A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using Sbepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects ' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978), assumes that subjects store category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. It is assumed that the same multidimensional perceptual representation underlies performance in both the identification and Categorization paradigms. However, because of the influence of selective attention, similarity relationships change systematically across the two paradigms. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained that subjects may have augmented their category representations with inferred exemplars. Implications of the results for theories of multidimensional scaling and categorization are discussed.

Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a `tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modifies an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been construct...

The relationship between subjects ' identification and categorization learning of integral-dimension stimuli was studied within the framework of an exemplar-based generalization model. The model was used to predict subjects ' learning in six different categorization conditions on the basis of data obtained in a single identification learning condition. A crucial assumption in the model is that because of selective attention to component dimensions, similarity relations may change in systematic ways across different experimental contexts. The theoretical analysis provided evidence that, at least under unspeeded conditions, selective attention may play a critical role in determining the identification-categorization relationship for integral stimuli. Evidence was also provided that similarity among exemplars decreased as a function of identification learning. Various alternative classification models, including prototype, multiple-prototype, average distance, and "value-on-dimensions" models, were unable to account for the results. This article seeks to characterize performance relations between the two fundamental classification paradigms of identification and categorization. Whereas in an identification paradigm people identify stimuli as unique items (a one-to-one

The ability to categorize and use concepts e#ectively is a basic requirementofany intelligent actor. The utility-based approach to categorization is founded on the thesis that categorization is fundamentally in service of action, i.e., the choice of concepts made by an actor is critical to its choice of appropriate actions. This is in contrast to classical and similarity-based approaches which seek logical completeness in concept description with respect to sensory data rather than action-oriented e#ectiveness. Utility-based categorization is normative and not descriptive. It prescribes howanintelligent agent ought to conceptualize to act e#ectively. It provides ideals for categorization, speci#es criteria for the design of e#ective computational agents, and provides a model of ideal competence. A decision-theoretic framework for utilitybased categorization whichinvolves reasoning about alternative categorization models of varying levels of abstraction is proposed. Categorization mode...

This paper, theoretically, examines concepts, propositions, and establishes the need for and develops an extension to Concept Maps (CMaps) called Cyclic Concept Maps (Cyclic CMaps). The Cyclic CMap is considered to be an appropriate tool for representing knowledge of functional or dynamic relationships between concepts. The Concept Map (CMap), on the other hand, is viewed as an appropriate tool for representing hierarchical or static knowledge. The two maps complement each other and collectively they capture a larger domain of knowledge, thus forming a more effective knowledge representation tool.

This thesis develops a cognitive linguistic approach to language comprehension. The cognitive approach differs from traditional linguistic approaches in that linguistic description is seen as an integral part of the description of cognition, and that the object of description is the nature of conceptual structures, the processes which relate these conceptual structures, and the effect of context upon these processes. As a cognitive description within cognitive science, a computational approach is adopted: language comprehension is described in terms of two modules, a linguistic processing module and a discourse processing module. Within these modules, conceptual structures and processes are given a uniform characterization: structures are characterized as partial objects which are extended by processes into (potentially) less partial objects. In the linguistic processing module, linguistic expressions are characterized as signs which combine as head and modifier. The conceptual structu...

An inportant form of inductive learning is inventing a meaningful classification of given objects or events. This chapter extends the authors' previous work on this problem based on conceptual clustering, i.e., grouping objects into conceptually simple classes. In contrast to the past work, the new method deals with classifying objects represented by structural descriptions rather than sequences of attribute values. These descriptions are expressed in Annotated Predicate Calculus (A_PC), which is a typed predicate logic calculus with additional operators.

this paper. We also thank Barbara Malt for providing the list of basic properties and the family resemblance weightings of properties used in Experiment 4. Correspondence concerning this article should be sent to W.-K. Ahn, Department of Psychology, Vanderbilt University, Wilson Hall, 111 21st Avenue South, Nashville, TN 37203 (e-mail: woo-kyoung.ahn@vander bilt.edu). ---Accepted by previous editorial team. Effect of theory-based feature correlations on typicality judgments WOO-KYOUNG AHN, JESSECAE K. MARSH, and CHRISTIAN C. LUHMANN Vanderbilt University, Nashville, Tennessee and KEVIN LEE Yale University, New Haven, Connecticut In the present study, we examine what types of feature correlations are salient in our conceptual representations

Medin and Schaffer's (1978) context theory of classification learning is interpreted in terms of Luce's (1963) choice theory and in terms of theoretical results obtained in multidimensional scaling theory. En route to this interpretation, quantitative relationships that may exist between identification and classification performance are investigated. It is suggested that the same basic choice processes may operate in the two paradigms but that the similarity parameters that determine performance change systematically according to the structure of the choice paradigm. In particular, when subjects are able to attend selectively to the component dimensions that compose the stimuli, the similarity parameters may tend toward what is optimal for maximizing performance. Medin and Schaffer (1978) proposed a In the empirical work for which the context quantitative model, termed the context theory. to account for subjects ' performance in clas-model has been verified thus far, the stimuli have varied along four binary-valued dimensification paradigms. The context theory has sions, and the subjects have been required to provided good fits to data in numerous clas- classify the stimuli as belonging in one of two sification experiments (Medin, 1982; Medin, categories. To simplify the discussion; this ba-Altom, Edelson, & Freko, 1982; Medin, sic structure is assumed in the initial part of