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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
- Authors
- Issue Date
- 2008
- Publisher
- Lawrence Erlbaum Associates Inc.
- Citation
- Cognitive Science, Vol.32 No.8, pp.1376-1402
- 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.
- ISSN
- 0364-0213
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