Title: Computational principles of event memory
Our ability to understand ongoing events depends critically on general knowledge about how different kinds of situations work (schemas), and also on recollection of specific instances of these situations that we have previously experienced (episodic memory). The consensus around this general view masks deep questions about how these two memory systems interact to support event understanding: How do we build our library of schemas? And how exactly do we use episodic memory in the service of event understanding? Given rich, continuous inputs, when do we store and retrieve episodic memory “snapshots,” and how are they organized so as to ensure that we can retrieve the right snapshots at the right time? I will develop predictions about how these processes work using memory augmented neural networks (i.e., neural networks that learn how to use episodic memory in the service of task performance), and I will present results from relevant fMRI and behavioral studies.
Dr. Norman is a Huo Professor in Computational and Theoretical Neuroscience and Chairperson of the Department of Psychology at Princeton Neuroscience Institute, Princeton University.
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