Categorization under complexity: A unified MDL account of human learning of regular and irregular categories

We present an account of human concept learning-that is, learning of categories from examples-based on the principle of minimum descrip-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-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-mans learn to categorize objects. It has been variously suggested that the underlying prin-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). (a) (b) (c) (d) (e) Figure 1: Categories similar to those previously studied. Lines represent contours of equal probability. All except (e) are unimodal