Princeton Neuroscience Institute

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. In my lab we use computational modeling and analytical tools in combination with human functional imaging. Our emphasis is on model- based experimentation: we use computational models to define precise hypotheses about data, to design experiments, and to analyze their results. In particular, we are 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 our 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 we are interested in are: How can working memory be used to assist in real-world learning, and how can learning assist in determining the content of working memory? How do animals determine when one piece of experience is similar to another (generalization) and thus the information from both should be combined, versus two different situations that should be encoded separately (discrimination)? What is the scope of dopaminergic prediction error signals 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?