Bayesian models of perception, cognition and learning

Bayesian memory and learning, how to go from observations to latent variables. Speaker: Máté Lengyel.

By Cajal Programme
Published Jan, 2018

Learning materials

External links

Description

Behavior. Internal models in perception, probabilistic models.

Mathematical introduction. Bayes' rule. Loss functions (a.k.a. utility functions). Posterior mean and median, maximum a posteriori (MAP).

Behavioral examples. Perception and likelihood ratio. Bayesian estimation motor task, different strategies. Combining priors and likelihood, different priors. Natural (learned human) priors. Ideal observer model, inverting the ideal observer model to study natural priors/internal representations. Cross-validation.

Prerequisites

Basic probablity theory.