A Mixed-Effects Expectancy-Valence Model for the Iowa Gambling Task

Abstract

The Iowa gambling task (Bechara, Damasio, Damasio, & Anderson, 1994) was developed to simulate real-life decision making under uncertainty. The task has been widely used to examine possible neuro-cognitive deficits in normal and clinical populations. Busemeyer and Stout (2002) proposed the expectancy-valence model to explicitly account for individual participants’ repeated choices in the Iowa gambling task. Parameters of the expectancy-valence 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 this paper, the expectancy-valence model is extended to include both fixed-effects and subject-specific random-effects. The mixed-effects expectancy-valence model fits naturally the nested structure of observations in the Iowa gambling task and provides a unified procedure for parameter estimation and comparisons among groups of populations. We illustrate the utility of the mixed-effects approach using a real data set. A simulation study is conducted to verify the advantages of this approach.