{"pk":25466,"title":"A Bayesian Latent Mixture Approach to Modeling Individual Differences\nin Categorization Using General Recognition Theory","subtitle":null,"abstract":"Decision-bound models of categorization like General Recognition\nTheory (GRT: Ashby & Townsend, 1986) assume that\npeople divide a stimulus space into different response regions,\nassociated with different categorization decisions. These models\nhave traditionally been applied to empirical data using standard\nmodel-fitting methods like maximum likelihood estimation.\nWe implement the GRT as a Bayesian latent mixture\nmodel to infer both qualitative individual differences in the\ntypes of decision bounds people use, and quantitative differences\nin where they place the bounds. We apply this approach\nto a previous data set with two category structures tested under\ndifferent cognitive loads. Our results show that different participants\ncategorize by applying diagonal, vertical, or horizontal\ndecision bounds. Various types of contaminant behavior are\nalso found, depending on the category structures and presence\nor absence of load. We argue that our Bayesian latent mixture\nframework offers a powerful approach to studying individual\ndifferences in categorization.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"category learning; decision bound models; General\nRecognition Theory; Bayesian inference; latent mixture\nmodel"}],"section":"Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/16d5b6f6","frozenauthors":[{"first_name":"Irina","middle_name":"","last_name":"Danileiko","name_suffix":"","institution":"Department of Cognitive Sciences, University of California, Irvine","department":""},{"first_name":"Michael","middle_name":"D","last_name":"Lee","name_suffix":"","institution":"Department of Cognitive Sciences, University of California, Irvine","department":""},{"first_name":"Michael","middle_name":"L","last_name":"Kalish","name_suffix":"","institution":"Psychology Department, Syracuse University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2015-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/25466/galley/15090/download/"}]}