How Features Create Knowledge of Kinds

How Features Create Knowledge of Kinds Shohei Hidaka (shhidaka@indiana.edu) Linda B. Smith (smith4@indiana.edu) Department of Psychological and Brain Sciences, Indiana University; 1101 East Tenth Street, Bloomington, IN 47405-7007, USA name, the children systematically extend the name to new in- stances by different features for different kinds (Imai & Gen- tner, 1997, etc.) Specifically, they extend the names for things with features indicative of animates by multiple similarities, for solid things with features typical of artifacts by shape, and for nonsolid substances by material. For these different kinds of things, young children have clearly solved the fea- ture selection problem and seem to know that different kinds of features matter for different kinds of things. They know what kinds of categories need to be formed. Abstract Given a single instance of a novel category, two- and three- year-old children systematically generalize its name to other novel things based on appropriate feature dimensions. We explain this in terms of a prediction of the probabilistic den- sity (category likelihood) in feature space from a single novel instance. In principle, observing more instances from a par- ticular probabilistic density, one can estimate the probabilistic density more accurately. In this sense, children’s success in generalization from a single instance seems to go beyond the theoretical limit. We provide a theoretical account for the phe- nomenon. In our theory, these kind of kind specific generaliza- tions, a fast mapping from a single instance to a whole category is due to the structure of the system of learned categories and a sort of optimization of the category organization. Category likelihood and feature selection Different categories are structured in different ways. For example, colors are relevant to categorizing foods but not to categorizing trucks. Further, some categories are decidedly incoherent and not formed by people. For example, people do not form categories that include fish and elephants but not lions (see also Murphy & Medin, 1985). A key ques- tion for a theory of categories, then, is how different features are selected for different kinds of categories, and how some categories but not others are selected. An understanding of very young children’s novel word generalizations may pro- vide an answer. Two and 3-year-old children generalize novel names for novel things in the “right” way given just a single instance of the category: generalizing names for novel arti- facts by shape, for novel animates by multiple features, and for substances by material. This fast mapping of a name for a single thing to a whole category surely facilitates early word learning. Children’s use of different features to form different kinds of categories in these tasks appears to directly reflect the category likelihoods of those features for known categories. Samuelson and Smith (see also Colunga & Smith, 2005) ex- amined the category structure of the first 312 nouns typi- cally known by children learning English (and in other stud- ies the first 300 nouns learned by children learning Japanese). They measured category structure by asking adults to judge the characteristic within-category similarities of typical in- stances of individual noun categories on four dimensions, shape, color, texture, and material. They found that individ- ual artifact categories (e.g., chairs, forks, spoons, cups) were judged to have instances that were highly similar in shape but variable in other properties, that animal categories were judged to have instances that were similar in all properties, and that substance categories were judged to have instances that were similar in material (and color). Thus, the impor- tance of features to different kinds of categories for children may reflect the expected distributions of those features for nearby categories, a point we expand on below. Kind specific generalizations Distribution of category likelihoods The phenomenon of interest derives from a widely used ex- perimental task of novel noun generalization (NNG) (see Carey & Bartlett, 1978). In these tasks, children are shown a single novel thing and are then asked to generalize that name to other things. One experimental variable in these studies is the properties of the objects themselves, for exam- ple, whether they have features typical of animates (e.g., eyes, legs, hands), features typical of artifacts (e.g, solid with an- gular parts, straight edges), or features typical of substances (e.g., nonsolid, rounded, flat forms, with irregular shapes). In general, a large literature indicates that when 2- and 3-year- old children are given a novel never-seen-before thing, told its name (“This is a dax”), and asked what other things have that Several recent studies further indicate that children’s differ- ent patterns of category generalization for different kinds of features may be geometrically organized in some larger fea- ture space (Imai & Gentner, 1997; Colunga & Smith, 2005). For example, Colunga & Smith (2005) showed that chil- dren’s generalizations of novel names by shape versus mate- rial shifted gradually as the presented novel instances varied incrementally from shapes typical of artifacts (complex, lots of angles) to shapes typical of substances (simple rounded shapes). Similarly, Colunga and Smith (under review, see also Yoshida & Smith, 2003) showed that children’s general- ization by shape versus material shifted gradually as (identi- cally shaped) instances were incrementally varied from solid Keywords: Fast mapping; Probability density estimation; Smooth feature space