We have an ongoing series of studies that use a patch-foraging paradigm from ecology in order to investigate how individuals make serial (stop/search) decisions. Using tools from machine learning and economics, we model the trial by trial data in an effort to understand how the decision variables are learned and incrementally updated with experience. We have begun to investigate how this type of decision making is represented in the brain by looking at the same class of tasks in a Parkinson’s.