Limitations of exemplar-based generalization and the abstraction of categorical information.

An evaluation of exemplar-based models of generalization was provided for illdefined categories in a category abstraction paradigm. Subjects initially classified 35 high-level distortions into three categories, defined by 5, 10, and 20 different patterns, followed by a transfer test administered immediately and after 1 wk. The transfer patterns included old, new, prototype, and unrelated exemplars, of which the new patterns were at one of five levels of similarity to a particular training (old) stimulus. In both experiments, increases in category size and oldnew similarity facilitated transfer performance. However, the effectiveness of old-new similarity was strongly attenuated by increases in category size and delay of the transfer test. It was concluded that examplar-based generalization may be effective only under conditions of minimal category experience and immediacy of test; with continued category experience, performance on the prototype determines classification accuracy. Categories are said to be ill defined (Neisser, 1967) when it is not obvious what dimensions characterize a category, and the variety among the potential members of a category is essentially infinite. Examples of ill-defined categories are quite diverse and would include the natural categories, musical style, hand-written letter As, and the class of sound patterns associated with a specific spoken word. How the human organism learns ill-defined categories, and how this knowledge is transferred to novel situations, has been a topic of considerable attention over the past 10 years. Posner and Keele (1968, 1970) argued that a prototype or central tendency is abstracted during the classification of distorted but related patterns. In their experiments, the subject initially sorted dot-pattern stimuli into a number of categories, with each category represented by a different reference pattern (objective prototype). ClasThis research was supported b y National Institute of