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To simulate or not? Comment on steingroever, Wetzels, and Wagenmakers (2014)

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dc.contributor.authorKonstantinidis, Emmanouil-
dc.contributor.authorSpeekenbrink, Maarten-
dc.contributor.authorStout, Julie C.-
dc.contributor.authorAhn, Woo-Young-
dc.contributor.authorShanks, David R.-
dc.date.accessioned2024-04-24T02:33:57Z-
dc.date.available2024-04-24T02:33:57Z-
dc.date.created2024-04-17-
dc.date.created2024-04-17-
dc.date.created2024-04-17-
dc.date.issued2014-
dc.identifier.citationDecision, Vol.1 No.3, pp.184-191-
dc.identifier.issn2325-9965-
dc.identifier.urihttps://hdl.handle.net/10371/199359-
dc.description.abstractSteingroever, Wetzels, and Wagenmakers (2014) conducted a detailed investigation of 3 popular reinforcement-learning models for the Iowa gambling task using 2 model comparison techniques: a post hoc fit criterion and a simulation method. However, these 2 methods yield inconsistent results regarding which model should be preferred as a description of underlying psychological processes. Here, we describe the benefits of each method in an attempt to develop a more balanced view of how to utilize these model comparison techniques, and we outline the risks of focusing on a single method to make inferences about the overall utility of a model. Also, we make several suggestions about how applied research should evaluate candidate cognitive models, and we offer guidelines for future research aimed at identifying "good" models for decomposing and explaining participants' performance.-
dc.language영어-
dc.publisherAmerican Psychological Association Inc.-
dc.titleTo simulate or not? Comment on steingroever, Wetzels, and Wagenmakers (2014)-
dc.typeArticle-
dc.identifier.doi10.1037/dec0000016-
dc.citation.journaltitleDecision-
dc.identifier.scopusid2-s2.0-85032867421-
dc.citation.endpage191-
dc.citation.number3-
dc.citation.startpage184-
dc.citation.volume1-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorAhn, Woo-Young-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordAuthorExperience-based decision-making-
dc.subject.keywordAuthorIowa gambling task-
dc.subject.keywordAuthorMathematical modeling-
dc.subject.keywordAuthorReinforcement learning-
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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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