Complexity ---------- A major challenge to maintaining good situation awareness is the complexity of many of the systems that must be operated. The more complex the systems are to operate, the greater the increase in the mental workload required to achieve a given level of situation awareness. When that demand exceeds human capabilities, situation awareness will suffer. System complexity may be somewhat moderated by the degree to which the person has a well-developed internal representation of the system to aid in directing attention, integrating data, and developing the higher levels of situation awareness - mechanisms that may be effective for coping with complexity. Developing those internal models, however, requires a considerable amount of training and may beyond the capabilities of many soldiers, as indicated by their ASVAB scores, as we discussed in Chapter 2. Models of Attention ------------------- Broadbent (1958) presented on of the first attempts to characterize capacity limits in human information processing. He proposed that comprehending multiple, unrelated spoken passages was impossible because of limited short-term memory capacity. Therefore, some messages had to be selected for admission to short-term memory, while irrelevant information was essentially blocked by a filter mechanism. The filter could be set to admit information on the basis of physical characteristics such as location, pitch, loudness, etc. As Gopher and Donchin (1986) point out, Broadbent's model of attention does have implications for systems design. If it is important for an operator to focus attention on one source of information, it should be clearly segregated from other sources by simple physical features, to allow efficient selection by the filter. This same prescription applies to information conveyed by visual displays. Simple physical features, such as color, line orientation, and motion, can be picked up by preattentive processes (Treisman and Gelade, 1980; Wolfe, 1994) and can rapidly guide attention to relevant display areas. The filter model, however, has relatively little to say about how and when divided attention is possible. Nor does it account for the idea that tasks vary in difficulty, which can at least partially be overcome by increased effort. Measures of Cognitive Workload ------------------------------ As the previous section suggest, there are severe limits in our ability to process multiple sources of information. Understanding these limits is essential in designing and evaluating components of the Land Warrior System. Current technology allows many options in terms of information display, graphical interface, input devices, etc. The availability of these options has the potential to produce severe information overload for an infantry soldier who is hot or cold, tired, and stressed. Comprehensive assessment of workload is therefore a critical part of the design process. The goal of cognitive workload measurement is to characterize the attentional demands that a task places on an operator. O'Donnell and Eggemeier (1986) suggest several criteria for evaluating measures of cognitive workload. Measures can be evaluated according to their: sensitivity (does the measure respond to variations in task difficulty or load?), diagnosticity (does it indicate what kind of attentional resource is being used?), intrusiveness (does the instrument interfere with performance of the task?), implementation requirements (does it require costly equipment or large amounts of time to complete?) and operator acceptance. Table 6-1 lists four classes of workload measurement with their pros and cons. Primary task performance and subjective measures can be used for the initial screening of designs. These are easy to administer, sensitive to task difficulty, and have good user acceptance. Subjective measures include structured interviews, rating scales focused on particular tasks, etc. They can be supplemented by looking at how easy it is for operators to combine various tasks together, using the embedded secondary tasks technique. Dual-task performance, using pairs of tasks that are likely to occur together in the course of a mission, should provide valuable information on potential bottlenecks. Several useful reviews of these techniques are available. Gopher and Donchin (1986) provide an overview of the concept of workload, O'Donnell and Eggemeier (1986) give a good description of various subjective scales of workload measurement, and Kramer (1991) has provided a recent analysis of physiological workload measures. In the section that follow we summarize the important points and consider their relevance to the Land Warrior System. Table 6-1 Method Pros Cons ------------------------------------------------------------------------------- Primary task High face validity Workload or performance? Measures assessed anyway No productivity Nonintrusive Secondary task Good diagnosticity Intrusive Sensitive Loading task in high Poor user acceptance situations Theory bound interpretation Subjective response High face validity Dissociation with primary User acceptable Largely post-hoc measures Easy to obtain Interscale replicability Physiological assessment Mostly nonintrusive Subject to artifacts Objective Poor user acceptance Data rich Difficult to administer Globally diagnostic Primary and Secondary Task Measures ----------------------------------- Primary task measures simply try to infer workload from performance on the task of interest. Primary task performance is obviously the critical variable. However, it isn't clear that primary task measures have much of a direct association with workload. Errors in performance do not necessarily indicate high workload imposed by the primary task. They can arise from a variety of sources, including workload levels that are too low, as in vigilance tasks in which the operator may miss signals because they are so infrequent. A better measure of workload is provided by secondary task performance, in which spare capacity is assessed by presenting operators with occasional probe signals that require them to press a key. Probe response time should be related to the difficulty of the primary task. For example, the difficulty of choosing various options of map presentation on the helmet-mounted display could be evaluated in terms of speed of response to occasional auditory tones. We pointed out earlier that there are a number of difficulties associated with using secondary task performance as a measure of spare capacity. Subjects may use various strategies for dealing with what is basically a dual-task situation; some may actively pres pare for the probe task despite instructions to treat it as secondary. In addition, the existence of multiple resources means that a given probe task, such as auditory detection, will vary in difficulty depending on its similarity to the primary task (due to variation in overlap of the particular resources used by each task). Finally, the secondary task can be intrusive and therefore disruptive of primary task performance. As we noted in connection with the model of dual-task interference (Pashler, 1994), even very simple and dissimilar tasks can produce interference when both of them require memory retrieval at the same time. These considerations suggest that secondary task performance should be viewed with caution as an index of workload. Of course, soldiers using Land Warrior equipment will often be in dual-task situations. For example, a soldier may be navigating terrain with the aid of the map display and GPS when an auditory message comes in. The message has to be checked for its importance relative to the navigation task; the speed and accuracy of response to such messages would be therefore expected to be a function of the ease of use of the map system. This is an example of an embedded secondary task method, in which the tasks are presented within the context of meaningful scenarios that are motivated by the kinds of dual-task combinations that are likely to occur in operational use. The panel recommends that this sort of dual-task analysis be carried out using a variety of realistic task combinations. At the very least, this approach would provide valuable information on which kinds of dual-task situations pose difficulty. It could prove valuable in design as well as test and evaluation.