The categorization ability of fully connected neural network models, with either discrete or continuous Q-state units, is studied in this work in replica symmetric mean-field theory. Hierarchically correlated multi-state patterns in a two level structure of ancestors and descendents (examples) are embedded in the network and the categorization task consists in recognizing the ancestors when the...

Four observers completed perceptual matching, identification, and categorization tasks using separable-dimension stimuli. A unified quantitative approach relating perceptual matching, identification, and categorization was proposed and tested. The approach derives from general recognition theory (Ashby & Townsend, 1986) and provides a powerful method for quantifying the separate influences of p...

An emerging theory of the neurobiology of category learning postulates that there are separate neural systems supporting the learning of categories based on verbalizeable rules (RB) or through implicit information integration (II). The medial temporal lobe (MTL) is thought to play a crucial role in successful RB categorization while the posterior regions of the caudate are hypothesized to suppo...

With the development of the web, large numbers of documents are available on the Internet. Digital libraries, news sources and inner data of companies surge more and more. Automatic text categorization becomes more and more important for dealing with massive data. However the major problem of text categorization is the high dimensionality of the feature space. At present there are many methods ...

An emerging theory of the neurobiology of category learning postulates that there are separate neural systems supporting the learning of categories based on verbalizeable rules (RB) or through implicit information integration (II). The medial temporal lobe (MTL) is thought to play a crucial role in successful RB categorization, whereas the posterior regions of the caudate are hypothesized to su...

Different models of categorization are often treated as competing accounts, but specific models are often used to understand individual differences, by estimating individual-level parameters. We develop an approach to understanding categorization that allows for individual differences both between and within models, using two prominent categorization models that make different theoretical assum...

Four observers completed identification and categorization tasks. Learning and attention processes were examined by applying General Recognition Theory (Ashby & Townsend, 1986) that separates low-level perceptual, high-level decisional, and attentional processes. Learning led to decision regions that became more nearly optimal. Learning had a small effect on perceptual processes in identificati...

Four observers completed identification and categorization tasks. Learning and attention processes were examined by applying general recognition theory (F. G. Ashby & J. T. Townsend, 1986), which separates perceptual, decisional, and attentional processes. Learning led to decision regions that became more nearly optimal. Learning had little effect on perceptual processes in identification and d...

In this article we present DUAL-PECCS, an integrated Knowledge Representation system aimed at extending artificial capabilities in tasks such as conceptual categorization. It relies on two different sorts of cognitively inspired common-sense reasoning: prototypical reasoning and exemplars-based reasoning. Furthermore, it is grounded on the theoretical tenets coming from the dual process theory ...

In this paper, we address the problem of how people understand predicative metaphors such as “The rumor flew through the office”, and argue that two-stage categorization is the process via which predicative metaphors are understood. In the two-stage categorization process, the verb of a predicative metaphor (e.g., fly) evokes an intermediate category, which in turn evokes a metaphorical categor...