Aging and Visual Crowding
Bibliographic record
Abstract
OBJECTIVES: The ability to perceive high spatial frequencies (i.e., fine detail) is impaired when contours are placed near the detail to be resolved (Bouma, H. [1970]. Interaction effects in parafoveal letter recognition. Nature, 226, 177-178. doi:10.1038/226177a0; Flom, M. C., Weymouth, F. W., & Kahneman, D. [1963]. Visual resolution and contour interaction. Journal of the Optical Society of America A, 53, 1026-1032. doi:10.1364/JOSA.53.001026.). This visual crowding is more pronounced outside of central vision and may be more pronounced in older adults. Thus, the motivation for the present study. METHOD: Younger (M = 20.95 years) and older adults (M = 70.32 years) detected gap orientation in a Landolt C presented at 3° or 6° either alone or flanked by bars of the same spatial scale. RESULTS: Both age groups demonstrated a visual crowding effect, in that acuity deteriorated in the flanking condition, an effect that grew with eccentricity. Older adults exhibited a larger crowding effect, particularly at 6°. Younger adults tested at reduced illumination did not show the crowding effect of older adults. Thus, age differences do not appear to result from reduced retinal illumination. When the crowding effect was operationalized as the ratio of crowded to uncrowded acuity, age differences were eliminated at both 3° and 6°. DISCUSSION: These data have implications for understanding age differences in functional vision, including reading and visual search.
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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 | 0.002 |
| 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 | 0.001 |
| 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".