Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
Bibliographic record
Abstract
Eye tracking has been used extensively as a proxy to gain insight into the cognitive, perceptual, and sensorimotor processes that underlie skill performance. Previous work has shown that traditional and advanced gaze metrics reliably demonstrate robust differences in pilot expertise, cognitive load, fatigue, and even situation awareness (SA). This study describes the methodology for using a wearable eye tracker and gaze mapping algorithm that captures naturalistic head and eye movements (i.e., gaze) in a high-fidelity flight motionless simulator. The method outlined in this paper describes the area of interest (AOI)-based gaze analyses, which provides more context related to where participants are looking, and dwell time duration, which indicates how efficiently they are processing the fixated information. The protocol illustrates the utility of a wearable eye tracker and computer vision algorithm to assess changes in gaze behavior in response to an unexpected in-flight emergency. Representative results demonstrated that gaze was significantly impacted when the emergency event was introduced. Specifically, attention allocation, gaze dispersion, and gaze sequence complexity significantly decreased and became highly concentrated on looking outside the front window and at the airspeed gauge during the emergency scenario (all p values < 0.05). The utility and limitations of employing a wearable eye tracker in a high-fidelity motionless flight simulation environment to understand the spatiotemporal characteristics of gaze behavior and its relation to information processing in the aviation domain are discussed.
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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 | 0.002 |
| 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) | 0.003 | 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".