Upcoming Talks by the Cognitive Neuroscience Faculty Candidates
The psychology department is excited to welcome four excellent candidates for the open cognitive neuroscience position. The upcoming dates and times of these candidates’ talks on their work is as follows:
11/28/17 (Tuesday) at 3:00 PM in 145 Crater Lake South
Matthew Nassar (Brown University), Learning as statistical inference: neural and computational mechanisms for normative learning
Talk 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.
11/30/17 (Thursday) at 3:00 PM in Gerlinger Lounge
Arielle Tambini (University of California, Berkley), Reactivation during awake rest: an opportunity for memory consolidation
12/04/17 (Monday) at 3:00 PM in Straub Hall 245
Anna Schapiro (Harvard University), Learning and consolidating patterns in experience
Talk Abstract: There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. This tension is resolved in classic memory systems theories by separating these processes anatomically: the hippocampus rapidly encodes individual episodes, while the cortex slowly extracts regularities over days, months, and years. This framework fails, however, to account for the full range of human learning and memory behavior, including: (1) how we often learn regularities quite quickly—within a few minutes or hours, and (2) how these memories transform over time and as a result of sleep. I will present evidence from fMRI and patient studies suggesting that the hippocampus, in addition to its well-established role in episodic memory, is in fact also responsible for our ability to rapidly extract regularities. I will then use computational modeling of the hippocampus to demonstrate how these two competing learning processes can coexist in one brain structure. Finally, I will present empirical and simulation work showing how these initial hippocampal memories are replayed during offline periods to help stabilize and integrate them into cortical networks. This work advocates a new comprehensive, mechanistic
12/07/17 Thursday) at 3:00 PM in EMU Gumwood Room
Sarah Dubrow (Princeton University), Memories, together and apart: How the brain segments and connects our experiences
All are welcome to attend.