Our lab investigates how neural circuits that underwent evolutionary expansion in primates help monkeys and humans learn that the things they see and do have affective consequences. We want to understand the function of these circuits underlying motivated cognition in enough detail that we can predict whether actions or objects will elicit appetitive or aversive responses and the strength of those responses.

Learning to approach or avoid objects or actions based on past experience is called reinforcement learning. One advantage to studying reinforcement learning is that the problems and solutions it encompasses are computationally tractable. This allows us to utilize the same tasks and algorithms to study learning and decision making in humans and monkeys with few modifications and at a variety of levels. Using the rhesus macaque brain as a model system, our team combines multiple techniques including neurophysiology, reversible methods for perturbing neural circuits, and molecular approaches to neuroanatomy, to build an understanding about cell-type specific and circuit level control of reinforcement learning.

We also use tools and theories from reinforcement learning to forward translate discoveries about the neural bases of reinforcement learning in the monkey into healthy humans and psychiatric populations. These efforts identify which neural mechanisms are relevant across primates. They also ensure that our research involving nonhuman primates is building a translational bridge, towards a quantitative understanding of human emotions and improved treatments for mental health disorders.