Work in philosophy and psychology has argued for a dissociation between perceptuallybased similarity and higher-level rules in conceptual thought. Although such a dissociation may be justified at times, our goal is to illustrate ways in which conceptual processing is grounded in perception, both for perceptual similarity and abstract rules. We discuss the advantages, power and influences of perceptually-based representations. First, many of the properties associated with amodal symbol systems can be achieved with perceptually-based systems as well (e.g. productivity). Second, relatively raw perceptual representations are powerful because they can implicitly represent properties in an analog fashion. Third, perception naturally provides impressions of overall similarity, exactly the type of similarity useful for establishing many common categories. Fourth, perceptual similarity is not static but becomes tuned over time to conceptual demands. Fifth, the original motivation or basis for sophisticated cognition is often less sophisticated perceptual similarity. Sixth, perceptual simulation occurs even in conceptual tasks that have no explicit perceptual demands. Parallels between perceptual and conceptual processes suggest that many mechanisms typically associated

Construal level theory proposes that temporal distance changes people’s responses to future events by changing the way people mentally represent those events. The greater the temporal distance, the more likely are events to be represented in terms of a few abstract features that convey the perceived essence of the events (high-level construals) rather than in terms of more concrete and incidental details of the events (low-level construals). The informational and evaluative implications of high-level construals, compared with those of low-level construals, should therefore have more impact on responses to distant-future events than near-future events. This article explores the implications of construal level theory for temporal changes in evaluation, prediction, and choice. The authors suggest that construal level underlies a broad range of evaluative and behavioral consequences of psychological distance from events.

The authors report a series of studies designed to determine whether effects similar to those observed in the innate categorical perception of color and phonemes are induced during the learning of simple unidimensional categories and more complex multidimensional ones. In Experiment 1 no evidence was found for such effects when stimuli varied on 1 dimension. Experiments 2 and 3 demonstrated a within-category compression effect but no between-category expansion effect for stimuli varying in 2 dimensions. Compression only was also shown in Experiment 4, which used pictures of actual objects. Multidimensional scaling analyses illustrate how within-category compression without expansion was sut~cient to produce categorical clustering of items in the similarity space. These analyses also show that learning changed the dimensional structure of similarity space. Results are compared with those from other studies exploring similar phenomena and with neural network simulations. Most contemporary models of category learning rely to

The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism: a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization. A fundamental aspect of human intelligence is the ability to form and manipulate relational representations. Examples of relational thinking include the ability to appreciate analogies between seemingly different objects or events (Gentner, 1983; Holyoak & Thagard, 1995), the ability to apply abstract rules in novel situations (e.g., Smith, Langston, & Nisbett, 1992), the ability to understand and learn language (e.g., Kim, Pinker, Prince, & Prasada, 1991), and even the ability to appreciate perceptual similarities

this article. We also thank Denise Hatton, Tisha Baldwin, Joshua Nathan, Helen Sullivan, and Julia Wenzlaff for collecting data. Some of the stimulus materials used in Experiments 1 and 2 are adapted from the stimulus materials used in Rehder and Hastie (1997) and we thank them for inspiring many of the features and objects used in these studies. This project was supported by a National Science Foundation Grant (NSF-SBR 9515085) and a National Institute of Mental Health Grant (RO1 MH57737) given to Woo-kyoung Ahn, a National Science Foundation Graduate Fellowship to Nancy Kim, and a National Institute of Mental Health Postdoctoral Fellowship (MH10888-01A1) to Mary Lassaline

Abstract not found

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.

ion and Categorization Eric J. Horvitz # and Adrian C. Klein Palo Alto Laboratory Rockwell International Science Center 444 High Street Palo Alto, CA 94301 Abstract We take a utility-based approach to categorization. We construct generalizations about events and actions by considering losses associated with failing to distinguish among detailed distinctions in a decision model. The utility-based methods transform detailed states of the world into more abstract categories comprised of disjunctions of the states. We show how we can cluster distinctions into groups of distinctions at progressively higher levels of abstraction, and describe rules for decision making with the abstractions. The techniques introduce a utility-based perspective on the nature of concepts, and provide a means of simplifying decision models used in automated reasoning systems. We demonstrate the techniques by describing the capabilities and output of TUBA, a program for utility-based abstraction. 1 INTRODUCTION...

We describe some of the implications of markup for document management systems. Markup’s properties are inherited from text, since it is embedded in text. These properties are most advantageous when document structure is reducible to substrings of characters, and when the update characteristics of the structure are similar to the update characteristics of the text. We describe situations in which these characteristics are disadvantageous. Markup is not a data model, but one of several possible techniques for representing structure. For this reason it should not be the

Investigations of patients with semantic category-specific deficits have revealed a wide range of performance and variability in categories that are impaired or spared; this variability presents a challenge to accounts of category specificity. Accounts based only on impairment to semantic features of a particular type (e.g., visual), as well as accounts based only on featural properties (e.g., feature intercorrelations), are insufficient to explain the variability of patients' performance. A first goal of the paper is to discuss how a hybrid account incorporating both a level of organization according to feature-types (a level of nonlinguistic conceptual representations) and a level of organization dictated by featural properties may provide a more comprehensive account of the cases reported in the literature. The second and most novel goal of the study reported here is to derive from our hybrid account a series of novel predictions concerning the representation and impairment of a different domain of knowledge: knowledge of actions and events, a domain of knowledge that has received remarkably little attention to date. Keywords: category-specificity, nouns, verbs, semantics, simulation The breakdown of semantic knowledge: Insights from a statistical model of meaning representation. The study of patients in whom semantic knowledge has been disrupted has led to a number of important inferences concerning the underlying architecture of the semantic system (Warrington, 1975). Particularly relevant are cases in which focal brain damage creates categoryspecific deficits (i.e., selective impairment of semantic knowledge along category boundaries). At present there are a substantial number of cases on record (approximately 89, according to Rogers & Plaut, 2002). Specificity in...