{"pk":32740,"title":"Learning, Development, and Nativism: Connectionist Implications","subtitle":null,"abstract":"Fedforward neural network models of cognitive development are reviewed within the framework of a functional distinction between learning and development. This analysis suggests that static architecture networks implement a learning theory, whereas generative architecture networks combine learning and development. Both types of networks are then evaluated m terms of genetic costs. Within a levels-of-innateness framework, generative architectures are viewed as more plausible than static ones. Static architecture networks appear to implement a form of nativistic elicitation.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Long Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/6n60x5td","frozenauthors":[{"first_name":"Sylvain","middle_name":"","last_name":"Sirois","name_suffix":"","institution":"Department of Psychology, McGill University","department":""},{"first_name":"Thomas","middle_name":"R.","last_name":"Shultz","name_suffix":"","institution":"Department of Psychology, McGill University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1999-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/32740/galley/23802/download/"}]}