The Effects of Mild Ocular Surface Stimulation and Concentration on Spontaneous Blink Parameters
Bibliographic record
Abstract
PURPOSE: This exploratory, pilot study compared the effects of concentrating on a visual task and a very mild ocular surface air stimulus on multiple blink parameters. METHODS: Ten subjects participated in this study. There were two visits, one with an ocular surface air stimulus (AS) and one without (NS). The AS was set at a level barely perceptible by subjects (approximately 0.6 m/s at the eye). At each visit, subjects performed a high-concentration (HC) and low-concentration (LC) task. Blinking was tracked and tear-film breakup (TBU) was monitored simultaneously to measure blink parameters, including the interblink interval (IBI), blink amplitude, duration, maximum velocity and TBU before and after each blink. RESULTS: During the HC tasks, IBI was significantly higher and blink duration was lower (repeated measures ANOVA, p < 0.05) than the LC tasks. The IBI in the AS-LC condition was significantly lower and less variable than in the NS-HC condition, whereas blink duration showed the opposite effect (Hotelling T² test, p < 0.005). There was high individual variation in correlations between blink amplitude and maximum velocity. The area of TBU was not significantly correlated with any blink parameter. CONCLUSIONS: The lack of correlation between TBU and blinking suggests that many blinks are stimulated by internal controls, rather than direct stimulation of the ocular surface by TBU. This pilot study suggests that even very mild ocular surface stimulation produces opposite effects on the timing and duration of the blink, when compared to concentrating on a visual task. The HC task tends to decrease blink frequency and duration, presumably to minimize interruption by the eyelids, whereas mild ocular surface AS increased blink frequency and duration, most likely to increase protection of the ocular surface.
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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) | 0.001 | 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) | 0.002 | 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".