The Assumption of Class-conditional Independence in Category Learning


This paper investigates the role of the assumption of class-conditional independence of object features in human classification learning. This assumption holds that object feature values are statistically independent of each other, given knowledge of the object's true category. Treating features as class-conditionally independent can in many situations substantially facilitate learning and categorization even if the assumption is not perfectly true. Using optimal experimental design principles, we designed a task to test whether people have this default assumption when learning to categorize. Results provide some supporting evidence, although the data are mixed. What is clear is that classification behavior adapts to the structure of the environment: a category structure that is unlearnable under the assumption of class-conditional independence is learned by all participants.

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