Research in the lab is focused on understanding brain substrates for flexible behavioral control and reinforcement learning (RL). We have extensive experience in behavioral, systems, and computational neuroscience and work in the lab combines multiple interdisciplinary approaches to study: (i) the functional properties of key brain decision-circuits, (ii) link identified circuit mechanisms to specific computational operations within normative theoretical frameworks, (iii) to ultimately understand how these circuit- and computational-specializations become leveraged during various behavioral demands.Our previous scientific contributions have reported novel empirical findings (including dopamine midbrain-forebrain dissociation, and striatal dopamine waves) that have significantly (re)shaped formalizations of dopamine's role in RL. The lab seeks to build on this trajectory to make deep contributions that integrate experimental findings into multilevel neurocomputational models for tandem and cyclical advances in the simulated and empirical understanding of brain mechanisms for valuation, selection, planning, and execution of behavioral goals.
