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Comparison of Decision Learning Models Using the Generalization Criterion Method

Cited 154 time in Web of Science Cited 182 time in Scopus

Ahn, Woo-Young; Busemeyer, Jerome R.; Wagenmakers, Eric-Jan; Stout, Julie C.

Issue Date
Lawrence Erlbaum Associates Inc.
Cognitive Science, Vol.32 No.8, pp.1376-1402
It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short-term predictions, and the third method again used a generalization criterion for long-term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short-versus long-term predictions on simple gambling tasks.
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  • College of Social Sciences
  • Department of Psychology
Research Area Addiction, computational neuroscience, decision neuroscience, 계산 신경과학, 의사결정 신경과학, 중독


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