SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUS-TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into

People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion—an illusion of explanatory depth. The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures or narratives. The illusion for explanatory knowledge is most robust where the environment supports real-time explanations with visible mechanisms. We demonstrate the illusion of depth with explanatory knowledge in Studies 1–6. Then we show differences in overconfidence about knowledge across different knowledge domains in Studies 7–10. Finally, we explore the mechanisms behind the initial confidence and behind overconfidence in Studies 11 and 12, and discuss the implications of our findings for the roles of intuitive theories in concepts and cognition.

Recognizing emotion from facial expressions draws on diverse psychological processes implemented in a large array of neural structures. Studies using evoked potentials, lesions, and functional imaging have begun to elucidate some of the mechanisms. Early perceptual processing of faces draws on cortices in occipital and temporal lobes that construct detailed representations from the configuration of facial features. Subsequent recognition requires a set of structures, including amygdala and orbitofrontal cortex, that links perceptual representations of the face to the generation of knowledge about the emotion signaled, a complex set of mechanisms using multiple strategies. Although recent studies have provided a wealth of detail regarding these mechanisms in the adult human brain, investigations are also being extended to nonhuman primates, to infants, and to patients with psychiatric disorders.

In many geoscientific disciplines concepts are regularly discovered and modified, but the architecture of our geospatial information systems is primarily aimed at supporting static conceptual structures. This results in a semantic gap between our evolving understanding of these concepts and how they are represented in our systems. The research reported here provides better database support for geographical concepts that evolve with particular situations. To reduce the potential for schema change in such environments, we develop an analysis of the structure and function of situated geographical concepts and directly model the results in an UML schema. The developed schema explicitly contextualizes geographic information and concepts, enabling the extraction of contexts and interpretations from databases. This aids (1) the addition of empirical components to a geoscientific ontology, (2) the representation of context in geo-databases, and (3) the uncovering of the implicit aspects of data, and the sharing of meaning, via the represented contexts. Prototype implementations that show promise for managing geoscientific ontologies and databases are also briefly discussed.

Categories are learned in many ways, but the focus of much category learning research has been on classification learning. In classification learning, the diagnosticity of features is a primary influence on learning and the category representation. In this paper, we assess this influence of diagnosticity on a different means of category learning, inference learning. In two experiments, each with a different dependent measure, we found the expected result that classification learning led to strong sensitivity to the diagnosticity of the features, even to the exclusion of prototypicality (when controlled for diagnosticity). However, inference learners were significantly less sensitive to the diagnostic value of the features, and were sensitive to the prototypicality. This result provides further evidence for the idea that different types of category learning differentially influence the category representation and provides a better understanding of inference learning.

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified. In this article, we address the representation of complex perceptual categories. Contrary to the conventional and widespread assumption that people’s representations are homogeneous and integrated, we show in two experiments that people often master a complex categorization task by forming independent components, or parcels, of knowledge. We also show that once a knowledge

This paper explores the development of categories shared in the field logging of a region by a team of geologists. Visualization, neural networks and spatial statistical tools are employed to gain insight into the complex space of attributes observed, and into the categories developed. Background material and a discussion of results examines the findings in the light of research into category development, and specifically how categories are thought to be formed and modified as part of the (geo)scientific process and the situations encountered. Results show that (1) category discrepancy exists between individuals; (2) category development or revision is evident among individuals; and (3) that some categories do not seem to be totally defined by observed data alone. The results imply that contextual factors should also be considered when adopting ontological approaches to information representation.

In addition to traditional supervised classification learning, people can also learn categories by predicting the features of category members. It has been proposed that feature inference learning promotes the learning of more within-category information and a prototype representation of the category, as compared to classification learning that promotes learning of diagnostic information. We tracked learners ' eye movements during inference learning and found (Expt. 1) that they indeed fixated other features (even though those features were not necessary to predict the missing feature), providing the opportunity to extract within-category information. But those fixations were limited to only those features that needed to be predicted on future trials (Expt. 2). In other words, inference learning promotes the acquisition of within-category information not because participants are motivated to learn that information, but rather because of the anticipatory learning it induces. Whenever a person classifies an object, describes a concept verbally, engages in problem solving, or infers missing information, they must access their conceptual knowledge. As a result, the study of concept acquisition has been a critical part of understanding how people experience the world and how they interact with it in appropriate ways. Concept researchers have developed sophisticated formal theories that explain certain aspects of concept acquisition. These theories are largely based on the study of what has come to be known as standard supervised classification—a task that occupies the majority of experimental research in this area (Solomon, Medin, & Lynch, 1999). However, an emerging literature is focused on expanding the range of tasks that can be used to inform our models of concept acquisition. By studying different learning tasks we can understand other aspects of concept acquisition, including the interplay between category use and the type of concept

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Models and theories of category learning may exaggerate the extent to which people adopt discriminative strategies because of a reliance on the traditional supervised classification task. In the present experiment, this task is contrasted with supervised observational learning as a way of exploring differences between discriminative and generative learning. Categories were defined by a simple unidimensional rule with a second dimension that was either less diagnostic (than the simple rule on the first dimension) or non-diagnostic. When the second dimension was less diagnostic, observational learners were more sensitive to its distributional properties compared to classification learners (though classification accuracy at test did not differ). Observational learners were also consistently more sensitive to distributional information about the highly diagnostic dimension. When the second dimension was non-diagnostic, neither learning group showed sensitivity to the distributional properties of this dimension.