{"pk":27118,"title":"Beliefs about sparsity affect causal experimentation","subtitle":null,"abstract":"What is the best way of figuring out the structure of a causalsystem composed of multiple variables? One prominent ideais that learners should manipulate each candidate variable inisolation to avoid confounds (known as the “Control of Vari-ables” strategy). Here, we demonstrate that this strategy is notalways the most efficient method for learning. Using an opti-mal learner model which aims to minimize the number of tests,we show that when a causal system is sparse, that is, whenthe outcome of interest has few or even just one actual causeamong the candidate variables, it is more efficient to test mul-tiple variables at once. In a series of behavioral experiments,we then show that people are sensitive to causal sparsity whenplanning causal experiments.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"information search; causal learning; hypothesistesting"}],"section":"Posters: Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/8s94f16h","frozenauthors":[{"first_name":"Anna","middle_name":"","last_name":"Coenen","name_suffix":"","institution":"New York University","department":""},{"first_name":"Neil","middle_name":"R.","last_name":"Bramley","name_suffix":"","institution":"New York University","department":""},{"first_name":"Azzurra","middle_name":"","last_name":"Ruggeri","name_suffix":"","institution":"Max Planck Institute for Human Developmen","department":""},{"first_name":"Todd","middle_name":"M.","last_name":"Gureckis","name_suffix":"","institution":"New York University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2017-01-01T13:00:00-05:00","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27118/galley/16754/download/"}]}