Topic: decision confidence

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

When making decisions, combining multiple pieces of information can increase the probability of making the right choice. A decision maker's estimate of this probability, referred to as decision confidence (Hangya et al., 2016), is useful for guiding subsequent actions and learning improvements in the decision policy (Drugowitsch et al., 2019). But studies of the neural computations that give rise to confidence have been limited by the difficulty of measuring confidence in animal subjects. One promising approach uses willingness to wait for reward after choice as a proxy for confidence (Lak, 2