Bayesian Modeling
Conceptually and methodologically, my work is related to a growing body of Bayesian statistical modeling work showing that various aspects of perception and learning, both its features and apparent bugs (such as visual illusions) can be understood as statistically normative (optimal) inferences about the world based on noisy data. Although the brain is unlikely to be exact-Bayesian (optimal) in general, Bayesian models provide a principled formalism from which to build computational and algorithmic understanding of brain processes. However, Bayesian statistical inference alone does not provide adequate formal tools to explain many aspects of cognition; in particular, it is chiefly concerned with the representation, integration, and propagation of inexact information, but it is silent on what course of actions or decisions ought to be taken based on that information, in order to achieve a desired behavioral outcome. As such, Bayesian analyses have had the most success in modeling aspects of cognition that only require observational analysis of incoming data, such as in perception and learning, and not those that require active manipulation of the state of the world or the nature of the incoming data. To overcome this limitation,
Decision, Control and Game Theory
I have been combining Bayesian modeling with decision theory, which deals with how to optimize terminal actions, control theory, which deals with how to optimize actions that affect future data collection (as in active sensing) and/or the state of the world, and game theory, which deals with strategic social interactions.
Methodologically, my lab typically uses behavioral data to constrain and develop the initial computational model, and then seek collaborations with neurophysiologists and psychiatrists to identify the underlying neural substrate that support the computations implied by the models in the healthy brain, as well as understanding the etiology and manifestation of impairments to component processes in psychiatric and neurological diseases.