Concept learning and the recognition and classification of exemplars

Barbara Hayes-Roth, Frederick Hayes-Roth

Expert InsightsPublished 1977

A model is proposed for concept learning and subsequent recognition and classification of OLD and NEW exemplars. The model, called the "property-set model," assumes that a learned exemplar is encoded in memory as a set of the component properties and combinations of properties of the exemplar. Recognition of a presented exemplar is assumed to be an increasing function of the memory strengths of its component property-sets, while classification of the exemplar is determined by its most diagnostic property-set. This model is contrasted with a number of alternative models, including prototype-plus-transformation, feature-frequency, and nearest neighbor models. In an experimental evaluation of alternative models, subjects attempted to learn two concepts by classifying exemplars in an anticipation paradigm. They then performed recognition and classification tasks with particular exemplars. On a within-subject basis, the property-set model was the best predictor of both recognition and classification performance.

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Hayes-Roth, Barbara and Frederick Hayes-Roth, Concept learning and the recognition and classification of exemplars. Santa Monica, CA: RAND Corporation, 1977. https://www.rand.org/pubs/papers/P5778.html.
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