{"pk":27135,"title":"Amortized Hypothesis Generation","subtitle":null,"abstract":"Bayesian models of cognition posit that people compute prob-ability distributions over hypotheses, possibly by construct-ing a sample-based approximation. Since people encountermany closely related distributions, a computationally efficientstrategy is to selectively reuse computations – either the sam-ples themselves or some summary statistic. We refer to thesereuse strategies as amortized inference. In two experiments,we present evidence consistent with amortization. When se-quentially answering two related queries about natural scenes,we show that answers to the second query vary systematicallydepending on the structure of the first query. Using a cog-nitive load manipulation, we find evidence that people cachesummary statistics rather than raw sample sets. These resultsenrich our notions of how the brain approximates probabilisticinference.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Amortization; hypothesis generation; Bayesian in-ference; Monte Carlo methods"}],"section":"Posters: Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/6cc8954w","frozenauthors":[{"first_name":"Ishita","middle_name":"","last_name":"Dasgupta","name_suffix":"","institution":"Harvard University","department":""},{"first_name":"Eric","middle_name":"","last_name":"Schulz","name_suffix":"","institution":"University College London","department":""},{"first_name":"Noah","middle_name":"D.","last_name":"Goodman","name_suffix":"","institution":"Stanford University","department":""},{"first_name":"Samuel","middle_name":"J.","last_name":"Gershman","name_suffix":"","institution":"Harvard University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2017-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27135/galley/16771/download/"}]}