Machine learning of visual object categorization: an application of the SUSTAIN model

Formal models of categorization are psychological theories that try to describe the process of categorization in a lawful way, using the language of mathematics. Their mathematical formulation makes it possible for the models to generate precise, quantitative predictions. SUSTAIN (Love, Medin & Gureckis, 2004) is a powerful formal model of categorization that has been used to model a range of human experimental data, describing the process of categorization in terms of an adaptive clustering principle. Love et al. (2004) suggested a possible application of the model in the field of object recognition and categorization. The present study explores this possibility, investigating at the same time the utility of using a formal model of categorization in a typical machine learning task. The image categorization performance of SUSTAIN on a well-known image set is compared with that of a linear Support Vector Machine, confirming the capability of SUSTAIN to perform image categorization with a reasonable accuracy, even if at a rather high computational cost

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

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

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

Optimal Categorization

The importance of categorical reasoning in human cognition is well-established in psychology and cognitive science, and it is generally acknowledged that one of the most important functions of categorization is to facilitate prediction. This paper provides a model of optimal categorization. In the beginning of each period a subject observes a two-dimensional object in one dimension and wants to predict the object's value in the other dimension. The subject partitions the space of objects into categories. She has a data base of objects that were observed in both dimensions in the past. The subject determines what category the new object belongs to on the basis of observation of its first dimension. The average value in the second dimension, of objects in this category in the data base, is used as prediction for the object at hand. At the end of each period the second dimension is observed and the observation is stored in the data base. The main result is that the optimal number of categories is determined by a trade-off between (a) decreasing the size of categories in order to enhance category homogeneity, and (b) increasing the size of categories in order to enhance category sample size.Categorization; Priors; Prediction; Similarity-Based Reasoning.

Selective self-categorization: Meaningful categorization and the in-group persuasion effect

Research stemming from self-categorization theory (Turner et al., 1987) has demonstrated that individuals are typically more persuaded by messages from their in-group than by messages from the out-group. The present research investigated the role of issue relevance in moderating these effects. In particular, it was predicted that in-groups would only be more persuasive when the dimension on which group membership was defined was meaningful or relevant to the attitude issue. In two studies, participants were presented with persuasive arguments from either an in-group source or an out-group source, where the basis of the in-group/out-group distinction was either relevant or irrelevant to the attitude issue. Participants' attitudes toward the issue were then measured. The results supported the predictions: Participants were more persuaded by in-group sources than out-group sources when the basis for defining the group was relevant to the attitude issue. However, when the defining characteristic of the group was irrelevant to the attitude issue, participants were equally persuaded by in-group and out-group sources. These results support the hypothesis that the fit between group membership and domain is an important moderator of self-categorization effects