Starting at NYU January 2023.
Research Question: How does the brain simulate the past and future to guide our decisions?
Our lab studies the neural computations that generate intelligent, goal-directed behavior. Using mathematical models, behavioral experiments, and neural recordings, we try to uncover how our memory systems build internal models of the world, and how we use these representations to simulate the future when making a decision. We draw heavily from the fields of Artificial Intelligence and Computational Neuroscience, focusing particularly on how the brain might implement model-based reinforcement learning.
Research in the lab consists of developing mathematical models of learning and decision making, typically expressed in the language of reinforcement learning, Bayesian statistics, and neural networks. To evaluate the theoretical predictions from these models, we conduct behavioral and neuroimaging experiments with humans. We also collaborate closely with other experimentalists, including those specialized in animal electrophysiology and psychiatric disorders in humans.
We hope our work will help us understand the computations underlying healthy and pathological cognition, and inform the development of brain-inspired AI algorithms that match (rather than surpass) human performance.