Vol inclu all ppers & postrs presntd at 2000 Cog Sci mtg & summaries of symposia & invitd addresses. Dealg wth issues of representg & modelg cog procsses, appeals to scholars in all subdiscip tht comprise cog sci: psy, compu sci, neuro sci, ling, & philo

This volume features the complete text of the material presented at the Twenty-Fourth Annual Conference of the Cognitive Science Society. 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. The volume includes all papers, posters, and summaries of symposia presented at this leading conference that brings cognitive scientists together. The 2002 meeting dealt with issues of representing and modeling cognitive processes as they appeal to scholars in all subdisciplines that comprise cognitive science: psychology, computer science, neuroscience, linguistics, and philosophy.

This book presents the complete collection of peer-reviewed presentations at the 1999 Cognitive Science Society meeting, including papers, poster abstracts, and descriptions of conference symposia. For students and researchers in all areas of cognitive science.

Vol. includes all papers and posters presented at 2001 Cog Sci Mtg & summaries of symposia & invited addresses. Deals w/ issues of repres & model'g cog processes. Appeals to scholars in subdisciplines that comprise Cog Sci: Psych, Computr Sci, Neuro, Lin

This volume features the complete text of the material presented at the Twenty-Fifth Annual Conference of the Cognitive Science Society. 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. This volume includes all papers, posters, and summaries of symposia presented at the leading conference that brings cognitive scientists together. The theme of this year's conference was the social, cultural, and contextual elements of cognition, including topics on collaboration, cultural learning, distributed cognition, and interaction.

In this book, a number of experts from various disciplines take a look at three different strands in learning to model. They examine the activity of modeling from disparate theoretical standpoints, taking into account the individual situation of the individuals involved. The chapters seek to bridge the modeling of communication and the modeling of particular scientific domains. In so doing, they seek to throw light on the educational communication that goes on in conceptual learning. Taken together, the chapters brought together in this volume illustrate the diversity and vivacity of research on a relatively neglected, yet crucially important aspect of education across disciplines: learning to model. A common thread across the research presented is the view that communication and interaction, as fundamental to most educational practices and as a repository of conceptual understanding and a learning mechanism in itself, is intimately linked to elaborating meaningful, coherent, and valid representations of the world. The editors hope this volume will contribute to both the fundamental research in its field and ultimately provide results that can be of practical value in designing new situations for teaching and learning modeling, particularly those involving computers.

This collection weitten by leading figures in cognitive science includes their lively debates with Dartnall about his call for a new epistemology, an alternative to the standard representational story in cognitive science. Dartnall aims to show that new epistemology is already with us in some leading-edge models of human creativity. Such an epistemology steers a middle road between the representationism of classical cognitive science and a radical anti-representationism that denies the existence or importance of representations. Dartnall, who debates contributors at each chapter's end, believes that creativity inheres—not only in big ticket items such as plays, poems, or sonatas—but in our ability to produce cognitive content at all, so that representations are the creative products of our knowledge, rather than its passive carriers.

The Psychology of Learning and Motivation publishes empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditioning to complex learning and problem solving. Volume 49 contains chapters on short-term memory, theory and measurement of working memory capacity limits, development of perceptual grouping in infancy, co-constructing conceptual domains through family conversations and activities, the concrete substrates of abstract rule use, ambiguity, accessibility, and a division of labor for communicative success, and lexical expertise and reading skill.

An argument that qualitative representations—symbolic representations that carve continuous phenomena into meaningful units—are central to human cognition. In this book, Kenneth Forbus proposes that qualitative representations hold the key to one of the deepest mysteries of cognitive science: how we reason and learn about the continuous phenomena surrounding us. Forbus argues that qualitative representations—symbolic representations that carve continuous phenomena into meaningful units—are central to human cognition. Qualitative representations provide a basis for commonsense reasoning, because they enable practical reasoning with very little data; this makes qualitative representations a useful component of natural language semantics. Qualitative representations also provide a foundation for expert reasoning in science and engineering by making explicit the broad categories of things that might happen and enabling causal models that help guide the application of more quantitative knowledge as needed. Qualitative representations are important for creating more human-like artificial intelligence systems with capabilities for spatial reasoning, vision, question answering, and understanding natural language. Forbus discusses, among other topics, basic ideas of knowledge representation and reasoning; qualitative process theory; qualitative simulation and reasoning about change; compositional modeling; qualitative spatial reasoning; and learning and conceptual change. His argument is notable both for presenting an approach to qualitative reasoning in which analogical reasoning and learning play crucial roles and for marshaling a wide variety of evidence, including the performance of AI systems. Cognitive scientists will find Forbus's account of qualitative representations illuminating; AI scientists will value Forbus's new approach to qualitative representations and the overview he offers.

In Order to Learn shows how order effects are crucial in human learning, instructional design, machine learning, and both symbolic and connectionist cognitive models. Each chapter explains a different aspect of how the order in which material is presented can strongly influence what is learned by humans and theoretical models of learning in a variety of domains. In addition to data, models are provided that predict and describe order effects and analyze how and when they will occur.