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{ "pk": 27760, "title": "Evidence for hierarchically-structured reinforcement learning in humans", "subtitle": null, "abstract": "Flexibly adapting behavior to different contexts is a critical\ncomponent of human intelligence. It requires knowledge to\nbe structured as coherent, context-dependent action rules, or\ntask-sets (TS). Nevertheless, inferring optimal TS is compu-\ntationally complex. This paper tests the key predictions of a\nneurally-inspired model that employs hierarchically-structured\nreinforcement learning (RL) to approximate optimal inference.\nThe model proposes that RL acts at two levels of abstrac-\ntion: a high-level RL process learns context-TS values, which\nguide TS selection based on context; a low-level process learns\nstimulus-actions values within TS, which guide action selec-\ntion in response to stimuli. In our novel task paradigm, we\nfound evidence that participants indeed learned values at both\nlevels: not only stimulus-action values, but also context-TS\nvalues affected learning and TS reactivation, and TS values\nalone determined TS generalization. This supports the claim\nof two RL processes, and their importance in structuring our\ninteractions with the world.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "Reinforcement Learning" }, { "word": "Structure learning" }, { "word": "Hierarchical representation" }, { "word": "Task sets" } ], "section": "Publication-based-Talks", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/3wx3881m", "frozenauthors": [ { "first_name": "Maria", "middle_name": "K", "last_name": "Eckstein", "name_suffix": "", "institution": "University of California, Berkley", "department": "" }, { "first_name": "Anne", "middle_name": "GE", "last_name": "Collins", "name_suffix": "", "institution": "University of California, Berkley", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2018-01-01T10:00:00-08:00", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27760/galley/17400/download/" } ] }