Daeyeol Lee,1,2,* Hyojung Seo,1,* and Min Whan Jung3,*
1Department of Neurobiology, Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, Connecticut 06510
2Department of Psychology, Yale University, New Haven, Connecticut 06520
3Neuroscience Laboratory, Institute for Medical Sciences, Ajou University School of Medicine, Suwon 443-721, Republic of Korea
* Corresponding author
Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describehowmuch future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood.
Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain.
prefrontal cortex, neuroeconomics, reward, striatum, uncertainty