Synthese 193 (12):3951-3985 (2016)

Carlos Zednik
Eindhoven University of Technology
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.
Keywords Bayesian Modeling  Levels of Analysis  Rational Analysis  Explanation in Cognitive Science  Reverse-engineering
Categories (categorize this paper)
DOI 10.1007/s11229-016-1180-3
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

PhilArchive copy

Upload a copy of this paper     Check publisher's policy     Papers currently archived: 56,141
Through your library

References found in this work BETA

View all 48 references / Add more references

Citations of this work BETA

Character and Theory of Mind: An Integrative Approach.Evan Westra - 2018 - Philosophical Studies 175 (5):1217-1241.
Being Realist About Bayes, and the Predictive Processing Theory of Mind.Matteo Colombo, Lee Elkin & Stephan Hartmann - forthcoming - British Journal for the Philosophy of Science:000-000.
Mechanisms in Cognitive Science.Carlos Zednik - 2017 - In Phyllis McKay Illari & Stuart Glennan (eds.), The Routledge Handbook of Mechanisms and Mechanical Philosophy. London: Routledge. pp. 389-400.

View all 6 citations / Add more citations

Similar books and articles

In Defense of Reverse Inference.Edouard Machery - 2014 - British Journal for the Philosophy of Science 65 (2):251-267.
Reverse Inference in Neuropsychology.Clark Glymour & Catherine Hanson - 2016 - British Journal for the Philosophy of Science 67 (4):1139-1153.
Bayesian Models and Simulations in Cognitive Science.Giuseppe Boccignone & Roberto Cordeschi - 2007 - Workshop Models and Simulations 2, Tillburg, NL.
Bayes in the Brain—On Bayesian Modelling in Neuroscience.Matteo Colombo & Peggy Seriès - 2012 - British Journal for the Philosophy of Science 63 (3):697-723.
Reverse-Engineering in Cognitive-Science.Marcin Miłkowski - 2013 - In Marcin Miłkowski & Konrad Talmont-Kaminski (eds.), Regarding Mind, Naturally. Cambridge Scholars Press. pp. 12-29.
Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).


Added to PP index

Total views
44 ( #226,989 of 2,404,065 )

Recent downloads (6 months)
8 ( #88,964 of 2,404,065 )

How can I increase my downloads?


My notes