Statistical learning is the capability to estimate the latent statistics of events in the world based on (sequences of) observations. In this talk, I ask whether human statistical learning is “Bayesian”, distinguishing different aspects of Bayesian inference. Bayesian inference is often used to define the optimal performance in a task; response accuracy is thus taken as evidence (although not sufficient) of a Bayesian process. Bayesian inference is also notorious for the use of priors, the estimation and use of uncertainty, and the use of conditional probabilities, making computations over hierarchical models possible. I will provide evidence based on behavioral experiments and neuroimaging (functional magnetic resonance imaging, magneto-encephalography) that human statistical learning exhibits, at least qualitatively, those different properties of Bayesian inference.
By Risto Ilmoniemi
By William Lytton
By Claudia Vargas
By Daniel Fraiman
By Oswaldo Baffa Filho
By Leonardo Cohen
By Yves Frégnac
By Maria Elisa Pimentel Piemonte , Rafael Bassi Stern
By Fabrizia Mealli
By Sylvain Le Corff
By Ronnie Pavlov
By Tamara Kucherenko
