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Objective This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload. Background Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic. Method This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation. Results The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms. Conclusion There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state. Application Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.

Objective This study investigates the cost of detection response task performance on cognitive load. Background Measuring system operator's cognitive load is a foremost challenge in human factors and ergonomics. The detection response task is a standardized measure of cognitive load. It is hypothesized that, given its simple reaction time structure, it has no cost on cognitive load. We set out to test this hypothesis by utilizing pupil diameter as an alternative metric of cognitive load. Method Twenty-eight volunteers completed one of four experimental tasks with increasing levels of cognitive demand (control, 0-back, 1-back, and 2-back) with or without concurrent DRT performance. Pupil diameter was selected as nonintrusive metric of cognitive load. Self-reported workload was also recorded. Results A significant main effect of DRT presence was found for pupil diameter and self-reported workload. Larger pupil diameter was found when the n-back task was performed concurrently with the DRT, compared to no-DRT conditions. Consistent results were found for mental workload ratings and n-back performance. Conclusion Results indicate that DRT performance produced an added cost on cognitive load. The magnitude of the change in pupil diameter was comparable to that observed when transitioning from a condition of low task load to one where the 2-back was performed. The significant increase in cognitive load accompanying DRT performance was also reflected in higher self-reported workload. Application DRT is a valuable tool to measure operator's cognitive load. However, these results advise caution when discounting it as cost-free metric with no added burden on operator's cognitive resources.

With vehicle automation becoming more commonplace, the role of the human driver is shifting from that of system operator to that of system supervisor. With this shift comes the risk of drivers becoming more disengaged from the task of supervising the system functioning, thus increasing the need for technology to keep drivers alert. This special issue includes the most up-to-date research on how drivers use vehicle automation, and the safety risks it may pose. It also investigates the accuracy that driver monitoring systems have in detecting conditions like driver distraction and drowsiness, and explores ways future drivers may respond to the broader introduction of this technology on passenger vehicles.

Objective This study investigates the effect of cognitive overload on assembly task performance and muscle activity. Background Understanding an operator's cognitive workload is an important component in assessing human–machine interaction. However, little evidence is available on the effect that cognitive overload has on task performance and muscle activity when completing manufacturing tasks. Method Twenty-two volunteers completed an assembly task while performing a secondary cognitive task with increasing levels of demand ( n-back). Performance in the assembly task (completion times, accuracy), muscle activity recorded as integrated electromyography (EMG), and self-reported workload were measured. Results Results show that the increasing cognitive demand imposed by the n-back task resulted in impaired assembly task performance, overall greater muscle activity, and higher self-reported workload. Relative to the control condition, performing the 2-back task resulted in longer assembly task completion times (+10 s on average) and greater integrated EMG for flexor carpi ulnaris, triceps brachii, biceps brachii, anterior deltoid, and pectoralis major. Conclusion This study demonstrates that working under high cognitive load not only results in greater muscle activity, but also affects assembly task completion times, which may have a direct effect on manufacturing cycle times. Application Results are applicable to the assessment of the effects of high cognitive workload in manufacturing.

Objective This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems. Background Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven. Method Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods. Results Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found. Conclusion Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo. Application The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.