This article describes an integrated theory of analogical access and mapping, instantiated in a

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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

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Metaphor comprehension involves an interaction between the meaning of the topic and vehicle terms of the metaphor. Meaning is represented by vectors in a high-dimensional semantic space. Predication modifies the topic vector by merging it with selected features of the vehicle vector. The resulting metaphor vector can be evaluated by comparing it with known landmarks in the semantic space. Thus, metaphorical predication is treated in the present model in exactly the same way as literal predication. Some experimental results concerning metaphor comprehension are simulated within this framework, such as the non-reversibility of metaphors, priming of metaphors with literal statements, and priming of literal statements with metaphors.

Theories of categorization have typically focused on the internal structure of categories. This paper is concerned with the external structure of categories. In particular , it is suggested that many categories specify the relational role that is played by category members. To support this claim, the paper distinguishes between traditional feature-based categories, relational categories (which specify a relational structure) and role-governed categories (which specify that an item plays a particular role within a relational structure). After discussing the relationship among these types of categories, the implications of this view for the study of category learning and category use are discussed.

In Latent Semantic Analysis (LSA) the meaning of a word is represented as a vector in a high-dimensional semantic space. Different meanings of a word or different senses of a word are not distinguished. Instead, word senses are appropriately modified as the word is used in different contexts. In N-VP sentences, the precise meaning of the verb phrase depends on the noun it is combined with. An algorithm is described to adjust the meaning of a predicate as it is applied to different arguments. In forming a sentence meaning, not all features of a predicate are combined with the features of the argument, but only those that are appropriate to the argument. Hence, a different "sense" of a predicate emerges every time it is used in a different context. This predication algorithm is explored in the context of four different semantic problems: metaphor interpretation, causal inferences, similarity judgments, and homonym disambiguation.

We present and evaluate an approach to the reasoning needed to handle a broad class of metaphorical ut-terances, and a computer program (ATT-Meta) partially implementing and further specifying that approach. The approach emanates from artificial intelligence but is offered also for consideration by cognitive scien-tists generally. The utterances of interest are ones that (a) rest on conceptual metaphors that are familiar to the understander but (b) transcend the mappings in the conceptual metaphors by using concepts not han-dled by the mappings. Our approach advocates possibly-extensive inferencing in the terms of the source (vehicle) domains of the conceptual metaphors, while avoiding as far as possible the extension of the map-pings to deal with the concepts they do not handle. The general approach is similar in flavor to those of a small number of other metaphor researchers, but we provide a more extensive analysis, additional principles and a more thorough-going implementation. The approach contains a number of “view-neutral mapping adjuncts, ” which are default mapping principles that enable important source-domain aspects to be mapped to the target domain, independently of which specific metaphorical views are in play. Many discussions of metaphor appear to assume that such mapping actions occur, but rarely address them systematically and explicitly. In addition, in the approach, a conceptual metaphor can consist not only of a between-domain mapping but also of special, ancillary assumptions that serve to enrich the source domain with specific de-tails needed by the metaphors. The implemented system supports ancillary assumptions but currently only has a preliminary handling of view-neutral mapping adjuncts. 2 1

Abstract: The interpretation of metaphorical utterances often results in the attribution of emergent properties, which are neither standardly associated with the individual constituents in isolation nor derivable by standard rules of semantic composition. An adequate pragmatic account of metaphor interpretation must explain how these properties are derived. Using the framework of relevance theory, we propose a wholly inferential account, and argue that the derivation of emergent properties involves no special interpretive mechanisms not required for the interpretation of ordinary, literal utterances.

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