Our laboratory studies the neural basis of decision-making; that is, how the brain can take uncertain information from a variety of sources and form categorical judgments that guide behavior. We are particularly interested in the role of experience in this process, including how we learn to optimize our decisions to achieve particular goals. We study these issues using three basic approaches:
1) Quantitative measures of behavior (“psychophysics”) combined with non-invasive measures of physiological variables like pupil diameter in human subjects. These studies allow us to prototype new behavioral tasks; make use of well-established psychophysical techniques to identify interesting behaviors and make inferences about the underlying neural mechanisms; and identify non-invasive measures that we can relate to our neurophysiological studies.
2) Psychophysics and electrophysiology in non-human primates. These studies allow us to test directly ideas about the relationship between neural activity in a particular brain region or regions and behavior.
3) Computational modeling. We develop computational and mathematical models for several purposes, including defining optimal limits on behavior for a particular task, which can provide a useful benchmark for interpreting behavioral data; linking in a quantitative and rigorous manner behavioral and neural data; and determining the computations that might be necessary and/or sufficient for a particular behavior.
The goal of our work is to provide new insights into the neural mechanisms that govern complex, learned behaviors and ultimately translate these insights into new approaches to understand, diagnose, and treat disorders of learning and cognition.
Updated January 17, 2013