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Challenges and promises for translating computational tools into clinical practice

DC Field Value Language
dc.contributor.authorAhn, Woo-Young-
dc.contributor.authorBusemeyer, Jerome R.-
dc.creator안우영-
dc.date.accessioned2019-04-25T00:14:19Z-
dc.date.available2020-04-05T00:14:19Z-
dc.date.created2018-10-29-
dc.date.created2018-10-29-
dc.date.created2018-10-29-
dc.date.created2018-10-29-
dc.date.created2018-10-29-
dc.date.issued2016-10-
dc.identifier.citationCurrent Opinion in Behavioral Sciences, Vol.11, pp.1-7-
dc.identifier.issn2352-1546-
dc.identifier.urihttps://hdl.handle.net/10371/148950-
dc.description.abstract© 2016 Elsevier Ltd.Computational modeling and associated methods have greatly advanced our understanding of cognition and neurobiology underlying complex behaviors and psychiatric conditions. Yet, no computational methods have been successfully translated into clinical settings. This review discusses three major methodological and practical challenges (A. precise characterization of latent neurocognitive processes, B. developing optimal assays, C. developing large-scale longitudinal studies and generating predictions from multi-modal data) and potential promises and tools that have been developed in various fields including mathematical psychology, computational neuroscience, computer science, and statistics. We conclude by highlighting a strong need to communicate and collaborate across multiple disciplines.-
dc.language영어-
dc.language.isoenen
dc.publisherElsevier Limited-
dc.titleChallenges and promises for translating computational tools into clinical practice-
dc.typeArticle-
dc.identifier.doi10.1016/j.cobeha.2016.02.001-
dc.citation.journaltitleCurrent Opinion in Behavioral Sciences-
dc.identifier.wosid000395323700002-
dc.identifier.scopusid2-s2.0-84958970780-
dc.description.srndOAIID:RECH_ACHV_DSTSH_NO:T201736003-
dc.description.srndRECH_ACHV_FG:RR00200001-
dc.description.srndADJUST_YN:-
dc.description.srndEMP_ID:A080561-
dc.description.srndCITE_RATE:0-
dc.description.srndDEPT_NM:심리학과-
dc.description.srndEMAIL:wahn55@snu.ac.kr-
dc.description.srndSCOPUS_YN:Y-
dc.citation.endpage7-
dc.citation.startpage1-
dc.citation.volume11-
dc.description.isOpenAccessY-
dc.contributor.affiliatedAuthorAhn, Woo-Young-
dc.identifier.srndT201736003-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordPlusDECISION-MAKING-
dc.subject.keywordPlusCOGNITIVE MODEL-
dc.subject.keywordPlusLEARNING-MODELS-
dc.subject.keywordPlusRISK-TAKING-
dc.subject.keywordPlusREWARD-
dc.subject.keywordPlusPERFORMANCE-
dc.subject.keywordPlusDAMAGE-
dc.subject.keywordPlusPSYCHIATRY-
dc.subject.keywordPlusFRAMEWORK-
dc.subject.keywordPlusBEHAVIOR-
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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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