{"pk":30475,"title":"A Layered Network Model for Learning-to-learn and Configuration in Classical Conditioning","subtitle":null,"abstract":"Networks composed of layers of adaptive elements provide a\nrigorous explanation for complex associative learning\nphenomena. In particular, a network composed of three\nadaptive elements can explain previously intractable\nphenomena, namely the rapid rate of reacquisitions,\nlearning-to-learn, spontaneous configuration, and negative\npatterning (the exclusive-OR problem). This paper will\ncompare the results of computer simulations to the\nbehavioral results of classical conditioning experiments\nusing the rabbit's nictitating membrane response.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Presented Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/7815x1vw","frozenauthors":[{"first_name":"E.","middle_name":"James","last_name":"Kehoe","name_suffix":"","institution":"University of New South Wales","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1986-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/30475/galley/20324/download/"}]}