Einstein said that "the whole of science is nothing more than a refinement of everyday thinking." David Klahr suggests that we now know enough about cognition—and hence about everyday thinking—to advance our understanding of scientific thinking. In this book he sets out to describe the cognitive and developmental processes that have enabled scientists to make the discoveries that comprise the body of information we call "scientific knowledge."
Over the past decade Klahr and his colleagues have conducted extensive laboratory experiments in which they create discovery contexts, computer-based environments, to evoke the kind of thinking characteristic of scientific discovery in the "real world." In attempting to solve the problems posed by the discovery tasks, experiment participants (from preschoolers through university students, as well as laypersons) use many of the same higher-order cognitive processes used by practicing scientists. Through this work Klahr integrates two disparate approaches—the content-based approach and the process-based approach—to present a comprehensive model of the psychology of scientific discovery.
Cognitive psychologists have found the production systems class of computer simulation models to be one of the most direct ways to cast complex theories of human intelligence. There have been many scattered studies on production systems since they were first proposed as computational models of human problem-solving behavior by Allen Newell some twenty years ago, but this is the first book to focus exclusively on these important models of human cognition, collecting and giving many of the best examples of current research.In the first chapter, Robert Neches, Pat Langley, and David Klahr provide an overview of the fundamental issues involved in using production systems as a medium for theorizing about cognitive processes, emphasizing their theoretical power.The remaining chapters take up learning by doing and learning by understanding, discrimination learning, learning through incremental refinement, learning by chunking, procedural earning, and learning by composition. A model of cognitive development called BAIRN is described, and a final chapter reviews John Anderson's ACT theory and discusses how it can be used in intelligent tutoring systems, including one that teaches LISP programming skills.In addition to the editors, the contributors are Yuichiro Anzai (Hokkaido University, Japan), Paul Rosenbloom (Stanford) and Allen Newell (Carnegie-Mellon), Stellan Ohlsson (University of Pittsburgh), Clayton Lewis (University of Colorado, Boulder), Iain Wallace and Kevin Bluff (Deakon University, Australia), and John Anderson (Carnegie-Mellon).David Klahr is Professor and Head of the Department of Psychology at Carnegie-Mellon University. Pat Langley is Associate Professor, Department of Information and Computer Science, University of California, Irvine, and Robert Neches is Research Computer Scientist at University of Southern California Information Sciences Institute. Production System Models of Learning and Development is included in the series Computational Models of Cognition and Perception, edited by Jerome A. Feldman, Patrick J. Hayes, and David E.Rumelhart. A Bradford Book.