About this Research Topic
Essential for survival is the ability to learn how stimuli in the environment signal rewards and punishers and to use that information to modify behavior. Reinforcement learning is a form of trial-and-error learning where associations are formed based upon the incentive properties of stimuli. Studies of reinforcement learning span multiple disciplines from computer science to psychiatry; and theoretical work in this field has generated learning algorithms that are used in diverse applications such as artificial intelligence and approximate dynamic programming as well as modeling the mammalian brain. The current Research Topic focuses on approaches using reinforcement learning models to understand how the brain solves the computational problem of associating previously neutral stimuli with rewarding or aversive outcomes.
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