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Comparison of Decision Learning Models Using the Generalization Criterion Method
Author(s) -
Ahn WooYoung,
Busemeyer Jerome R.,
Wagenmakers EricJan,
Stout Julie C.
Publication year - 2008
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1080/03640210802352992
Subject(s) - generalization , generalizability theory , a priori and a posteriori , task (project management) , term (time) , artificial intelligence , computer science , machine learning , mathematics , statistics , philosophy , physics , management , epistemology , quantum mechanics , economics , mathematical analysis
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.