Computational Cognitive Modeling of Pilot Performance in Pre-flight and Take-off Procedures
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
While the current practice of pilot training relies on flight instructors’ subjective assessment, computational cognitive modeling may be used to support future objective assessment and diagnosis of pilot performance. We built two models in a cognitive architecture to simulate pilot flight performance during pre-flight and take-off tasks. Modeling results were compared with human results collected from the same tasks using X-Plane 11 flight simulator. The models were able to capture human pilot performance and workload results from both tasks with good levels of fitness (percentage errors ranging from 0.8% to 13.2%). This work demonstrated the capability and advantage of this theory-driven modeling approach for supporting general aviation pilot training. We expect that this type of cognitive model will be complementary to data-driven machine learning models, and the current work provides the foundation for future work to expand the modeling capability and test practical applications in general aviation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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