Title:
Rethinking Memory Systems for Statistical Learning

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dc.contributor.author Turk-Browne, Nicholas
dc.contributor.corporatename Georgia Institute of Technology. School of Psychology en_US
dc.contributor.corporatename Yale University. Dept.of Psychology en_US
dc.date.accessioned 2019-03-07T14:51:43Z
dc.date.available 2019-03-07T14:51:43Z
dc.date.issued 2019-02-27
dc.description Presented on February 27, 2019 at 3:00 p.m. in the J. S. Coon Building, Room 250. en_US
dc.description Nicholas Turk-Browne is the 2016 winner of the Elsevier/VSS Young Investigator Award. Trained at the University of Toronto and then at Yale University, Nicholas Turk-Browne was awarded a PhD in Cognitive Psychology in 2009 under the supervision of Marvin Chun and Brian Scholl. Following his PhD, Nick took up a position at Princeton University before moving on to become a Professor of Psychology at Yale, where he currently resides. Dr. Turk-Browne uses behavioural, neuroimaging, computational and patient studies to develop an integrated understanding of the mind and brain. en_US
dc.description Runtime: 73:03 minutes en_US
dc.description.abstract Dogma states that memory can be divided into distinct types, based on whether conscious or not, one-shot or incremental, autobiographical or factual, sensory or motor, etc. These distinctions have been supported by dissociations in brain localization, task performance, developmental trajectories, and pharmacological interventions, among other techniques. A natural consequence is the assumption of a one-to-one mapping between brain systems and memory behaviors. Aside from theoretical concerns and dissociation logic, there have also now been several empirical demonstrations of where these boundaries break down, from contributions of the hippocampus to reward learning and motor behavior to rapid episodic-like learning in frontal cortex. These considerations suggest that behavior is overdetermined by multiple brain systems and that the dependence on any particular brain system reflects the specific computations required for that behavior. As a case study, I will describe a series of neuroimaging, neuropsychological, and computational studies implicating the hippocampal system in statistical learning, a function more traditionally ascribed to cortical systems. I will end by considering some open questions that arise from this perspective, including about how memory systems support predictive coding and change over development. en_US
dc.format.extent 73:03 minutes
dc.identifier.uri http://hdl.handle.net/1853/60922
dc.language.iso en_US en_US
dc.relation.ispartofseries Psychology Colloquium
dc.subject Behavior en_US
dc.subject Memory en_US
dc.subject Statistical learning en_US
dc.title Rethinking Memory Systems for Statistical Learning en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename College of Sciences
local.contributor.corporatename School of Psychology
local.relation.ispartofseries School of Psychology Colloquiua
relation.isOrgUnitOfPublication 85042be6-2d68-4e07-b384-e1f908fae48a
relation.isOrgUnitOfPublication 768a3cd1-8d73-4d47-b418-0fc859ce897d
relation.isSeriesOfPublication da9098fa-29c9-4bda-a0d0-bb2f2a5f2bd0
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