Schrater's domain is in the development of predictive models of human behavior, with a focus on perception, action, decision-making, learning and motivation. The approach is rooted in the idea that human behavior is a rational adaptive response to the problems of surviving and reproducing in our environment given limited information. He uses probabilistic methods like hierarchical probabilistic models, Bayesian Reinforcement learning, Bayesian decision theory, etc. to construct normative (optimal) solutions to perception, action, decision, and learning problems faced by humans. His lab uses behavioral experiments to test these ideas via one of several lab setups, including a video game lab, and through collaboration with the Multi-Sensory Perception lab.
Ph.D. in Neuroscience, University of Pennsylvania (1999) B.A. in Neuroscience, California State University, Long Beach (1992)
Paul Schrater holds the joint appointment between Psychology and Computer Science and Engineering. He received his Ph.D. in Neuroscience in 1999 from the University of Pennsylvania and has been a Post Doc with Dan Kersten in the Computational Vision Lab for the past 3 years researching human and computer vision and motor control. Schrater's research interests include statistical pattern recognition, human and computer vision, multi-modal sensory integration and motor control.