Nathaniel Daw (New York University)
Neuroscience Seminar Series
I study how people and animals learn from trial and error (and from rewards and punishments) to make decisions, combining computational, neural, and behavioral perspectives. Having trained partly in computer science, I am particularly interested in machine learning, reinforcement learning, and Bayesian techniques as frameworks for understanding and analyzing biological decision making. I therefore focus on how the brain copes with the sorts of computationally demanding decision situations that these methods address, such as choice under uncertainty and in tasks (such as mazes or chess) requiring many decisions to be made in sequence.
Learned decision making: Beyond reinforcement
Free and open to the university community and the public