Abstract
Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.
Highlights
M ENTAL workload is one of the most crucial factors that affect the efficiency of human operators as they function in complex interactive work environments
Our findings demonstrate that the brain dynamics during each of these tasks can be estimated from the eye activity, heart rate variability (HRV) and performance during the tasks
HRV correlates to mental workload variations for tracking and collision prediction tasks are successfully unravelled
Summary
M ENTAL workload is one of the most crucial factors that affect the efficiency of human operators as they function in complex interactive work environments. Wickens and Tsang [1] defined mental workload as the dynamic relationship between the cognitive resources demanded by a task and the capability of the operator to afford those resources. As mental workload has a negative influence on the performance of the operator, it results in human error commission [4], compromising system efficiency and safety [5]. Mental workload must be maintained at an optimal level, avoiding both underload and overload [6] as the performance is known to fall at both overload and underload conditions [7], [8]. Predicting an operator’s mental workload and thereby adapting the system behaviour by modifying task allocation can avoid the loss of situational awareness, maintaining high performance. Accurate and reliable measurement of the mental workload of an operator is crucial, especially in a safetycritical work environment, by providing better work environments and human-machine interactions [9], [10]
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