Topic: approximate inference through active sampling

Bayesian computations are intractable and expensive, but this is rarely accounted for in existing Bayesian observer models. In this work, we propose that a) the brain can only compute imprecise (noisy) estimates of likelihoods and posteriors, and, b) since computations are expensive, the brain actively chooses which computations to perform to refine such estimates. We call our framework approximate inference through active sampling (AIAS) and study its implications in N-alternative categorization. While it is common in Bayesian observer models to assume that the agent makes noisy measurements