The long-term goal of our research is to understand computational and neural mechanisms that allow humans and other animals to be flexible, a crucial aspect of intelligence, and when/how these mechanisms fail. Specifically, we aim to elucidate cognitive processes and neural mechanisms underlying adaptive learning and decision making, as well as cognitive heuristics and biases animals adopt to overcome challenges of real-world learning and decision making. To achieve these goals, we use a combination of computational methods and human experiments while extensively collaborating with a wide range experimentalists using different animal models (including mice, rats, monkeys, and humans; see current collaborators).
To that end, we have created computational models to capture adaptive learning and decision making across multiple scales (from synaptic to system level). Moreover, we have developed computational methods for estimating timescales of neural dynamics and quantifying consistency in learning and choice behavior, and applied these methods to data across different species.
Our research is supported by fundings from NIH (NIDA) and NSF.