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Comparison of Decision Learning Models Using the Generalization Criterion Method
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Woo-Young | - |
dc.contributor.author | Busemeyer, Jerome R. | - |
dc.contributor.author | Wagenmakers, Eric-Jan | - |
dc.contributor.author | Stout, Julie C. | - |
dc.date.accessioned | 2024-05-20T00:43:19Z | - |
dc.date.available | 2024-05-20T00:43:19Z | - |
dc.date.created | 2024-05-16 | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | Cognitive Science, Vol.32 No.8, pp.1376-1402 | - |
dc.identifier.issn | 0364-0213 | - |
dc.identifier.uri | https://hdl.handle.net/10371/203423 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.publisher | Lawrence Erlbaum Associates Inc. | - |
dc.title | Comparison of Decision Learning Models Using the Generalization Criterion Method | - |
dc.type | Article | - |
dc.identifier.doi | 10.1080/03640210802352992 | - |
dc.citation.journaltitle | Cognitive Science | - |
dc.identifier.wosid | 000261413600007 | - |
dc.identifier.scopusid | 2-s2.0-42549101410 | - |
dc.citation.endpage | 1402 | - |
dc.citation.number | 8 | - |
dc.citation.startpage | 1376 | - |
dc.citation.volume | 32 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Ahn, Woo-Young | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | STATISTICAL-THEORY | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | EXPERIENCE | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | EVENTS | - |
dc.subject.keywordPlus | ERROR | - |
dc.subject.keywordPlus | STATE | - |
dc.subject.keywordAuthor | Generalization criterion | - |
dc.subject.keywordAuthor | Reinforcement learning | - |
dc.subject.keywordAuthor | Decision making | - |
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