Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

The Bayesian-Laplacian Brain

View through CrossRef
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 brain’s inferential systems operate: in one category are biological priors ( β priors ) and in the other artifactual 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 to 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 not, however, always totally distinct and can merge into one another to varying extents, resulting in posteriors that draw upon both categories.
Title: The Bayesian-Laplacian Brain
Description:
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 brain’s inferential systems operate: in one category are biological priors ( β priors ) and in the other artifactual 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 to 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 not, however, always totally distinct and can merge into one another to varying extents, resulting in posteriors that draw upon both categories.

Related Results

[RETRACTED] Gro-X Brain Reviews - Is Gro-X Brain A Scam? v1
[RETRACTED] Gro-X Brain Reviews - Is Gro-X Brain A Scam? v1
[RETRACTED]➢Item Name - Gro-X Brain➢ Creation - Natural Organic Compound➢ Incidental Effects - NA➢ Accessibility - Online➢ Rating - ⭐⭐⭐⭐⭐➢ Click Here To Visit - Official Website - ...
Sample-efficient Optimization Using Neural Networks
Sample-efficient Optimization Using Neural Networks
<p>The solution to many science and engineering problems includes identifying the minimum or maximum of an unknown continuous function whose evaluation inflicts non-negligibl...
Figs S1-S9
Figs S1-S9
Fig. S1. Consensus phylogram (50 % majority rule) resulting from a Bayesian analysis of the ITS sequence alignment of sequences generated in this study and reference sequences from...
The spectrum and metric dimension of Indu–Bala product of graphs
The spectrum and metric dimension of Indu–Bala product of graphs
Given a connected graph [Formula: see text], the distance Laplacian matrix [Formula: see text] is defined as [Formula: see text], and the distance signless Laplacian matrix [Formul...
Brain Biochemistry and Its Disease
Brain Biochemistry and Its Disease
The human brain is one of the important organs in the human body. It is the most complex of all organs. The brain is an organ composed of billions of nerve cells. It has parts of t...
Hydatid Disease of The Brain Parenchyma: A Systematic Review
Hydatid Disease of The Brain Parenchyma: A Systematic Review
Abstarct Introduction Isolated brain hydatid disease (BHD) is an extremely rare form of echinococcosis. A prompt and timely diagnosis is a crucial step in disease management. This ...
A note on the Seidel and Seidel Laplacian matrices
A note on the Seidel and Seidel Laplacian matrices
In this paper we investigate the spectrum of the Seidel and Seidel Laplacian matrix of a graph. We generalized the concept of Seidel Laplacian matrix which denoted by Seidel matrix...

Back to Top