Cognitive Engineering Examples

[Prospero Barela]

Structuredas a four-part cycle of gather, analyze, design, and evaluate, the cognitive engineering toolkit methods and lessons-learned outlined in this report provide guidance on how to ensure technology innovation effectively supports human cognitive work in high consequence mission environments.

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Cognitive engineering is a user-centred approach to systems design that emphasises the study of how users think and reason rather than just what they do when using systems or tools to perform tasks such as controlling a process.

The aim of this approach is to develop systems that will have the range of capabilities needed by a human operator to control and manage the variety of demands that the controlled system can present the user.

Cognitive engineering draws from fields such as psychology, human factors, human-computer interaction, and socio-technical systems, using methods such as cognitive task analysis and cognitive work analysis. Cognitive engineers first seek to understand the nature of the problems and identifying how human expertise is applied to these problems. Such expertise can be described in terms of cognitive strategies. Cognitive engineers also seek to model the cognitive work encountered in complex dynamic work domains using methods such as cognitive work analysis. Using this knowledge, designers can then apply principles from representation design (e.g. Bennett & Flach, 2011) to create user interfaces and visual representations that are compatible with the work and strategies that enables users to maximise the application of their expertise in that particular domain.

We have found this approach to be very useful. For example, during the FP7 VALCRI research project, we used it to help us define the requirements that drive the design of tools and interfaces for criminal intelligence analysts and investigators to design functions that augment human intelligence. Cognitive engineering methods make it possible to identify the needed complementarity between human cognition and machine algorithms to create better human-machine teams.

Health information systems are rapidly being implemented in a variety of healthcare contexts, including Emergency Departments (EDs). These systems offer promising solutions to challenges related to cost, efficiency, patient safety, and medical errors.

Our goal is to provide a fundamental and comprehensive picture of the difficult sensemaking, decision-making, and planning/replanning tasks in the ED, along with the individual and team expertise required to meet those challenges.

Additionally, the research will develop and evaluate exemplar solutions for a targeted set of needs that will be identified through the cognitive engineering analysis, thus providing a methodological example and "proof-of-concept" for translating cognitive engineering analyses into designs.

We would like to thank our internal research team, MedStar Health collaborators, and partners from the Department of Industrial Engineering at the University at Buffalo State University of New York, Roth Cognitive Engineering, and Department of Emergency Medicine at the University of Florida for their support, dedication, and teamwork in completing this research in an exemplary fashion.

The overview above reflects work completed while the MedStar Health National Center for Human Factors in Healthcare was part of the MedStar Institute for Innovation. In July 2020, the Human Factors Center transitioned to its new organizational home, MedStar Health Research Institute, and still remains a key collaborator of MI2. Visit the Human Factors Center website for the latest information on its work.

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 challenge for cognitive engineers is to better understand the reasons underlying these reservations and to overcome them by demonstrating and communicating more effectively their concepts, approaches, and proposed solutions. To contribute to this goal, the current volume presents concrete examples of cognitive engineering research and design. It is an attempt to complement the already existing excellent literature on cognitive engineering in domains other than aviation and to introduce professionals and students in a variety of domains to this rather young discipline.

The editors of this book, and the authors whose work is included, subscribe to the need to evaluate work in context. Accepting new paradigms for the study of humans working in complex environments, they view the human as an asset--indeed a necessity--in human-machine systems and they accept and take advantage of variations in human behavior. In addition, they recognize that much or most error is the result of mismatches between human capabilities and the demands placed on those humans by the machines which they use in the environments in which they are placed. As a whole, this volume illustrates how far we've come in understanding the cognitive bases of human work in complex human-machine systems.

Cognitive systems engineering (CSE) is a field of study that examines the intersection of people, work, and technology, with a focus on safety-critical systems. The central tenet of cognitive systems engineering is that it views a collection of people and technology as a single unit that is capable of cognitive work, which is called a joint cognitive system.[1]

CSE draws on concepts from cognitive psychology and cognitive anthropology, such as Edwin Hutchins's distributed cognition, James Gibson's ecological theory of visual perception, Ulric Neisser's perceptual cycle, and William Clancey's situated cognition.[2] CSE techniques include cognitive task analysis[3] and cognitive work analysis.[4]

Cognitive systems engineering emerged in the wake of the Three Mile Island (TMI) accident.[5] At the time, existing theories about safety were unable to explain how the operators at TMI could be confused about what was actually happening inside of the plant.[6]

Following the accident, Jens Rasmussen did early research on cognitive aspects of nuclear power plant control rooms.[7] This work influenced a generation of researchers who would later come to be associated with cognitive systems engineering, including Morten Lind, Erik Hollnagel, and David Woods.[5]

Following the publication of a textbook on cognitive systems engineering by Kim Vicente in 1999 the techniques employed to establish a cognitive work analysis (CWA) were used to aid the design of any kind of system were humans have to interact with technology. The tools outlined by Vicente were not tried and tested, and there are few if any published accounts of the five phases of analysis being implemented.[8]

Although the term cognitive engineering had already been introduced by Don Norman, Hollnagel and Woods deliberately introduced new terminology. They were unhappy with the framing of the term cognitive engineering, which they felt focused too much on improving the interaction between humans and computers, through the application of cognitive science. Instead, Hollnagel and Woods wished to emphasize a shift in focus from human-computer interaction to joint cognitive systems as the unit of analysis.[9]

As mentioned in the Origins section above, one of the key tenets of cognitive systems engineering is that the base unit of analysis is the joint cognitive system. Instead of viewing cognitive tasks as being done only by individuals, CSE views cognitive work as being accomplished by a collection of people coordinating with each other and using technology to jointly perform cognitive work as a system.[1]

CSE researchers focus their studies on work in situ, as opposed to studying how work is done in controlled laboratory environments.[11] This research approach, known as macrocognition, is similar to the one taken by naturalistic decision-making. Examples of studies of work done in context include Julian Orr's ethnographic studies of copy machine technicians,[12] Lucy Suchman's ethnographic studies of how people use photocopiers,[13] Diane Vaughan's study of engineering work at NASA in the wake of the Space Shuttle Challenger disaster,[14] and Scott Snook's study of military work in the wake of the 1994 Black Hawk shootdown incident.[15]

A general thread that runs through cognitive systems engineering research is the question of how to design joint cognitive systems that can deal effectively with complexity, including common patterns in how such systems can fail to deal effectively with complexity.[16][11][17][18]

As mentioned in the Origins section above, CSE researchers were influenced by TMI. One specific application of coping with complexity is the work that human operators must do when they are supervising a process such as nuclear power plant, and they must then deal with a problem that arises. This work is sometimes known as anomaly response[11][19] or dynamic fault management.[20] This type of work often involves uncertainty, quickly changing conditions, and risk tradeoffs in deciding what remediation actions to take.

Because joint cognitive systems involve multiple agents that must work together to complete cognitive tasks, coordination is another topic of interest in CSE. One specific example is the notion of common ground[21] and its implications for building software that can contribute effectively as agents in a joint cognitive system.[22]

CSE researchers study how people use technology to support cognitive work and coordinate this work across multiple people. Examples of such cognitive artifacts, which have been studied by researchers, include "the bed book" used in intensive care units,[23] "voice loops" used in space operations,[24] "speed bugs" used in aviation,[25] drawings and sketches in engineering work,[26] and the various tools used in marine navigation.[27]

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