{"pk":32721,"title":"A Feedback Neural Network Model of Causal Learning and Causal Reasoning","subtitle":null,"abstract":"We present a feedback or recurrent, auto-associative model that captures several important aspects of causal learning and causal reasoning that cannot be handled by feed-forward models. First, our model learns asymmetric relations between cause and effect, and can reason in both directions between cause and effect. As a result it can represent an important distinction in causal reasoning, that between necessary and sufficient causes. Second, it predicts cue competition among effects and provides a mechanism for them, something which can only be done with feed-forward models by assuming that two separate networks are learned, a highly non parsimonious assumption. Finally, we show that contrary to previous claims, a feed-forward model cannot handle Discounting and Augmenting in causal  reasoning, although a feedback model can. The success of our feedback model argues for a greater focus on such models of causal learning and reasoning.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Long Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/6rw6z0rp","frozenauthors":[{"first_name":"Stephen","middle_name":"J.","last_name":"Read","name_suffix":"","institution":"Department of Psychology, University of Southern California","department":""},{"first_name":"Jorge","middle_name":"A.","last_name":"Montoya","name_suffix":"","institution":"Department of Psychology, University of Southern California","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/32721/galley/23784/download/"}]}