There is a resurgence of interest in mental models due to advances in our understanding of how they can be used to help design and due to the development of practical methods to elicit them. This book brings both areas together with a focus on reducing domestic energy consumption. The book focuses on how mental models can be applied in design to bring out behaviour change resulting in increased achievement of home heating goals (reduced waste and improved comfort). This book also offers a method to extract and apply mental models to interface design. The approach enables mental models to be applied across domains when behaviour change was sought, and is validated as a useful design method.
In 1966 the first meeting of the Association for the Study of Attention and Performance was held in the Netherlands to promote the emerging science of cognitive psychology. This volume is based on the most recent conference, held in Israel thirty years later. The focus of the conference was the interaction between theory and application. The organizers chose the specific topic, cognitive regulation of performance, because it is an area where contemporary theories of cognitive processes meet the everyday challenges posed by human interactions with complex systems. Present-day technological systems impose on the operator a variety of supervisory functions, such as input and output monitoring, allocation of cognitive resources, choice of strategies, and regulation of cognitive operations. A challenge for engineers and designers is to accommodate the cognitive requirements called for by these systems. The book is divided into four sections: the presentation and representation of information, cognitive regulation of acquisition and performance, consciousness and behavior, and special populations: aging and neurological disorders. Contributors Nicole D. Anderson, Moshe Bar, Lynn Bardell, Alice E. Barnes, Irving Biederman, Robert A. Bjork, Richard A. Block, Fergus I. M. Craik, Heiner Deubel, John Dunlosky, Ido Erev, Ronald Fisher, John M. Flach, Barry Goettl, Morris Goldsmith, Daniel Gopher, Lynn Hasher, Okihide Hikosaka, Larry L. Jacoby, Peter Kalocsai, Colleen Kelley, David E. Kieras, Roberta Klatzky, Asher Koriat, Arthur F. Kramer, Elisabetta Ladavas, John L. Larish, Susan J. Lederman, John Long, Cynthia P. May, Guiliana Mazzoni, Brian McElree, David Meyer, Satoru Miyauchi, Neville Moray, Louis Narens, Thomas O. Nelson, Raymond S. Nickerson, Lynne Reder, J. Wesley Regian, Ian Robertson, Wolfgang Schneider, Christian D. Schunn, Wayne Shebilske, Shinsuke Shimojo, Suresh Subramaniam, Tom N. Trainham, Jehoshua Tsal, Timothy A. Weber, Christopher Wickens, Rose T. Zacks, Dan Zakay
This volume investigates our ability to capture, and then apply, expertise. In recent years, expertise has come to be regarded as an increasingly valuable and surprisingly elusive resource. Experts, who were the sole active dispensers of certain kinds of knowledge in the days before AI, have themselves become the objects of empirical inquiry, in which their knowledge is elicited and studied -- by knowledge engineers, experimental psychologists, applied psychologists, or other experts -- involved in the development of expert systems. This book achieves a marriage between experimentalists, applied scientists, and theoreticians who deal with expertise. It envisions the benefits to society of an advanced technology for capturing and disseminating the knowledge and skills of the best corporate managers, the most seasoned pilots, and the most renowned medical diagnosticians. This book should be of interest to psychologists as well as to knowledge engineers who are "out in the trenches" developing expert systems, and anyone pondering the nature of expertise and the question of how it can be elicited and studied scientifically. The book's scope and the pivotal concepts that it elucidates and appraises, as well as the extensive categorized bibliographies it includes, make this volume a landmark in the field of expert systems and AI as well as the field of applied experimental psychology.
Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage.
M. David Merrill has been active in the field of instructional technology for almost 40 years. His contributions range from basic instructional principles and instructional design theory to development and implementation of learning environments. Innovations in Instructional Technology is a collection of original essays written by leading scholars and practitioners who have worked with and been inspired by Professor Merrill. The chapters in this book represent a sampling of key innovations in the instructional technology field and include knowledge of how people learn, how people solve problems, how designers conceptualize learning spaces, how teachers implement learning activities, and how evaluators assess outcomes. This volume is divided into five basic areas of research in instructional technology, mirroring the diverse contributions of Dr. Merrill's work: *four chapters on learning objects and the notion of reusable components; *three chapters that discuss fundamental aspects of learning and the design of instruction; *three chapters that address innovations in the area of assessment, evaluation, and model validation; *three chapters that concern theories of learning and instruction; and *three chapters on instructional design practice. The book concludes with a chapter outlining Dr. Merrill's responses to challenges, comments, and questions on the future of the field--ranging from the notion of initial passions with regard to instructional technology to connections between theory and practice to questions of conscience--from an expert panel comprised of many of the contributors to the book. As Dave Merrill's work will continue to be required reading for students of instructional technology, Innovations in Instructional Technology is a book that will appeal to students, researchers, and practitioners in the field.
At present, there is a general consensus on the nature of learning programming, but there are different opinions on what forms an effective environment for it. It is generally recognized that the development of a mental model is a formidable task for the student and that learning programming is a complex activity that depends heavily on metacognitive skills. This book, based on a NATO workshop, presents both pure cognitive models and experimental learning environments, and discusses what characteristics can make a learning model effective, especially in relation to the learning environment (natural or computerized). The papers cover cognitive models related to different aspects of programming, classes of learners, and types of environment, and are organized in three groups: theoretical and empirical studies on understanding programming, environments for learning programming, and learning programming in school environments. Comprehension, design, construction, testing, debugging, and verification are recognized as interdependent skills, which require complicated analysis and may develop independently, and indifferent orders, in novices. This book shows that there is unlikely to be asingle path from novice to expert and that the structure of the final product (the program) may not constrain the process by which it comes into being as much as some would advocate.
What the Book Is About This book is about the problem of organizational learning, that is the analysis of organizations conceived as learning systems. In order to survive in a period of a rapid change, organizations must innovate and than to develop and exploit their abilities to learn. The most innovative organizations are those that can respond with great efficiency to internal and external changes. They respond to and generate technological change by acting as effective learning systems. They maximize the learning potential of ongoing and "normal" work activities. The organizational structure and the technology allow members to learn while the organizations itself learns from its members. So organizations reach high levels of innovation when structured to take advantage of the social, distributed, participative, situated processes of learning developed by its members in interaction with the technological environment. Organizations should consider learning as an explicit "productive" objective. They must create integrated learning mechanisms, that encompass technological tools, reward and incentive systems, human resource practices, belief systems, access to information, communication and mobility patterns, performance appraisal systems, organizational practices and structures. The design of efficient learning organizations requires cognitive, technological and social analyses. All the computer-based technologies (e. g. office automation, communication and group decision support) not only those devoted to and used in training activities, have to be considered as tools for organizational learning and innovation.
A clear, concise introduction to the quickly growing field of complexity science that explains its conceptual and mathematical foundations What is a complex system? Although "complexity science" is used to understand phenomena as diverse as the behavior of honeybees, the economic markets, the human brain, and the climate, there is no agreement about its foundations. In this introduction for students, academics, and general readers, philosopher of science James Ladyman and physicist Karoline Wiesner develop an account of complexity that brings the different concepts and mathematical measures applied to complex systems into a single framework. They introduce the different features of complex systems, discuss different conceptions of complexity, and develop their own account. They explain why complexity science is so important in today's world.
