Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Implementation of Probabilistic Models of Cognition   Bayesian models of cognition hypothesize that human brains make sense of data
by representing probability distributions and applying Bayes' rule to find the
best explanation for available data. Understanding the neural mechanisms
underlying probabilistic models remains important because Bayesian models
provide a computational framework, rather than specifying mechanistic
processes. Here, we propose a deterministic neural-network model which
estimates and represents probability distributions from observable events --- a
phenomenon related to the concept of probability matching. Our model learns to
represent probabilities without receiving any representation of them from the
external world, but rather by experiencing the occurrence patterns of
individual events. Our neural implementation of probability matching is paired
with a neural module applying Bayes' rule, forming a comprehensive neural
scheme to simulate human Bayesian learning and inference. Our model also
provides novel explanations of base-rate neglect, a notable deviation from
Bayes.

36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga