The effect of mask contrast on spatiotemporal masking in younger and older subjects
Bibliographic record
Abstract
Although there is evidence for weaker centre-surround interactions in older subjects in tasks using dynamic stimuli (Betts et al., Neuron, 45(3), 361-6, 2005; Betts et al., J Vis, 9(1):25, 1–15, 2009), the effect of the surround is much stronger for older adults in tests of perceived contrast (Karas et al., J Vis, 9(5):11, 1–9, 2009). Previously, we measured detection thresholds for a horizontal 3.5 cycles-per-degree Gabor target masked by a small central sine wave mask, a surround sine wave annulus, and a combination mask (centre-plus-surround) of the same spatial frequency. Target onset, relative to the 40% contrast mask, varied across conditions. The shapes of the overlay masking functions obtained from younger and older subjects were similar to those found for young subjects by Saarela and Herzog (J Vis, 8(3):23, 1–10, 2008), but the overall level of masking was lower in older subjects. One potential explanation for this age difference is older subjects had lower contrast sensitivity for the mask. The current experiment tests this hypothesis by measuring masking functions with different mask contrasts ranging from 10% to 80%. We found that varying contrast had different effects on masking in younger and older subjects, and that the age difference could not be explained by a difference in contrast sensitivity. Our results suggest that the relative strength of age-related changes in centre-surround interactions depend on the spatiotemporal properties of the stimulus.
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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 | 0.000 | 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".