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.

A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedural-learning-based) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trial-by-trial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.

haracterized by descriptions of each module and how each serves in those tasks for which it is best suited. However, these theories often do not emphasize how modules interact in producing responses and in learning. In this article we will develop a modular theory of categorization that follows from two distinct accounts of this behavior. The first account is that of rule-based theories of categorization. These theories emerge from a philosophical tradition in which concepts and categorization are described in terms of definitional rules. For example, if a living thing has a wide, flat tail and constructs dams by cutting down trees with its This work was supported by Indiana University Cognitive Science Program Fellowships and by NIMH ResearchTraining Grant PHS-T32-MH19879-03 to Erickson, and in part by NIMH FIRST Award 1-R29-MH51572-01 to Kruschke. This research was reported as a poster at the 1996 Cognitive Science Society Conference in San Diego, CA. We than

When ratings of judged similarity or frequencies of stimulus identification are averaged across subjects, the psychological structure ofthe data is fundamentally changed. Regardless of the structure of the individual-subject data, the averaged similarity data will likely be well fit by a standard multidimensional scaling model, and the averaged identification data will likely be well fit by the similarity-choice model. In fact, both models often provide excellent fits to averaged data, even if they fail to fit the data of each individual subject. Thus, a good fit of either model to averaged data cannot be taken as evidence that the model describes the psychological structure that characterizes individual subjects. We hypothesize that these effects are due to the increased symmetry that is a mathematical consequence of the averaging operation. It is common practice to average across subjects when analyzing

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 &

An important problem in accessing and retrieving visual information is to provide efficient similarity matching in large databases. Though much work is being done on the investigation of suitable perceptual models and the automatic extraction of features, little attention is given to the combination of useful representations and similarity models with efficient index structures. In this paper

this article may be addressed to either Michael Kahana or Robert Sekuler, Volen National Center for Complex Systems, MS 013, Brandeis University, Waltham, MA 02254-9110. E-mail may be sent to kahana @brandeis.edu or sekuler@brandeis.edu plex multidimensional stimulus spaces (Nosofsky, 1992; Maddox & Ashby, 1996; Ashby & Perrin, 1988), with decision rules that can predict performance in a variety of classification paradigms (Nosofsky & Palmeri, 1998; Nosofsky & Alfonso-Reese, 1999; Maddox & Ashby, 1996). Although models of classification and models of visual discrimination share many assumptions about stimulus representation and subjects' decision rules, models of classification have been primarily developed to explain subjects' classification of combinations of simple geometric forms, whereas models of discrimination have been developed to explain subjects ' discrimination of elemental visual stimuli, including sinusoidal luminance gratings. Because such stimuli can be combined to synthesize more complex images such as textures and natural scenes, they represent a natural test-bed for assessing theories' power and generalizability

From laser-scanned data to feature human model: a system based on

How does the brain represent visual objects? In simple perceptual generalization tasks, the human visual system performs as if it represents the stimuli in a low-dimensional metric psychological space (Shepard, 1987). In theories of 3D shape recognition, the role of feature-space representations (as opposed to structural (Biederman, 1987) or pictorial (Ullman, 1989) descriptions) has been for a long time a major point of contention. If shapes are indeed represented as points in a feature space, patterns of perceived similarity among different objects must reflect the structure of this space. The feature space hypothesis can then be tested by presenting subjects with complex parameterized 3D shapes, and by relating the similarities among subjective representations, as revealed in the response data by multidimensional scaling (Shepard, 1980), to the objective parameterization of the stimuli. The results of four such tests, accompanied by computational simulations, support the notion that...

Many currently popular models of categorization are either strictly parametric (e.g., prototype models, decision bound models) or strictly nonparametric (e.g., exemplar models) (Ashby & Alfonso-Reese, 1995). In this article, a family of semi-parametric classifiers is investigated where categories are represented by a finite mixture distribution. The advantage of these mixture models of categorization is that they contain several parametric models and nonparametric models as a special case. Specifically, it is shown that both decision bound models (Ashby & Maddox, 1992, 1993) and the generalized context model (Nosofsky, 1986) can be interpreted as two extreme cases of a common mixture model. Furthermore, many other (semi-parametric) models of categorization can be derived from the same generic mixture framework. In this article, several examples are discussed, and a parameter estimation procedure for fitting these models is outlined. To illustrate the approach, several specific models are fitted to a data set collected by McKinley and Nosofsky (1995). The results suggest that semi-parametric models are a promising alternative for future model development. Formal models of categorization are often closely related to statistical methods of probability density estimation (Ashby & Alfonso-Reese, 1995). In statistics, a distinction is made between parametric estimators, that make strong assumptions about the distribution of the sample data, and nonparametric estimators that make only weak distributional assumptions. In accord with this distinction, Ashby and Alfonso-Reese defined parametric classifiers as those classifiers that make strong assumptions about the functional form of the category distributions, and nonparametric classifiers as classifiers that make almost no assumptions about the category form. Prototype models (Reed, 1972) and decision bound models (Ashby & Maddox, 1992, 1993) are parametric classifiers, because they make strong assumptions about category structure. Decision bound models, for example, assume that the category distributions are multivariate normal (see Ashby, 1992, for a motivation). Despite this strong assumption (and the fact that these models can only predict linear or quadratic decision bounds), Ashby and Maddox (1992, 1993)