Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)
Charles Kemp, Alan Jern
Many categories are better described by providing relational information than listing characteristic features. We present a hierarchical generative model that helps to explain how relational categories are learned and used. Our model learns abstract schemata that specify the relational similarities shared by members of a category, and our emphasis on abstraction departs from previous theoretical proposals that focus instead on comparison of concrete instances. Our first experiment suggests that our abstraction-based account can address some of the tasks that have previously been used to support comparison-based approaches. Our second experiment focuses on one-shot schema learning, a problem that raises challenges for comparison-based approaches but is handled naturally by our abstraction-based account.