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 description 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 intrinsic complexity of a concept (that is, its description length) systematically influences its learnability.