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The Bayesian-Laplacian Brain

Authors: Semir Zeki; Oliver Y. Chén;

The Bayesian-Laplacian Brain

Abstract

AbstractWe 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 artifactual ones (α priors). We argue thatβ 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 fromβ priorshaving universal assent and agreement is high. By contrast, αpriors, 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 theβ priorscompared toα priors, another cardinal distinction between the two is that the probability of posteriors generated fromβ priorshaving universal agreement is greater than that forα priors. The two categories are not, however, always totally distinct and can merge into one another to varying extents, resulting in posteriors that draw upon both categories.

Country
United Kingdom
Keywords

colour vision, artifactual priors, Brain, Humans, biological priors, Bayes Theorem, aesthetic experiences, Bayesian brain operations

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
21
Top 10%
Top 10%
Average
Green
hybrid