#  Cognition, Brain, & Behavior Job Talk - Matthew Nassar 

 



####  calendar\_today Date and Time 

 **October 25, 2017** 

 04:00PM - 05:15PM EDT 

####  pin\_drop Location 

 **WJH Basement Auditorium**  



 

 



 

 **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.



 

 



 

 

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