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W138582014 abstract "Learning a Hierarchical Organization of Categories Steven Verheyen (steven.verheyen@psy.kuleuven.be) University of Leuven, Department of Psychology Tiensestraat 102, B-3000, Leuven Belgium Eef Ameel (eef.ameel@psy.kuleuven.be) University of Leuven, Department of Psychology Tiensestraat 102, B-3000, Leuven Belgium Timothy T. Rogers (ttrogers@wisc.edu) University of Wisconsin-Madison, Department of Psychology 1202 West Johnson Street, Madison, WI 15206 USA Gert Storms (gert.storms@psy.kuleuven.be) University of Leuven, Department of Psychology Tiensestraat 102, B-3000, Leuven Belgium models are even more rare (Estes, 1993; Palmeri, 1999). Thus the adequacy of such models to explain critical aspects of human category learning remains in question. Palmeri (1999) considered how vertical category learning might challenge the class of exemplar models. To see this, let us follow Palmeri in thinking about the issue within the context of a hierarchically organized category structure and by investigating the manner in which classification probabilities are computed in Medin and Schaffer’s context model (1978), the precursor of all current exemplar models. Let us assume that a particular set of stimuli belongs to category A, while a different set of stimuli belongs to a separate category B. Let us further assume that category A is comprised of two subordinate categories C and D. That is, each of the A stimuli also belongs either to C or to D. A similar hierarchical relationship holds between category B and subordinate categories E and F. According to the context model, the probability of assigning a particular stimulus to one of the specific categories (C, D, E, F) can never exceed the probability of classifying the stimulus in the specific category’s superordinate (A or B). More specifically, the context model proposes that evidence E X for classifying a stimulus in a particular category X is accumulated by summing the similarity of each of the category’s exemplars to the stimulus. Classification probability P(X) is then taken to be the ratio of the evidence E X to the evidence that the stimulus belongs to any of the categories at the abstraction level of the target category. The probability of classifying a stimulus in category A is written accordingly as: Abstract Although exemplar models of category learning have been successfully applied to a wide range of classification problems, such models have only rarely been tested on their ability to deal with vertical category learning, that is, cases where the same stimuli may be classified at multiple levels of abstraction. We report an experiment in which participants learned to classify artificial stimuli at both levels of a nested hierarchy and displayed more accurate classification of these items at the lower level of the hierarchy than at the more general level. Some authors have suggested that exemplar models would have great difficulty accounting for this phenomenon, but we show that the ALCOVE exemplar model effectively captures the behavioral pattern arising in the experiment. Despite suggestions to the contrary, superior performance at the lower level of a nested hierarchy does not necessarily invalidate the class of exemplar models. Keywords: context model; exemplar models; vertical category learning; hierarchies; basic-level effect. Introduction When developing and testing formal models of category learning, researchers have primarily relied upon experimental paradigms in which artificial stimuli need to be classified into one of several categories at the same level of abstraction. While the wealth of classification models currently available testifies to the fruitfulness of emphasizing what Rosch (1978) termed the horizontal dimension of categories, doubts remain about their usefulness when it comes to determining category membership at different levels of abstraction. Vertical relationships between categories, of the kind that exist between natural language categories such as bulldog, dog, mammal, and animal, have rarely been studied using traditional artificial category learning methods (see Lassaline, Wisniewski, & Medin, 1992; Mervis & Crisafi, 1982; and Murphy & Smith, 1982, for a few notable exceptions), and efforts to address such data with formal P(A) = E A / (E A + E B ), while the probability of appointing the stimulus to category C becomes: P(C) = E C / (E C + E D + E E + E F )." @default.
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W138582014 title "Learning a hierarchical organization of categories" @default.
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