Abstract not found

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

Theory-based Bayesian models of inductive reasoning 2 Theory-based Bayesian models of inductive reasoning

Analogy is often viewed as a partial similarity match between domains. But not all partial similarities qualify as analogy: There must be some selection of which commonalities count. Three experiments tested o particular selection constraint in anological mapping, namely, systemoticity. That is, we tested whether a given predicate is more likely to figure in the interpretation of and prediction from on analogy if the predicate participates in a common system of relations. In Experiment 1, subjects judged two matches to be included in on analogy: an isolated match, and a match embedded in. a larger matching system. Subjects preferred the embedded match. In Experiments 2 and 3, subjects mode analogical predictions about a target domain. Subjects predicted information that followed from a causal system that matched the base domain, rather than information that was equally plausible, but that created an isolated match with the base. Results support Gentner's (1983, 1989) structure. mopping theory in that anological mopping concerns systems and not individual predicates, and that attention to shored systematic structure constrains the selection of information to include in an analogy.

Similarity-based and rule-based accounts of cognition are often portrayed as opposing accounts. In this paper we suggest that in learning and development, the process of comparison can act as a bridge between similarity-based and rule-based processing. We suggest that comparison involves a process of structural alignment and mapping between two representations. This kind

this article we outline the foundations of such a theory, working in the general framework of Bayesian inference. Much of our proposal for extending Shepard's theory to the cases of multiple examples and arbitrary stimulus structures has already been introduced in other papers (Griffiths & Tenenbaum, 2000; Tenenbaum, 1997, 1999a, 1999b; Tenenbaum & Xu, 2000). Our goal here is to make explicit the link to Shepard's work and to use our framework to make connections between his work and other models of learning (Feldman, 1997; Gluck & Shanks, 1994; Haussler, Kearns & Schapire, 1994; Kruschke, 1992; Mitchell, 1997), generalization (Nosofsky, 1986; Heit, 1998), and similarity (Chater & Hahn, 1997; Medin, Goldstone & Gentner, 1993; Tversky, 1977). In particular, we will have a lot to say about how our generalization of Shepard's theory relates to Tversky's (1977) well-known set-theoretic models of similarity. Tversky's set-theoretic approach and Shepard's metric space approach are often considered the two classic -- and classically opposed -- theories of similarity and generalization. By demonstrating close parallels between Tversky's approach and our Bayesian generalization of Shepard's approach, we hope to go some way towards unifying these two theoretical approaches and advancing the explanatory power of each. The plan of our article is as follows. In Section 2, we recast Shepard's analysis of generalization in a more general Bayesian framework, preserving the basic principles of his approach in a form that allows us to apply the theory to situations with multiple examples and arbitrary (non-spatially represented) stimulus structures. Sections 3 and 4 describe those extensions, and Section 5 concludes by discussing some implications of our theory for the internalization of...

Electronic hypertext catalogues provide an important channel for information provision. However, static hypertext documents cannot be dynamically adapted to help the user find what he/she is looking for. We demonstrate that natural language generation techniques can be used to produce tailored hypertext documents, and we focus on two key benefits of the resulting DYNAMIC HYPERTEXT. First, documents can be tailored more precisely to an individual's needs and background, thus aiding the search process. Secondly, the incorporation of techniques for comparing catalogue items allows the user to search still more effectively. We describe the automatic generation of hypertext documents containing comparisons, with illustrations from two implemented systems. KEYWORDS: adaptive hypertext, dynamic hypertext, natural language generation, user modelling, discourse history INTRODUCTION The advent of on-line distributed hypertext systems and the world wide web (WWW) has led to the extensive popul...

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

to all of the category's features. However, people's knowledge of real-world categories often consists of many "rote " features that are not related to their prior knowledge. Five experiments found that even minimal prior knowledge (1 knowledge-relevant feature and 5 rote features per exemplar) can facilitate category learning. Posttests revealed that although the knowledge aided learning, subjects also acquired the rote features that were not related to knowledge, contradicting predictions of an attentional expla-nation of the knowledge effect. The results of Experiment 6 suggested that subjects attempt to link even rote features to their knowledge.

Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference -- hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples -- can provide a complete picture of how people generalize concepts in even this simple setting. This thesis prop...