Recent Views of Conceptual Structure

ness. Second, every attribute specified for a concept is shared by more than one instance of the concept. Thus, the information contained in a concept is an abstraction across instances of the concept. The overlapping networks of shared attributes thus formed hold conceptual categories together. In this respect, the family resemblance view is like the classical view: Both maintain that the instances of a concept cohere because they are similar to one another by virtueof sharing certain attributes. Weighted attributes. An object that shares attributes with many members of a category bears greater family resemblance to that category than an object that shares attributes with few members. This suggests that attributes that are shared by many members confer a greater degree of family resemblance than those that are shared by a few. A third characteristic of the family resemblance view is that it assumes that concept attributes are "weighted" according to their relevance for conferring family resemblance to the category. In general, that relevance is taken to be a function of the number of category instances (and perhaps noninstances) that share the attribute. Presumably, if the combined relevance weights of the attributes of some novel object exceed a certain level (what might be called the membership threshold or criterion), that object will be 2 Here and throughout, I use relevance to include both relevance and salience as used by Ortony, Vondruska, Foss, and Jones (1985). 504 LLOYD K. KOMATSU considered an instance of the category (Medin, 1983; Rosch & Mervis, 1975; E. E. Smith & Medin, 1981). The greater the degree to which the combined relevance weights exceed the threshold, the more typical an instance it is (see also Shafir, Smith, & Osherson, 1990). By this measure, an object must have a large number of heavily weighted attributes to be judged highly typical of a given category. Because such heavily weighted attributes are probably shared by many category instances and relatively few noninstances, an object highly typical of a category is likely to lie near the central tendencies of the category (see Retention of Central Tendencies, below), and is not likely to be typical of or lie near the central tendencies of any other category. Independence and additive combination of weights: Linear separability. Attribute weights can be combined using a variety of methods (cf. Medin & Schaffer, 1978; Reed, 1972). In the method typically associated with the family resemblance view (adapted from Tversky's, 1977, contrast model of similarity), attribute weights are assumed to be independent and combined by adding (Rosch & Mervis, 1975; E. E. Smith & Medin, 1981). This leads to a fourth characteristic of the (modal) family resemblance view: It predicts that instances and noninstances of a concept can be perfectly partitioned by a linear discriminant function (i.e., if one was to plot a set of objects by the combined weights of their attributes, all instances would fall to one side of a line, and all noninstances would fall on the other side; Medin & SchafFer, 1978; Medin & Schwanenflugel, 1981; Nakamura, 1985; Wattenmaker, Dewey, Murphy, & Medin, 1986). Thus the (modal) family resemblance view predicts that concepts are "linearly separable." Retention of central tendencies. The phrase family resemblance is used in two ways. In the sense that I have focused on until now, the family resemblance of an object to a category increases as the similarity between that object and all other members of the category increases and the similarity between that object and all nonmembers of the category decreases. This use of family resemblance (probably the use more reflective of Wittgenstein's, 1953, original ideas) has an extensional emphasis: It describes a relationship among objects and makes no assumptions about how the category of objects is represented mentally (i.e., about the intension of the word or what I have been calling the concept). In the second sense, family resemblance increases as the similarity between an object and the central tendencies of the category increases (Hampton, 1979). This use of family resemblance has an intentional emphasis: It describes a relationship between objects and a mental representation (of the central tendencies of a category). Although these two ways of thinking about family resemblance, average similarity to all instances and similarity to a central tendency, are different (cf. Reed, 1972), Barsalou (1985, 1987) points out that they typically yield roughly the same outcome, much as the average difference between a number and a set of other numbers is roughly the same as the difference between that number and the average of that set of other numbers. (For example, consider the number 2 and the set of numbers 3, 5, and 8. The average difference between 2 and 3, 5, and 8 is 3.33, and the difference between 2 and the average of 3,5, and 8 is 3.33.) Barsalou argues that although for most purposes the two ways of thinking about family resemblance are equivalent (one of the reasons the exemplar and family resemblance views are often difficult to distinguish empirically; see below), computation in terms of central tendencies may be more plausible psychologically (because fewer comparisons are involved in comparing an object with the central tendencies of a concept than with every instance and noninstance of the concept; see also Barresi, Robbins, & Shain, 1975). This suggests a fifth characteristic of the family resemblance view: A concept provides a summary of a category in terms of the central tendencies of the members of that category rather than in terms of the representations of individual instances. Economy, Informativeness, Coherence, and Naturalness Both the classical and the family resemblance views explain conceptual coherence in terms of the attributes shared by the members of a category (i.e., the similarity among the instances of a concept). The critical difference between the two views lies in the constraints placed on the attributes shared. In the classical view, all instances are similar in that they share a set of necessary and sufficient attributes (i.e., the definition). The family resemblance view relaxes this constraint and requires only that every attribute specified by the concept be shared by more than one instance. Although this requirement confers a certain amount of economy to the family resemblance view (every piece of information applies to several instances), removing the definitional constraint allows family resemblance representations to include nondefinitional information. In particular, concepts are likely to specify information beyond that true of all instances or beyond that strictly needed to understand what Medin and Smith (1984) call linguistic meaning (the different kinds of relations that hold among words such as synonymy, antynomy, hyponomy, anomaly, and contradiction as usually understood; cf. Katz, 1972; Katz & Fodor, 1963) to include information about how the objects referred to may relate to one another and to the world. It is not clear whether this loss in economy results in a concomitant increase in informativeness: Although in the family resemblance view more information may be associated with a concept than in the classical, not all of that information applies to every instance of the concept. In the family resemblance view, attributes can be inferred to inhere in different instances only with some level of probability. Thus the informativeness of the individual attributes specified is somewhat compromised. With no a priori constraint on the nature (or level) of similar3 There are several different ways to approach the representation of the central tendencies of a category. E. E. Smith and Medin (1981), for example, identified three approaches to what they called the probablistic view: the featural, the dimensional, and the holistic. E. E. Smith and Medin provided ample evidence for rejecting the holistic approach on both empirical and theoretical grounds (see also McNamara & Miller, 1989). They also argued that the similarities between the featural and dimensional approaches suggest that they might profitably be combined into a single position that could be called the "component" approach (E. E. Smith & Medin, 1981, p. 164) and concluded that the component approach is the only viable variant. RECENT VIEWS OF CONCEPTS 505 ity shared by the instances of a concept, the family resemblance view has difficulty specifying which similarities count and which do not when it comes to setting the boundaries between concepts. A Great Dane and a Bedlington terrier appear to share few similarities, but they share enough so that both are dogs. But a Bedlington terrier seems to share as many similarities with a lamb as it does with a Great Dane. Why is a Bedlington terrier a dog and not a lamb? Presumably, the family resemblance view would predict that the summed weights of Bedlington terrier attributes lead to its being more similar to other dogs than to lambs and result in its being categorized as a dog rather than a lamb. But to determine those weights, we need to know how common those attributes are among dogs and lambs. This implies that the categorization of Bedlington terriers must be preceded by the partitioning of the world into dog and lamb. Without that prior partitioning, the dog versus lamb weights of Bedlington terrier attributes cannot be determined. To answer the question of what privileges the categorization of a Bedlington terrier with the Great Dane rather than the lamb requires answering what privileges the partitioning of the world into dogs and lambs. Rosch (Rosch, 1978; Rosch & Mervis, 1975) argues that certain partitionings of the world (including, presumably, into dogs and lambs) are privileged, more immediate or direct, and arise naturally from the interaction of our perceptual apparatus and the environment. Thus whereas the classical view