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

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)657-663
Number of pages7
JournalBehavior Research Methods
Volume41
Issue number3
DOIs
Publication statusPublished - 2009 Aug 1

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Gambling
Task Performance and Analysis
Population Groups
Population
Uncertainty
Decision Making
Psychology
Expectancy
Valence
Datasets

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Psychology (miscellaneous)
  • Psychology(all)

Cite this

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A mixed-effects expectancy-valence model for the Iowa gambling task. / Cheng, Chung-Ping; Sheu, Ching-Fan; Yen, Nai Shing.

In: Behavior Research Methods, Vol. 41, No. 3, 01.08.2009, p. 657-663.

Research output: Contribution to journalArticle

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