Summary: Danks, D. (2006). (Not) learning a complex (but learnable) category. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th
annual meeting of the cognitive science society (pp. 1186-1191). Mahwah, N.J.: Lawrence Erlbaum Associates.
(Not) Learning a Complex (but Learnable) Category
David Danks (firstname.lastname@example.org)
Department of Philosophy, Carnegie Mellon University, 135 Baker Hall
Pittsburgh, PA 15213 USA; and
Institute for Human & Machine Cognition, 40 S. Alcaniz St.
Pensacola, FL 32502 USA
Recent theoretical research has argued that multiple
psychological theories of categorization are mathematically
identical to inference in probabilistic graphical models (a
framework developed in statistics and computer science).
These results imply that the major extant psychological
theories can all be represented mathematically as special cases
of inference in (subclasses of) chain graphs, a particular type
of probabilistic graphical models. These formal results
suggest that people should be capable of learning significantly
more complicated category structures than can be expressed
in the standard psychological theories. In this paper, we