Modeling Classification and Inference Learning

Bradley C. Love and Arthur B. Markman, The University of Texas at Austin; Takashi Yamauchi, University of Pittsburgh

Human categorization research is dominated by work in classification learning. The field may be in danger of equating the classification learning paradigm with the more general phenomenon of category learning. This paper compares classification and inference learning and finds that different patterns of behavior emerge depending on which learning mode is engaged. Inference learning tends to focus subjects on the internal structure of each category, while classification learning highlights information that discriminates between the categories. The data suggest that different learning modes lead to the formation of different internal representations. SUSTAIN successfully models inference and classification learning by developing different internal representations for different learning modes. Other models do not fair as well.

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