A Novel Visual Psychometric Test for Light-Induced Discomfort Using Red and Blue Light Stimuli Under Binocular and Monocular Viewing Conditions
Bibliographic record
Abstract
Purpose: To develop an objective psychophysical method to quantify light-induced visual discomfort, and to measure the effects of viewing condition and stimulus wavelength. Methods: Eleven visually normal subjects participated in the study. Their pupils were dilated (2.5% phenylephrine) before the experiment. A Ganzfeld system presented either red (1.5, 19.1, 38.2, 57.3, 76.3, 152.7, 305.3 cd/m2) or blue (1.4, 7.1, 14.3, 28.6, 42.9, 57.1, 71.4 cd/m2) randomized light intensities (1 s each) in four blocks. Constant white-light stimuli (3 cd/m2, 4 s duration) were interleaved with the chromatic trials. Participants reported each stimulus as either "uncomfortably bright" or "not uncomfortably bright." The experiment was done binocularly and monocularly in separate sessions, and the order of color/viewing condition sequence was randomized across participants. The proportion of "uncomfortable" responses was used to generate individual psychometric functions, from which 50% discomfort thresholds were calculated. Results: Light-induced discomfort was higher under blue compared with red light stimulation, both during binocular (t(10) = 3.58, P < 0.01) and monocular viewing (t(10) = 3.15, P = 0.01). There was also a significant difference in discomfort between viewing conditions, with binocular viewing inducing more discomfort than monocular viewing for blue (P < 0.001), but not for red light stimulation. Conclusions: The light-induced discomfort characteristics reported here are consistent with features of the melanopsin-containing intrinsically photosensitive retinal ganglion cell light irradiance pathway, which may mediate photophobia, a prominent feature in many clinical disorders. This is the first psychometric assessment designed around melanopsin spectral properties that can be customized further to assess photophobia in different clinical populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 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".