Flight emotions unleashed: Navigating training phases and difficulty levels in simulated flying
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Flying accuracy is influenced by pilots' affective reactions to task demands. A better understanding of task‐related emotions and flying performance is needed to enhance pilot training. Objective Understand pilot trainees' performance and emotional dynamics (intensity, frequency and variability) based on training phase and difficulty level in a flight simulator. Methods Twenty‐three volunteers performed basic flight manoeuvres. Trials were divided into three phases: Introduction (trials 1–7), session A (trials 8–15) and session B (trials 16–22). Three task difficulty levels were implemented (low, medium and high). Flying performance was evaluated using root mean square error (RMSE) and expert ratings. Emotional intensity was inferred from physiological (electrodermal activity) and behavioural (facial expressions) emotional responses. Emotional variability was calculated to understand fluctuations among multiple emotions. Emotional responses were mapped into task‐relevant emotions, like sadness with boredom, and fear with anxiety. Results and Conclusions The most frequent facial expressions neutral, anger and surprise. Neutral and anger were interpreted as deep focus states. Surprise was likely a response to unexpected events. Flying performance and emotional dynamics varied across training phases and difficulty levels. During introduction, performance was less accurate, and emotions were less frequent. During session A, performance improved while participants experienced more physiological arousal and emotional variability. During session B, performance was the most accurate. In high‐difficulty tasks, performance was the least accurate, participants expressed emotions with more frequency, more variability and higher physiological arousal. Future studies can use simulated flying tasks for trainees to familiarize with their emotional reactions to task demands expecting to improve training outcomes.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 it