Unravelling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks: Implications for Air Traffic Controllers
John, A. R.; Singh, A. K.; Do, T.-T. N.; Eidels, A.; Nalivaiko, E.; Gavgani, A. M.; Brown, S.; Bennett, M.; Lal, S.; Simpson, A. M.; Gustin, S. M.; Double, K.; Walker, F. R.; Kleitman, S.; Morley, J.; Lin, C.-T.
Show abstract
ObjectiveWe have designed tracking and collision prediction tasks to elucidate the differences in the physiological response to the workload variations in basic ATC tasks to untangle the impact of workload variations experienced by operators working in a complex ATC environment. BackgroundEven though several factors influence the complexity of ATC tasks, keeping track of the aircraft and preventing collision are the most crucial. MethodsPhysiological 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. ResultsThe neurometrics of workload variations in the tracking and collision prediction tasks were markedly distinct, indicating that neurometrics can provide insights on the type of mental workload. The pupil size, number of blinks and HRV metric, root mean square of successive difference (RMSSD), varied significantly with the mental workload in both these tasks in a similar manner. ConclusionOur 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. ApplicationThese 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. PrecisThis article identifies the physiological correlates of mental workload variation in basic ATC tasks. The findings assert that neurometrics can provide more information on the task that contributes to the workload, which can aid in the design of intelligent mental workload adaptive system.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- EEG in game user analysis: A framework for expertise classification during gameplay 95%
- Low Physiological Arousal in Mental Fatigue: Analysis of Heart Rate Variability during Time-on-task, Recovery, and Reactivity 95%
- A Measure of Event-Related Potentials (ERP) Indices of Motivation During Cycling 95%
Similar papers in this journal
- Short-Term Meditation Training Alters Brain Activity and Sympathetic Responses at Rest, but not during the meditation 95%
- The Relationship between Stability of Interpersonal Coordination and Inter-Brain EEG Synchronization during Anti-phase Tapping 95%
- Immersive virtual prism adaptation therapy with depth sensing camera: A feasibility study with functional near infrared spectroscopy in healthy adults 94%
Similar papers in this journal
- Classification of complex emotions using EEG and virtual environment: proof of concept and therapeutic implication 95%
- Gamma Music: A New Acoustic Stimulus for Gamma-frequency Auditory Steady-State Response 94%
- Brain Functional Connectivity Correlates of Anomalous Interaction Between Sensorily Isolated Monozygotic Twins 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.