- Professor, Psychology
- Professor, Neuroscience
- Professor, Cognitive Neuroscience
- Behavioral and Neurobiological Mechanisms of Associative Learning
- Extinction Learning
- Reward Processing & Reward Prediction Error Circuits
- Interval Timing & Comparative Cognition
- Computational and Neural Net Models of Associative Learning
- Ph.D., Dalhousie University
- Post-Doctoral Fellow, University of Pennsylvania
- Brooklyn College
My research focuses on investigations of the psychological and neurobiological foundations of simple forms of learning and decision-making, mostly with laboratory rodents. We ask how the rat learns to represent different aspects of reward (e.g., what it is, when it occurs, its value) and use that information to guide its behavior. One guiding principle is that different psychological and neurobiological systems underlie learning about different reward features, and we explore this using various sophisticated behavioral methods (e.g., reward devaluation, Pavlovian-to-instrumental transfer tasks) together with popular neurobiological techniques (DREADDs, local brain inactivation, lesion, protein expression). Some specific ongoing projects concern: distinguishing between reward identity and time encoding (with dorsal striatal and amygdala manipulations), investigating the mechanisms of goal-directed actions and habits, effects of extinction manipulations on reward identity, time, and value encoding in Pavlovian learning, extinction of conditioned flavor preference learning, connectionist network and experimental approaches to complex conditional discrimination learning and interval timing, human reaction time (RT) measures of associative priming.
I am currently the director of the CCP training program at CUNY and also participate as a faculty member in the CUNY Neuroscience Collaborative, have served as past president of the Eastern Psychological Association and the Pavlovian Society, and am the Editor of the Journal of Experimental Psychology: Animal Learning & Cognition.
- Brooklyn College