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{ "pk": 27136, "title": "The Causal Sampler: A Sampling Approach to Causal Representation, Reasoningand Learning", "subtitle": null, "abstract": "Although the causal graphical model framework has achievedsuccess accounting for numerous causal-based judgments, akey property of these models, the Markov condition, is con-sistently violated (Rehder, 2014; Rehder & Davis, 2016). Anew process model—the causal sampler—accounts for theseeffects in a psychologically plausible manner by assumingthat people construct their causal representations using theMetropolis-Hastings sampling algorithm constrained to onlya small number of samples (e.g., < 20). Because it assumesthat Markov violations are built into people’s causal represen-tations, the causal sampler accounts for the fact that those vio-lations manifest themselves in multiple tasks (both causal rea-soning and learning). This prediction was corroborated by anew experiment that directly measured people’s causal repre-sentations.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "Causal Learning" }, { "word": "causal reasoning" }, { "word": "Sampling" } ], "section": "Posters: Papers", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/9sb0h2p7", "frozenauthors": [ { "first_name": "Zachary", "middle_name": "J.", "last_name": "Davis", "name_suffix": "", "institution": "New York University", "department": "" }, { "first_name": "Bob", "middle_name": "", "last_name": "Rehder", "name_suffix": "", "institution": "New York University", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2017-01-02T00:00:00+06:00", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27136/galley/16772/download/" } ] }