Human Corneal Adaptation to Mechanical, Cooling, and Chemical Stimuli
Bibliographic record
Abstract
PURPOSE: To psychophysically investigate adaptation in human corneas using the Belmonte pneumatic esthesiometer. METHODS: Twenty, 8, and 20 healthy subjects were enrolled in the mechanical, cool, and chemical experiments, respectively. Thresholds were estimated using an ascending method of limits and three intensities (subthreshold, threshold, and suprathreshold, in random order) were each presented 10 or 20 times, and subjects scaled the intensity of the stimuli (0-4 [no stimulus to very intense stimulus]). Friedman nonparametric ANOVA was used to analyze the rating data. RESULTS: There was measurable adaptation with both mechanical and cool stimuli. For both suprathreshold mechanical and cool stimuli, the earlier stimuli were rated more intensely than subsequent stimuli (both P < 0.05). However, this was not the case for subthreshold and threshold mechanical and cool stimuli (all P > 0.05). Paradoxically, for the chemical stimuli, there was adaptation to threshold stimuli (P = 0.03) but no adaptation for subthreshold and suprathreshold stimuli (P = 0.19 and 0.11, respectively). CONCLUSIONS: Both mechanical (mechanosensory or polymodal) and cold receptors on human corneas show adaptation to repeated suprathreshold stimuli with a reduction in perceived intensity after multiple exposures to the same physical stimulus intensity. This is in accord with the results found in electrophysiological and psychophysical experiments of somatosensation elsewhere in the body (and in other animals). The response to chemical stimuli was different, and this might reflect proximal and distal neural or stimulus-specific effects.
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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) | 0.003 | 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".