Neural Implementation of Probabilistic Models of Cognition

TL;DR

This paper presents a neural network model that learns to estimate and represent probability distributions through experience, implementing Bayesian inference and explaining cognitive phenomena like base-rate neglect.

Contribution

It introduces a deterministic neural network that learns probabilities from event patterns and integrates a Bayesian module, advancing neural models of probabilistic cognition.

Findings

Neural model successfully estimates probabilities from data.

Model simulates Bayesian inference in neural terms.

Provides explanations for base-rate neglect phenomena.

Abstract

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.