CRC 1436 lecture with Matthew Nassar

We would like to cordially invite you to our next CRC External Speaker Lecture. It will take place on May 27th, 2024 at 3:30pm at House 64 DZNE / IKND (room 121). At this lecture Prof. Matthew Nassar, Professor in the Department of Neuroscience at Brown University, will give an overview of his recent work.

Invitation for lecture with Matthew Nassar

Guest: Matthew Nassar

Title: Dynamic representations for behavioral flexibility

When: Monday, May 27th, 3.30 pm CET

Where: DZNE building, room 121

Abstract: People flexibly adjust their use of information according to context. The same piece of information, for example the unexpected outcome of an action, might be highly influential on future behavior in one situation — but ignored in another one. Bayesian models have provided insight into why people display this sort of behavior, and even identified potential neural mechanisms that link to behavior in specific tasks and environments, but to date have fallen short of providing broader mechanistic insights that generalize across tasks or statistical environments. Here I’ll examine the possibility that such broader insights might be gained through careful consideration of task structure. I’ll show that we can think about a large number of sequential tasks as requiring the same inference problem — that is to infer the latent states of the world and the parameters of those latent states — with the primary distinctions within the class defined by transition structure. Then I’ll talk about how a neural network that updates latent states according to a known transition structure and learns “parameters” of the world for each latent state can explain adaptive learning behavior across environments and provide the first insights into neural correlates of adaptive learning across environments. Finally, I will present a computational model that can learn the structure of the environment de novo, and show that the model can capture behavioral features of structure learning in humans performing changepoint, oddball, reversal, and sequence tasks.

All are welcome!