Neural Implementation of Probabilistic Models of Cognition
This work addresses the gap between computational Bayesian frameworks and neural mechanisms in cognitive science, offering a mechanistic model for probabilistic reasoning.
The authors tackled the problem of how the brain implements Bayesian models of cognition by proposing a deterministic neural network that learns probability distributions from event patterns and integrates Bayes' rule, simulating human learning and inference while explaining deviations like base-rate neglect.
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.