Analogical Modeling (AM) is an exemplar-based general theory of description that uses both neighbors and non-neighbors (under certain well-defined conditions of homogeneity) to predict language behavior. This book provides a basic introduction to AM, compares the theory with nearest-neighbor approaches, and discusses the most recent advances in the theory, including psycholinguistic evidence, applications to specific languages, the problem of categorization, and how AM relates to alternative approaches of language description (such as instance families, neural nets, connectionism, and optimality theory). The book closes with a thorough examination of the problem of the exponential explosion, an inherent difficulty in AM (and in fact all theories of language description). Quantum computing (based on quantum mechanics with its inherent simultaneity and reversibility) provides a precise and natural solution to the exponential explosion in AM. Finally, an extensive appendix provides three tutorials for running the AM computer program (available online).

1. Structuralist Versus Analogical Descriptions ONE important purpose of this book is to compare two completely dif ferent approaches to describing language. The first of these approaches, commonly called stnlctllralist, is the traditional method for describing behavior. Its methods are found in many diverse fields - from biological taxonomy to literary criticism. A structuralist description can be broadly characterized as a system of classification. The fundamental question that a structuralist description attempts to answer is how a general contextual space should be partitioned. For each context in the partition, a rule is defined. The rule either specifies the behavior of that context or (as in a taxonomy) assigns a name to that context. Structuralists have implicitly assumed that descriptions of behavior should not only be correct, but should also minimize the number of rules and permit only the simplest possible contextual specifications. It turns out that these intuitive notions can actually be derived from more fundamental statements about the uncertainty of rule systems. Traditionally, linguistic analyses have been based on the idea that a language is a system of rules. Saussure, of course, is well known as an early proponent of linguistic structuralism, as exemplified by his characterization of language as "a self-contained whole and principle of classification" (Saussure 1966:9). Yet linguistic structuralism did not originate with Saussure - nor did it end with "American structuralism".

1. Structuralist Versus Analogical Descriptions ONE important purpose of this book is to compare two completely dif ferent approaches to describing language. The first of these approaches, commonly called stnlctllralist, is the traditional method for describing behavior. Its methods are found in many diverse fields - from biological taxonomy to literary criticism. A structuralist description can be broadly characterized as a system of classification. The fundamental question that a structuralist description attempts to answer is how a general contextual space should be partitioned. For each context in the partition, a rule is defined. The rule either specifies the behavior of that context or (as in a taxonomy) assigns a name to that context. Structuralists have implicitly assumed that descriptions of behavior should not only be correct, but should also minimize the number of rules and permit only the simplest possible contextual specifications. It turns out that these intuitive notions can actually be derived from more fundamental statements about the uncertainty of rule systems. Traditionally, linguistic analyses have been based on the idea that a language is a system of rules. Saussure, of course, is well known as an early proponent of linguistic structuralism, as exemplified by his characterization of language as "a self-contained whole and principle of classification" (Saussure 1966:9). Yet linguistic structuralism did not originate with Saussure - nor did it end with "American structuralism".

Analogy has been the focus of extensive research in cognitive science over the past two decades. Through analogy, novel situations and problems can be understood in terms of familiar ones. Indeed, a case can be made for analogical processing as the very core of cognition. This is the first book to span the full range of disciplines concerned with analogy. Its contributors represent cognitive, developmental, and comparative psychology; neuroscience; artificial intelligence; linguistics; and philosophy. The book is divided into three parts. The first part describes computational models of analogy as well as their relation to computational models of other cognitive processes. The second part addresses the role of analogy in a wide range of cognitive tasks, such as forming complex cognitive structures, conveying emotion, making decisions, and solving problems. The third part looks at the development of analogy in children and the possible use of analogy in nonhuman primates. Contributors Miriam Bassok, Consuelo B. Boronat, Brian Bowdle, Fintan Costello, Kevin Dunbar, Gilles Fauconnier, Kenneth D. Forbus, Dedre Gentner, Usha Goswami, Brett Gray, Graeme S. Halford, Douglas Hofstadter, Keith J. Holyoak, John E. Hummel, Mark T. Keane, Boicho N. Kokinov, Arthur B. Markman, C. Page Moreau, David L. Oden, Alexander A. Petrov, Steven Phillips, David Premack, Cameron Shelley, Paul Thagard, Roger K.R. Thompson, William H. Wilson, Phillip Wolff

The International Conference on Cognitive Modeling brings together researchers who develop computational models to explain and predict cognitive data. The core theme of the 2004 conference was "Integrating Computational Models," encompassing an integration of diverse data through models of coherent phenomena; integration across modeling approaches; and integration of teaching and modeling. This text presents the proceedings of that conference. The International Conference on Cognitive Modeling 2004 sought to grow the discipline of computational cognitive modeling by providing a sophisticated modeling audience for cutting-edge researchers, in addition to offering a forum for integrating insights across alternative modeling approaches in both basic research and applied settings, and a venue for planning the future growth of the discipline. The meeting included a careful peer-review process of 6-page paper submissions; poster-abstracts to include late-breaking work in the area; prizes for best papers; a doctoral consortium; and competitive modeling symposia that compare and contrast different approaches to the same phenomena.

The psychologist William James observed that "a native talent for perceiving analogies is... the leading fact in genius of every order." The centrality and the ubiquity of analogy in creative thought have been noted again and again by scientists, artists, and writers, and understanding and modeling analogical thought have emerged as two of the most important challenges for cognitive science.Analogy-Making as Perception is based on the premise that analogy-making is fundamentally a high-level perceptual process in which the interaction of perception and concepts gives rise to "conceptual slippages" which allow analogies to be made. It describes Copycat - a computer model of analogymaking, developed by the author with Douglas Hofstadter, that models the complex, subconscious interaction between perception and concepts that underlies the creation of analogies.In Copycat, both concepts and high-level perception are emergent phenomena, arising from large numbers of low-level, parallel, non-deterministic activities. In the spectrum of cognitive modeling approaches, Copycat occupies a unique intermediate position between symbolic systems and connectionist systems a position that is at present the most useful one for understanding the fluidity of concepts and high-level perception.On one level the work described here is about analogy-making, but on another level it is about cognition in general. It explores such issues as the nature of concepts and perception and the emergence of highly flexible concepts from a lower-level "subcognitive" substrate.Melanie Mitchell, Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, is a Fellow of the Michigan Society of Fellows. She is also Director of the Adaptive Computation Program at the Santa Fe Institute.

The International Conference on Cognitive Modeling brings together researchers who develop computational models that explain and predict cognitive data. The 2004 conference encompassed an integration of diverse data through models of coherent phenomena;

This volume features the complete text of the material presented at the Nineteenth Annual Conference of the Cognitive Science Society. Papers have been loosely grouped by topic and an author index is provided in the back. As in previous years, the symposium included an interesting mixture of papers on many topics from researchers with diverse backgrounds and different goals, presenting a multifaceted view of cognitive science. In hopes of facilitating searches of this work, an electronic index on the Internet's World Wide Web is provided. Titles, authors, and summaries of all the papers published here have been placed in an online database which may be freely searched by anyone. You can reach the web site at: www-csli.stanford.edu/cogsci97.

This book brings together powerful ideas and new developments from internationally recognised scholars and classroom practitioners to provide theoretical and practical knowledge to inform progress in science education. This is achieved through a series of related chapters reporting research on analogy and metaphor in science education. Throughout the book, contributors not only highlight successful applications of analogies and metaphors, but also foreshadow exciting developments for research and practice. Themes include metaphor and analogy: best practice, as reasoning; for learning; applications in teacher development; in science education research; philosophical and theoretical foundations. Accordingly, the book is likely to appeal to a wide audience of science educators –classroom practitioners, student teachers, teacher educators and researchers.

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