Zednik, C. ; Jäkel, F. (2016)
Bayesian reverse-engineering considered as a research strategy for cognitive science.
In: Synthese
doi: 10.1007/s11229-016-1180-3
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Zednik, C. ; Jäkel, F. |
| Art des Eintrags: | Bibliographie |
| Titel: | Bayesian reverse-engineering considered as a research strategy for cognitive science |
| Sprache: | Englisch |
| Publikationsjahr: | 2016 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Synthese |
| DOI: | 10.1007/s11229-016-1180-3 |
| URL / URN: | https://doi.org/10.1007/s11229-016-1180-3 |
| Kurzbeschreibung (Abstract): | 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. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie 03 Fachbereich Humanwissenschaften > Institut für Psychologie > Modelle höherer Kognition Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) |
| Hinterlegungsdatum: | 09 Jul 2018 09:27 |
| Letzte Änderung: | 12 Okt 2020 09:20 |
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