Variation of Pilots’ Mental Workload Under Emergency Flight Conditions Induced by Different Equipment Failures: A Flight Simulator Study
Bibliographic record
Abstract
Pilots’ excessive mental workload could reduce their ability to perform concurrent tasks during emergency flights, which is one of the most critical aviation safety concerns. Several past efforts have attempted to investigate the underlying issues, but all had limited success owing to the challenge of collecting representative data under realistic operating conditions. This study aimed to address this challenge by conducting a flight simulator study involving a comparatively large number of participants, who were pilot cadets with flight experience, and using noninvasive functional near-infrared spectroscopy (fNIRS) to collect the pilots’ brain activity data. Pilots’ subjective ratings and brain activity records were collected over a total of 75 simulated flights under three subtask scenarios comprising different equipment failures. A statistical analysis was carried out on the subjective ratings and on the changes observed in the saturation of the oxyhemoglobin (ΔOxyHb) of individual fNIRS channels. The mental workload of the pilots was classified using a support vector machine hierarchical combination classifier, focusing on the question of whether it is feasible to classify pilots’ mental workload using brain activity signals (i.e., ΔOxyHb). The results suggested that the pilots’ mental workload levels were highly associated with the ΔOxyHb measures as well as with the activities of different brain regions, including the prefrontal-, motor-, and occipital cortex. The findings from this study could provide a reference for optimizing pilot training systems and improving pilot performance during emergency flight operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".