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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
  • 566 Downloads

Abstract

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.

Keywords

Bulimia Nervosa Choice Probability Iowa Gambling Task Response Time Distribution Motivation Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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