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The congeries of theoretical views collectively referred to as "situated action" (SA) claim that humans and their interactions with the world cannot be understood using symbol-system models and methodology, but only by observing them within real-world contexts or building nonsymbolic models of them. SA claims also that rapid, real-time interaction with a dynamically changing environment is not amenable to symbolic interpretation of the sort espoused by the cognitive science of recent decades. Planning and representation, central to symbolic theories, are claimed to be irrelevant in everyday human activity. We will contest these claims, as well as their proponents ' characterizations of the symbol-system viewpoint. We will show that a number of existing symbolic systems perform well in temporally demanding tasks embedded in complex environments, whereas the systems usually regarded as exemplifying SA are thoroughly symbolic (and representational), and, to the extent that they are limited in these respects, have doubtful prospects for extension to complex tasks. As our title suggests, we propose that the goals set forth by the proponents of SA can be attained only within the framework of symbolic systems. The main body of empirical evidence supporting our view resides in the numerous symbol systems constructed in the past 35 years that have successfully simulated broad areas of human cognition. During the past few years a point of view has emerged in artificial intelligence, often under the label of "situated action " (henceforth, SA), that denies that intelligent systems are correctly characterized as physical symbol systems, and especially denies that symbolic processing lies at the heart of

This paper outlines the main assumptions of relevance theory (Sperber & Wilson 1985, 1995, 1998, 2002, Wilson & Sperber 2002), an inferential approach to pragmatics. Relevance theory is based on a definition of relevance and two principles of relevance: a Cognitive Principle (that human cognition is geared to the maximisation of relevance), and a Communicative Principle (that utterances create expectations of optimal relevance). We explain the motivation for these principles and illustrate their application to a variety of pragmatic problems. We end by considering the implications of this relevance-theoretic approach for the architecture of the mind. 1

It is proposed to conceive of representation as an emergent phenomenon that is supervenient on patterns of activity of coarsely tuned and highly redundant feature detectors. The computational underpinnings of the outlined theory of representation are (1) the properties of collections of overlapping graded receptive fields, as in the biological perceptual systems that exhibit hyperacuity-level performance, and (2) the sufficiency of a set of proximal distances between stimulus representations for the recovery of the corresponding distal contrasts between stimuli, as in multidimensional scaling. The present preliminary study appears to indicate that this concept of representation is computationally viable, and is compatible with psychological and neurobiological data. 1 Introduction A perceptual system confronted with a stimulus must (i) decide whether the stimulus belongs to an already encountered category, and (ii) if necessary, create a new category record for the stimulus a...

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Findings in the social psychology literatures on attitudes, social perception, and emotion demonstrate that social information processing involves embodiment, where embodiment refers both to actual bodily states and to simulations of experience in the brain’s modality-specific systems for perception, action, and introspection. We show that embodiment underlies social information processing when the perceiver interacts with actual social objects (online cognition) and when the perceiver represents social objects in their absence (offline cognition). Although many empirical demonstrations of social embodiment exist, no particularly compelling account of them has been offered. We propose that theories of embodied cognition, such as the Perceptual Symbol Systems (PSS) account (Barsalou, 1999), explain and integrate these findings, and that they also suggest exciting new directions for research. We compare the PSS account to a variety of related proposals and show how it addresses criticisms that have previously posed problems for the general embodiment approach. Consider the following findings. Wells and Petty (1980) reported that nodding the head (as in agreement)

1.1. Conceptual systems 621 1.2. Semantic memory 621

Examination of the systemic properties and forms of interaction that characterize classification and categorization reveals fundamental syntactic differences between the structure of classification systems and the structure of categorization systems. These distinctions lead to meaningful differences in the contexts within which information can be apprehended and influence the semantic information available to the individual. Structural and semantic differences between classification and categorization are differences that make a difference in the information environment by influencing the functional activities of an information system and by contributing to its constitution as an information environment.

Six experiments addressed the role of phonological information in visual word recognition using a semantic-decision task. Experiment 1 replicated Van Orden's (1987) finding that Ss make more false-positive errors on homophone foils than on spelling controls, indicating phonological activation of meaning. Experiment 2 showed that only lower frequency words yield this effect when broader categories are used. In Experiments 3 and 4, the homophony effect for lower frequency words remained, even though the stimuli included a large proportion of homophones, suggesting that activation of phonological information cannot be strategically inhibited. Experi-ments 5 and 6 examined effects of homophony on targets that were correct category exemplars and yielded similar results. These studies indicate that in skilled readers, phonological information contributes to the activation of word meaning only for low-frequency words. The word's orthographies represent different solutions to the problem of representing spoken language in written form. Orthographies differ in the extent to which the written sym-bols encode phonological information. Historically, orthogra-phies have evolved toward more direct representations of

890 Finding typical instances is an effective approach to understand and analyze large data sets. In this paper, we apply the idea of typicality analysis from psychology and cognition science to database query answering, and study the novel problem of answering top-k typicality queries. We model typicality in large data sets systematically. To answer questions like “Who are the top-k most typical NBA players?”, the measure of simple typicality is developed. To answer questions like “Who are the top-k most typical guards distinguishing guards from other players?”, the notion of discriminative typicality is proposed. Computing the exact answer to a top-k typicality query requires quadratic time which is often too costly for online query answering on large databases. We develop a series of approximation methods for various situations. (1) The randomized tournament algorithm has linear complexity though it does not provide a theoretical guarantee on the quality of the answers. (2) The direct local typicality approximation using VP-trees provides an approximation quality guarantee. (3) A VP-tree can be exploited to index a large set of objects. Then, typicality queries can be answered efficiently with quality guarantees by a tournament method based on a Local Typicality Tree data structure. An extensive performance study using two real data sets and a series of synthetic data sets clearly show that top-k typicality queries are meaningful and our methods are practical.