{"pk":30117,"title":"The language of causation","subtitle":null,"abstract":"People use varied language to express their causal understand-ing of the world. But how does that language map onto peo-ple’s underlying representations, and how do people choosebetween competing ways to best describe what happened? Inthis paper we develop a model that integrates computationaltools for causal judgment and pragmatic inference to addressthese questions. The model has three components: a causalinference component which computes counterfactual simula-tions that capture whether and how a candidate cause madea difference to the outcome, a literal semantics that mapsthe outcome of these counterfactual simulations onto differentcausal expressions (such as “caused”, “enabled”, “affected”,or “made no difference”), and a pragmatics component thatconsiders how informative each causal expression would befor figuring out what happened. We test our model in an ex-periment that asks participants to select which expression bestdescribes what happened in video clips depicting physical in-teractions.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"causality; language; counterfactuals; pragmatics;intuitive physics."}],"section":"Poster Session 3","is_remote":true,"remote_url":"https://escholarship.org/uc/item/07d3n8s6","frozenauthors":[{"first_name":"Ari","middle_name":"","last_name":"Beller","name_suffix":"","institution":"Stanford University","department":""},{"first_name":"Erin","middle_name":"","last_name":"Bennett","name_suffix":"","institution":"Stanford University","department":""},{"first_name":"Tobias","middle_name":"","last_name":"Gerstenberg","name_suffix":"","institution":"Stanford University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2020-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/30117/galley/19971/download/"}]}