DEPARTMENT of PSYCHOLOGY
2017-2018 COLLOQUIUM SERIES
presents
Matthew Nassar, PhD.
Postdoctoral Fellow
Department of Cognitive, Linguistic and Psychological Sciences
Brown University
“Learning as Statistical Inference: Neural and Computational Mechanisms for Normative Learning”
Successful decision making often requires learning from prediction errors, but how much should we learn from any given error? I will examine this question in detail, drawing on an optimal inference model to formalize how we should learn in dynamic environments and a computationally efficient approximation to provide insight into how we could do so by adjusting the rate of learning from moment to moment. I will show behavioral data validating key model predictions in humans, demonstrate a role for the arousal system in setting the learning rate, and dissect the computational roles of neural subsystems upstream of learning rate implementation. I will explore the possibility that learning deficits might emerge from a failure to correctly determine how much should be learned, rather than a failure to represent prediction errors per se, and provide evidence for such an explanation in the case of healthy aging. Finally I will re-examine neural architecture of error-driven learning in the context of these results and discuss some future directions emerging from this work.
Monday, January 29, 2018
3:30 p.m.
Gilmer 190
Coffee/cookies at 3:15pm.
Reception will be held after the talk.