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No two events are exactly alike. But still, we learn, which means that we implicitly decide what events are similar enough that experience with one can inform us about what to do in another. Yael Niv and her colleagues have suggested that this relies on an implicit parsing of incoming information into ‘clusters’ according to inferred hidden (latent) causes. Moreover, they have proposed that unexpected information (that is, a prediction error) is key to this separation into clusters.
In this talk, Niv will demonstrate these ideas through behavioral experiments showing evidence for clustering in animals and humans and by illustrating the effects of prediction errors on the organization of memory. Finally, she will tie the different findings together into a hypothesis about how our brains organize information about events.
Niv received her master’s degree in psychobiology from Tel Aviv University and her Ph.D. in computational neuroscience from the Hebrew University in Jerusalem. She conducted a major part of her thesis research at the Gatsby Computational Neuroscience Unit at University College London. She is a professor at Princeton University in the psychology department and the Princeton Neuroscience Institute. Her lab studies the neural and computational processes underlying reinforcement learning and decision making with a particular focus on how the cognitive processes of attention, memory and learning interact in constructing task representations that allow efficient learning and decision-making. In addition, she is a co-founder and co-director of the Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry, where she is applying ideas from reinforcement learning to questions pertaining to psychiatric disorders within the new field of computational psychiatry.