In this chapter, we review several strands of research that share one common theme: the close conceptual and empirical links between categorization and recognition memory. We demonstrate that these links are strong, and often surprising. We explore the relation between recognition and unsupervised learning, and we investigate how perceptual processes can provide a unifying link between categori...

We suggest a model of categorization based on prototypes. A set of entities, identified with some finite dimensional Euclidian space, is partitioned into a finite number of categories. Such a categorization is said to be generated by extended prototypes if there is a set of distinguished entities, one for each category, such that the categorization is determined by proximity to these prototypic...

A large part of our history as well as our daily lives is captured in visual data. Understanding visual collections requires careful categorization to reveal expected as well as hidden relations. Performing this categorization manually is a demanding and cumbersome process. On the other hand automatic methods still have limitations in performance. An optimal approach brings together the power o...

Categorization relies upon the vocabulary of features that comprise the target objects. Previous theoretical work (Schyns, Goldstone, & Thibaut, 1998) has argued this vocabulary may change through learning and experience. Goldstone (2000) demonstrated this perceptual learning during a categorization task when new features are added that create a single feature unit from multiple existing units....

The Exemplar-Based Random Walk (EBRW) model (Nosofsky & Palmeri, 1997; Palmeri, 1997) incorporates elements of Nosofsky’s (1986) generalized context model (GCM) of categorization and Logan’s (1988) instance theory of automaticity. The model assumes that categories are represented in terms of stored exemplars. Exemplars are represented as points in a multidimensional psychological space with sim...

The number of patent documents is currently rising rapidly worldwide, creating the need for an automatic categorization system to replace time-consuming and labor-intensive manual categorization. Because accurate patent classification is crucial to search for relevant existing patents in a certain field, patent categorization is a very important and useful field. As patent documents are structu...

Rule-based category learning was examined in 4-11 year-olds and adults. Participants were asked to learn a set of novel perceptual categories in a classification learning task. Categorization performance improved with age, with younger children showing the strongest rule-based deficit relative to older children and adults. Model-based analyses provided insight regarding the type of strategy bei...

We review neuropsychological and neuroimaging evidence for the existence of three qualitatively different categorization systems. These categorization systems are themselves based on three distinct memory systems: working memory (WM), explicit long-term memory (explicit LTM), and implicit long-term memory (implicit LTM). We first contrast categorization based on WM with that based on explicit L...

Today, the number of text documents in digital form is progressively increasing and text categorization becomes the key technology of dealing with organizing text data. A major problem of text categorization is a huge-scale number of features. Most of those are useless, irrelevant or redundant for text categorization. Therefore, these features can decrease the classification performance. In ord...

The ability to form object categories is an important milestone in human infant development (Cohen 2003). We propose a framework that allows a robot to form a unified object categorization from several interactions with objects. This framework is consistent with the principle that robot learning should be ultimately grounded in the robot’s perceptual and behavioral repertoire (Stoytchev 2009). ...