Shorter Viewing Distance Reduces Contrast in Man-Made Environments
Bibliographic record
Abstract
Purpose Myopia is associated with increased near-work, time spent indoors and urban environments. This may in part stem from the spatial frequency (SF) composition of the visual environment in such scenarios compared to nature. We investigated the SF content of natural and man-made environments and how the relative contrast experienced may be affected by viewing distance. Study Design Experimental study of environmental contrast. Subjects, Participants, and/or Controls Digital images (n=594) from natural (n=151), mixed-urban (n=172), urban (n=136) and indoor (n=135) environments across Singapore. Methods The FFT was computed and radially averaged and the slope of the amplitude spectra with SF was determined. The total contrast energy within each SF octave ( f –2 f ) was measured to determine exposure deficits between environments, and whether environmental contrast was scale invariant (unaffected by changes in viewing distance). Main Outcome Measures Between environment differences in SF gradient, total contrast and scale invariance. Results As expected, indoor environments displayed the steepest SF gradients at -1.36±0.1, more than urban (-1.22±0.1, p <0.01), mixed-urban (-1.09±0.1, p <0.01) and natural (-1.00±0.1, p <0.01) environments. Specifically, mixed-urban, urban and indoor environments all lacked contrast at middle and higher SFs compared with natural scenes ( p <0.01 each). Contrast energy/octave was approximately equal for natural environments, signifying the relative SF composition was scale invariant. This was not true for man-made environments. Contrast energy/octave decreased as a function of SF in mixed-urban, urban and, most rapidly, in indoor environments ( p <0.001 each), signifying that the total contrast experienced in such environments is directly dependent on viewing distance. Consequently, halving viewing distance when indoors would reduce total perceived middle SF contrast by 35%, despite indoor environments already being deficient compared to nature. Conclusions The SF content of natural environments is consistent regardless of viewing distance, signifying scale invariance. However, this does not apply in man-made environments where contrast at all SFs directly depend on viewing distance. This implies that short viewing distances in man-made environments, may fail to stimulate the eye with sufficient contrast compared to natural environments and may be the reason that such environments induce myopia. Artificial environments cause deprivation because they inherently lack contrast at middle and higher SFs, and these deficits are exacerbated the shorter the viewing distance.
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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".