{"pk":29324,"title":"Bayesian Inference Causes Incoherence in Human Probability Judgments","subtitle":null,"abstract":"Human probability judgements appear systematically biased, in apparent tension with Bayesian models of cognition. Butperhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a processof sampling, as used in computational probabilistic models in statistics. The Bayesian sampling viewpoint provides asimple rational model of probability judgements, which generates biases such as conservatism. The Bayesian samplerprovides a single framework for explaining phenomena associated with diverse biases and heuristics, including availabilityand representativeness. The approach turns out to provide a rational reinterpretation of noise in an important recent modelof probability judgement, the probability theory plus noise model (Costello & Watts, 2014; 2016; 2017; Costello, Watts,& Fisher, 2018), and captures the empirical data supporting this model.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Member Abstracts","is_remote":true,"remote_url":"https://escholarship.org/uc/item/11g9c4bd","frozenauthors":[{"first_name":"Jianqiao","middle_name":"","last_name":"Zhu","name_suffix":"","institution":"University of Warwick","department":""},{"first_name":"Adam","middle_name":"","last_name":"Sanborn","name_suffix":"","institution":"University of Warwick","department":""},{"first_name":"Nicholas","middle_name":"","last_name":"Chater","name_suffix":"","institution":"University of Warwick","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2019-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29324/galley/19195/download/"}]}