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Dynamic hierarchical Bayesian inference underpins human social learning, by Andreea DIOCANESCU / University of Toronto| old_uid | 18055 |
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| title | Dynamic hierarchical Bayesian inference underpins human social learning, by Andreea DIOCANESCU / University of Toronto |
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| start_date | 2019/10/28 |
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| schedule | 11h |
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| online | no |
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| summary | 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. |
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| responsibles | Blancho |
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