Colombo, Matteo and Hartmann, Stephan (2014) Bayesian Cognitive Science, Unification, and Explanation. [Preprint]

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Abstract

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal-mechanical explanation.

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Item Type:	Preprint
Creators:	Creators Email ORCID Colombo, Matteo m.colombo@uvt.nl Hartmann, Stephan s.hartmann@lmu.de 0000-0001-8676-2177
Keywords:	Bayesian modelling; unification; mechanical explanation; cue integration; computational neuroscience
Subjects:	Specific Sciences > Cognitive Science General Issues > Confirmation/Induction General Issues > Explanation
Depositing User:	Dr. Matteo Colombo
Date Deposited:	12 Jan 2015 16:28
Last Modified:	12 Jul 2024 17:25
Item ID:	11262
Subjects:	Specific Sciences > Cognitive Science General Issues > Confirmation/Induction General Issues > Explanation
Date:	June 2014
URI:	https://philsci-archive.pitt.edu/id/eprint/11262

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