Assessing cognitive load through eye metrics in in-motion vs. stationary environments
Bibliographic record
Abstract
OBJECTIVE: Quantifying cognitive load during whole-body motion is crucial for professions such as aircraft pilots and paramedics. This study incorporates eye metrics to monitor cognitive load changes during aiming tasks with different levels of difficulty (ID) while the participant was either stationary or experiencing whole-body motion (in-motion condition). METHOD: 25 participants completed reciprocal aiming tasks under both in-motion and stationary conditions. The IDs were modified by altering target distances and sizes. Eye metrics, including pupil size, blink rate, fixation dispersion, and eye saccadic movements, were analyzed. RESULTS: Under in-motion conditions, significantly larger pupil dilation was observed by more than 100 %; Blink rate decreased by 27 % for difficult tasks; Participants displayed significantly larger fixation dispersion by 29 %. Participants' eyes frequently overshot targets while scanning between targets, followed by a series of corrective adjustments, for both conditions. CONCLUSIONS: The in-motion condition significantly increased cognitive load, exaggerated pupil dilation, suppressed eye blinks, and intensified fixation dispersion. APPLICATIONS: Eye metrics assess cognitive load in moving environments, offering reliable tools to study the effects of motion. This aids in developing training protocols to minimize negative impacts on individuals working under whole-body motion conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".