Cognition in the cockpit: assessing instructional modalities in pilot training simulations
Bibliographic record
Abstract
Introduction: Flight Simulators (FS) play a critical role in pilot training, yet the increasing use of automated modules in FS raises questions about how instructional delivery methods influence learning. This study investigates how different FS instruction modalities affect student pilots' cognitive states and performance. Methods: A between-subjects experiment was conducted with 30 flight-school students using Microsoft Flight Simulator 2020 under Visual Flight Rules (VFR). Participants were randomly assigned to one of three instruction modalities: audio-only, text-only, or combined audio-text. Each participant completed two tasks: (1) an instructional flight with guided instructions and (2) a solo evaluation flight without guidance. Measures included visual transition entropy (to assess visual scanning), emotional valence, cognitive load, motivation, and flight performance metrics. Results: During the evaluation flight, the text-only and combined audio-text groups showed significantly lower visual transition entropy, indicating more organized visual scanning. The text-only group also exhibited higher emotional valence, reflecting greater motivation and engagement. No significant differences were found in overall flight performance or cognitive load, although trends suggested higher perceived immersion and motivation in the text-only condition. Discussion: Textual instructional delivery appears to support more efficient visual scanning and greater engagement, aligning with the Cognitive Theory of Multimedia Learning while highlighting its boundary conditions in aviation contexts. Although performance metrics were unaffected in this short session, textual information may be advantageous for specific flight segments and scenarios lacking live instruction. Further research should examine longer or repeated training sessions.
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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 | 0.004 |
| 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 | 0.000 |
| 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".