title: Comparison of decision learning models using the generalization criterion method.
abstract: 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.
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University, AustraliaDepartment of Psychology, University of Amsterdam, AustraliaSchool of Psychology, Psychiatry, and Psychological Medicine, Monash University, Australia.
authors.forename: Woo-Young
authors.initials: WY
authors.lastname: Ahn
authors.forename: Jerome R
authors.initials: JR
authors.lastname: Busemeyer
authors.forename: Eric-Jan
authors.initials: EJ
authors.lastname: Wagenmakers
authors.forename: Julie C
authors.initials: JC
authors.lastname: Stout