Monitoring pilot trainees’ cognitive control under a simulator-based training process with EEG microstate analysis
Bibliographic record
Abstract
The objective of pilot training is to equip trainees with the knowledge, judgment, and skills to maintain control of an aircraft and respond to critical flight tasks. The present research aims to investigate changes in trainees' cognitive control levels during a pilot training process while they underwent basic flight maneuvers. EEG microstate analysis was applied together with spectral power features to quantitatively monitor trainees' cognitive control under varied flight tasks during different training sessions on a flight simulator. Not only could EEG data provide an objective measure of cognitive control to complement the current subjective assessments, but the application of EEG microstate analysis is particularly well-suited for capturing rapid dynamic changes in cognitive states that may happen under complex human activities in conducting flight maneuvers. Comparisons were conducted between two types of tasks and across different training stages to monitor how pilot trainees' cognitive control responds to varied flight task types and training stages. The present research provides insights into the changes in trainees' cognitive control during a pilot training process and highlights the potential of EEG microstate analysis for monitoring cognitive control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".