The Bayesian-Laplacian Brain

Abstract

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 brains inferential systems operate: in one category are biological priors ({beta} priors) and in the other artifactual ones ( priors). We argue that {beta} 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 {beta} priors having universal assent and agreement is high. By contrast, 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 {beta} priors compared to priors, another cardinal distinction between the two is that the probability of posteriors generated from {beta} priors having universal agreement is greater than that for 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.