Bayesian techniques for analyzing group differences in the Iowa Gambling Task: A case study of intuitive and deliberate decision-makers

• Published in: Psychonomic bulletin & review 2018-06, Vol.25 (3), p.951-970
• Main Authors: Steingroever, Helen, Pachur, Thorsten, Šmíra, Martin, Lee, Michael D.
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Bayesian analysis; Behavioral Science and Psychology; Cognition & reasoning; Cognitive Psychology; Data Interpretation, Statistical; Decision making; Decision Making - physiology; Economic models; Estimates; Executive Function - physiology; Gambling; Humans; Hypothesis testing; Methods; Models, Psychological; Neuropsychological Tests; Open access publishing; Parameter estimation; Probability; Psychology; Reinforcement, Psychology; Theoretical Review
• ISSN: 1069-9384; 1531-5320
• DOI: 10.3758/s13423-017-1331-7

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.