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How to best encode and represent information and later retrieve it from memory is a challenging computational problem. Our lab combines computational modeling, behavioral methods, and neural imaging to uncover the cognitive mechanisms that allows human memory to solve this problem. We do this by contrasting the behavior of human memory to machine learning algorithms when both are pitted against the same memory tasks. Understanding what gives rise to “optimal” memory also lays the theoretical foundations for unlocking more performance from our own memories.

We are grateful for the funding support from the National Science Foundation, provided through a grant from the PAC program and a collaborative grant from the NCS program.