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A Few Implications of an Ecological Approach to Human Factors
Published in John Flach, Peter Hancock, Jeff Caird, Kim Vicente, Global Perspectives on the Ecology of Human-Machine Systems, 2018
So far, this chapter has tried to argue that adopting an ecological approach to human factors can have important implications. It can lead to models, methods, and experiments that differ considerably from those of traditional human factors. One point that has yet to be mentioned, however, is that an ecological approach to human factors already exists in the area which has come to be known as cognitive engineering (Rasmussen, 1986; Woods & Roth, 1988). Cognitive engineering deals with the human factors challenges associated with introducing information technology into complex work domains, such as power plants, air traffic control, flexible manufacturing systems, and hospitals. In dealing with these complex systems, researchers have been forced to confront the inadequacies of traditional human factors practices (Rasmussen, 1988) and in the process have independently adopted many of the fundamental tenets of the ecological approach (Flach, 1989, 1990; Vicente, 1991; Vicente & Rasmussen, 1990, 1992; Woods & Roth, 1988).
Future Directions for Cognitive Engineering
Published in Philip J. Smith, Robert R. Hoffman, Cognitive Systems Engineering, 2018
Karen M. Feigh, Zarrin K. Chua
The aim of any science is the creation of explanatory models sufficiently detailed as to enable explanation and prediction. Furthermore, the aim of any engineering discipline is the application of scientific models to the design and creation of things. We seek, as Dave has argued, not to simply inform design but to use our tools and methods to create products. Traditional physics- or chemistry-based engineering disciplines focus almost exclusively on the creation of engineered systems or technology through the application of models of the appropriate physical or chemical mechanisms. Cognitive engineering applies psychology and cognitive and computational sciences to the design of work, procedures, and technology. There is a fundamental need, therefore, to develop predictive models of various aspects of cognition. From the microcognitive elements (such as perception, attention, and memory) to macrocognitive components (such as judgment, decision making, and ideation) to meta cognition (such as situation awareness and problem solving), and through macrocognition, which stretches across individuals, explanatory models having predictive power are needed in order to anticipate and design for cognitive work.
Cognitive Engineering: Designing for Situation Awareness
Published in Eduardo Salas, Aaron S Dietz, Situational Awareness, 2017
Sometimes, information-processing approaches to human factors create the impression that the human is a collection of biases and processing limitations that greatly constrains performance of complex systems. The human is portrayed as the weak link in the system. The focus of human factors design, then, takes the perspective of protecting the system against human error. Cognitive engineering tends to view the human in a more positive light. In many cases, it is the creativity and insight of the human that makes these systems work. The air transportation system and tactical aviation systems work because of the pilots and other personnel, not in spite of them. In considering the knowledge and processing links that pilots contribute to make the system work, it is not just a question of amount—how much knowledge, how many links, and so on. There are important qualitative distinctions in terms of the type of knowledge needed for particular links. In the context of Fig. 6.4, the goal of cognitive engineering is to design systems so that the knowledge states are comprehensive and so that there are adequate links between processing stages to provide efficient and robust application of that knowledge. The goal is to engineer systems that leverage the unique capabilities for awareness that humans bring to these systems against the complex problems that arise.
A review of methodologies for integrating human factors and ergonomics in engineering design
Published in International Journal of Production Research, 2019
Xiaoguang Sun, Rémy Houssin, Jean Renaud, Mickaël Gardoni
Cognitive engineering, as well known as cognitive ergonomics in later studies, concentrates on the analysis of the cognitive process regarding diagnosis, workload, situation awareness, decision-making, and planning (Hollnagel and Woods 1983; Jones 1995; Parasuraman, Sheridan, and Wickens 2008; Lawler, Hedge, and Pavlovic-Veselinovic 2011). The objective of cognitive engineering is to improve the performance of cognitive tasks in dynamic, technology-intensive environments through designing effective support, including understanding the fundamental principles behind human action and performance associated with engineering design development principles, and developing user-friendly systems.
Muddling between science and engineering: an epistemic strategy for developing human factors and ergonomics as a hybrid discipline
Published in Theoretical Issues in Ergonomics Science, 2018
In terms of HFE, even though there are examples of engineering knowledge in terms of methods and frameworks, the engineering epistemological dimension is not widely accepted and discussions thereof are quite minimal (e.g. see Lawson 1979; Papantonopoulos 2004 for notable exceptions). In addition, sometimes due to the self-characterisation of HFE as a scientific discipline the engineering and design dimension of HFE is subdued and explicitly labelled as ‘applied science.’ An example of this trend can be obtained from the subfield of HFE, which calls itself cognitive engineering/cognitive systems engineering, where a few researchers characterise it as applied cognitive science (Woods and Roth 1988, 415; also Wilson, Helton, and Wiggins 2013): Cognitive engineering is an applied cognitive science that draws on the knowledge and techniques of cognitive psychology and related disciplines to provide the foundation for principle driven design of person-machine systems (Woods and Roth 1988, 415).Further, the overarching shadow of science2 having epistemic primacy over engineering, the engineering dimension and concepts of cognitive engineering get subdued. Therefore, as mentioned before, the problem is not that HFE does not recognise engineering, but the problem lies in the way certain aspects of HFE characterise engineering knowledge and activity specifically limited to ‘applications of science.’ It should be reiterated that the current polarity, between science and engineering, as described in the article is a necessary simplification. In actuality, the relation between science and engineering in HFE, as well as in general is extremely complex. However, the simplification serves its purpose in terms of making the differences between the two epistemologies more salient.