Similarity and categorisation: getting dissociations in perspective

Dissociations between similarity and categorization have constituted critical counter-evidence to the view that categorization is similarity-based. However, there have been difficulties in replicating such dissociations. This paper reports three experiments. The first provides evidence of a double dissociation between similarity and categorization. The second and third show that by asking participants to make their judgments from particular perspectives, this dissociation disappears or is much reduced. It is argued that these data support a perspectival view of concepts, in which categorization is similarity-based, but where the dimensions used to make similarity and categorization judgments are partially fixed by perspective

Collaborative Categorization on the Web

Collaborative categorization is an emerging direction for research and innovative applications. Arguably, collaborative categorization on the Web is an especially promising emerging form of collaborative Web systems because of both, the widespread use of the conventional Web and the emergence of the Semantic Web providing with more semantic information on Web data. This paper discusses this issue and proposes two approaches: collaborative categorization via category merging and collaborative categorization proper. The main advantage of the first approach is that it can be rather easily realized and implemented using existing systems such as Web browsers and mail clients. A prototype system for collaborative Web usage that uses category merging for collaborative categorization is described and the results of field experiments using it are reported. The second approach, called collaborative categorization proper, however, is more general and scales better. The data structure and user interface aspects of an approach to collaborative categorization proper are discussed

A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model

According to the Threshold Theory (Hampton, 1995, 2007) semantic categorization decisions come about through the placement of a threshold criterion along a dimension that represents items' similarity to the category representation. The adequacy of this theory is assessed by applying a formalization of the theory, known as the Rasch model (Rasch, 1960; Thissen & Steinberg, 1986), to categorization data for eight natural language categories and subjecting it to a formal test. In validating the model special care is given to its ability to account for inter- and intra-individual differences in categorization and their relationship with item typicality. Extensions of the Rasch model that can be used to uncover the nature of category representations and the sources of categorization differences are discussed

Categorization by Groups

Categorization is a core psychological process central to consumer and managerial decision-making. While a substantial amount of research has been conducted to examine individual categorization behaviors, relatively little is known about the group categorization process. In two experiments, we demonstrate that group categorization differs systematically from that of individuals: groups created a larger number of categories with fewer items in each category. This effect is mediated by groups’ larger knowledge base and moderated by groups’ ease in achieving consensus. While neither broader nor narrower categories are normatively superior, more integration or distinction among concepts may be desirable for a given objective. Thus, it is important for those relying on the outputs of categorization tasks, such as web site designers, store managers, product development teams, and product marketing managers, to understand and consider the systematic differences between group and individual categorization.Decision-making;Categorization;Group and Individual Categorization

Using bag-of-concepts to improve the performance of support vector machines in text categorization

This paper investigates the use of concept-based representations for text categorization. We introduce a new approach to create concept-based text representations, and apply it to a standard text categorization collection. The representations are used as input to a Support Vector Machine classifier, and the results show that there are certain categories for which concept-based representations constitute a viable supplement to word-based ones. We also demonstrate how the performance of the Support Vector Machine can be improved by combining representations

Sequence effects in categorization of simple perceptual stimuli

Categorization research typically assumes that the cognitive system has access to a (more or less noisy) representation of the absolute magnitudes of the properties of stimuli and that this information is used in reaching a categorization decision. However, research on identification of simple perceptual stimuli suggests that people have very poor representations of absolute magnitude information and that judgments about absolute magnitude are strongly influenced by preceding material. The experiments presented here investigate such sequence effects in categorization tasks. Strong sequence effects were found. Classification of a borderline stimulus was more accurate when preceded by a distant member of the opposite category than by a distant member of the same category. It is argued that this category contrast effect cannot be accounted for by extant exemplar or decision-bound models of categorization. The effect suggests the use of relative magnitude information in categorization. A memory and contrast model illustrates how relative magnitude information may be used in categorization