human-factors engineering

human-factors engineering: see ergonomics.

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human-factors engineering [′hyü·mən ¦fak·tərz ‚en·jə′nir·iŋ]

(engineering)

The area of knowledge dealing with the capabilities and limitations of human performance in relation to design of machines, jobs, and other modifications of the human's physical environment. Also known as human engineering.

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Human-factors engineering

The application of experimental findings in behavioral science and physiology to the design and operation of technical systems in which humans are users or operators. This includes design of hardware, software, training, and documentation as well as manufacturing and maintenance. Human-factors professionals are trained in some combination of experimental or cognitive psychology, physiology, and engineering—typically industrial, mechanical, electrical, or software engineering. Human-factors engineering seeks to ensure that humans' tools and environment are best matched to their physical size, strength, and speed and to the capabilities of the senses, memory, cognitive skill, and psychomotor preferences. These objectives are in contrast to forcing humans to conform or adapt to the physical environment.

Human-factors engineering has also been termed human factors, human engineering, engineering psychology, applied experimental psychology, ergonomics, and biotechnology. It is related to the field of human-machine systems engineering but is more general, comprehensive, and empirical and not so wedded to formal mathematical models and physical analysis. See Human-machine systems

Among the problems of human-factors engineering are design of visual displays for ease and speed of interpretation; design of tonal signaling systems and voice communication systems for accuracy of communication; design of seats, workplaces, cockpits, and consoles in terms of humans' physical size, comfort, strength, and visibility. Human-factors engineering addresses problems of physiological stresses arising from such environmental factors as heat and cold, humidity, high and low atmospheric pressure, vibration and acceleration, radiation and toxicity, illumination or lack of it, and acoustic noise. Finally, the field includes psychological stresses of work speed and load and problems of memory, perception, decision making, and fatigue.

A fundamental problem of ever-increasing importance for human-factors engineers is what tasks should be assigned to people and what to machines. It is a fallacy to think that any given whole task can be accomplished best either by a human or by a machine without the aid of the other, because often some elements of both provide a mixture superior to either alone. Machines are superior in speed and power; are more reliable for routine tasks, being free of boredom and fatigue; can perform computations at higher rates; and can store and recall specific quantitative facts from memory faster and more dependably. Humans, by contrast, have remarkable sensory capacities which are difficult to duplicate in range, size, and power with artificial instruments (the ratio of the greatest to the least energy which people can either see or hear is about 10¹³). Humans' ability to perceive patterns, make relevant associations in memory, and induce new generalizations from empirical data remains far superior to that of any computer existing or planned. Thus, while people's overt information-processing rate in simple skills is low, their information-processing rate for these pattern recognition and inductive- reasoning capabilities (of which little is understood) appears far greater.

In cognitive engineering, one of the major issues in human-factors engineering is the concern that modern sophisticated hardware and software technology may be too complex for the people who will eventually use it. Requiring people to perform difficult or cognitively complex tasks is perhaps the leading cause of human–machine errors or accidents. Tasks can be cognitively difficult for a number of reasons. The number of steps required to use the system may exceed people's memory limitations, the user may be required to divide his or her attention between several different sources of information, or the person may be required to perform difficult mental operations. All of these factors burden an individual's cognitive capacity and, if that capacity is exceeded, errors may occur.

Two major trends have led to increased emphasis on the cognitive complexity of human-machine systems. One of these is the move toward larger and more complex systems where human error can have serious consequences for the systems' users and the general public. The other trend is the rapid development of modern information technology based upon powerful yet inexpensive microcomputers.

An important aspect in addressing this problem is the early identification and control of the cognitive complexity or mental difficulty of performing a task required of the new technology application. The best procedure (in terms of cost and effectiveness) for addressing this problem is to use cognitive analysis to develop specifications or guidelines that can be used during the initial design technology application. Through such design guidance, human cognitive limitations are controlled early in the technology application design when it is easiest and most cost-effective to make changes. If the technology application has developed to the point where design guides would no longer be useful (for example, much design work has already been completed), an alternative approach is to use cognitive engineering to evaluate the design as it exists. The results of the cognitive analysis will indicate which aspects of the design may be too difficult for people to perform and could lead to human–machine errors. The final use of cognitive engineering analysis is in preparing training materials. Cognitive analyses can identify the aspects of a person–machine interface that will be most difficult for people to perform. These aspects can then be given special training aids or more intensive hands-on training in order to reduce the potential of human–machine error.