{"pk":28267,"title":"A mouse-tracking study of how exceptions to a probabilistic generalization are learned","subtitle":null,"abstract":"How are exceptions to a probabilistic generalization learned? The present results suggest exceptions are learned in partby selectively suppressing the competing category, as opposed to only increasing knowledge of exceptions. Participantswere exposed to a mini-artificial language with a probabilistic generalization (80-20%) that mapped labels to categories ofimages (faces and scenes). Mouse-tracking trajectories determined the degree to which the generalization served as a lureto exceptions, compared to a separate baseline condition. Over time, the generalization became suppressed in a context-sensitive way: for exception items only. This extends retrieval induced forgetting, in which a particular item is suppresseddue to competition from partial retrieval, to include the entire conceptual category. Post-test revealed high item-specificaccuracy, even though category recognition was sufficient for the task.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Abstracts-Posters","is_remote":true,"remote_url":"https://escholarship.org/uc/item/4n49q2d6","frozenauthors":[{"first_name":"Karina","middle_name":"","last_name":"Tachihara","name_suffix":"","institution":"Princeton","department":""},{"first_name":"Kenneth","middle_name":"","last_name":"Norman","name_suffix":"","institution":"Princeton","department":""},{"first_name":"Nicholas","middle_name":"","last_name":"Turk-Browne","name_suffix":"","institution":"Yale","department":""},{"first_name":"Adele","middle_name":"","last_name":"Goldberg","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/28267/galley/17926/download/"}]}