Biomedical Graduate Studies

Description of Research Expertise

Many aspects of higher brain function rely on two closely related capacities, inference and learning. Inference is the process of drawing conclusions from uncertain data, like forming a percept from noisy sensory information or predicting the most rewarding future outcome from the recent history of outcomes. These inferences often inform decisions that determine behavior. Learning uses experience to shape how these kinds of inference and decision processes function, often optimizing them to meet particular goals. Recent work has begun to identify how and where in the brain inference processes are implemented, particularly in the service of perceptual and reward-based decision-making. Research in my laboratory focuses on how these processes are shaped by learning to provide the flexibility a decision-maker needs to survive in a complex and dynamic world.

We use several complementary approaches to study this complex issue.

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, identify and quantify interesting behaviors, and begin to make inferences about and understand the underlying neural mechanisms.

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. These studies help to define optimal limits on behavior, characterize relationships between behavioral and neural data, and identify particular computations that can drive complex behaviors.

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.