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The Bayesian‐Laplacian brain
Author(s) -
Zeki Semir,
Chén Oliver Y.
Publication year - 2020
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/ejn.14540
Subject(s) - prior probability , bayesian probability , computer science , artificial intelligence
We outline what we believe could be an improvement in future discussions of the brain acting as a Bayesian‐Laplacian system. We do so by distinguishing between two broad classes of priors on which the brain's inferential systems operate: in one category are biological priors (β priors ) and in the other artefactual ones (α priors ). We argue that β priors, of which colour categories and faces are good examples, are inherited or acquired very rapidly after birth, are highly or relatively resistant to change through experience, and are common to all humans. The consequence is that the probability of posteriors generated from β priors having universal assent and agreement is high. By contrast, α priors, of which man‐made objects are examples, are acquired post‐natally and modified at various stages throughout post‐natal life; they are much more accommodating of, and hospitable to, new experiences. Consequently, posteriors generated from them are less likely to find universal assent. Taken together, in addition to the more limited capacity of experiment and experience to alter the β priors compared with α priors, another cardinal distinction between the two is that the probability of posteriors generated from β priors having universal agreement is greater than that for α priors . The two categories are distinct at the extremes; there is, however, a middle range where they merge into one another to varying extents, resulting in posteriors that draw upon both categories.