Daniel Bullock

Professor Bullock’s research is focused on the use of integrated computational models of local circuits implicated in reinforcement learning, planning of action sequences (including speech), and motivated decision making. Current models focus on forebrain circuits within or linking: laminar frontal cortex, the striatum and other parts of the basal ganglia, and midbrain dopaminergic areas. The long-term goal is construction of quantitative models of sufficient accuracy to predict effects of many pharmacological manipulations on decision-making, voluntary behavior, and skill learning.

For further information: Daniel H. Bullock, Ph.D.