Abstract
An experiment is reported, testing whether a typically gradient can be observed in well-defined artificial categories. Subjects learned to classify a dot appearing at a random position in a square outline. A deterministic classification rule was used: either a left-right or a middle-edge categorization. In each condition half of the subjects were presented a task in which they were to discover the correct classification; the other half learned to apply the rule which was given with the instructions. In addition, the frequency distribution of the instances along the relevant dimension was varied. In all conditions a clear typicality gradient was obtained which was best predicted from the extent to which the instance represents the relevant property. Frequency of instantiation was a poor predictor, but family resemblance weighted for frequency was a moderate predictor of this gradient. The relations of the present findings to other research and their implications for some current theories of concept learning and schema abstraction are discussed.
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