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MENTAL WORKLOAD
Instructor: Dr. Peter Hancock Lecture Overview Just how hard are you working right now? I am assuming that you’re reading these lecture notes and looking to understand the information they contain. The action of reading takes some effort and if I look at your eyes I could tell they were moving, but other than that I would have some rather severe difficulties in measuring the demands currently imposed on you and your reaction to them. But this is not so for physical work! Here, I can use all the methods of physics, biochemistry, biomechanics, indeed even ergonomics and the like to ask simple and soluble questions about your current physical workload and your muscular response. And herein lies the problem. Traditional work measurement in Industrial Engineering and Ergonomics has been predominantly about physical effort where the muscle is the engine of action. Now, when we move on to the brain as the major source of work we deal with a very different form of measurement challenge. What might be surprising to the uninitiated is that the brain takes about a third of the resting metabolic energy produced by the body, and this can increase during especially intense mental work. Thus, although specific signals within the brain are faint and difficult to distinguish, the mass action of the brain itself is extensive. As we shall see, recent brain imaging techniques each try to evaluate certain aspects of this mass action and use various indicators to achieve this aim. However, the four primary methods of mental workload assessment have held sway for the last two decades and continue to dominate, even as these new techniques emerge. Thus, we shall here deal with these four techniques, but with your awareness that brain measurement technologies are a volatile and changing enterprise in which new developments are constantly emerging.
Primary Task Performance If one wishes to know how well an individual is performing cognitive work, the first and most obvious method is to measure their outcome efficiency – in short, their primary task performance. In many practical situations, these measures, which emerge from the very origin of time and motion studies, are the sole representation that the assessor requires. For example, in piece work, the rate of production combined with the rate of item rejection is used to calculate the remuneration for the individual. The faster and more accurately the individual works, the higher the pay level and presumably, the higher the level of cognitive demand. However, this remains an issue for employers. For example, what if an individual is highly productive in cognitive work but is working with much spare capacity? Does this mean the employer could get more out of that individual? What happens when the cognitive work is creative and not repetitive? How many great ideas equals a number of units of rote work? These are difficult questions to answer and actually underlie the intrinsic contract that work creates between the worker and the employer. No wonder the question mental of workload is not one of scientific definition and interest alone. However, the fundamental shortfall of primary task measures is that they reflect work as is being presently accomplished. Let’s suppose we are trying to use primary task measures to assess a critical process. We know that if we impose too much cognitive load on the individual (say 25 aircraft on an air-traffic controller), they will not be able to accomplish the task and catastrophe may follow. However, this failure is non-linear (see Hancock & Warm, 1989), and so as we add aircraft we will not see the failure coming if primary task performance is all that we have. In essence, primary task reflections are good for the present moment but can be very dangerous if we want to use them to predict future cognitive performance limits. In some cases, the process is not one that will suffer excessively from this handicap, in other processes it is the difference between life and death. Thus, while primary
proceed to an additional form which is composed of subjective measures. The rationale here is simple – if you want to know how hard someone is working, simply ask them! There are many legitimate concerns about these subjective responses. How do you scale between individuals? How do you know someone is telling the truth? How do you turn perceptions into numbers? Indeed, these are all important issues but they are not insuperable barriers and as we, among others, have sought to show, subjective measures have their place. The major such methods, The NASA-TLX and the SWAT are fundamentally similar in that they try to present descriptive adjectives relating to cognitive work and then provide a numerical representation on these scales. In this sense they follow a great tradition in psychology of trying to render the mind transparent. To the degree that any such endeavors are successful, subjective measures of mental workload are successful and often they are simple and convenient to collect making them attractive for researcher and practitioner alike. As with all the other methods, we shall examine the advantages and disadvantages of this method further in discussion (and see Meshkati, Hancock, Rahimi, & Dawes, (1995). Physiological Reflections There are many situations in which primary task performance is sacrosanct. In these performance crucial situations, such as aviation, vehicle control, surgery, combat, etc, any method which serves to interrupt the on- going performance can itself induce catastrophic failures. Further, in these circumstances, such as emergency response, law enforcement and the like, operators typically will not react to external questions and interruptions and data from techniques like the secondary task paradigm, or subjective response simply cannot derive useful information. As a consequence, in these crucial, real-world situations, in which we would really like the most reliable and diagnostic information, our major methods actually fail to function effectively. As a result, we have to seek an alternative avenues
through which to derived mental workload values. Becoming more and more popular as techniques evolve in sophistication and reliability, physiological measures are currently in vogue. As indicated by Hancock, Meshkati and Robertson (1985), one can either measure reflections in the peripheral or the central nervous system. The degree to which one gets accurate and reliable data often depends upon the proximity of the measurement (both physically and systemically) to the site of action. That is, measuring memory demands may be done via toe-nail growth rate, but this is a remote site and has poor resolution. It is much better to examine brain activation in the memory- stimulated regions. In class we shall discuss several such measurement techniques. I would ask you to identify one and be prepared to talk about its relative advantages and disadvantages. Application Areas Understanding mental workload and being able to provide a reliable and accurate measure of this form of workload on an individual basis may be a very satisfying scientific achievement. However, the realization of such a goal goes well beyond the realm of academics. There are an almost limitless vista of potential applications and here we consider two recent and highly pertinent examples, with your recognition that there are many, many others. Earlier in our class, we talked about human interaction with automated and semi-automated systems. One of the major advances in that realm was the idea of adaptive human-machine systems. This conception seeks to understand the state of the machine and the state of the human and then reconcile these respective states with the on-going needs of the combined human-machine system toward some mutual goal. Obviously, to accomplish this goal we need to know about the machine and need to be able to express this status in human terms. However, we also need to be able to capture the operator state and express their situation in machine terms (largely quantitative assessment). Accurate mental workload measures are thus absolutely vital here.
LECTURE READINGS Hancock, P.A. & Meshkati, N. (Eds.). (1988). Human mental workload. Amsterdam: North-Holland. Hancock, P.A., Meshkati, N., & Robertson, M.M. (1985). Physiological reflections of mental workload. Aviation , Space , and Environmental Medicine, 56, 1110-1114. Hancock, P.A., & Chignell, M.H. (1988). Mental workload dynamics in adaptive interface design. IEEE Transactions on Systems , Man , and Cybernetics , 18 , 647-658. Hancock, P.A., & Szalma, J.L. (2003). The future of Neuroergonomics. Theoretical Issues in Ergonomic Science, 4 (1) , 238-249. Meshkati, N., Hancock, P.A., & Rahimi, M. (1989). Techniques of mental workload assessment. In: J. Wilson (Ed.). Evaluation of human work : p ractical ergonomics methodology. (pp. 605-627) London: Taylor and Francis. Meshkati, N., Hancock, P.A., Rahimi, M., & Dawes, S.M. (1995). Techniques of mental workload assessment. In: J. Wilson and E.N. Corlett, (Eds.). Evaluation of human work : A practical ergonomics methodology****. (pp. 749-782) (Second Edition), London: Taylor and Francis. Parasuraman, R. (2003). Neuroergonomics: Research and practice. Theoretical Issues in Ergonomic Science , 4 (1), 5-20. Wickens, C.D. (1984). Processing resources in attention. In: R, Parasuraman and D.R. Davies (Eds.). Varieties of attention. (pp. 63-102), Orlando: Academic Press.
