and generated several arguments regarding emotionrelated behavior and psychology (Ahn, Busemeyer, Wagenmakers, and Stout, 2008; Dunn, Dalgleish, and Lawrence, 2006; Heims, Critchley, Dolan, Mathias, and Cipolotti, 2004; Leland and Grafman, 2005; Lin, Chiu, Lee, and Hsieh, 2007; Maia and McClelland, 2004, 2005; North and O'Carroll, 2001; O'Carroll and Papps, 2003; Rolls, 1999, 2005, 2007). Moreover, the IGT has been utilized in over 100 scientific studies to test subjects for psychiatric and neurological deficits (Bowman, Evans, and Turnbull, 2005). This neuropsychological tool has also been employed for clinical assessments of 13 neurological and psychiatric conditions—focal brain lesions, decision function of aged adults (aging), substance addiction, pathological gambling, schizophrenia, obsessive-compulsive disorder, anorexia nervosa, obesity, chronic pain, attention deficit hyperactivity disorder (ADHD), aggression disorders, affective disorders, and Huntington’s disease (Bechara, 2007). However, many IGT studies have generated conflicting outcomes for control groups (Fernie and Tunney, 2006; Fum, Napoli, and Stocco, 2008; MacPherson, Phillips, and Della Sala, 2002; Sevy, et al., 2007; Wilder, Weinberger, and Goldberg, 1998); these studies indicated that healthy decision-makers may be not foresighted. Serious confounding factors may exist in the IGT for control groups; for example, many studies have suggested that healthy decision-makers were primarily influenced by gain-loss frequency, not long-term outcome, in the IGT. Furthermore, some studies (Dunn, et al., 2006) have demonstrated that the lesion site on the brain (Fellows and Farah, 2005), disease subtype (Fridberg, et al., 2010; RodriguezSanchez, et al., 2005), gender (Overman, et al., 2004), age (Crone, Bunge, Latenstein, and van der Molen, 2005; Crone and van der Molen, 2004; MacPherson, et al., 2002), and individual differences (Eveline A. Crone, Vendel, and Molen, 2003; Weller, Levin, and Bechara, 2009) were considerable factors for IGT interpretation. However, the IGT has been validated by several clinical studies related to the 13 neurological and psychiatric conditions. Therefore, IGT validity must be reassessed. The conventional IGT has four decks and some rules that simulate uncertainty in real-life decision-making. Bad decks A and B in the IGT have relatively large and immediate gainloss values (+$100 – -$1250) that seduce decision-makers choosing these bad decks results in a long-term net loss. Conversely, good decks C and D have relatively small and immediate gain-loss values (+$50 – -$ 250) and a long-term net gain. The Iowa group defined ―bad‖ and ―good‖ based on the long-term outcome of decks. Moreover, bad deck A and good deck C have the same gain-loss frequency (10 gains and 5 losses), whereas bad deck B has the same gain-loss frequency as good deck D (10 gains and 1 loss) (Bechara, et al., 1994; Bechara, Tranel, Damasio, and Damasio, 2000). The IGT results acquired by Bechara et al. (Bechara, et al., 1994) demonstrated that healthy decision-makers preferred good decks over bad decks, and subjects with ventromedial prefrontal cortex (VMPFC) lesions chose bad decks over good decks. Based on these findings, the Iowa group posited that healthy subjects are foresighted and those with VMPFC lesions are myopic to long-term benefit. Furthermore, Bechara et al. (Bechara, Damasio, Damasio, and Lee, 1999) utilized a preference trend to determine the performance of those with amygdala lesions or VMPFC lesions, and that of normal decision-makers during the IGT.

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The average preference tendency (A+B vs. C+D) demonstrated that healthy decisionmakers preferred advantageous decks to disadvantageous decks. As described by Damasio, the turning point of the preference trend (bad vs. good decks) exists during the first 30 trials. Namely, subjects gradually started avoiding bad decks A and B and gradually began preferring good decks C and D. Deck B is an important index demonstrating the inhibition function (Miller and Cummings, 2007; Rolls, 2007; Takano, Takahashi, Tanaka, and Hironaka, 2010; Tranel, 2002) of the VMPFC (Rosenzweig, 2002), a brain region associated with the somatic marker hypothesis (SMH) (Damasio, 1994; Damasio, et al., 1990; Damasio, Tranel, and Damasio, 1996; Damasio, Graboeski, Frank, Galaburda, and Damasio, 1994). Deck B has a large loss after high-frequency gains (in an average of 10 trials) in this uncertain context, and has a bad long-term outcome. According to the SMH, healthy decision-makers avoid bad deck B based on its large loss out of 10 trials, and gradually shift their preference to good decks. Namely, one large loss (inhibitory stimulus) overrides frequent gains (inducing an impulsive tendency) in deck B, thereby inducing healthy subjects to choose good decks correctly. In other words, the VMPFC inhibits subject preference for bad deck B after a large loss. An increasing number of investigations, however, identified various choice patterns in this IGT configuration. A few studies observed the prominent deck B (PDB) phenomenon (Caroselli, Hiscock, Scheibel, and Ingram, 2006; Chiu and Lin, 2007; Lin, et al., 2007; MacPherson, et al., 2002; Wilder, et al., 1998). The PDB phenomenon in this study is defined as a player choosing bad deck B more times, or with near equal frequency, than choosing good decks C or D (Lin, et al., 2007). The PDB phenomenon indicates that subjects preferred the deck with a bad long-term outcome, as demonstrated by mean number of cards chosen. This phenomenon clearly demonstrates that many subjects cannot alter their preference for bad deck B after a few trials with large losses. If the PDB phenomenon exists, most decisionmakers are not foresighted, as Bechara et al. suggested. The PDB phenomenon in the IGT was first defined by Lin et al. (2007), Chiu and Lin (2007), and in other empirical studies (Chiu and Lin, 2007; Lin, et al., 2007). These researchers argued that the PDB phenomenon was primarily due to guidance provided by gain-loss frequency. Therefore, subjects likely prefer deck B with its high-frequency gains over deck A with its low-frequency gains. The effect of gain-loss frequency was identified using modified IGTs (Fum, et al., 2008; Stocco, Fum, and Napoli, 2009) (e.g., the Soochow Gambling Task (SGT)) (Chiu, et al., 2008). Notably, Stocco et al. (2009) provided a framework for explaining some choice-behavior debates in the IGT. They suggested that decision-makers can be influenced by the autonomic system (immediate feedback) and selfcontrolled system (long-term payoffs) simultaneously. In fact, in the original IGT, we named it concurrent IGT (cIGT) here temporally. The cIGT has a concurrent payment in a trial during 100 trials. For instance, when a subject in a certain trial chooses deck A, then he/she receives a $100 gain and a $150 loss (the concurrent payment). However, Lin et al. (2007) and Chiu and Lin (2007) developed the net value version of the IGT (a revised net payment mode by summing the gain and loss in a trial. For example, when a subject in a certain trial chooses deck A, he/she receives a $50 loss (nIGT). The cIGT likely has a more complex gain-loss procedure and more uncertainty than the nIGT (Overman, et al., 2006).

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This study first conducts a literature review of IGT-related studies to identify the PDB phenomenon. The Damasio group indicated that subjects infer long-term outcomes of good and bad decks during from the first 30 trials in the IGT. Namely, if the PDB phenomenon does not exist, healthy subjects would gradually infer the long-term outcomes of good and bad decks within the first 30 trials. The second goal of this study is to conduct an empirical experiment using the cIGT and nIGT to elucidate inconsistencies between these two IGT versions.

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METHODS This study first surveyed a collection of studies to investigate the PDB phenomenon for healthy decision-makers based on the following two lines of investigation: (1) Iowa group data obtained using the IGT during 1994–2008 (15 studies); and, (2) other research results obtained using the IGT. The significant number (over 200 studies) of studies were collected using Google Scholar, an Internet search engine, and library databases such as PubMed, Medline, Scidirect and PsychINFO. Twenty studies were collected via a meta-review of studies by Dunn et al. (Dunn, et al., 2006). In the second phase of this empirical study, 48 college students, 24 females and 24 males—most were freshman at China Medical University, Taiwan. Most were aged 18-19 years. These subjects were randomly assigned to two groups. Subjects in the first group (12 females and 12 males) played the nIGT, in which subjects receive a single net monetary gain or loss during each trial (only a net gain or loss). Subjects in the second group (12 females and 12 males) played the cIGT, in which subjects receive one monetary feedback (gain) or two monetary feedbacks (one gain and one loss) during each trial. Figures 1a and 1b show the experimental procedure for each trial in the cIGT and nIGT, respectively. This experiment was conducted using small groups of 2–4 subjects. Each subject was instructed to read the task introduction translated from the original IGT (Bechara, et al., 1999; Bechara, Tranel, et al., 2000). The introduction instructed (1) subjects to try to earn as much money as possible or avoid losing as much money as possible. (2) Subjects were unaware of game rules at the start of the game. (3) Subjects were told that card color is not related to gain or loss. (4) Subjects were also unaware of when the game would end. After instructions were given, each subject was encouraged to play at his/her own pace on a computer. To identify the turning point in a preference trend, this study conducted 300 trials (3 times the number of trials in the standard IGT) to investigate choice behavior (Ahn, et al., 2008; Crone, et al., 2005; Crone and van der Molen, 2004; Crone, et al., 2003; Fernie and Tunney, 2006; Reavis and Overman, 2001; Turnbull and Evans, 2006). The computerized nIGT and cIGT games on Matlab 7.1 were utilized to monitor and analyze subject performance (Bechara, 2001). The introduction instructed (1) subjects to try to earn as much money as possible or avoid losing as much money as possible. (2) Subjects were unaware of game rules at the start of the game. (3) Subjects were told that card color is not related to gain or loss. (4) Subjects were also unaware of when the game would end.

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a)

b) Figure 1. Experimental procedure of each trial in the cIGT and nIGT. After the game introduction, subjects were free to pick any card from any of the four decks in each trial. When the mouse button was pressed, the chosen card was turned over and the red or black color was disclosed to the player. The upper box on the selected card shows (1a) both gain and loss monetary values simultaneously in a single trial, and (1b) summarized net monetary value, namely, only a gain or loss in a single trial. Accordingly, the top two bars represent the cumulative loan and bonus.

After instructions were given, each subject was encouraged to play at his/her own pace on a computer. To identify the turning point in a preference trend, this study conducted 300 trials (3 times the number of trials in the standard IGT) to investigate choice behavior (Ahn, et al., 2008; Crone, et al., 2005; Crone and van der Molen, 2004; Crone, et al., 2003; Fernie and Tunney, 2006; Reavis and Overman, 2001; Turnbull and Evans, 2006). The computerized nIGT and cIGT games on Matlab 7.1 were utilized to monitor and analyze subject performance (Bechara, 2001).

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RESULTS Most IGT-related studies presented data using the ―two-category‖ format (bad [A+B] vs. good [C+D]); therefore, single selection frequency for deck B was not identified. The twocategory presentation format was adopted by most Iowa group studies (Bechara and Damasio, 2002; Bechara, Damasio, and Damasio, 2000; Bechara, et al., 1999; Bechara, Damasio, Tranel, and Anderson, 1998; Bechara, Damasio, Tranel, and Damasio, 1997; Bechara, et al., 2001; Antoine Bechara, Dolan, and Hindes, 2002; Bechara, Tranel, et al., 2000; Brand, Recknor, Grabenhorst, and Bechara, 2007; Denburg, Recknor, Bechara, and Tranel, 2006; Denburg, Tranel, and Bechara, 2005; Kester, et al., 2006) (Table 1), with the exception of those by Bechara et al. (Bechara, et al., 1994), Sevy et al. (Sevy, et al., 2007), and Johnson et al. (Johnson, et al., 2008). In these last three studies, the Iowa Group utilized the four-deck format (decks A, B, C, and D) to present IGT data (Table 1). Selection frequency for deck B (31 trials) in the study by Sevy et al. (Sevy, et al., 2007) (Table 1) was approximately double that for deck B in the study by Bechara et al. (Bechara, et al., 1994). Additionally, this study identified 16 studies (Table 2) that presented data using the fourdeck format. Thirteen of these 16 studies demonstrated the PDB phenomenon (Table 2) (Ahn, et al., 2008; Bark, Dieckmann, Bogerts, and Northoff, 2005; Caroselli, et al., 2006; Crone, Somsen, van Beek, and van Der Molen, 2004; Fernie and Tunney, 2006; Fridberg, et al., 2010; Fum, et al., 2008; Martino, Bucay, Butman, and Allegri, 2007; O'Carroll and Papps, 2003; Overman, et al., 2004; Ritter, Meador-Woodruff, and Dalack, 2004; RodriguezSanchez, et al., 2005; Toplak, Jain, and Tannock, 2005; Wilder, et al., 1998). Additionally, in a behavioral and modeling study by Ahn et al. (2008), bad deck B was chosen significantly more times than bad deck A [20]. In sum, a large number of studies that presented data using the four-deck format identified the PDB phenomenon. Some studies used ―frequency/ probability‖ to explain this unexpected phenomenon in their IGT data. For data analysis in the second phase of this study, the two-factor repeated measurement ANOVA (nIGT vs. cIGT; decks A, B, C, and D) was utilized to test factors. The main effect of deck by Greenhouse-Geisser correction, was statistically significant (F(2.44, 44)= 16.193, p