How to set the switches on this thing.
Reinforcement learning (RL) has become a dominant computational paradigm for modeling psychological and neural aspects of affectively charged decision-making tasks. RL is normally construed in terms of the interaction between a subject and its environment, with the former emitting actions, and the latter providing stimuli, and appetitive and aversive reinforcement. However, there is recent emphasis on redrawing the boundary between the two, with the organism constructing its own notion of reward, punishment and state, and with internal actions, such as the gating of working memory, being treated on an equal footing with external manipulation of the environment. We review recent work in this area, focusing on cognitive control.
Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR, United Kingdom. dayan@gatsby.ucl.ac.uk
Peter
P
Dayan
D000818:Animals; D001070:Appetitive Behavior / Q000502:physiology*; D001362:Avoidance Learning / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D008960:Models, Psychological; D012054:Reinforcement, Psychology; D012201:Reward