{"pk":26389,"title":"Vector Space Semantic Models\nPredict Subjective Probability Judgments for Real-World Events","subtitle":null,"abstract":"We examine how people judge the probabilities of real-world\nevents, such as natural disasters in different countries. We\nfind that the associations between the words and phrases that\nconstitute these events, as assessed by vector space semantic\nmodels, strongly correlate with the probabilities assigned to\nthese events by participants. Thus, for example, the semantic\nproximity of “earthquake” and “Japan” accurately predicts\njudgments regarding the probability of an earthquake in\nJapan. Our results suggest that the mechanisms and\nrepresentations at play in language are also active in high-\nlevel domains, such as judgment and decision making, and\nthat existing insights regarding these representations can be\nused to make precise, quantitative, a priori predictions\nregarding the probability estimates of individuals.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Judgement and decision making; Subjective\nprobability; Semantic representation; Semantic space models"}],"section":"Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/12v27809","frozenauthors":[{"first_name":"Sudeep","middle_name":"","last_name":"Bhatia","name_suffix":"","institution":"University of Pennsylvania","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2016-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/26389/galley/16025/download/"}]}