P021 Associations between Virtual Reality (VR) Ocular Assessments and Vigilance during Extended Wakefulness – Preliminary Analysis
Bibliographic record
Abstract
Abstract Introduction Sustained vigilance is essential for safety in high-risk workplaces, making rapid and accessible alertness failure vulnerability assessments extremely desirable. We are piloting existing technology, the NeuroFlex® Platform, which takes ocular performance measurements of both prosaccade and antisaccade eye-movements using a portable Virtual Reality (VR) headset. We’ve conducted preliminary comparisons between these measures and Psychomotor Vigilance during extended wakefulness. Methods Sixteen young-adults (females= 8; age M= 25.13, SD= 4.30) completed five test batteries starting 1-hour post-wake where repeated testing encapsuled more than 24-hours of extended wakefulness. Each battery consisted of two 10-minute visual Psychomotor Vigilance Tasks, in addition to three administrations of 60-second prosaccade and antisaccade assessments using the NeuroFlex® VR platform. Results Time main effects occurred for reciprocal reaction time, F(3.83,57.37) = 20.54, p <.001, and proportions of lapses/trials, F(3.55,48.33) = 7.36, p <.001. Vigilance troughs occurred around the circadian nadir (19-hours post-wake) with some recovery at final administrations (25-hours post-wake). No associations were found between prosaccade latency and vigilance metrics. Moderate negative (r=-.63, p=.01 & r=-.54, p=.03) associations were found between antisaccade latency and reciprocal reaction time near the nadir, but not the number of lapses per trial. Discussion This preliminary analysis has demonstrated potential sensitivity of the NeuroFlex® platforms ocular measurements to vigilance. Though we have not detected associations between lapses and eye-movement latency, we were limited to the existing output generation of the NeuroFlex® platform in this preliminary investigation. With refined data analyses, we see promise in the NeuroFlex® platforms capability of predicting alertness failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".