{"pk":31329,"title":"Adaptation of Cue-Specific Learning Rates in Network Models of Human Category Learning","subtitle":null,"abstract":"Recent engineering considerations have prompted an improvement to the least mean squares (LMS) learning rule for training one-layer adaptive networks; incorporating a dynamically modifiable learning rate for each associative weight accellerates overall learning and provides a mechanism for adjusting the salience of individual cues (Sutton, 1992a,b). Prior research has established that the standard L M S rule can characterize aspects of animal learning (Rescorla & Wagner, 1972) and human category learning (Gluck & Bower, 1988a,b). W e illustrate here how this enhanced L M S rule is analogous to adding a cue-salience or attentional component to the psychological model, giving the network model a means for discriminating between relevant and irrelevant cues. W e then demonstrate the effectiveness of this enhanced L M S rule for modeling human performance in two non-stationary learning tasks for which the standard L M S network model fails to adequately account for the data (Hurwitz, 1990; Gluck, Glauthier, & Sutton, in preparation).","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/0mc4c8j8","frozenauthors":[{"first_name":"Mark","middle_name":"A.","last_name":"Gluck","name_suffix":"","institution":"Rutgers University","department":""},{"first_name":"Paul","middle_name":"T.","last_name":"Glauthier","name_suffix":"","institution":"Rutgers University","department":""},{"first_name":"Richard","middle_name":"S.","last_name":"Sutton","name_suffix":"","institution":"GTE Laboratories Incorporated","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1992-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/31329/galley/22398/download/"}]}