{"pk":28050,"title":"Learning distributions as they come: Particle filter models for onlinedistributional learning of phonetic categories","subtitle":null,"abstract":"Human infants have the remarkable ability to learn any hu-man language. One proposed mechanism for this ability isdistributional learning, where learners infer the underlyingcluster structure from unlabeled input. Computational mod-els of distributional learning have historically been principledbut psychologically-implausible computational-level models,or ad hoc but psychologically plausible algorithmic-level mod-els. Approximate rational models like particle filters can po-tentially bridge this divide, and allow principled, but psycho-logically plausible models of distributional learning to be spec-ified and evaluated. As a proof of concept, I evaluate one suchparticle filter model, applied to learning English voicing cate-gories from distributions of voice-onset times (VOTs). I findthat this model learns well, but behaves somewhat differentlyfrom the standard, unconstrained Gibbs sampler implementa-tion of the underlying rational model.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Computational modeling; Rational models; Parti-cle filters; Language learning; Distributional learning; Speechperception"}],"section":"Publication-based-Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/7k03p783","frozenauthors":[{"first_name":"Dave","middle_name":"F","last_name":"Kleinschmidt","name_suffix":"","institution":"Princeton","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2018-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/28050/galley/17689/download/"}]}