Behavior Research Methods

, Volume 41, Issue 3, pp 657–663 | Cite as

A mixed-effects expectancy-valence model for the Iowa gambling task

  • Chung-Ping Cheng
  • Ching-Fan Sheu
  • Nai-Shing Yen
Society for Computers in Psychology


The Iowa gambling task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994) was developed to simulate real-life decision making under uncertainty. The task has been widely used to examine possible neurocognitive deficits in normal and clinical populations. Busemeyer and Stout (2002) proposed the expectancy-valence (EV) model to explicitly account for individual participants’ repeated choices in the IGT. Parameters of the EV model presumably measure different psychological processes that underlie performance on the task, and their values may be used to differentiate individuals across different populations. In the present article, the EV model is extended to include both fixed effects and subject-specific random effects. The mixed-effects EV model fits the nested structure of observations in the IGT naturally and provides a unified procedure for parameter estimation and comparisons among groups of populations. We illustrate the utility of the mixed-effects approach with an analysis of gender differences using a real data set. A simulation study was conducted to verify the advantages of this approach.


Bulimia Nervosa Choice Probability Iowa Gambling Task Response Time Distribution Motivation Parameter 
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  1. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7–15.CrossRefPubMedGoogle Scholar
  2. Bechara, A., & Damasio, H. (2002). Decision-making and addiction (Part I): Impaired activation of somatic states in substance-dependent individuals when pondering decisions with negative future consequences. Neuropsychologia, 40, 1675–1689.CrossRefPubMedGoogle Scholar
  3. Boeka, A. G., & Lokken, K. L. (2006). The Iowa gambling task as a measure of decision making for women with bulimia nervosa. Journal of the International Neuropsychological Society, 12, 741–745. doi:10.10170S1355617706060887CrossRefPubMedGoogle Scholar
  4. Bolla, K. I., Eldreth, D. A., Matochik, J. A., & Cadet, J. L. (2004). Sex-related differences in a gambling task and its neural correlates. Cerebral Cortex, 14, 1226–1232. doi:10.1093/cercor/bhh083CrossRefPubMedGoogle Scholar
  5. Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14, 252–262.CrossRefGoogle Scholar
  6. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah, NJ: Erlbaum.Google Scholar
  7. Davis, C., Patte, K., Tweed, S., & Curtis, C. (2007). Personality traits associated with decision-making deficits. Personality & Individual Differences, 42, 279–290. doi:10.1016/j.paid.2006.07.006CrossRefGoogle Scholar
  8. Hoffman, L., & Rovine, M. J. (2007). Multilevel models for experimental psychologists: Foundations and illustrative examples. Behavior Research Methods, 39, 101–117.PubMedGoogle Scholar
  9. Isella, V., Mapelli, C., Morieli, N., Pelati, O., Franceschi, M., & Appollonio, I. M. (2008). Age-related qualitative and quantitative changes in decision making ability. Behavioral Neurology, 19, 59–63.PubMedGoogle Scholar
  10. Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First- and higher-order factor models and their invariance across groups. Psychological Bulletin, 97, 562–582.CrossRefGoogle Scholar
  11. O’Neill, R. (1971). Algorithm AS 47: Function minimization using a simplex procedure. Applied Statistics, 20, 338–345.CrossRefGoogle Scholar
  12. Pinheiro, J. C., & Bates, D. M. (1995). Approximations to the loglikelihood function in the nonlinear mixed-effects model. Journal of Computational & Graphical Statistics, 4, 12–35.CrossRefGoogle Scholar
  13. Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects model in S and S-Plus. New York: Springer.CrossRefGoogle Scholar
  14. R Development Core Team (2006). R: A language and environment for statistical computing [Computer software and manual]. Vienna: R Foundation for Statistical Computing.Google Scholar
  15. Roediger, H. L., III, & McDermott, K. B. (1995). Creating false memories: Remembering words not presented in lists. Journal of Experimental Psychology: Learning, Memory, & Cognition, 21, 803–814.CrossRefGoogle Scholar
  16. Rouder, J. N., Lu, J., Speckman, P. L., Sun, D., & Jiang, Y. (2005). A hierarchical model for estimating response time distributions. Psychonomic Bulletin & Review, 12, 195–223.Google Scholar
  17. SAS Institute, Inc. (2004). SAS/STAT user’s guide (Version 9.1). Cary, NC: Author.Google Scholar
  18. Sheu, C.-F., Chen, C.-T., Su, Y.-H., & Wang, W.-C. (2005). Using SAS PROC NLMIXED to fit item response theory models. Behavior Research Methods, 37, 202–218.PubMedGoogle Scholar
  19. Sheu, C.-F., Lee, Y.-H., & Shih, P.-Y. (2008). Analyzing recognition performance with sparse data. Behavior Research Methods, 40, 722–727.CrossRefPubMedGoogle Scholar
  20. Stockard, J., O’Brien, R. M., & Peters, E. (2007). The use of mixed models in a modified Iowa gambling task and a prisoner’s dilemma game. Judgment & Decision Making, 2, 9–22.Google Scholar
  21. Stout, J. C., Busemeyer, J. R., Lin, A., Grant, S. J., & Bonson, K. R. (2004). Cognitive modeling analysis of the decision-making processes used by cocaine abusers. Psychonomic Bulletin & Review, 11, 742–747.Google Scholar
  22. van den Bos, R., Lasthuis, W., den Heijer, E., van der Harst, J., & Spruijt, B. (2006). Toward a rodent model of the Iowa gambling task. Behavior Research Methods, 38, 470–478.PubMedGoogle Scholar
  23. Verdejo-Garcia, A., Benbrook, A., Funderburk, F., David, P., Cadet, J. L., & Bolla, K. I. (2007). The differential relationship between cocaine use and marijuana use on decision-making performance over repeat testing with the Iowa gambling task. Drug & Alcohol Dependence, 90, 2–11. doi:10.1016/j.drugalcdep.2007.02.004CrossRefGoogle Scholar
  24. Wetzels, R., Vandekerckhove, J., Tuerlinckx, F., & Wagenmakers, E.-J. (2008, July). Bayesian parameter estimation in the expectancy valence model of the Iowa gambling task. Paper presented at the 41st Mathematical Psychology Meeting, Washington, DC.Google Scholar
  25. Wolfinger, R. D. (1999). Fitting nonlinear mixed models with the new NLMIXED procedure. In Proceedings of the 24th Annual SAS Groups International Conference (SUGI24) (Paper 287). Gary, NC: SAS Institute.Google Scholar
  26. Yechiam, E., Busemeyer, J. R., Stout, J. C., & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision making deficits. Psychological Science, 16, 973–978. doi:10.1111/j.1467-9280.2005.01646.xCrossRefPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2009

Authors and Affiliations

  • Chung-Ping Cheng
    • 1
  • Ching-Fan Sheu
    • 2
  • Nai-Shing Yen
    • 1
  1. 1.Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
  2. 2.National Chengchi UniversityTainan CityTaiwan

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