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 classi- fication learning. This assumption holds that object feature values are statistically independent of each other, given knowl- edge 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 princi- ples, we designed a task to test whether people have this de- fault 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.