Effects of Lighting on Reading Speed as a Function of Letter Size
Bibliographic record
Abstract
OBJECTIVE: We sought to determine under what conditions brighter lighting improves reading performance. METHOD: Thirteen participants with typical sight and 9 participants with age-related macular degeneration (AMD) read sentences ranging from 0.0 to 1.3 logMAR under luminance levels ranging from 3.5 to 696 cd/m². RESULTS: At the dimmest luminance level (3.5 cd/m²), reading speeds were slowest at the smaller letter sizes and reached an asymptote for larger sizes. When luminance was increased to 30 cd/m², reading speed increased only for the smaller letter sizes. Additional lighting did not increase reading speeds for any letter size. Similar size-related effects of luminance were observed in participants with AMD. CONCLUSION: In some instances, performance on acuity-limited tasks might be improved by brighter lighting. However, brighter lighting does not always improve reading; the magnitude of the effect depends on the text size and the relative changes in light level.
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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 | 0.004 |
| 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".