A Comparison of Three Measures of the Association Between a Feature and a Concept

A Comparison of Three Measures of the Association Between a Feature and a Concept Matthew D. Zeigenfuse (mzeigenf@msu.edu) Department of Psychology, Michigan State University East Lansing, MI 48823 USA Michael D. Lee (mdlee@uci.edu) Department of Cognitive Sciences, University of California, Irvine Irvine, CA 92697 USA measures of feature association from measures predicting people’s preferences for particular category structures. In the second, we describe an experiment comparing the three measures. In the third, we present the results of this experiment. Finally, we discuss what these results mean for other areas of cognitive psychology such as cat- egorization and similarity judgment. Abstract In this paper, we develop three measures of association between concepts and features from three measures of category structure preference. These measures are to- tal cue validity, feature possession score, and category utility. We compare these measures experimentally us- ing stimuli from the Leuven Natural Concept Database (de Deyne et al., 2008). We find the measure developed from feature possession score, collocation, best captures human associations between concepts and features, fol- lowed by the measure developed from category utility, and finally the measure developed from total cue valid- ity. We discuss how these results can be applied to open questions in categorization and similarity judgment such as domain differences in representation and modeling the effects of context. Keywords: Mental representation; categorization; sim- ilarity judgment Association Measures Each of the category structure preference measures used here were originally developed to explain basic level pref- erence. Within a category hierarchy, the basic level is the preferred level of abstraction for describing objects. Key findings are objects are categorized into basic-level categories more quickly than sub- or super-ordinate cat- egories, basic level objects are named faster, objects are described preferentially with basic level names, more fea- tures are listed at the basic level than at the superordi- nate level, basic level names are learned before names at other levels, and basic level names tend to be shorter (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). How basic people consider a category to be is a mea- sure of their preference for its category structure. Since people prefer basic level categories to non-basic level cat- egories for a large number of mental operations related to categorization, it seems reasonable to think of these categories as the standard for what people prefer cate- gories to look like. Consequently, as how basic a category is considered to be decreases, so does people’s preference for its category structure. We derive measures of association between a feature and a concept for each of three measures of category structure preference: total cue validity (Rosch et al., 1976), feature possession score (Jones, 1983), and cat- egory utility (Corter & Gluck, 1992). To do this, we apply a principle we term the feature impact hypothesis. It states that the features people associate with a con- cept should be the features that have the largest impact on category structure preference for the category consist- ing of the concept’s instances. Categories are abstracted from objects at least in part to help infer whether a novel object has one or more unobserved features given one or more features that have already been observed. Thus, it Suppose someone asks you which of the features has wings and sings you associate more with the concept birds. What about the relationship between the two fea- tures and birds determines your answer? One possibility is that it is determined by how many birds have each feature. Almost all birds have wings but only a few can sing, so you would associate has wings with birds more than sings. Another possibility is that it is determined by how specific the feature is to birds. A number of animals besides birds have wings, for instance bats and wasps, but no other animals sing, so you would associate sings with birds more than has wings. Associations between concepts and features form the basis of many theories of mental representation. For ex- ample, feature-based theories of mental representation hold that a concept is represented in terms of some num- ber of (normally discrete) features. The associations people form between concepts and features determine which features will be used to represent a particular con- cept. Alternatively, dimensional theories of representa- tion hold that a concept is represented by its values on some number of continuous dimensions. People’s associ- ations determine which dimensions will be used to rep- resent a concept and what the values on each dimension will be. The purpose of this paper is to compare empirically three different measures of feature association using their predictions of which feature you would choose. We pro- cede as follows. In the first section, we develop the three