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A new theory of classification and feature inference learning: An exemplar fragment model

By Robert M. Colner and Bob Rehder

Abstract

In addition to supervised classification learning, people can also learn categories by predicting the features of category members. One account of feature inference learning is that it induces a prototype representation of categories. Another is that it results in a set of category-to-feature rules. Because neither model provides an adequate account of existing data, we propose instead that inference learning induces an anticipatory learning strategy in which learners attend to aspects of training items they think will be needed in the future, and by so doing incidentally encode information about the category’s internal structure. The proposal is formalized by an exemplar fragment model (EFM) that represents partial exemplars, namely, those parts that are attended during training. EFM’s attention weights are approximated by eyetracking data, resulting in fewer free parameters as compared to competing theories. When people classify objects, problem solve, describe concepts, or infer missing information, they must access conceptual knowledge. Thus, the question of how people learn and represent concepts has been central to the overall mission of cognitive psychology. Researchers have developed sophisticated formal theories that explain many aspects of concept acquisition. These theories are largely based on supervised classification learning in which subjects classify items whose category membership is unknown and receive immediate feedback. Recently, to understand the interplay between how categorical knowledge is used and the concept acquired, researchers have begun to investigate a wider range of learning task

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.412.5329
Provided by: CiteSeerX
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