{"pk":32666,"title":"Modeling Perceptual Learning of Abstract Invariants","subtitle":null,"abstract":"We present the beginnings of a model of the human capacity to learn abstract invariants, such as square. The model is founded on four primary assumptions, which we believe to be neurally plausible and generic: Metric space, Topology, Comparison operations  (subtraction, greater-than/less-than), and Extraction of vertices. The model successfully learns to discriminate simple planar quadrilaterals, and generalizes that learning across variations in viewpoint and modest variations in shape.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Long Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/4pj6j35b","frozenauthors":[{"first_name":"Philip","middle_name":"J.","last_name":"Kellman","name_suffix":"","institution":"University of California, Los Angeles","department":""},{"first_name":"Timothy","middle_name":"","last_name":"Burke","name_suffix":"","institution":"University of California, Los Angeles","department":""},{"first_name":"John","middle_name":"E.","last_name":"Hummel","name_suffix":"","institution":"University of California, Los Angeles","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/32666/galley/23729/download/"}]}