TU Darmstadt / ULB / TUbiblio

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

Zednik, C. and Jäkel, F. (2016):
Bayesian reverse-engineering considered as a research strategy for cognitive science.
In: Synthese, pp. 3951-3985, DOI: 10.1007/s11229-016-1180-3,
[Online-Edition: https://doi.org/10.1007/s11229-016-1180-3],
[Article]

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.

Item Type: Article
Erschienen: 2016
Creators: Zednik, C. and Jäkel, F.
Title: Bayesian reverse-engineering considered as a research strategy for cognitive science
Language: English
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.

Journal or Publication Title: Synthese
Divisions: 03 Department of Human Sciences
03 Department of Human Sciences > Institute for Psychology
03 Department of Human Sciences > Institute for Psychology > Models of Higher Cognition
Date Deposited: 09 Jul 2018 09:27
DOI: 10.1007/s11229-016-1180-3
Official URL: https://doi.org/10.1007/s11229-016-1180-3
Export:
Suche nach Titel in: TUfind oder in Google

Optionen (nur für Redakteure)

View Item View Item