Bayesian reverse-engineering considered as a research strategy for cognitive science

DC FieldValueLanguage
dc.contributor.authorZednik, Carlos
dc.contributor.authorJaekel, Frank
dc.date.accessioned2021-12-23T16:11:12Z-
dc.date.available2021-12-23T16:11:12Z-
dc.date.issued2016
dc.identifier.issn00397857
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/9581-
dc.description.abstractBayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and are often deployed unsystematically, Bayesian reverse-engineering avoids several important worries that have been raised about the explanatory credentials of Bayesian cognitive science: the worry that the lower levels of analysis are being ignored altogether; the challenge that the mathematical models being developed are unfalsifiable; and the charge that the terms `optimal' and `rational' have lost their customary normative force. But while Bayesian reverse-engineering is therefore a viable and productive research strategy, it is also no fool-proof recipe for explanatory success.
dc.language.isoen
dc.publisherSPRINGER
dc.relation.ispartofSYNTHESE
dc.subjectBRAINS
dc.subjectCATEGORIZATION
dc.subjectCONNECTIONIST
dc.subjectDISCOVERY
dc.subjectHistory & Philosophy Of Science
dc.subjectIdeal observers
dc.subjectINFERENCE
dc.subjectLevels of analysis
dc.subjectPERCEPTION
dc.subjectPhilosophy
dc.subjectProbabilistic modeling
dc.subjectPROBABILISTIC MODELS
dc.subjectRational analysis
dc.subjectREPRESENTATIONS
dc.subjectReverse-engineering
dc.subjectScientific explanation
dc.subjectSTATISTICS
dc.subjectUNCERTAINTY
dc.titleBayesian reverse-engineering considered as a research strategy for cognitive science
dc.typejournal article
dc.identifier.doi10.1007/s11229-016-1180-3
dc.identifier.isiISI:000389190600010
dc.description.volume193
dc.description.issue12, SI
dc.description.startpage3951
dc.description.endpage3985
dc.contributor.orcid0000-0002-9702-7706
dc.contributor.researcheridH-3753-2019
dc.identifier.eissn15730964
dc.publisher.placeVAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
dcterms.isPartOf.abbreviationSynthese
dcterms.oaStatusGreen Submitted
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