Unravelling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks: Implications for Air Traffic Controllers

Abstract

The surge in air traffic increases the workload experienced by air traffic controllers (ATC) while they organise traffic-flow and prevent conflicts between aircraft. Even though several factors influence the complexity of ATC tasks, keeping track of the aircraft and preventing collision are the most crucial. We have designed tracking and collision prediction tasks to elucidate the differences in the physiological response to the workload variations in these basic ATC tasks to untangle the impact of workload variations experienced by operators working in a complex ATC environment. Physiological measures, such as 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. The mental workload in the tracking task was found to be positively correlated with the frontal theta power and negatively correlated with the occipital alpha power. In contrast, for the collision prediction task, the frontal theta, parietal theta, occipital delta, and theta power were positively correlated, and parietal alpha power was negatively correlated with the increases in mental workload. The pupil size, number of blinks and HRV metric, root mean square of successive difference (RMSSD), also varied significantly with the mental workload in both these tasks in a similar manner. Our findings indicate that variations in task load are sensitively reflected in physiological signals, such as EEG, eye activity and HRV, in these basic ATC-related tasks. 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 ATC and beyond.