Cognitive Systems Engineering

This volume provides an exceptional perspective on the nature, evolution, contributions and future of the field of Cognitive Systems Engineering (CSE). It is a resource to support both the teaching and practice of CSE. It accomplishes this through its organization into two complementary approaches to the topic. The first is an historical perspective: In the retrospections of leaders of the field, what have been the seminal achievements of cognitive human factors? What are the "lessons learned" that became foundational to CSE, and how did that foundation evolve into a broader systems view of cognitive work? The second perspective is both pedagogical and future-looking: What are the major conceptual issues that have to be addressed by CSE and how can a new generation of researchers be prepared to further advance CSE? Topics include studies of expertise, cognitive work analysis, cognitive task analysis, human performance, system design, cognitive modeling, decision making, human-computer interaction, trust in automation, teamwork and ecological interface design. A thematic focus will be on systems-level analysis, and such notions as resilience engineering and systems-level measurement. The book features broad coverage of many of the domains to which CSE is being applied, among them industrial process control, health care, decision aiding and aviation human factors. The book’s contributions are provided by an extraordinary group of leaders and pathfinders in applied psychology, cognitive science, systems analysis and system design. In combination these chapters present invaluable insights, experiences and continuing uncertainties on the subject of the field of CSE, and in doing so honor the career and achievements of Professor David D. Woods of Ohio State University.

Nothing has been more prolific over the past century than human/machine interaction. Automobiles, telephones, computers, manufacturing machines, robots, office equipment, machines large and small; all affect the very essence of our daily lives. However, this interaction has not always been efficient or easy and has at times turned fairly hazardous.

Our fascination with new technologies is based on the assumption that more powerful automation will overcome human limitations and make our systems 'faster, better, cheaper,' resulting in simple, easy tasks for people. But how does new technology and more powerful automation change our work? Research in Cognitive Systems Engineering (CSE) l

Powerful information technologies and the complex support systems they engender are evolving faster than people’s ability to adjust to them. In the workplace, this leads to troublesome task performance, added stress on users, increased organizational inefficiency, and, in some cases, a heightened risk of wide-scale .disaster. In the marketplace, it makes for consumer dissatisfaction. Clearly, traditional human-computer interaction (HCI) and system design (SD) solutions to this dilemma have proven woefully inadequate. What is needed is a fresh multidisciplinary approach offering a broader, more dynamic framework for assessing needs and designing usable, efficient systems. Taking modeling concepts from engineering, psychology, cognitive science, information science, and computer science, cognitive systems engineering (CSE) provides such a framework. This book is the first comprehensive guide to the emerging new field of CSE. Providing equal parts theory and practice, it is based on the authors’ many years of experience with work systems in a wide range of work domains, including process control, manufacturing, hospitals, and libraries. Throughout, the emphasis is on powerful analytical techniques that enhance the systems designer’s ability to see the "big picture," and to design for all crucial aspects of human-work interaction. Applicable to highly structured technical systems such as process plants, as well as less structured user-driven systems like libraries, these analytical techniques form the basis for the evaluation and design guidelines that make up the bulk of this book. And since the proof is in the pudding, the authors provide a chapter-length case history in which they demonstrate the success of their approach when applied to a full-scale software design project. The project, a retrieval system for public libraries, is described in detail, from field studies to concept validation experiments, and, of course, the empirical evaluation of the system while in use by the library users and personnel. Computer-based information systems are rapidly becoming a fundamental part of the human landscape. How that landscape evolves over the next decade or so, whether it becomes a hostile one or one that generously supports the needs of future generations, is in the hands of all those involved with the study and design of information systems.

This volume seeks to answer the question: "Can findings from cognitive science enhance the user-computer interaction process?" In so doing, it recognizes that user-computer interfaces (UCIs) are often essential parts of an information or decision support system -- and often critical components of software-intensive systems of all kinds. From the outset, the authors note that the design, prototyping, and evaluation of user-computer interfaces are part of larger systems and are therefore ideally designed, developed, and evaluated as part of a larger design and developmental process or "life cycle." Thus, this book describes the process by which functional, nonfunctional, or display-oriented requirements are converted first into prototypes and then into working systems. While the process may at times seem almost mysterious, there is in fact a methodology that drives the process -- a methodology that is defined in terms of an adaptive life cycle. There are a number of steps or phases that comprise the standard life cycle, as well as methods, tools and techniques that permit each step to be taken. Describing the effort to implement this process to enhance user-computer interaction, this book presents a methodological approach that seeks to identify and apply findings from cognitive science to the design, prototyping, and evaluation of user-computer interfaces.

This handbook is the first to provide comprehensive coverage of original state-of-the-science research, analysis, and design of integrated, human-technology systems.

Cognitive systems engineering has been widely and successfully applied in the design of safety critical systems such as nuclear power, aviation, and military command-and-control. More recently, these methods are being applied to the design of health and medical systems in order to improve health care quality, reduce errors and adverse events, and improve efficiencies. Cognitive Systems Engineering in Health Care provides an overview of cognitive systems engineering principles in the context of health care. It contains state-of-the-art examples of cognitive systems applications that can be adapted by health care practitioners interested in systematic engineering approaches to systems improvement. The book highlights current cognitive engineering-oriented research, analyses, and applications in settings such as cardiac surgery, obstetrics, and emergency medicine. It focuses on the impact cognitive engineering analyses can have in supporting communication and coordination with health care teams. The text then demonstrates the use of cognitive engineering methods to inform the design of information technology. It then details the systematic adaptation and application of specific cognitive engineering methods in the medical domain. The book concludes with examples of how in-depth cognitive engineering analyses can lead to demonstrated improvements in health care environments. Through a series of sample studies, the book gives you a deeper understanding of how cognitive engineering approaches might be applied in the health care domain. You’ll see common themes that underline the complexity of the health care domain and this insight can build a deep respect for the expertise of the practitioners who work in it. By identifying the abstractions that hold constant in this domain, you can build solutions for that will evolve to handle new applications, challenges, and approaches.

Although cognitive engineering has gained widespread acceptance as one of the most promising approaches to addressing and preventing difficulties with human-machine coordination and collaboration, it still meets with considerable skepticism and resistance in some of the industries that could benefit from its insights and recommendations. The challe