My research focuses on the neural and computational processes underlying learning and decision-making – the ongoing day-to-day processes by which we learn from trial and error and without explicit instructions, to predict future events and to act upon the environment so as to maximize reward and minimize punishment. The data of interest come from decades of animal conditioning literature, and the myriad of more recent investigations into the neural underpinnings of conditioned behavior and human decision-making. My approach is to use computational modeling techniques and analytical tools, specifically from reinforcement learning, Bayesian inference and machine learning, in combination with experimental investigations of human functional imaging and rat behavior. In particular, I am interested in normative explanations of behavior, ie, models that offer a principled understanding of why our brain mechanisms use the computational algorithms that they do, and in what sense, if at all, these are optimal. The main goal of computational models, in my hands, is not to simulate the system, but rather to understand what high-level computations is that system realizing, and to what purpose? That is, what functionality do these computations fulfill?
Some examples of questions I am interested in are: What is the optimal learning rule for prediction learning in a stochastic environment and what are its behavioral implications? How should motivational states (such as hunger or satiety) affect action selection and response rates? Through what neural mechanisms are these effects realized, and can this explain why dopamine influences response vigor? How does the brain identify which are the critical aspects of a task that should be represented and learned about? What are the implications of this fundamental learning process on the interactions between attention systems in the prefrontal cortex and reinforcement learning systems in the basal ganglia?