Journal Article

Bayes in the Brain—On Bayesian Modelling in Neuroscience

Matteo Colombo and Peggy Seriès

in The British Journal for the Philosophy of Science

Published on behalf of British Society for the Philosophy of Science

Volume 63, issue 3, pages 697-723
Published in print September 2012 | ISSN: 0007-0882
Published online February 2012 | e-ISSN: 1464-3537 | DOI: http://dx.doi.org/10.1093/bjps/axr043
Bayes in the Brain—On Bayesian Modelling in Neuroscience

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According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ([1982]). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms.

1Introduction

2Theoretical Neuroscientists meet Bayes

3Is Perception Bayesian Inference?

4How Should we Understand the Inference from Bayesian Observers to Bayesian Brains?

5How Could we Discover that Brains are Bayesian?

6Conclusion

Journal Article.  10279 words.  Illustrated.

Subjects: Philosophy of Science ; Science and Mathematics

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