**Faculty Candidate - Cognitive Neuroscience**

During the course of our day-to-day we make multitudes of decisions involving uncertain outcomes, from the simple (choosing where to eat lunch) to the very complex (trading stocks). Despite their differences, solving these problems relies on similar computations: we use expectations of reward, estimates of risk, and information about the past to guide our choices and learn from experience. Neural correlates of these and related signals have been widely demonstrated in both human and animal brains using primarily fMRI and single-unit approaches. Here, I propose the use of intracranial recordings, which provide unprecedented access to human neurophysiological activity, to build on and expand these insights. I will show evidence that sub-second electrophysiological (high gamma) and electrochemical (dopaminergic) activity from cortical and subcortical areas map onto different computational aspects of decision-making. Cortical activity can support a wide array of overlapping computations related to external and internal information, whereas striatal human dopamine function shows unique features not captured by current models derived from animal data. Invasive approaches allow the study of previously inaccessible aspects of human brain function, which will provide new insights into the neural basis of human cognition and inform future neuromodulation strategies.