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Being Realist about Bayes, and the Predictive Processing Theory of Mind

Colombo, Matteo and Elkin, Lee and Hartmann, Stephan (2018) Being Realist about Bayes, and the Predictive Processing Theory of Mind. [Preprint]

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Abstract

Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach is overall better supported by the empirical evidence. So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted. Naturalistic philosophers of mind should adopt instead an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true. For continuing on with an exclusive praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of actual scientific practice as well as the architecture of the mind.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Colombo, Matteom.colombo@uvt.nl
Elkin, Leeljelkin3@gmail.com
Hartmann, StephanS.Hartmann@lmu.de
Keywords: Bayesian cognitive science; representing uncertainty; naturalistic philosophy of mind; scientific realism; underdetermination thesis
Subjects: Specific Sciences > Cognitive Science
General Issues > Confirmation/Induction
General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
Depositing User: Dr. Lee Elkin
Date Deposited: 09 Jun 2018 18:46
Last Modified: 09 Jun 2018 18:46
Item ID: 14767
Subjects: Specific Sciences > Cognitive Science
General Issues > Confirmation/Induction
General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
Date: February 2018
URI: http://philsci-archive.pitt.edu/id/eprint/14767

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