Feature Relations and Feature Salience in Natural Categories

Feature Relations and Feature Salience in Natural Categories Jonathan R. Rein (jrein@mail.utexas.edu) Bradley C. Love (brad_love@psy.utexas.edu) Arthur B. Markman (markman@psy.utexas.edu) Department of Psychology, 1 University Station A8000 Austin, TX 78712 USA instance, the bird example above would be represented as [1,1,1]. This is the preferred format for artificial categories and mathematical models, with the features and their corresponding values usually being defined by the experimenter (e.g. Love, Medin, & Gureckis, 2004; Medin & Shaffer, 1978; Nosofsky, 1986; see McRae, de Sa, & Seidenberg, 1997 for a model employing spatial representations derived from feature lists). Features and dimensions may be differentially weighted in these formats, but weights are generally not influenced by the relationships among features. In the next section, we describe ways that features’ salience can differ when those features are represented strictly independently. We then review previous research that has employed structured representations in categorization. From these findings, we suggest a relationship between features’ salience and connectivity in a structure of feature relations, which will motivate the current study. Abstract Feature salience is a poorly understood construct in the study of category knowledge. Independent-feature representations are limited in potential explanations, compared to more complex structured representations. We explore the relationship between features’ connectivity in a network of relational knowledge and those features’ relative salience, along with their potential for inference. Comparing values of individual features, we illustrate a unique relationship between features’ salience, inferential potency, and structural connectivity. We argue that these results provide evidence for the explanatory power of structured representation beyond independent-feature representation. Keywords: concepts representation and categories, memory, Introduction Cognitive scientists often invoke salience as an explanatory construct. When responses are biased toward some items or features over others, differential salience provides a fairly simple account. While such explanations may match intuition, they are not theoretically powerful unless one can further explicate why this difference exists. One area in which salience has a powerful impact is categorization. For example, more salient dimensions have greater initial weights or biases in mathematical models of category learning (e.g. Kruschke, 1992; Nosofsky, Palmeri, & McKinley, 1994). These weights are assigned according to arbitrary intuition or existing similarity data. The determinants of salience are generally left to mystery. We suggest that this explanatory limitation is partially the result of assumptions about the representational form of categories. The majority of published work assumes (explicitly or implicitly) that categories are sets of unstructured, unrelated features. These independent-feature representations are instantiated in two main ways. One is as a list of verbally described features, such as “has feathers”, “has wings”, and “flies” for the category bird. Feature lists are generally obtained for natural categories by querying semantic knowledge of normal participants (e.g. McRae, Cree, Seidenberg, & McNorgan, in press; Rosch & Mervis, 1975; Tversky, 1977). The other type of independent-feature representation is a spatial one, in which features are numerical values in a vector. For Independent Features and Salience There is a limited number of ways in which one can explain salience differences among features, given an independent-feature category representation. Tversky first suggested some of these in his seminal work on features and similarity (1977). One determinant of salience is our natural predisposition toward intensity, a contingent result of the design of our cognitive systems. This is most obviously the case for perceptual dimensions of stimuli, such as bright lights and loud sounds. Salience may also be influenced by the statistics of the environment. Two statistical properties of features that could have a straightforward relationship with salience are diagnosticity and frequency. A common measure of diagnosticity in categorization is cue validity, the conditional probability of category membership given a particular feature, or P(bird|”feathers”). A common measure of frequency is category validity, the conditional probability of the presence of a feature given category membership, or P(“feathers”|bird). One can obtain these statistics through simple arithmetic on tallies of independent features. Critically, both of these measures only involve the relationship between the feature and category label. More complex statistics could incorporate information across features and dimensions. Features may also differ in salience according to the