We present statistical analyses of the large-scale structure of three types of semantic networks: word associations, WordNet, and Roget's thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path-lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the world wide web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and also suggests one possible mechanistic basis for the effects of learning history variables (age-ofacquisition, usage frequency) on behavioral performance in semantic processing tasks.

this article. We also thank Denise Hatton, Tisha Baldwin, Joshua Nathan, Helen Sullivan, and Julia Wenzlaff for collecting data. Some of the stimulus materials used in Experiments 1 and 2 are adapted from the stimulus materials used in Rehder and Hastie (1997) and we thank them for inspiring many of the features and objects used in these studies. This project was supported by a National Science Foundation Grant (NSF-SBR 9515085) and a National Institute of Mental Health Grant (RO1 MH57737) given to Woo-kyoung Ahn, a National Science Foundation Graduate Fellowship to Nancy Kim, and a National Institute of Mental Health Postdoctoral Fellowship (MH10888-01A1) to Mary Lassaline

Ahn and Lassaline [Ahn, W., Lassaline, M.E., 1995. Causal structure in categorization.

People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion—an illusion of explanatory depth. The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures or narratives. The illusion for explanatory knowledge is most robust where the environment supports real-time explanations with visible mechanisms. We demonstrate the illusion of depth with explanatory knowledge in Studies 1–6. Then we show differences in overconfidence about knowledge across different knowledge domains in Studies 7–10. Finally, we explore the mechanisms behind the initial confidence and behind overconfidence in Studies 11 and 12, and discuss the implications of our findings for the roles of intuitive theories in concepts and cognition.

Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature’s importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category’s causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category’s causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes.

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The theory-based model of categorization posits that concepts are represented as theories rather than as feature lists. Thus, it is particularly interesting that the DSM-IV (American Psychiatric Association, 1994), establishes a set of atheoretical guidelines for diagnosis in the domain of mental disorders. Five experiments investigated how clinicians handle an atheoretical nosology. Clinical psychologists' causal theories for DSM-IV disorders and their responses on diagnostic and memory tasks were measured. Participants were more likely to diagnose a hypothetical patient with a disorder if that patient had causally central rather than causally peripheral symptoms according to their theory of the disorder. They also showed biased memory for the causally central symptoms. Clinicians are cognitively driven to form and apply theories despite decades of training and practice with the DSM's atheoretical guidelines. Clinical Psychologists' Theory-Based Representations of Mental Disorders Predict their Diagnostic Reasoning and Memory The theory-based view of categorization proposes that concepts are represented as theories or causal explanations. Murphy and Medin (1985) suggested that our nave theories about the world hold the features of a concept together in a cohesive package. For instance, a layperson's concept of anorexia not only contains the features "fear of becoming fat" and "refuses to maintain minimal body weight," but also the notion that the fear of becoming fat helps cause the refusal to maintain minimal body weight (Kim & Ahn, 2002). Indeed, a growing body of evidence supports the notion that the human mind constantly seeks out rules and explanations that make sense of incoming data concerning its surroundings, and forms concepts based on its theories about the ...

this paper is to demonstrate that the features that are central for determining the name of an object are not always the features that are central for determining how we think about the object. Naming and thinking about objects impose systematically different demands on the importance that we assign to the objects' various aspects. More specifically, we describe a condition distinguishing features that show convergence in centrality judgments between naming and conceiving from features that show divergence

Abstract. Geographic information is based on observations or measurements. The Open Geospatial Consortium (OGC) has developed an implementation specification for observations and measurements (O&M). It specifies precisely how to encode information. Yet, the O&M conceptual model does not specify precisely which real-world entities are denoted by the specified information objects. We provide formal semantics for the central O&M terms by aligning them to the foundational ontology DOLCE. The alignment to a foundational ontology restricts the possible interpretations of the central elements in the O&M model and establishes explicit relations between categories of real world entities and classes of information objects. These relations are essential for assessing semantic interoperability between geospatial information sources.

A feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that a category has two or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature's dependencies was manipulated by varying the similarity of category pairs. Participants' ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well-specified and vague dependency structures.