| What are the computational mechanisms that underlie perceptual and cognitive behavior? Any answer to this question must start with the observation that the brain has to work with uncertain information at every level of analysis. The presence of uncertainty has the consequence that the problem of computation in the brain becomes one of probabilistic inference. Indeed, we can recast all cognitive processing as comprising sequential stages of probabilistic inference, performed over data of varying abstraction. In this framework, the goal of processing at a particular level is to infer the variable of interest given the input information and the goal of learning at a particular level is to improve the quality of the inference that is being carried out.;In this thesis we explore and computationally characterize the inference that underlies cognitive processing at multiple levels, using multiple research methodologies. At the neural level, we derive a simple analytic expression that allows for the relation of network properties to the quality of the inference being carried out during neural representation and transmission. This derivation provides an important tool that can be used to elucidate mechanisms leading to efficient inference. We then use this expression to explore the neural mechanisms that underlie the improvements in behavioral performance, observed during perceptual learning. We report that perceptual learning can be neurally mediated through an improvement in the inference process in early sensory areas. Importantly, this model, in addition to accounting for the training induced changes in behavioral performance, also captures the training induced changes in neural properties.;Finally, at the behavioral level, we show that human multi-sensory integration during categorical speech perception is well described by a normative model for optimal inference, thereby providing behavioral evidence for efficient inference in the brain. As opposed to previous studies, the study described here computationally and experimentally probes cue integration in categorical tasks, thereby representing an important extension of previous work since most real-world perceptual tasks involve judgments over categorical dimensions. |
