Categories in the middle level of a taxonomic hierarchy tend to be highly differentiated in that they have both high levels of within-category similarity and low levels of between-category similarity. Research on similarity reveals a distinction between pairs of categories that are seen as dissimilar because they have few commonalities and pairs that are seen as dissimilar because they have many psychologically relevant alignable differences. The authors suggest that the low between-category similarity proposed for neighboring basic-level categories is actually a matter of having many psychologically relevant differences. In contrast, the low between-category similarity of superordinates is a result of their having few commonalities. The authors evaluate this claim in 4 experiments using a variety of natural stimuli and converging measures. The data support the importance of aliguable differences for distinguish-ing between pairs of basic-level categories. People typically categorize objects at a number of levels of generality. For example, an object on top of a coffee table that has a rectangular shape, contains pages of printed text, and describes a mysterious murder can be called a murder

A host of experimental data has been accumulating on the properties and mechanisms of object recognition in cortex. We review the main findings, and summarize them using a quantitative, biologically plausible, Standard Model. The model is a tool to interpret and understand the available data, and generate questions and predictions for new experiments.

Previous research has shown that background knowledge affects the ease of concept learning, but little research has examined its effects on speeded categorization of instances after the category is well learned. Subjects in 4 experiments first learned novel categories. At test, they categorized a new set of novel stimuli that were either consistent or inconsistent with background knowledge given about the categories. Background knowledge affected catego-rization responses in an untimed task, with usual reaction time instructions, with a response deadline, or when the stimuli were presented for 50 ms followed by a mask. Three other experiments using a part-detection task showed that subjects were more likely to notice missing parts that were critical than noncritical according to background knowledge. The mechanisms by which background knowledge affects categorization and part detection are discussed. Human categorization is a cognitive proceSs in which people decide whether an instance is a member of a cate-gory by comparing the instance with their conceptual rep-resentations. Categorization research in the 1970s and early

Are visual and verbal processing systems functionally independent ? Two experiments (one using line drawings of common objects, the other using faces) explored the relationship between the number of syllables in an object's name (one or three) and the visual inspection of that object. The tasks were short-term recognition and visual search. Results indicated more fixations and longer gaze durations on objects having three-syllable names when the task encouraged a verbal encoding of the objects (i.e., recognition). No effects of syllable length on eye movements were found when implicit naming demands were minimal (i.e., visual search). These findings suggest that implicitly naming a pictorial object constrains the oculomotor inspection of that object, and that the visual and verbal encoding of an object are synchronized so that the faster process must wait for the slower to be completed before gaze shifts to another object. Both findings imply a tight coupling between visual and linguistic processing, and highlight the utility of an oculomotor methodology to understand this coupling.

The aim of this paper is to present a software artifact for machine supported understanding and modelling of prototypebased categorization. This software system is able to perform discovery of syndromes (seen as prototypes) into a given data set of clinical observations. A new genetic algorithm is used to achieve an unsupervised categorization of observation via adaptive learning of number and features of prototypes. Its evolutionary learning is oriented to maximize specificity and distinctiveness of categories. Experimental results show that prototype-based categorization of clinical observation is suitable for syndrome-based categorization. The trichotomy of categorizations (superordinate, basic and subordinate) is explained by trade off between specificity and distinctiveness. Moreover the natural basic level is also related to epistemic adequacy of found prototypes.

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