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 Fass, David


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

Neural Information Processing Systems

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 ofa concept (that is, its description -length) systematically influences its leamability.


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

Neural Information Processing Systems

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 complexityofa concept (that is, its description -length) systematically influences its leamability.