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 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 | 0.000 |
| 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) | 0.000 | 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".