Supra‐threshold contrast matching and the effects of contrast threshold and age
Bibliographic record
Abstract
BACKGROUND: The effects of age on contrast threshold are well known but little is known about its effect on supra-threshold contrast perception. This study examines supra-threshold contrast matching and the effects of age in naïve observers. METHODS: Two age groups (from 20 to 50 years with 14 subjects and 51 years and older with 15 subjects) participated in the study. Contrast threshold and supra-threshold contrast matching up to 8.53 cycles per degree were measured. RESULTS: Both age groups demonstrated some degree of contrast constancy at medium and higher contrasts but this was not perfect even at the highest contrast tested (55.9 per cent). There was no overall effect of age on supra-threshold contrast matching (p = 0.086) but there was an interaction between age and spatial frequency (p < 0.001). The plots of matched contrast against standard contrast showed that for some spatial frequencies, the slope was significantly different from unity, indicating a gain in the visual system for supra-threshold perception. This was still true when corrected for threshold differences. CONCLUSION: Contrast constancy exists in a larger group of naïve subjects of different ages but does not perfectly compensate for the differences in thresholds. The results are discussed in terms of the currently proposed models of contrast perception.
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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 | 0.006 |
| 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".