**Faculty Candidate Search - Cognitive Neuroscience**

Reinforcement learning provides a rich computational framework for understanding how value-based decisions are made, and how they are implemented in the brain. However, reinforcement learning models typically neglect the diverse array of cognitive resources humans may bring to decision problems, and thus only capture a narrow subset of choice behaviors and their neural underpinnings. My research seeks to understand how the brain utilizes cognitive processes beyond mere reinforcement in order to make decisions. In this talk, I present learning models that incorporate semantic and relational knowledge about decision tasks by leveraging the neural circuitry that is central to simple learning from reinforcement: the basal ganglia and the dopamine system. Using behavioral, genetic, and neuroimaging measures, I show affirmative evidence for these models, explaining how information beyond reinforcement enters into the neural computation of decision variables and affects choice behavior.