{"pk":29217,"title":"Drawing conclusions from spatial coincidences: a cumulative clustering account","subtitle":null,"abstract":"Spatial coincidences allow us to infer the presence of latent causes in the world. For instance, an unusually large clusterof ants allows us to infer the presence of a food source. The leading cognitive model for such inferences is Bayesian,but the Bayesian algorithm is computationally taxing. Humans likely employ a more efficient, approximative algorithm.To characterize the cognitive algorithms used, we had subjects judge whether a set of dots was drawn from a uniformdistribution or from a mixture of a uniform and a gaussian source (tending to produce clusters). Responses systematicallydeviate from Bayesian optimality: as the number of dots increase, subjects more often report a latent cause where noneexists. The bias is accounted for by a Bayesian clustering algorithm that cumulatively considers the next-nearest dot to aputative source. This finding helps characterize our tendency to perceive causal patterns where none exist.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Member Abstracts","is_remote":true,"remote_url":"https://escholarship.org/uc/item/6fx2922n","frozenauthors":[{"first_name":"Jennifer","middle_name":"","last_name":"Lee","name_suffix":"","institution":"New York University","department":""},{"first_name":"Wei","middle_name":"Ji","last_name":"Ma","name_suffix":"","institution":"New York University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2019-01-01T13:00:00-05:00","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29217/galley/19088/download/"}]}