The Daw Lab at Princeton University studies how people and animals learn from trial and error (and from rewards and punishments) to make decisions, combining computational, neural, and behavioral perspectives. They focus on understanding how subjects cope with computationally demanding decision situations, notably choice under uncertainty and in tasks (such as mazes or chess) requiring many decisions to be made sequentially. In engineering, these are the key problems motivating reinforcement learning and bayesian decision theory. They are particularly interested in using these computational frameworks as a basis for analyzing and understanding biological decision making.