Bayesian models of perception, cognition and learning
Bayesian memory and learning, how to go from observations to latent variables. Speaker: Máté Lengyel.
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.
Basic probablity theory.