{"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":"2017-01-01T21:00:00+03:00","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27136/galley/16772/download/"}]}