Bayesian Computation and Optimal Decision Making in Primate Brains

Abstract

This dissertation investigates the computational principles underlying the brains’ remarkable capacity to perceive, learn and act in environments of constantly varying uncertainty. Bayesian probability theory has suggested that optimal perception, learning and action rely on computing probability distributions over task-relevant world variables.This suggests the nervous system may maintain internal probabilistic generative models for what caused its sensory input. In this dissertation, we examine many aspects of primate perceptual and motor behaviors and model them under the framework of Bayesian inference and optimality principle.