{"pk":27026,"title":"Structure Learning in Motor Control:A Deep Reinforcement Learning Model","subtitle":null,"abstract":"Motor adaptation displays a structure-learning effect: adapta-tion to a new perturbation occurs more quickly when the sub-ject has prior exposure to perturbations with related structure.Although this ‘learning-to-learn’ effect is well documented, itsunderlying computational mechanisms are poorly understood.We present a new model of motor structure learning, approach-ing it from the point of view of deep reinforcement learning.Previous work outside of motor control has shown how recur-rent neural networks can account for learning-to-learn effects.We leverage this insight to address motor learning, by import-ing it into the setting of model-based reinforcement learning.We apply the resulting processing architecture to empiricalfindings from a landmark study of structure learning in target-directed reaching (Braun et al., 2009), and discuss its implica-tions for a wider range of learning-to-learn phenomena.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"motor adaptation; reinforcement learning; learn-ing to learn; structure learning; system identification"}],"section":"Talks: Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/35v0x009","frozenauthors":[{"first_name":"Ari","middle_name":"","last_name":"Weinstein","name_suffix":"","institution":"Deepmind","department":""},{"first_name":"Matthew","middle_name":"M.","last_name":"Botvinick","name_suffix":"","institution":"Deepmind","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2017-01-01T13:00:00-05:00","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27026/galley/16662/download/"}]}