title: Neurocomputational mechanisms of reinforcement-guided learning in humans: a review.
abstract: Adapting decision making according to dynamic and probabilistic changes in action-reward contingencies is critical for survival in a competitive and resource-limited world. Much research has focused on elucidating the neural systems and computations that underlie how the brain identifies whether the consequences of actions are relatively good or bad. In contrast, less empirical research has focused on the mechanisms by which reinforcements might be used to guide decision making. Here, I review recent studies in which an attempt to bridge this gap has been made by characterizing how humans use reward information to guide and optimize decision making. Regions that have been implicated in reinforcement processing, including the striatum, orbitofrontal cortex, and anterior cingulate, also seem to mediate how reinforcements are used to adjust subsequent decision making. This research provides insights into why the brain devotes resources to evaluating reinforcements and suggests a direction for future research, from studying the mechanisms of reinforcement processing to studying the mechanisms of reinforcement learning.
authors.affiliation: University of California, Davis, California, USA. mikexcohen@gmail.com
authors.forename: Michael X
authors.initials: MX
authors.lastname: Cohen
mesh_terms: D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D007206:Individuality; D007858:Learning / Q000502:physiology*; D008960:Models, Psychological; D012054:Reinforcement, Psychology