Engineering Psychology Research Paper

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As its name implies, engineering psychology is a branch of psychology that is concerned with design. In particular, it seeks to ensure that human capabilities, limitations, and tendencies are considered in the design of things people use—from ordinary consumer products to sophisticated technologies—with the aim of maximizing performance and safety. The colloquial term ‘user-friendliness’ captures the essence of this goal, and in pursuing it, engineering psychologists draw upon and contribute to many domains of psychological knowledge.

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1. Interdisciplinary And Disciplinary Relationships

Throughout its history, which dates back to the Second World War era, engineering psychology has struggled to establish a clear identity. Partly this is a result of its interdisciplinary perspective and partly it stems from a confusing array of labels with which it has been associated. To appreciate the nature and current status of the specialty, therefore, it is necessary to sort out these relationships.

The core concept that engineering psychology shares with specialists in other disciplines—including some in engineering, computer and cognitive sciences, biomechanics, medical sciences, architecture, and gerontology—is that of the human–machine system: the idea that human and machine function as partners in a collective enterprise rather than as independent operators. Whereas design professionals typically focus on the efficiency and reliability of the machine component, and personnel specialists, on the proficiency of the human component, the system perspective emphasizes the importance of their interaction. A machine that is difficult to use will result in poor system performance no matter how well it functions as a machine.

For example, airplane accidents officially attributed to ‘pilot error’ were at one time quite commonly found to involve misreading the cockpit altimeter. Since this instrument provided highly accurate altitude information and rarely malfunctioned, it was beyond reproach from an engineering standpoint. From a human–machine systems standpoint, however, it was seriously deficient in that altitude readings were easily confused. Hence if a pilot under stress mistook 1,000 for 10,000 feet, it was as much the fault of the display design as it was of the human operator. Fortunately, this problem was diagnosed by engineering psychologists and corrected through human-oriented design (Sanders and McCormick 1987). The moral: good design requires joint consideration of human and machine characteristics.

The approximately 11,000 professionals who subscribe to this philosophy are known as ‘human factors specialists’ or ‘ergonomists,’ and despite a variety of disciplinary backgrounds, their shared interest has evolved into a discipline of its own (Van Cott and Huey 1992). In the USA, it is most commonly referred to as ‘human factors’; in the rest of the world, as ‘ergonomics.’ Since nearly half of these professionals were trained as research psychologists, engineering psychology represents a substantial component of this interdisciplinary field. Within the field of psychology, however, where the number of professionals is well in excess of 150,000 and growing, it constitutes but a tiny specialty area—one that few other psychologists could readily identify. Moreover, larger, well-established specialties such as cognitive, experimental, educational, environmental, and industrial organizational, (I/O) psychology have also developed an interest in design applications. For this reason, it has been suggested that the engineering psychology specialty is in transition and may eventually be absorbed into the others (Howell 1993). Its relationships with cognitive, experimental, and I O specialties are particularly noteworthy as we shall now see.

2. Background And Changing Emphases

Although it had many precursors, engineering psychology emerged as a recognized specialty following the Second World War. During the war, teams of research psychologists and engineers had been assembled to investigate—and correct—problems such as the excessive number of accidents in certain aircraft, the difficulty of maneuvering large ships, and the failure of radar operators to detect critical signals. Most of the psychologists recruited for this purpose were academic scientists whose peacetime work had involved studies of basic human functions such as sensation, perception, motivation, and learning. Within the field they were known as experimental psychologists because they relied chiefly on the experimental methods of science in their investigations.

The success of these wartime ‘human engineering’ efforts, as they were called, had four noteworthy consequences. First, it convinced the engineers and psychologists who had been thrown together to rectify specific design problems that the collaboration had much broader potential. The engineers discovered that psychological knowledge is useful for design; the psychologists discovered new applications and directions for their research.

Second, it prompted transition of the psychology–engineering collaboration into peacetime applications. Military human-engineering units continued their research and development (R&D) activities, and extended their influence by writing user-oriented requirements into contracts for new systems, and by supporting civilian researchers. Consequently, some large military contractors (e.g., aircraft manufacturers) and civilian industries (e.g., The Bell Telephone system) saw fit to form human-engineering groups of their own. These groups comprised mainly of psychologists who reviewed plans and prototypes, defined usability requirements, and conducted research as a basis for setting those requirements.

The third consequence of the wartime human engineering effort was the effect it had on the subsequent work of the participating psychologists. When they returned to their respective laboratories, many of them continued lines of research stimulated by their wartime activities, often under military sponsorship. For instance, Cornell University psychologist James J. Gibson developed a whole new theory of perception based on his wartime experience, one that revolutionized thinking on how we construct a three-dimensional world out of a two-dimensional retinal image (Gibson 1966).

The final consequence was the institutionalization of the amalgam of psychology and design with the establishment of the Society of Engineering Psychologists and the interdisciplinary Human Factors Society, both in 1958. Specialized journals (e.g., Human Factors, Ergonomics), graduate training programs, textbooks, courses, research laboratories, and consulting firms followed.

Then as now, the content of engineering psychology was dictated largely by the nature of the prevailing technologies and the accompanying task requirements for human operators (Howell 1993). At mid-century, humans exercised manual control over most systems, hence the focus was on the design of information displays (mostly dials), manual controls, and their interaction. The most relevant psychological knowledge was that involving sensation, perception, motor functions, reaction time, and perceptual-motor skill.

Today’s technologies present operators with the daunting combination of more information, less direct control, and greater responsibility. Often the human’s task is that of monitoring complex, powerful, highly automated machines and acting only when something goes wrong or there is reason to alter the program—a role commonly referred to as ‘supervisory control’ (Sheridan 1992). Consequently the psychological emphasis has shifted to more ‘mental’ topics such as attention, cognition, mental workload, information-processing capacity, decision making, and problem solving.

Throughout its history, engineering psychology has maintained an active involvement in both science and application. When the literature provides a plausible scientific basis for a design decision, that knowledge is incorporated; when it does not, it prompts new lines of research. Some of this research seeks specific answers to narrowly defined questions, such as whether to use menus or icons in a particular software program. Some, however, addresses much broader, more fundamental issues— such as the nature and limits of human attention— and in so doing expands the body of psychological knowledge.

In this regard, it is interesting to note that the work of engineering psychologists played an important role in a comprehensive ‘paradigm shift’ that overtook psychology, starting in the 1960s. At a time when the dominant psychological philosophy (behaviorism) held that mental events are unsuitable subjects for scientific investigation because they are not open to public inspection, British engineering psychologist Donald E. Broadbent and his colleagues were gathering data on radar operators that would culminate in a general model of how humans process information (Broadbent 1958).

Broadbent’s model, particularly his theory of attention, was thus instrumental in the ‘revolution’ that rekindled psychologists’ interest in cognition (i.e., mental processes). So, too, were contributions such as the Tanner–Swets theory of signal detectability (Swets et al. 1961), which established that the seemingly mechanistic task of sensing things includes an important cognitive element (decision making); Hick’s Law (Hick 1952), which described human reaction time in terms of the mental operations required to translate a stimulus into a response; and a number of others. Because engineering psychologists dealt with applied problems such as detecting radar signals, interpreting messages obscured by noise, following moving targets, and remembering strings of letters and numbers, they found it necessary—and useful—to explore the mental functions that underlay these skills, thereby challenging the tenets of behaviorism.

Whereas engineering psychology’s origins and current orientation are closely linked to experimentation (hence the experimental and cognitive branches of the field), it also has ties with another specialty—I/O psychology—that evolved from a very different tradition: psychometrics, or the measurement of individual differences. The experimental approach seeks to understand how the typical human functions under a particular set of circumstances—in the present context, in order to design for the modal user. The individual-differences approach seeks to develop measures (e.g., tests, questionnaires) that enable one to understand and measure how people differ—in the context of I/O psychology, in order to select and place them in jobs that best fit their knowledge, skills, and abilities. Simply put, the former tradition tries to fit the machine (or work setting) to the human; the latter, the human to the machine (or work setting).

Over the years, the two traditions have drawn closer together philosophically and methodologically in recognition of the fact that both derive from the same basic model (complex goal-oriented systems) and seek the same end (achieving a good fit between the system components). I/O psychologists now include experimentation among the tools they use in organizational research, and engineering psychologists recognize that measuring individual differences is important for design because one size does not fit all. Increased ethnic and gender diversity among today’s pilots, for example, requires rethinking a number of the cockpit design features that were geared to the fairly homogenous male pilot population of yesteryear. And our aging general population requires considering agerelated differences in the design of consumer products, home-care medical devices, highways, and working conditions.

What is perhaps surprising is that despite these converging trends and the occasional reference of one to the other in modern texts, engineering and I/O psychology remain far apart in their institutional existence (Ilgen and Howell in press). There is very little overlap between their respective membership in professional organizations, journal content, graduate training programs, research efforts, or job titles.

The one notable exception is the content area of training in which both specialties have a long-standing interest. Engineering psychologists are particularly interested in the design and evaluation of devices (e.g., simulators, computer-based instructional systems) used for developing skills, whereas I/O psychologists are concerned with a much wider array of training issues (e.g., from simple job training to team building and even management or organizational development). It is worth noting, however, that whatever the focus, training constitutes a third strategic leg on the system performance stool—a complement to the design and selection strategies favored by engineering and I O psychologists respectively.

3. Engineering Psychology Today: Examples

Whether or not engineering psychology survives as an independent specialty, the philosophy that it represents has clearly established itself within psychology and, by whatever name, design-oriented research will continue to grow. Thus it is important to grasp the essential nature of such research, and that is best done through examples. Two illustrations are presented below: the first, an example of fundamental research that was prompted by a design issue; the second, an example of the application of existing psychological knowledge to a design problem. These examples also illustrate methodologies typical of engineering psychology.

3.1 Mental Models

Many technologically advanced systems have failed to perform as effectively as they might because the logic built into the machine component either is not transparent to the user, or is inconsistent with his or her conception of it, or both. That is, the ‘artificial intelligence’ and the ‘human intelligence’ involved are not congruent.

For example, computer-based systems designed to educate students in a particular subject matter (socalled ‘intelligent tutoring devices’) have often failed because they could not adequately diagnose the thinking behind the learner’s answers and hence could not provide feedback in a useful form. And highly automated ‘process-control’ systems, such as those built into nuclear power plants, have encountered problems when something went awry because the humans responsible for diagnosing the fault were not sufficiently familiar with how the machine executed its control function. This was one of the many factors that contributed to the highly publicized accident at the Three Mile Island plant in 1979 (Rubinstein and Mason 1979).

Overcoming this limitation in the design of intelligent tutors, process-control automation, and other such systems cannot be achieved without an understanding of how people conceptualize the underlying physical or logical processes. Realization of this fact has led to a great deal of research on the mental representation of such structures, commonly referred to as ‘mental models,’ some of it aimed at specific applications and some at teasing out general cognitive principles.

A key requirement for either endeavor, of course, is devising ways to access private mental content—to describe these mental models and validate them. And that, in turn, has produced a number of new ‘knowledge elicitation’ and ‘cognitive task analysis’ techniques that are helping us gain a deeper understanding of human cognition (Cooke 1992). Some, known as ‘process tracing’ methods, involve collecting verbal protocols in a systematic way from individuals while they are performing a task, and then analyzing those protocols to infer the underlying thought processes. Others, such as the ‘Pathfinder’ technique and a collection of so-called ‘policy-capturing’ methods, deduce these processes using sophisticated statistical analysis applied to a lot of relatively simple judgments or choices required of the operator.

One technique, known as SAGAT, was designed explicitly to capture the operator’s mental picture (or situation awareness’) at various stages in the performance of highly complex tasks such as air-to-air combat or command decision making. This approach involves introducing systematic ‘knowledge probes’ at various points in the conduct of a simulated exercise, thereby piecing together the evolution of the operator’s mental model (Endsley 1995).

Taken together, therefore, the design problems posed by the growing complexity of today’s systems, plus the frequent disconnects between human and machine modes of thinking, has given rise to new insights into how the mind works as well as new methods for extracting that information.

3.2 The TADMUS Project

In 1988 a civilian airliner was mistakenly shot down by the USS Vincennes, a cruiser equipped with the latest intelligence technology, and the incident prompted international concern. It also prompted a Congressional investigation and a commitment by the US Navy to diagnose and rectify whatever went wrong. For many years, the Navy’s Office of Naval Research (ONR) had been supporting fundamental research on human decision making—if fact, had been its leading sponsor. Therefore, ONR accepted the challenge of applying this vast body of knowledge to the general situation involved in the Vincennes incident, and the Tactical Decision Making Under Stress (TADMUS) project was born.

The aim of this project was to examine everything that was known about how humans make decisions; how their performance is affected by the complexities, uncertainties, and urgency (i.e., the stresses) typical of military ‘command and control’ operations; and how to ameliorate the effects of stress.

Needless to say, this ambitious goal called for—and received—a substantial investment in both time and money: more that $10 million was committed over an eight-year period, and the results were impressive. Drawing on basic cognitive literatures, hypotheses were developed and tested in a high-fidelity simulation of the shipboard Combat Information Center (CIC), and promising innovations were evaluated by operational personnel in research conducted at training sites, and even in actual shipboard demonstrations (Cannon-Bowers and Salas 1998).

At the end of the 1990s, TADMUS-based concepts on how to train teams to perform this vital function and sustain their performance, especially in the face of stress, were being incorporated into training curricula, and proven design improvements were being considered in planning the next generation of CIC interface equipment. The interface innovations include better (user-oriented) display of the massive amount of information available to decision-makers, and machine ‘decision aids’ to help them process this information rapidly and accurately. The literature on ‘mental models’ played an important role in both the training and design innovations.

In sum, the TADMUS project, mounted to address an urgent problem, has brought a wide variety of psychological knowledge to bear on that problem and, as a result, is virtually certain to result in improved CIC performance in the future. Moreover in the process, new knowledge was gained about team training, display for ‘situation awareness,’ decision aiding, and a number of other generic issues.

Viewed together, these two examples—mental models research and the TADMUS project—should provide ample material from which to construct a valid mental model of the specialty of engineering psychology as it exists today. Both its enduring features (the goal of improving human–machine systems, the bidirectional linking of design issues with psychological research, the multidisciplinary perspective, the driving role of technology) and its current focuses (cognitive functions; complex, heavily automated systems) are vividly illustrated. Additional sources of general information on engineering psychology can be found in two widely used texts (Wickens 1992, Sanders and McCormick 1987), the Handbook of Human Factors (Salvendy 1987), a chapter in the Handbook of I O Psychology (Howell 1991), and chapters devoted to the topic that have appeared periodically in the Annual Review of Psychology (see Howell 1993).


  1. Broadbent D E 1958 Perception and Communications. Pergamon Press, London
  2. Cannon-Bowers J A, Salas E (eds.) 1998 Making Decisions under Stress: Implications for Individual and Team Training. American Psychological Association, Washington, DC
  3. Cooke N J 1992 Eliciting semantic relations for empirically derived networks. International Journal of Man–Machine Studies 37: 721–50
  4. Endsley M R 1995 Toward a theory of situational awareness. Human Factors 37: 32–64
  5. Gibson J J 1966 The Senses Considered as Perceptual Systems. Houghton-Mifflin, Boston, MA
  6. Hick W E 1952 On the rate of gain of information. Quarterly Journal of Experimental Psychology 4: 11–26
  7. Howell W C 1991 Human factors in the workplace. In: Dunnette M D, Hough L M (eds.) Handbook of Industrial and/or ganizational Psychology. Consulting Psychologists Press, Palo Alto, CA, Vol. 2, pp. 209–69
  8. Howell W C 1993 Engineering psychology in a changing world. Annual Review of Psychology 44: 231–63
  9. Ilgen D R, Howell W C in press The National Research Council Committee on Human Factors (or human factors: It’s more than you think). The I/O Psychologist 36
  10. Rubinstein T, Mason A F 1979 The accident that shouldn’t have happened: An analysis of Three Mile Island. IEEE Spectrum November: 33–57
  11. Salvendy G 1997 Handbook of Human Factors, 2nd edn. Wiley, New York
  12. Sanders M S, McCormick E J 1987 Human Factors in Engineering and Design 6th edn. McGraw-Hill, New York
  13. Sheridan T B 1992 Telerobotics, Automation, and Human Supervisory Control. MIT Press, Cambridge, MA
  14. Swets J A, Tanner W P, Birdsall T G 1961 Decision processes in perception. Psychological Review 68: 301–40
  15. Van Cott H P, Huey B M (eds.) 1992 Human Factors Specialists’ Education and Utilization. National Academy Press, Washington, DC
  16. Wickens C D 1992 Engineering Psychology and Human Performance, 2nd edn. Harper Collins, New York
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