The Impact of Reduced Vision on Simulated Flight Performance in Novice Pilots: Toward Establishing Performance-Based and Operationally Representative Visual Acuity Standards
Bibliographic record
Abstract
Objective To investigate the effect of visual degradation on simulated flight performance, perceived stress, and perceived task difficulty. Background Establishing visual standards for pilots is crucial, although it may limit the pool of eligible candidates and impact pilot retention. Despite this, there is limited understanding regarding the influence of vision on pilot performance. Method Twenty participants (0-300 flight hours) completed a flight simulation task using the ALSIM AL250 in two experiments. Distance static visual acuity (VA) ranged from 6/6 (20/20) to 6/60, with scenarios including no vision. Experiment 1 ( n = 10) tested landing performance for 6 VA conditions, while experiment 2 ( n = 10) involved a more difficult circuit task (traffic pattern) with 8 VA conditions. Participants completed stress and difficulty questionnaires between trials. Flight performance variables assessed were vertical speed, altitude, attitude, pitch, and roll. Results In both flight simulation experiments, vision degradation did not affect novice pilots’ landing performance, but complete loss of vision led to loss of control. Participants in experiment 1 experienced stress at lower perturbation level than in experiment 2. Conclusion Vision degradation up to 6/60 had no discernible impact on novice pilots’ simulated approach to landing or flight circuit and landing. Total vision loss led to loss of aircraft control. Perceived stress and difficulty increased with reduced vision. Application This research opens the door to reexamine the visual standards for pilots and serve as a simple tool to manipulate perceived stress and difficulty in operational tasks.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".