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

Abstract not found

this article should be addressed to Robert Goldstone, Psychology Department, Indiana University, Bloomington, Indiana 47405. Electronic mail may be sent to rgoldsto @ ucs.indiana.edu

It is argued that working memory limitations are best defined in terms of the complexity of relations that can be processed in parallel. Relational complexity is related to processing loads in problem solving, and discriminates between higher animal species, as well as between children of different ages. Complexity is defined by the number of dimensions, or sources of variation, that are related. A unary relation has one argument and one source of variation, because its argument can be instantiated in only one way at a time. A binary relation has two arguments, and two sources of variation, because two argument instantiations are possible at once. Similarly, a ternary relation is three dimensional, a quaternary relation is four dimensional, and so on. Dimensionality is related to number of chunks, because both attributes on dimensions and chunks are independent units of information of arbitrary size. Empirical studies of working memory limitations indicate a soft limit which corresponds to processing one quaternary relation in parallel. More complex concepts are processed by segmentation or conceptual chunking. Segmentation entails breaking tasks into components which do not exceed processing capacity, and which are processed serially. Conceptual chunking entails "collapsing" representations to reduce their dimensionality and consequently their processing load, but at the cost of making some relational information inaccessible. Parallel distributed processing implementations of relational representations show that relations with more arguments entail a higher computational cost, which corresponds to empirical observations of higher processing loads in humans. Empirical evidence is presented that relational complexity discriminates between higher species...

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

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

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.

Retrieving relevant cases is a crucial component of case-based reasoning systems. The task is to use user-defined query to retrieve useful information, i.e., exact matches or partial matches which are close to query-defined request according to certain measures. The difficulty stems from the fact that it may not be easy (or it may be even impossible) to specify query requests precisely and completely - resulting in a situation known as a fuzzy-querying. It is usually not a problem for small domains, but for a large repositories which store various information (multifunctional information bases or a federated databases), a request specification becomes a bottleneck. Thus, a flexible retrieval algorithm is required, allowing for imprecise query specification and for changing the viewpoint. Efficient database techniques exists for locating exact matches. Finding relevant partial matches might be a problem. This document proposes a context-based similarity as a basis for flexible retrieval...

While most approaches to similarity assessment are oblivious of knowledge and goals, there is ample evidence that these elements of problem solving play an important role in similarity judgements. This paper is concerned with an approach for integrating assessment of similarity into a framework of problem solving that embodies central notions of problem solving like goals, knowledge and learning. We review empirical findings that unravel characteristics of similarity assessment most of which have not been covered by purely syntactic models of similarity. A formal account of similarity assessment that allows for the integration of central ideas of problem solving is developed. Given a goal and a domain theory, an appropriate perspective is taken that brings into focus only goal-relevant features of a problem description as input to similarity assessment. Key words: problem solving, case-based reasoning, similarity, EBG 1 Introduction In recent years, there has been an upsurge of inter...

The dominating models of information processes have been based on symbolic representations of information and knowledge. During the last decades, a varietyof non-symbolic models have been proposed as superior. The prime examples of models within the non-symbolic approach are neural networks. However, to a large extent theylack a higher-level theoryof representation. In this paper, conceptual spaces are suggested as an appropriate framework for non-symbolic models. Conceptual spaces consist of a number of “qualitydimensions ” that often are derived from perceptual mechanisms. It will be outlined how conceptual spaces can represent various kind of information and how theycan be used to describe concept learning. The connections to prototype theory will also be presented.