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{ "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/" } ] }