Eye tracking, point of gaze, and performance degradation during disorientation.
Bibliographic record
Abstract
BACKGROUND: The cognitive cockpit concept has been proposed as a potential disorientation countermeasure. It involves monitoring the pilot's physiological, behavioral and subjective responses during disorientation. This data is combined to provide a real time model of pilot state, which is used as a basis for optimizing pilot performance. This study attempts to investigate whether there are consistent behavioral or physiological "markers" that can be monitored during a specific disorientation scenario. METHODS: An Integrated Physiological Trainer with interactive aircraft controls and an eye-tracking device was employed. Fourteen subjects proficient in maintaining straight-and-level flight and who have acquired the skills in changing attitude participated in the study. They were exposed to a flight profile consisting of straight-and-level flying and change in attitude without exposure to a head roll (control condition) and a profile with exposure to a head roll (experimental conditions) during constant yaw rotation. Flight performance parameters and subjects' eye movements and point of gaze behavior were monitored continuously. RESULTS: Immediately on the return to upright head position, all subjects reported a strong apparent pitch displacement that lasted < or = 20 s and a lesser sensation of lateral movement. Significant differences (p < 0.01) were noted on a number of scanning behaviors between the control and the experimental conditions. The appearance of nystagmus was apparent as indicated by the number of involuntary saccades during disorientation. Flight performance decrement in the experimental conditions was reflected by a significant deviation in maintaining airspeed (p < 0.01). CONCLUSION: It appears that the pitch illusion consistently affects visual scanning behavior and is responsible for the decrement in flight performance observed in the simulator.
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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 | 0.001 |
| 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 | 0.000 |
| 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".