Contrast sensitivity, healthy aging and noise
Bibliographic record
Abstract
At least three studies have used external noise paradigms to investigate the cause of contrast sensitivity losses due to healthy aging. These studies have used noise that was spatiotemporally localized on the target. Yet, Allard and Cavanagh (2011) have recently shown that the processing strategy can change with localized noise thereby violating the noise-invariant processing assumption and compromising the application of external noise paradigms. The present study reassessed the cause of age-related contrast sensitivity losses using spatiotemporally extended external noise (i.e., full-screen, continuously displayed dynamic noise). Contrast thresholds were measured for young (mean=24 years) and older adults (mean=69 years) at 3 spatial frequencies (1, 3 and 9 cpd) and 3 noise conditions (noise-free, local noise and extended noise). At the two highest spatial frequencies, the results were similar with local and extended noise: the sensitivity loss was mainly due to lower calculation efficiency. At the lowest spatial frequency, age-related contrast sensitivity losses were attributed to the internal equivalent noise when using extended noise and, like in previous studies, due to calculation efficiency with local noise. These results show that the interpretation of external noise paradigms can drastically differ depending on the noise type suggesting that external nose paradigms should use external noise that is spatiotemporally extended like internal noise to avoid triggering a processing strategy change. Contrary to previous studies, we conclude that healthy aging does not affect the calculation efficiency of the detection process at low spatial frequencies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".