Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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David Fass, Jacob Feldman


We present an account of human concept learning-that is, learning of categories from examples-based on the principle of minimum descrip(cid:173) tion length (MDL). In support of this theory, we tested a wide range of two-dimensional concept types, including both regular (simple) and highly irregular (complex) structures, and found the MDL theory to give a good account of subjects' performance. This suggests that the intrin(cid:173) sic complexity of a concept (that is, its description -length) systematically influences its leamability.

1- The Structure of Categories

A number of different principles have been advanced to explain the manner in which hu(cid:173) mans learn to categorize objects. It has been variously suggested that the underlying prin(cid:173) ciple might be the similarity structure of objects [1], the manipulability of decision bound~ aries [2], or Bayesian inference [3][4]. While many of these theories are mathematically well-grounded and have been successful in explaining a range of experimental findings, they have commonly only been tested on a narrow collection of concept types similar to the simple unimodal categories of Figure 1(a-e).