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• W4366829666 abstract "Abstract Background Cognitive load (CL) management is essential in safety‐critical fields so that professionals can monitor and control their cognitive resources efficiently to perform and solve scenarios in a timely and safe manner, even in complex and unexpected circumstances. Thus, cognitive load theory (CLT) can be used to design virtual reality (VR) training programmes for professional learning in these fields. Objectives We studied CL management performance through behavioural indicators in authentic VR flight training and explored if and to what extent physiological data was associated with CL management performance. Methods The expert ( n = 8) and novice pilots ( n = 6) performed three approach and landing scenarios with increasing element interactivity . We used video recordings of the training to assess CL management performance based on the behavioural indicators. Then, we used the heart rate (HR) and heart rate variability (HRV) data to study the associations between the physiological data and CL management performance. Results and Conclusions The pilots performed effectively in CL management. The experience of the pilots did not remarkably explain the variation in CL management performance. The scenario with the highest element interactivity and an increase in the very low‐frequency band of HRV were associated with decreased performance in CL management. Takeaways Our study sheds light on the association between physiological indicators and CL management performance, which has traditionally been assessed with behavioural indicators in professional learning in safety‐critical fields. Thus, physiological measurements can be used to supplement the assessment of CL management performance, as relying solely on behavioural indicators can be time consuming." @default.
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• W4366829666 date "2023-04-24" @default.
• W4366829666 modified "2023-10-14" @default.
• W4366829666 title "Capturing cognitive load management during authentic virtual reality flight training with behavioural and physiological indicators" @default.
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• W4366829666 doi "https://doi.org/10.1111/jcal.12817" @default.
• W4366829666 hasPublicationYear "2023" @default.
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