{"pk":31250,"title":"An Invesitgation of Balance Scale Success","subtitle":null,"abstract":"The success of a connectionist model of cognitive development on the balance scale task is due to manipulations which impede convergence of the backpropagation learning algorithm. The model was trained at different levels of a biased training environment with exposure to a varied number of training instances. The effects of weight updating method and modifying the network topology were also examined. In all cases in which these manipulations caused a decrease in convergence rate, there was an increase in the proportion of psychologically realistic nms. W e conclude that incremental connectionist learning is not sufficient for producing psychologically successful connectionist balance scale models, but must be accompanied by a slowing of convergence.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/823935gw","frozenauthors":[{"first_name":"William","middle_name":"C.","last_name":"Schmidt","name_suffix":"","institution":"McGill University","department":""},{"first_name":"Thomas","middle_name":"R.","last_name":"Shultz","name_suffix":"","institution":"McGill University","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/31250/galley/22319/download/"}]}