Investigating the origin of visual loss during the normal aging process using an adapted Landolt-C technique.
Bibliographic record
Abstract
Purpose. The objective of the present study was to investigate the change of visual functioning with age at both ocular and neural levels. This was done using an adapted Landolt-C technique where the “C”s were either defined by luminance-contrast or texture-contrast, the latter necessitating increased neural processing to be perceived. Methods. Performance was measured psychophysically by asking each participant to indicate the orientation of the “C” gap-opening, presented in either of 4 orientations (up, down, left or right) at different levels of luminance-contrast (first-order condition) or texture-contrast (second-order condition). A 4AFC constant stimuli procedure was used to measure gap-opening orientation-identification thresholds for 4 age groups; 18–35 years, 35–50 years, 50–65 years and 65+ years. Only participants presenting a Snellen acuity of 6/7.5 or better were tested. Older participants presenting nuclear cataracts of grade 3 (or worse) or other ocular pathologies were excluded. Inclusion criteria were confirmed by a thorough optometric examination. Results. Gap-opening orientation-identification thresholds for the texture-defined “C”s increased with age at a faster rate compared to the thresholds for the luminance-defined “C”s. Conclusions. The results suggest that neurally-based complex visual information processing becomes progressively compromised with age, possibly reflecting less efficient neuro-integrative functioning within early visual areas of the aging brain. Furthermore, the results demonstrate that this novel technique is sensitive enough to measure subtle neural dysfunction during the aging process and can be used to dissociate peripheral (ocular) from centrally (neural) mediated visual loss.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".