Our laboratory is interested in answering the following question:
How is the brain able to accomplish complex goals, with limited computational resources, in an uncertain world?
In addressing this question, our research encompasses a broad range of topics, including visual perception, memory, motor control, and reinforcement learning. In each case however, we apply a common theoretical framework. Simply stated, the goal of information processing in the brain is to transform information into action. An optimal cognitive system is one that maximizes the utility of action, subject to constraints on the ability to store and process information.
To test this idea, we develop computational models, and conduct behavioral experiments on human learning, perception, and memory. Our laboratory is equipped with state of the art technology for mobile eye tracking and full-body motion capture that allows us to measure and record behavior in fine-grained detail across a range of tasks. To understand human performance in these laboratory tasks, we develop computational cognitive models that draw inspiration from machine learning, Bayesian statistics, and information theory.
In everyday life, the incredible complexity of the problems solved by the brain is hidden from our awareness. Oftentimes, it is only when we try to replicate the breadth and robustness of human performance in a computational model is the extent of the mind's complexity apparent. Computational models of human cognition serve an important role in all of the research carried out in our laboratory. They serve as an explicit statement of a scientific theory, but they can also generate novel predictions that can be tested experimentally. In many cases, human performance exceeds that of any existing algorithm (for example, in categorizing images or recognizing faces). By studying human behavior and building computational models of cognition, we can also advance the state of the art in engineering and applied sciences.