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{ "pk": 26499, "title": "Inferring priors in compositional cognitive models", "subtitle": null, "abstract": "We apply Bayesian data analysis to a structured cognitivemodel in order to determine the priors that support humangeneralizations in a simple concept learning task. We mod-eled 250,000 ratings in a “number game” experiment wheresubjects took examples of a numbers produced by a program(e.g. 4, 16, 32) and rated how likely other numbers (e.g. 8vs. 9) would be to be generated. This paper develops a dataanalysis technique for a family of compositional “Language ofThought” (LOT) models which permits discovery of subjects’prior probability of mental operations (e.g. addition, multi-plication, etc.) in this domain. Our results reveal high cor-relations between model mean predictions and subject gener-alizations, but with some qualitative mismatch for a stronglycompositional prior.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "Concepts and categories; learning; Bayesian mod-eling; machine learning" } ], "section": "Papers", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/43s5z8jj", "frozenauthors": [ { "first_name": "Eric", "middle_name": "J.", "last_name": "Bigelow", "name_suffix": "", "institution": "University of Rochester", "department": "" }, { "first_name": "Steven", "middle_name": "T.", "last_name": "Piantadosi", "name_suffix": "", "institution": "University of Rochester", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2016-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/26499/galley/16135/download/" } ] }