{"pk":25682,"title":"Upsetting the contingency table: Causal induction over sequences of point events","subtitle":null,"abstract":"Data continuously stream into our minds, guiding our learn-\ning and inference with no trial delimiters to parse our experi-\nence. These data can take on a variety of forms, but research\non causal learning has emphasized discrete contingency data\nover continuous sequences of events. We present a formal\nframework for modeling causal inferences about sequences\nof point events, based on Bayesian inference over nonhomo-\ngeneous Poisson processes (NHPPs). We show how to apply\nthis framework to successfully model data from an experiment\nby Lagnado and Speekenbrink (2010) which examined human\nlearning from sequences of point events.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"causal inference; continuous time; stochastic pro-\ncesses; Bayesian models"}],"section":"Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/0cr5s8z1","frozenauthors":[{"first_name":"Michael","middle_name":"D","last_name":"Pacer","name_suffix":"","institution":"UCB","department":""},{"first_name":"Thomas","middle_name":"L","last_name":"Griffiths","name_suffix":"","institution":"UCB","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2015-01-01T21:00:00+03:00","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/25682/galley/15306/download/"}]}