"Rethinking Memory Systems for Statistical Learning"

A School of Psychology Colloquium Featuring Dr. Nicholas Turk-Brown, PhD, Professor, Yale University

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."

Reception following the lecture.

 

About the Speaker

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.

The theme of his research is that cognitive processes such as perception, attention, learning and memory are inherently interactive, and that exploring such interactions can be an especially effective way to understand how these processes work. For example, he has published extensively on ‘statistical learning’ – an automatic, often unconscious process by which we extract and represent regularities in our experience and use them to generate predictions and facilitate processing.

 

Event Details

Date/Time:

  • Wednesday, February 27, 2019
    3:00 pm - 4:30 pm