Contour integration and aging: effects of inter-element distance, distracter density, and stimulus duration
Bibliographic record
Abstract
Contour integration - the ability to group information across space to extract contours - declines with aging (DelViva & Agostini, IOVS, 2007; Roudaia et. al., Vis.Res., 2008, 2011). Here, we examined how age-related changes in contour integration depend on inter-element distance, contour element collinearity, stimulus duration, and distracter density. In a 4AFC task, younger (mean age: 25 y.) and older (mean age: 66 y.) subjects discriminated the global orientation of spiral-shaped contours sampled with Gabor elements (λ= 0.3 deg, σ=0.11 deg, 90% contrast) and embedded in a field of randomly oriented distracter Gabors. In Experiment 1, stimuli were presented for 1s and their minimum inter-element distance was varied across blocks between 2λ and 8λ. Within each block, contour element collinearity was disrupted by the addition of 5 levels of orientation jitter, ranging from 0 - 60 deg. There was a constant age-related decline in accuracy for all inter-element distances and orientation jitter levels. Experiment 2 examined the effect of stimulus duration on the discrimination of collinear and non-collinear contours with 2λ and 6λ inter-element distances. Stimuli were blocked by contour type and were presented for 0.04 - 0.8s. Older subjects’ accuracy declined more with decreasing stimulus duration than younger subjects’ accuracy, however this effect varied with contour type. Experiment 3 examined the effect of increasing distracter density on discrimination of collinear contours with 3λ and 6λ inter-element distances. Both age groups showed higher accuracy for 3λ compared to 6λ contours for all distracter density levels. However, older subjects showed greater sensitivity to increasing density than younger subjects for contours with small inter-element distances. In sum, although the spatial range of contour integration does not change with age, the process is slower and less tolerant to the relative contour/distracter density. Meeting abstract presented at VSS 2012
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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 | 0.003 |
| 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 | 0.000 |
| 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".