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

Similarity and analogy are fundamental in human cognition. They are crucial for recognition and classification, and have been associated with scientific discovery and creativity. Any adequate understanding of similarity and analogy requires the integration of theory and data from diverse domains. This interdisciplinary volume explores current development in research and theory from psychological, computational, and educational perspectives, and considers their implications for learning and instruction. The distinguished contributors examine the psychological processes involved in reasoning by similarity and analogy, the computational problems encountered in simulating analogical processing in problem solving, and the conditions promoting the application of analogical reasoning in everyday situations.

For a long time researchers have believed that children are incapable of reasoning by analogy. This book argues that this is far from the case, and that analogical reasoning may be available very early in development.

Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.

For this book, the editors invited and called for contributions from indispensable research areas relevant to "chance discovery," which has been defined as the discovery of events significant for making a decision, and studied since 2000. From respective research areas as artificial intelligence, mathematics, cognitive science, medical science, risk management, methodologies for design and communication, the invited and selected authors in this book present their particular approaches to chance discovery. The chapters here show contributions to identifying rare or hidden events and explaining their significance, predicting future trends, communications for scenario development in marketing and design, identification effects and side-effects of medicines, etc. The methods presented in this book are based on the interaction of human, machine, and human's living environment, rather than based purely automated predictions of the future. This is a promising direction of computer-supported decision of human in a radically changing environment.

Analogy—recalling familiar past situations to deal with novel ones—is a mental tool that everyone uses. Analogy can provide invaluable creative insights, but it can also lead to dangerous errors. In Mental Leaps two leading cognitive scientists show how analogy works and how it can be used most effectively. Keith Holyoak and Paul Thagard provide a unified, comprehensive account of the diverse operations and applications of analogy, including problem solving, decision making, explanation, and communication. Holyoak and Thagard present their own theory of analogy, considering its implications for cognitive science in general, and survey examples from many other domains. These include animal cognition, developmental and social psychology, political science, philosophy, history of science, anthropology, and literature. Understanding how we draw analogies is important for people interested in the evolution of thinking in animals and in children; for those whose focus is on either creative thinking or errors of everyday reasoning; for those concerned with how decisions are made in law, business, and politics; and for those striving to improve education. Mental Leaps covers all of this ground, emphasizing the principles that govern the use of analogy and keeping technical matters to a minimum. A Bradford Book