The problem of determining baselines for human performance measurement is neither peculiar to people concerned with military system performance nor to those associated with educational systems. It has traditionally been easier to compare performance of, for example, the experimental group to the control group or system “a” to system “b”, than it has been to determine some base of performance characteristic of a group of people and then to measure the effect of change from there. In education, the question of not only philosophical but very practical consequence is how do we know when someone is working at his level? Do attempts to standardize presentation methodology and time consider performer variations adequately? In engineering, the human factors specialist is also concerned with workload and overload in terms of system performance decrement. If the pilot of a high performance tactical fighter must perform a precise tracking task, and at the same time navigate and monitor his aircraft systems while subject to intense “g” loadings, and if he fails to do so, the concern is with the increment which resulted in this failure—i.e., which added duty or which increment of psychological or physiological stress was the last straw? Baseline performance measurement is confounded by other problems as well. The largest of these is the tremendous reserve capacity for both continued performance and dramatic performance increase found among humans at all age and ability levels. This is clearly a motivational artifact because, when so motivated, people can program their activities in such a way as to have enormously increased capacities for work or cognition. The overloaded pilot, suddenly faced with a fire warning indication, in seconds becomes a far more sophisticated analog computer than anything he has on-board, rapidly relegating certain tasks to low priority (e.g., navigation or energy management) and others to the highest priority (e.g., fault isolation, logic assessment of spurious indications). The child in the classroom, plodding along at one moment, is, in the next moment, able to take on vast increases in information when his interest is sparked. How can these baselines be measured when they are seemingly made of some superstretch material? How could capacities be quantified at some level so that one could know that the addition of some increment would or would not effect system performance learning or achievement? Over the years, techniques have developed in response to such engineering questions as: will control system “a” result in a greater workload than system “b”? These were typically performance based questions, since what was ultimately desired was some statement of how the above would influence mission performance. Similarly, educators have devised systems of measuring learner activity levels, but most dramatically, recent innovations in remote measurement of psychophysiological states perhaps may provide some breakthroughs. This paper will trace the development of baseline performance measurement techniques from human factors task loading studies to those of brain wave and physiological state measurements and offer several recommendations for further study.
Not all sensors are created equal: a framework for evaluating human performance measurement technologies
