Effects of unexpected event urgency and flight scenario familiarity on pilot trainees performance and stress responses
Bibliographic record
Abstract
Pilot trainees’ ability to manage unexpected events is crucial for aviation safety, yet the impact of event urgency and flight scenario familiarity on pilot performance remains under-explored. This study investigates how different urgency levels of unexpected events influence pilot trainees’ flight performance, heart rate, and anxiety in both familiar (single-engine) and less familiar (twin-engine) flight scenarios. Two controlled experiments were conducted using flight simulators: Experiment 1 involved 27 pilot trainees operating a Cessna 172 single-engine simulator under low-urgency and high-urgency conditions, while Experiment 2 involved 25 pilot trainees using a FTD D40/D42 twin-engine simulator, introducing an additional no-event baseline. In the single-engine flight scenario, high-urgency unexpected events significantly impaired pilot trainees’ performance (Cohen’d = 0.454) and increased anxiety (ηp2 = 0.229). In the twin-engine flight scenario, high-urgency unexpected events significantly impaired flight performance (MEI increased, ηp2 = 0.737), elevated heart rate (ηp2 = 0.516), and increased anxiety levels (ηp2 = 0.442) compared to low-urgency events, which had minimal effects and, in some cases, improved pilot trainees focus. Additionally, pilot trainees performed better and exhibited lower anxiety in familiar scenarios, suggesting that task familiarity mitigates the negative impact of high-urgency unexpected events. These findings highlight the importance of incorporating urgency-based training scenarios and cross-aircraft training to enhance pilot trainees’ adaptive responses to unexpected events, ultimately improving flight safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".