Bayesian reverse-engineering considered as a research strategy for cognitive science.
Bayesian 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 formula...
| Publicado en: | Synthese Vol. 193; no. 12; pp. 3951 - 3986 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Dec2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=119806953&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 119806953 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Dec2016 vid: 193 iid: 12 pid: 237 pub: Springer Nature artinfo: ui: 119806953 10.1007/s11229-016-1180-3 ppf: 3951 ppct: 35 formats: fmt: @attributes: type: P size: 754KB tig: atl: Bayesian reverse-engineering considered as a research strategy for cognitive science. aug: au: Zednik, Carlos Jäkel, Frank affil: Institut III - Philosophie , Otto-von-Guericke-Universität Magdeburg , Zschokkestraße 32 39104 Magdeburg Germany Institut für Kognitionswissenschaft , Universität Osnabrück , Albrechtstraße 28 49076 Osnabrück Germany su: Reverse engineering Cognitive science Cognition Behavior Artificial intelligence Machine learning Cognitive psychology Neurosciences sug: subj: Reverse engineering Cognitive science Cognition Behavior Artificial intelligence Machine learning Cognitive psychology Neurosciences keyword: Ideal observers Levels of analysis Probabilistic modeling Rational analysis Reverse-engineering Scientific explanation ab: Bayesian 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2016. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
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