Matthew Nassar zu Gast in der SFB Speaker Series

Am Montag den 27. Mai 2024 um 15:30 Uhr setzt der SFB 1436 die Reihe „External Speaker Series“ im Haus 64 DZNE / IKND (Raum 121) fort. Dieses Mal ist Matthew Nassar, Professor am Department of Neuroscience der Brown University, zu Gast und wird mit seinem Vortrag „Dynamic representations for behavioral flexibility“ einen Überblick über seine jüngsten Arbeiten geben.

Invitation for lecture with Matthew Nassar

Gast: Matthew Nassar

Titel: Dynamic representations for behavioral flexibility

Zeit: Montag, 27.05.2024, 15:30 Uhr

Ort: Gebäude 64, Raum 121

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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.