Acquisition of Concepts with Characteristic and Defining Features
نویسندگان
چکیده
Concepts with defining features have sufficiently rigid boundaries that examples of one concept are rarely confused with another concept, whereas probabilistic concepts have vague boundaries, producing frequent misclassifications. It has been argued that, although neural learning might account for probabilistic (or fuzzy) concepts, it is incapable of accounting for acquisition of concepts with defining features. Simulations reported here with encoder networks call this view into question. These sibling-descendant cascade-correlation networks account for a variety of phenomena with concepts positioned along the probabilistic to defining-features continuum. Classification of exemplars by our networks was affected by both isolation of the concept in semantic feature space and by dispersion of its examples around a prototype. The detection of defining features cured networks from the effects of conceptual crowding and example dispersion. Simulations captured the developmental shift from characteristic to defining features in a natural way.
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