Rao, Rajesh P.N.Huang, Yanping2015-09-292015-09-292015-09-292015Huang_washington_0250E_14500.pdfhttp://hdl.handle.net/1773/33689Thesis (Ph.D.)--University of Washington, 2015This 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.application/pdfen-USCopyright is held by the individual authors.Bayesian Computation; Decision making; Markov Decision Process; Neural Networks; Reinforcement learning; Sequential ModelArtificial intelligenceCognitive psychologyNeurosciencescomputer science and engineeringThesis