The effects of aging on contrast discrimination
Bibliographic record
Abstract
Abstract It is well established that contrast sensitivity for sine wave gratings is reduced in older observers, but comparatively little is known about how aging affects the perception of supra-threshold contrast. The current experiments therefore examined contrast discrimination in groups of younger (n=11; mean age = 23 years) and older (n=12; mean age = 69 years) observers. In experiment one, the target — a horizontal 1.5 c/deg Gabor pattern — was added to a mask grating of the same spatial frequency, orientation, and spatial phase. Threshold-vs-contrast (TvC) curves were obtained by measuring detection thresholds for the target as a function of mask contrast, which ranged from zero to 0.32. As was reported by Beard et al. (1994), TvC curves had similar dipper shapes in both age groups. Contrast discrimination thresholds were higher in older observers, but the age differences were reduced greatly after discrimination thresholds were normalized by dividing them by detection thresholds (i.e., thresholds measured with a zero contrast mask). In a second experiment, TvC curves were measured using a vertically-oriented mask. As expected, using a mask that was orthogonal to the target significantly altered the shapes of the TvC curves, which were nearly flat and increased slightly only at the highest mask contrast. Contrast discrimination thresholds were higher in older observers, but, as was found in the first experiment, age differences were eliminated by normalizing discriminating thresholds by detection thresholds. Hence the results from both experiments suggest age differences in supra-threshold contrast discrimination can be explained by age differences in contrast sensitivity.
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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 | 0.002 |
| 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) | 0.001 | 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".