Hostname: page-component-8448b6f56d-cfpbc Total loading time: 0 Render date: 2024-04-23T08:22:51.967Z Has data issue: false hasContentIssue false
  • English
  • Français

Shifted risk preferences in pathological gambling

Published online by Cambridge University Press:  30 August 2012

R. Ligneul ,
G. Sescousse ,
G. Barbalat ,
P. Domenech  and
J.-C. Dreher
R. Ligneul
Affiliation:
Reward and Decision-Making Group, Cognitive Neuroscience Center, CNRS, Bron, France, University of Lyon I
G. Sescousse
Affiliation:
Reward and Decision-Making Group, Cognitive Neuroscience Center, CNRS, Bron, France, University of Lyon I
G. Barbalat
Affiliation:
Reward and Decision-Making Group, Cognitive Neuroscience Center, CNRS, Bron, France, University of Lyon I
P. Domenech
Affiliation:
Reward and Decision-Making Group, Cognitive Neuroscience Center, CNRS, Bron, France, University of Lyon I
J.-C. Dreher*
Affiliation:
Reward and Decision-Making Group, Cognitive Neuroscience Center, CNRS, Bron, France, University of Lyon I
*
*Address for correspondence: J.-C. Dreher, Ph.D., CNRS, Reward and Decision-Making Group, Cognitive Neuroscience Center, 67 Bd Pinel, 69675 Bron, France. (Email: dreher@isc.cnrs.fr)
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Pathological gambling (PG) is an impulse control disorder characterized by excessive monetary risk seeking in the face of negative consequences. We used tools from the field of behavioral economics to refine our description of risk-taking behavior in pathological gamblers. This theoretical framework allowed us to confront two hypotheses: (1) pathological gamblers distort winning probabilities more than controls; and (2) pathological gamblers merely overweight the whole probability range.

Method

Eighteen pathological gamblers and 20 matched healthy participants performed a decision-making task involving choices between safe amounts of money and risky gambles. The online adjustment of safe amounts, depending on participants' decisions, allowed us to compute ‘certainty equivalents’ reflecting the subjective probability weight associated with each gamble. The behavioral data were then fitted with a mathematical function known as the ‘probability weighting function’, allowing us to disentangle our two hypotheses.

Results

The results favored the second hypothesis, suggesting that pathological gamblers' behavior reflects economic preferences globally shifted towards risk, rather than excessively distorted probability weighting. A mathematical parameter (elevation parameter) estimated by our fitting procedure was found to correlate with gambling severity among pathological gamblers, and with gambling affinity among controls.

Conclusions

PG is associated with a specific pattern of economic preferences, characterized by a global (i.e. probability independent) shift towards risky options. The observed correlation with gambling severity suggests that the present ‘certainty equivalent’ task may be relevant for clinical use.

Keywords

Behavioral addictiondecision makinggamblingprobability discountingprobability weightingprospect theoryrisk seeking
Type
Original Articles
Information
Psychological Medicine , Volume 43 , Issue 5 , May 2013 , pp. 1059 - 1068
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdellaoui, M, Diecidue, E, Onculer, A (2011). Risk preferences at different time periods: an experimental investigation. Management Science 57, 975–987.CrossRefGoogle Scholar
Andrade, LF, Petry, NM (2012). Delay and probability discounting in pathological gamblers with and without a history of substance use problems. Psychopharmacology 219, 491–499.CrossRefGoogle ScholarPubMed
APA (2000). Diagnostic and Statistical Manual of Mental Disorders (4th edn, text revision). American Psychiatric Association: Washington, DC.Google Scholar
Bechara, A, Damasio, AR, Damasio, H, Anderson, SW (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50, 7–15.CrossRefGoogle ScholarPubMed
Brainard, DH (1997). The Psychophysics Toolbox. Spatial Vision 10, 433–436.CrossRefGoogle ScholarPubMed
Brand, M, Kalbe, E, Labudda, K, Fujiwara, E, Kessler, J, Markowitsch, HJ (2005). Decision-making impairments in patients with pathological gambling. Psychiatry Research 133, 91–99.CrossRefGoogle ScholarPubMed
Breen, R, Zuckerman, M (1999). ‘Chasing’ in gambling behavior: personality and cognitive determinants. Personality and Individual Differences 27, 1097–1111.CrossRefGoogle Scholar
Cavedini, P, Riboldi, G, Keller, R, D'Annucci, A, Bellodi, L (2002). Frontal lobe dysfunction in pathological gambling patients. Biological Psychiatry 51, 334–341.CrossRefGoogle ScholarPubMed
Clark, L (2010). Decision-making during gambling: an integration of cognitive and psychobiological approaches. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 365, 319–330.CrossRefGoogle ScholarPubMed
Dagher, A, Robbins, TW (2009). Personality, addiction, dopamine: insights from Parkinson's disease. Neuron 61, 502–510.CrossRefGoogle ScholarPubMed
Fox, CR, Poldrack, RA (2008). Prospect theory and the brain. In Neuroeconomics Decision Making and the Brain (ed. Glimcher, P., Camerer, C. F., Fehr, E. and Poldrack, R. A.), pp. 145–173. Academic Press: New York, NY.Google Scholar
Gonzalez, R, Wu, G (1999). On the shape of the probability weighting function. Cognitive Psychology 166, 129–166.CrossRefGoogle Scholar
Goodie, AS (2005). The role of perceived control and overconfidence in pathological gambling. Journal of Gambling Studies 21, 481–502.CrossRefGoogle ScholarPubMed
Goudriaan, AE, Oosterlaan, J, de Beurs, E, Van den Brink, W (2004). Pathological gambling: a comprehensive review of biobehavioral findings. Neuroscience and Biobehavioral Reviews 28, 123–141.CrossRefGoogle ScholarPubMed
Griffiths, M, Wardle, H, Orford, J, Sproston, K, Erens, B (2009). Internet gambling, health, smoking and alcohol use: findings from the 2007 British Gambling Prevalence Survey. International Journal of Mental Health and Addiction 9, 1–11.CrossRefGoogle Scholar
Heatherton, TF, Kozlowski, LT, Frecker, RC, Fagerström, KO (1991). Fagerström test for nicotine dependence. British Journal of Addiction 86, 1119–1127.CrossRefGoogle ScholarPubMed
Hertwig, R, Barron, G, Weber, EU, Erev, I (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science 15, 534–539.CrossRefGoogle ScholarPubMed
Hertwig, R, Erev, I (2009). The description-experience gap in risky choice. Trends in Cognitive Sciences 13, 517–523.CrossRefGoogle ScholarPubMed
Holt, D (2003). Is discounting impulsive? Evidence from temporal and probability discounting in gambling and non-gambling college students. Behavioural Processes 64, 355–367.CrossRefGoogle ScholarPubMed
Hsu, M, Krajbich, I, Zhao, C, Camerer, CF (2009). Neural response to reward anticipation under risk is nonlinear in probabilities. Journal of Neuroscience 29, 2231–2237.CrossRefGoogle ScholarPubMed
Kahneman, D, Tversky, A (1979). Prospect theory: an analysis of decision under risk. Econometrica 47, 263–291.CrossRefGoogle Scholar
Labudda, K, Wolf, OT, Markowitsch, HJ, Brand, M (2007). Decision-making and neuroendocrine responses in pathological gamblers. Psychiatry Research 153, 233–243.CrossRefGoogle ScholarPubMed
Lattimore, PK, Baker, JR, Witte, AD (1992). The influence of probability choice: a parametric examination. Journal of Economic Behavior and Organization 17, 377–400.CrossRefGoogle Scholar
Lawrence, AJ, Luty, J, Bogdan, N, Sahakian, BJ, Clark, L (2009). Problem gamblers share deficits in impulsive decision-making with alcohol-dependent individuals. Addiction 104, 1006–1015.CrossRefGoogle ScholarPubMed
Ledgerwood, DM, Orr, ES, Kaploun, KA, Milosevic, A, Frisch, GR, Rupcich, N, Lundahl, LH (2012). Executive function in pathological gamblers and healthy controls. Journal of Gambling Studies 28, 89–103.CrossRefGoogle ScholarPubMed
Lesieur, HR, Blume, SB (1987). The South Oaks Gambling Screen (SOGS): a new instrument for the identification of pathological gamblers. American Journal of Psychiatry 144, 1184–1188.Google ScholarPubMed
Madden, GJ, Petry, NM, Johnson, PS (2009). Pathological gamblers discount probabilistic rewards less steeply than matched controls. Experimental and Clinical Psychopharmacology 17, 283–290.CrossRefGoogle ScholarPubMed
Martins, SS, Tavares, H, da Silva Lobo, DS, Galetti, AM, Gentil, V (2004). Pathological gambling, gender, and risk-taking behaviors. Addictive Behaviors 29, 1231–1235.CrossRefGoogle ScholarPubMed
Michalczuk, R, Bowden-Jones, H, Verdejo-Garcia, A, Clark, L (2011). Impulsivity and cognitive distortions in pathological gamblers attending the UK National Problem Gambling Clinic: a preliminary report. Psychological Medicine 41, 2625–2635.CrossRefGoogle Scholar
Miedl, SF, Peters, J, Buchel, C (2012). Altered neural reward representations in pathological gamblers revealed by delay and probability discounting. Archives of General Psychiatry 69, 177–186.CrossRefGoogle ScholarPubMed
Milosevic, A, Ledgerwood, DM (2010). The subtyping of pathological gambling: a comprehensive review. Clinical Psychology Review 30, 988–998.CrossRefGoogle ScholarPubMed
Nurnberger, JI, Blehar, MC, Kaufmann, CA, York-Cooler, C, Simpson, SG, Harkavy-Friedman, J, Severe, JB, Malaspina, D, Reich, T (1994). Diagnostic Interview for Genetic Studies: rationale, unique features, and training. Archives of General Psychiatry 51, 849–859.CrossRefGoogle ScholarPubMed
Patton, JH, Stanford, MS, Barratt, ES (1995). Factor structure of the Barratt Impulsiveness Scale. Journal of Clinical Psychology 51, 768–774.3.0.CO;2-1>CrossRefGoogle ScholarPubMed
Peters, J, Büchel, C (2009). Overlapping and distinct neural systems code for subjective value during intertemporal and risky decision making. Journal of Neuroscience 29, 15727–15734.CrossRefGoogle ScholarPubMed
Petry, NM (2012). Discounting of probabilistic rewards is associated with gambling abstinence in treatment-seeking pathological gamblers. Journal of Abnormal Psychology 121, 151–159.CrossRefGoogle ScholarPubMed
Potenza, MN (2008). The neurobiology of pathological gambling and drug addiction: an overview and new findings. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 363, 3181–3189.CrossRefGoogle ScholarPubMed
Potenza, MN, Steinberg, MA, McLaughlin, SD, Rounsaville, BJ, O'Malley, SS (2000). Illegal behaviors in problem gambling: analysis of data from a gambling helpline. Journal of the American Academy of Psychiatry and the Law 28, 389–403.Google ScholarPubMed
Powell, J, Hardoon, K, Derevensky, JL, Gupta, R (2009). Gambling and risk-taking behavior among university students. Substance Use and Misuse 34, 1167–1184.CrossRefGoogle Scholar
Richards, JB, Zhang, L, Mitchell, SH, de Wit, H (1999). Delay or probability discounting in a model of impulsive behavior: effect of alcohol. Journal of the Experimental Analysis of Behavior 71, 121–143.CrossRefGoogle ScholarPubMed
Richter, MK (1966). Revealed Preference Theory. Econometrica 34, 635–638.CrossRefGoogle Scholar
Saunders, JB, Aasland, OG, Babor, TF, de la Fuente, JR, Grant, M (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption – II. Addiction 88, 791–804.CrossRefGoogle ScholarPubMed
Schonberg, T, Fox, CR, Poldrack, RA (2010). Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience. Trends in Cognitive Sciences 15, 11–19.CrossRefGoogle Scholar
Strong, D, Breen, R, Lejuez, C (2004). Using item response theory to examine gambling attitudes and beliefs. Personality and Individual Differences 36, 1515–1529.CrossRefGoogle Scholar
Takahashi, H, Matsui, H, Camerer, C, Takano, H, Kodaka, F, Ideno, T, Okubo, S, Takemura, K, Arakawa, R, Eguchi, Y, Murai, T, Okubo, Y, Kato, M, Ito, H, Suhara, T (2010). Dopamine D1 receptors and nonlinear probability weighting in risky choice. Journal of Neuroscience 30, 16567–16572.CrossRefGoogle ScholarPubMed
Tobler, PN, Fletcher, PC, Bullmore, ET, Schultz, W (2007). Learning-related human brain activations reflecting individual finances. Neuron 54, 167–175.CrossRefGoogle ScholarPubMed
Tom, SM, Fox, CR, Trepel, C, Poldrack, RA (2007). The neural basis of loss aversion in decision-making under risk. Science 315, 515–518.CrossRefGoogle ScholarPubMed
Trepel, C, Fox, CR, Poldrack, RA (2005). Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk. Brain Research. Cognitive Brain Research 23, 34–50.CrossRefGoogle Scholar
Tversky, A, Kahneman, D (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty 5, 297–323.CrossRefGoogle Scholar
Vachon, DD, Bagby, RM (2009). Pathological gambling subtypes. Psychological Assessment 21, 608–615.CrossRefGoogle ScholarPubMed
van Holst, RJ, van den Brink, W, Veltman, DJ, Goudriaan, AE (2010). Why gamblers fail to win: a review of cognitive and neuroimaging findings in pathological gambling. Neuroscience and Biobehavioral Reviews 34, 87–107.CrossRefGoogle Scholar
Weber, BJ, Huettel, SA (2008). The neural substrates of probabilistic and intertemporal decision making. Brain Research 1234, 104–115.CrossRefGoogle ScholarPubMed
Welte, JW, Barnes, GM, Tidwell, MC, Hoffman, JH (2008). The prevalence of problem gambling among U.S. adolescents and young adults: results from a national survey. Journal of Gambling Studies 24, 119–313.CrossRefGoogle Scholar
Zigmond, AS, Snaith, RP (1983). The hospital anxiety and depression scale (HADS). Acta Psychiatrica Scandinavica 67, 361–370.CrossRefGoogle Scholar
Zuckerman, M (2007). The Sensation Seeking Scale V (SSS-V): still reliable and valid. Personality and Individual Differences 43, 1303–1305.CrossRefGoogle Scholar
Supplementary material: File

Ligneul Supplementary Material

Appendix

Download Ligneul Supplementary Material(File)
File 68.2 KB