Contour integration and aging: the effects of element spacing, orientation alignment and stimulus duration
Bibliographic record
Abstract
The ability to extract contours in cluttered visual scenes, which is a crucial step in visual processing, declines with healthy aging, but the reasons for this decline are not well understood. In three experiments, we examined how the effect of aging on contour discrimination varies as a function of contour and distracter inter-element spacing, collinearity, and stimulus duration. Spiral-shaped contours composed of Gabors were embedded within a field of distracter Gabors of uniform density. In a four alternative forced-choice task, younger and older subjects were required to report the global orientation of the contour. In Experiment 1, the absolute contour element spacing varied from two to eight times the Gabor wavelength and contour element collinearity was disrupted with five levels of orientation jitter. Contour discrimination accuracy was lower in older subjects, but the effect of aging did not vary with contour spacing or orientation jitter. Experiment 2 found that decreasing stimulus durations from 0.8 to 0.04 s had a greater effect on older subjects' performance, but only for less salient contours. Experiment 3 examined the effect of the background on contour discrimination by varying the spacing and orientation of the distracter elements for contours with small and large absolute spacing. As in Experiment, the effect of aging did not vary with absolute contour spacing. Decreasing the distracter spacing, however, had a greater detrimental effect on accuracy in older subjects compared to younger subjects. Finally, both groups showed equally high accuracy when all distracters were iso-oriented. In sum, these findings suggest that aging does not affect the sensitivity of contour integration to proximity or collinearity. However, contour integration in older adults is slower and is especially vulnerable when distracters are denser than contour elements.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".