Hierarchical Bayesian inference in the brain: psychological models and neural implementation

Hierarchical Bayesian inference in the brain: Psychological models and neural implementation by Lei Shi Doctor of Philosophy in Neuroscience University of California, Berkeley Professor Thomas Griffiths, Chair The human brain effortlessly solves problems that still pose a challenge for modern computers, such as recognizing patterns in natural images. Many of these problems can be formulated in terms of Bayesian inference, including planning motor movements, combining cues from different modalities, and making predictions. Recent work in psychology and neuroscience suggests that human behavior is often consistent with Bayesian inference. However, most research using probabilistic models has focused on formulating the abstract problems behind cognitive tasks and their optimal solutions, rather than considering mechanisms that could implement these solutions. Therefore, it is critical to understand the psychological models and neural implementations that carry out these notoriously challenging computations. Exemplar models are a successful class of psychological process models that use an inventory of stored examples to solve problems such as identification, categorization, and function learning. We show that exemplar models can be used to perform a sophisticated form of Monte Carlo approximation known as importance sampling, and thus provide a way to perform approximate Bayesian inference. Simulations of Bayesian inference in speech perception, generalization along a single dimension,