Bruner, Goodnow, and Austin's (1956) research on concept development is re-examined from a connectionist perspective. A neural network was constructed which associates positive and negative instances of a concept with their corresponding attribute values. Two methods were used to help preserve the ecological validity of the input: 1) closely mapping the input to the actual visual stimuli; and 2) structuring the output layer based on Gagne's (1962, 1985) work on human concept learning. This resulted in the addition of output units referred to as attribute context constraints. These units required the network to demonstrate the identification of attributes both relevant and irrelevant to the task of classification. Results suggest that the simultaneous learning of attributes guided the network in constructing a faster and more generalizable representation than when attribute constraints were absent. Results are discussed with respect to the advantages of computational approaches to studying learning.
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