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With complex multimedia data, we see the emergence of database systems in which the fundamental operation is similarity assessment. Before database issues can be addressed, it is necessary to give a definition of similarity as an operation. In this paper we develop a similarity measure, based on fuzzy logic, that exhibit several features that match experimental findings in humans. The model is dubbed Fuzzy Feature Contrast (FFC) and is an extension to a more general domain of the Feature Contrast model due to Tversky. We show how the FFC model can be used to model similarity assessment from fuzzy judgment of properties, and we address the use of fuzzy measures to deal with dependencies among the properties.

Current input device taxonomies and other frameworks typically emphasize the mechanical structure of input devices. We suggest that selecting an appropriate input device for an interactive task requires looking beyond the physical structure of devices to the deeper perceptual structure of the task, the device, and the interrelationship between the perceptual structure of the task and the control properties of the device. We atllrm that perception is key to understanding performance of multidimensional input devices on multidimensional tasks. We have therefore extended the theory of processing of perceptual structure to graphical interactive tasks and to the control structure of input devices. This allows us to predict task and device combinations that lead to better performance and hypothesize that performance is improved when the perceptual structure of the task matches the control structure of the device. We conducted an experiment in which subjects performed two tasks with different perceptual structures, using two input devices with correspondingly different control structures, a three-dimensional tracker and a mouse. We analyzed both speed and accuracy, as well as the trajectories generated by subjects as they used the unconstrained three-dimensional tracker to perform each task. The results support our hypothesis and confirm the importance of matching the perceptual structure of the task and the control structure of the input device. Categories and Subject Descriptors: H.1.2 [Models and Principles]: User/Machine

A new theory of similarity, rooted in the detection and recognition literatures, is developed. The general recognition theory assumes that the perceptual effect of a stimulus is random but that on any single trial it can be represented as a point in a multidimensional space. Similarity is a function of the overlap of perceptual distributions. It is shown that the general recognition theory contains Euclidean distance models of similarity as a special case but that unlike them, it is not constrained by any distance axioms. Three experiments are reported that test the empirical validity of the theory. In these experiments the general recognition theory accounts for similarity data as well as the cur-rently popular similarity theories do, and it accounts for identification data as well as the long-standing "champion " identification model does. The concept of similarity is of fundamental importance in psychology. Not only is there a vast literature concerned directly with the interpretation of subjective similarity judgments (e.g., as in multidimensional scaling) but the concept also plays a cru-cial but less direct role in the modeling of many psychophysical tasks. This is particularly true in the case of pattern and form recognition. It is frequently assumed that the greater the simi-larity between a pair of stimuli, the more likely one will be con-fused with the other in a recognition task (e.g., Luce, 1963; Shepard, 1964; Tversky & Gati, 1982). Yet despite the poten-tially close relationship between the two, there have been only a few attempts at developing theories that unify the similarity and recognition literatures. Most attempts to link the two have used a distance-based similarity measure to predict the confusions in recognition ex-

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In this article, the relation between the identification, similarity judgment, and categorization of multidimensional perceptual stimuli is studied. The theoretical analysis focused on general recognition theory (GRT), which is a multidimensional generalization of signal detection theory. In one application, 2 Ss first identified a set of confusable stimuli and then made judgments of their pairwise similarity. The second application was to Nosofsky's (1985b, 1986) identificationcategorization experiment. In both applications, a GRT model accounted for the identification data better than Luce's (1963) biased-cboice model. The identification results were then used to predict performance in the similarity judgment and categorization conditions. The GRT identification model accurately predicted the similarity judgments under the assumption that Ks allocated attention to the 2 stimulus dimensions differently in the 2 tasks. The categorization data were predicted successfully without appealing to the notion of selective attention. Instead, a simpler GRT model that emphasized the different decision rules used in identification and categorization was adequate. The perceptual processes involved when subjects identify, categorize, or judge the pairwise similarity of multidimensional perceptual stimuli are closely related (e.g., Ashby &

Image databases will force us to rethink many of the concepts that led us so far. One of these is matching. We argue that the fundamental operation in a content-indexed image database should not be matching the query against the images in the database in search of a "target" image that best matches the query. The basic operation in query-by-content will be ranking portions of the database with respect to similarity with the query. What kind of similarity measure should be used is a problem we begin exploring in this paper. We let psychological experiments guide us in the quest for a good similarity measure, and devise a measure derived from a set-theoretic measure proposed in the psychological literature, modified by the introduction of fuzzy logic. 1 Introduction What makes a multimedia database di#erent from the databases we have been using until now? Many things, one might say: there are di#erent ways to express a query (a sketch, an image...), a di#erence of several orders of mag...

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Abstract. Determining the grade of semantic similarity between geospatial concepts is the basis for evaluating semantic interoperability of geographic information services and their users. Geometrical models, such as conceptual spaces, offer one way of representing geospatial concepts, which are modelled as n-dimensional regions. Previous approaches have suggested to measure semantic similarity between concepts based on their approximation by single points. This paper presents a way to measure semantic similarity between conceptual regions—leading to more accurate results. In addition, it allows for asymmetries by measuring directed similarities. Examples from the geospatial domain illustrate the similarity measure and demonstrate its plausibility. 1

This paper introduces a psychological notion of categorization into economics and derives its implications for economic decision making. We show, using a tractable model of social cognition, that a decision maker in (efficiently) assigning past experiences to categories, will sort experiences of interaction with larger (majority) groups more finely than experiences with smaller (minority) groups. We then apply the model to understand simple forms of discrimination and social identity. It is shown that discrimination in hiring can result from such cognitive processes even when there is no malevolent taste to do so and workers' qualifications are fully observable. The model also provides a framework that is equipped to investigate the social psychological concept of identity, where identity is viewed as self-categorization.