Research in the Niv lab focuses on the neural and computational processes underlying reinforcement learning and decision-making. We study the ongoing day-to-day processes by which animals and humans learn from trial and error, without explicit instructions, to predict future events and to act upon the environment so as to maximize reward and minimize punishment. In particular, we are interested in how attention and memory processes interact with reinforcement learning to create representations that allow us to learn to solve new tasks so efficiently.
Our emphasis is on model-based experimentation: we use computational models to define precise hypotheses about data, to design experiments, and to analyze results. In particular, we are interested in normative explanations of behavior: 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. In our hands, the main goal of computational models is not to simulate the system, but rather to understand what high-level computations is that system realizing, and what functionality do these computations fulfill.
A new focus of the lab is computational cognitive neuropsychiatry. Here our aim is to use the computational toolkit that we have developed for quantifying dynamical behavioral processes in order to better diagnose, understand, and treat psychiatric illnesses such as depression, OCD, schizophrenia and addiction. This work is done under the auspices of the new Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry.