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{ "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": "2017-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27026/galley/16662/download/" } ] }