Exploring Task Prioritization in VR Flight Environments: Can Eye-Tracking Uncover Cognitive Control?

Cognitive control is important for multitasking performance, which is essential for safety and efficiency in human-machine interaction domains such as aviation. Previous research has shown that the stability-flexibility dilemma of cognitive control can be manipulated through task prioritization in low-fidelity flight environments, with eye-tracking metrics effectively classifying this dilemma in a low-fidelity flight environment using a machine learning approach. However, it is unclear whether these results extend to higher-fidelity environments. This study examines the applicability of this approach to eye-tracking metrics assessed in a VR flight simulator. Linear mixed-effects models reveal significant differences in fixation duration, relative number of fixations, coefficient K, stationary entropy, and explore-exploit ratio across flight scenarios with varying task prioritization. Additionally, these metrics were used as input features in a machine learning model to classify the mission scenarios. The findings have important implications for designing adaptive assistance systems aiming at supporting multitasking performance in safety-critical environments.