For the auditorily inclined: An informal podcast about what the lab does (starts around min 9)
Research in the Niv Lab focuses on the neural and computational processes underlying day to day reinforcement learning and decision-making: how we learn, from trial and error, to predict future events and to maximize reward and minimize punishment. In particular, we are interested in how attention and memory processes interact with 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 make precise hypotheses about data, to design experiments, and to analyze results. 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, for which we are looking for new students, is computational cognitive neuropsychiatry. Here, our aim is to use the computational toolkit that we have developed for quantifying dynamical behavioral processes to better diagnose, understand, and treat psychiatric illnesses such as depression, bipolar disorder, OCD and addiction. As the lab is moving in this new clinical direction, we especially welcome students and postdocs who have a clinical background and/or a computational background and strong clinical interests.
Read more about us in the press:
|Niv Lab // Department of Psychology // Princeton Neuroscience Institute|
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