Research in our lab focuses on the neural and computational processes underlying reinforcement learning and decision-making. We study the ongoing day-to-day processes by which we 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.
The data of interest come from decades of animal conditioning literature, and from more recent investigations into the neural basis of conditioned behavior and human decision-making. In the 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 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.
Some examples of questions we are interested in are:
|Niv Lab // Department of Psychology // Princeton Neuroscience Institute|
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