Hierarchical Bayesian inference underpins human social learning
Abstract: Bayesian theories of brain function view perception as an active process with the brain constantly generating predictions about the causes of sensory inputs to anticipate future events. Since our knowledge about states in the world is incomplete, the extent to which surprising signals (or prediction errors) update predictions depends on their uncertainty. The role of uncertainty and (its inverse, precision) is particularly critical for social contexts where predictions about others’ hidden intentions are based on incomplete or ambiguous inputs. Consistent results from EEG and functional MRI studies suggest that the brain utilizes Bayesian inference machinery, in particular hierarchical precision-weighted prediction errors to generate a model of another person and his/her intentions. This computational approach is now extended to psychiatry, in the context of disorders where theory of mind deficits predominate.
Brief Bio: Dr Andreea Diaconescu is an Independent Scientist at the Krembil Centre for Neuroinformatics (CAMH) and Assistant Professor in the Department of Psychiatry at the University of Toronto.
See below for link to join via zoom:
Topic: KCN Event: Andreea Diaconescu
Time: Dec 10, 2020 10:00 Eastern Time (US and Canada)
Meeting ID: 896 8176 7784
Recording available on kcnhub youtube channel: