Matthew Nassar (Brown University - Frank Lab)
Title: "Learning as Statistical Inference: Neural and Computational Mechanisms for Normative Learning"
Abstract: 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.
LOCATION:WJH Basement Auditorium STATUS:CONFIRMED DTSTART:20171025T200000Z DTEND:20171025T211500Z END:VEVENT END:VCALENDAR