Concepts, Perceptions and Their Interactions

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The proposed experiments and model explore how people learn new concepts. The central thesis is that concept learning often changes how objects are perceived. That is, concepts and categories may be constructed on the basis of the perceptual features of objects, but these concepts may in turn influence what we see as the perceptual features. The aim of the proposed work is to specify and provide a formal account of such interactions between concept learning and perception. The influences of concept learning on subsequent perceptual judgments are investigated in a first series of experiments. The possibility that concept learning causes new object descriptions (based on perceptual features) to be created is considered in the second series of experiments. A neutral network computer model of concept learning is proposed. In this model, the concepts to be acquired alter the perceptual features used for categorization. Rather than assuming that fixed perceptual features are combined to determine categorization rules, this model allows for a mutual and simultaneous influence between concepts and perceptions. The research will have two tangible benefits. The first benefit will be to further our understanding of how people naturally learn. Unlike most laboratory studies, concept learning in the real world does typically involve alterations in what is perceived. For example, specialized experts learn to improve their perception and come to be able to classify better, for example, to classify some patients as suffering from cancer, some tumors as malignant, or some oil fields as promising. If we can understand how experts learn to improve their perception with experience, we will better be able to effectively train new personnel. The second tangible benefit will be to aid the construction of automatic devices for human-like categorization. By understanding the mechanisms that people use to create new categories, we will be in a better position to develop procedures that also can categorize objec ts in the world in an intelligent manner.