SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUS-TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into

The authors examined inductive reasoning among experts in a domain. Three types of tree experts (landscapers, taxonomists, and parks maintenance personnel) completed 3 reasoning tasks. In Experi-ment 1, participants inferred which of 2 novel diseases would affect "more other kinds of trees " and provided justifications for their choices. In Experiment 2, the authors used modified instructions and asked which disease would be more likely to affect "all trees. " In Experiment 3, the conclusion category was eliminated altogether, and participants were asked to generate a list of other affected trees. Among these populations, typicality and diversity effects were weak to nonexistent. Instead, experts ' reasoning was influenced by "local " coverage (extension of the property to members of the same folk family) and causal-ecological factors. The authors concluded that domain knowledge leads to the use of a variety of reasoning strategies not captured by current models of category-based induction. Cognitive psychologists are increasingly interested in concep-tual functions beyond categorization (e.g., Barsalou & Hale, 1992; Markman, Yamauchi, & Makin, 1997; Pazzani, 1991; Ross, 1996, 1997; Wisniewski, 1995). Particularly, they have focused on the use of categories in reasoning and have proposed a number of formal models of category-based reasoning (e.g., Heit, 1998; Mc-

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As computer scientists, we are constantly seeking ways to understand user behaviors so that we can build better information environments and applications. Therefore, Web site designers, producers, and maintainers often ask the questions: What are my users trying to do on my Web site? What's the mixture of my user traffic? In this paper, we introduce and describe a method to discover major types of information goals of Web surfers automatically. As part of this technique, we use Multi-Modal Clustering (MMC), the Longest Repeating Subsequences (LRS), and Inferring User Need by Information Scent (IUNIS) algorithms to extract significant user paths from the Web server logs, and then we represent these user path profiles using multi-modal vectors that encompass various sources of information, including Content, Topology, and URL. To confirm our method's utility in the real-world, we apply this technique to three different Web sites of varying sizes and purposes, and present the results.

to all of the category's features. However, people's knowledge of real-world categories often consists of many "rote " features that are not related to their prior knowledge. Five experiments found that even minimal prior knowledge (1 knowledge-relevant feature and 5 rote features per exemplar) can facilitate category learning. Posttests revealed that although the knowledge aided learning, subjects also acquired the rote features that were not related to knowledge, contradicting predictions of an attentional expla-nation of the knowledge effect. The results of Experiment 6 suggested that subjects attempt to link even rote features to their knowledge.

Theories of categorization have typically focused on the internal structure of categories. This paper is concerned with the external structure of categories. In particular , it is suggested that many categories specify the relational role that is played by category members. To support this claim, the paper distinguishes between traditional feature-based categories, relational categories (which specify a relational structure) and role-governed categories (which specify that an item plays a particular role within a relational structure). After discussing the relationship among these types of categories, the implications of this view for the study of category learning and category use are discussed.

world, that the human conceptual system is designed to create systematic categories, and that people have theories about the world that bind together seemingly unrelated features. The authors have suggested that the need to establish reference in communication also influences category coherence. This proposal was tested in 2 studies involving a referential communica-tion task. In these studies, consistency was promoted between individuals by communication, which synchronized the category structures of different people. Further, people were focused on the commonalities of objects and on the differences related to the commonalities by communication--a pattern that is compatible with what has been observed in existing categories. These results suggest that categorization research must incorporate communication tasks into the canon of methodologies used to study category structure. The human conceptual system is notable both for its rich structure and its profound flexibility. For example, studies of taxonomic categories demonstrate that people have hierarchi-cally organized category structures and that they often name pictures of objects with a term at a middle level of

conditioning, etc.). Piaget spelled out a list of "formal operations," such as modus ponens, the probability schema, etc., which he regarded as the fundamental deductive and inductive rule schemas necessary to understand the world. The cognitive revolution, from its earliest incarnation in the work of such theorists as George Miller and Herbert Simon, until nearly the end of the 20 century, essentially embraced Piaget's position of extreme formalism and content independence of inferential rules. Cognitive scientists' endorsement of the formalist, universalistic position was undoubtedly encouraged by the analogy between the human mind and the computer: brain = hardware, cognitive procedures = operating principles and factory-installed software (Block, 1995). This analogy both encouraged the universality assumption and discouraged any assumption that cognitive procedures might be alterable. The heuristics and biases movement of Kahneman and Tversky (1974) and their colleagues in social

Abstract. This paper describes a cross-cultural research project on the relation between how people conceptualize nature (their mental models) and how they act in it. Mental models of nature differ dramatically among and within populations living in the same area and engaged in more or less the same activities. This has novel implications for environmental decision making and management, including dealing with commons problems. Our research also offers a distinct perspective on models of culture, and a unified approach to the study of culture and cognition. We argue that cultural transmission and formation does not consist primarily in shared rules or norms, but in complex distributions of causally-connected representations across minds in interaction with the environment. The cultural stability and diversity of these representations often derives from rich, biologically-prepared mental mechanisms that limit variation to readily transmissible psychological forms. This framework addresses a series of methodological issues, such as the limitations of conceiving culture to be a well-defined system or bounded entity, an independent variable, or an internalized component of minds. 2 I. Introduction.

this article should be addressed to Simone Nguyen, Department of Psychology, University of North Carolina, 601 South College Road, Wilmington, NC, 28403-5612. Electronic mail may be sent to nguyens@uncw.edu