No abstract

Dynamic and data-rich domains, like those found in the military, primarily rely on multiple operators’ visual attention. Of interest is to understand how shared visual attention impacts performance when workload changes and whether this informs the adaptation process between collaborators. Ten pairs of participants completed a simulated Unmanned Aerial Vehicle control task under two different workload conditions - first under low workload and then under high workload. The best and worst performing pairs were identified and further analyzed by assessing pairs’ percent gaze overlap and strategy when workload changed. The findings showed the best performing pairs not only had higher levels of percent gaze overlap on average, but also increased their percent gaze overlap as workload increased. Additionally, the best performing pairs engaged in the adaptation process with not only their actions, but their overall visual attention allocation strategy. These findings suggest systems and technology in these domains should allow operators to have access to their collaborator’s visual data, in order to provide the opportunity to adapt and dynamically collaborate under different workload conditions.

Automation reliance and functionality are ever increasing, especially in supervisory control environments like unmanned aerial vehicle (UAV) missions. Of particular relevance is understanding how automation transparency, i.e., explaining the capabilities and limitations of automation to the human in real-time, can improve human-automation performance across automated systems that vary in reliability. Two hundred seventy one Naval Aviation trainees completed a simulated multi-UAV supervisory control mission for 42 minutes with three automated systems that varied in reliability. Participants were never explicitly told the reliability varied, but halfway through the mission, they were alerted that the least reliable system may falter. Results indicated human-automation performance improved after the alert for this specific system, but not as a whole, as one system’s human-automation performance deteriorated. This work suggests uncertainty communication should not only include the specific, real-time capabilities of the automation, but also communicate unintentional consequences it may have on the whole environment.

High mental workload, in addition to changes in workload, can negatively affect operators, but it is not clear how sudden versus gradual workload transitions influence performance and visual attention allocation. This knowledge is important as sudden shifts in workload are common in multitasking domains. The objective of this study was to investigate, using performance and eye tracking metrics, how constant versus variable levels of workload affect operators in the context of a dual-task paradigm. An unmanned aerial vehicle command and control simulation varied task load between low, high, gradually transitioning from low to high, and suddenly transitioning from low to high. Performance on a primary and secondary task and several eye tracking measures were calculated. There was no significant difference between sudden and gradual workload transitions in terms of performance or attention allocation overall; however, both sudden and gradual workload transitions changed participants’ strategy in dealing with the primary and secondary task as compared to low/high workload. Also, eye tracking metrics that are not frequently used, such as transition rate and stationary entropy, provided more insight into performance differences. These metrics can potentially be used to better understand operators’ strategies and could form the basis of an adaptive display.