Spatial characteristics of center-surround antagonism in younger and older adults
Bibliographic record
Abstract
Sensitivity to motion direction is affected by stimulus size, contrast (D. Tadin & J. S. Lappin, 2005; D. Tadin, J. S. Lappin, L. A. Gilroy, & R. Blake, 2003), and observer age (L. R. Betts, C. P. Taylor, A. B. Sekuler, & P. J. Bennett, 2005). Here, we investigated the effect of spatial frequency on motion discrimination and how sensitivity changes in older adulthood. We measured stimulus duration thresholds for younger (18-30 years) and older (60-75 years) observers using drifting Gabor gratings that differed in size, spatial frequency, and contrast. A simple model characterized age differences in the threshold-vs.-size functions. The model parameter fits were consistent with an age-related decrease in the strength of spatial suppression. The model also provided good fits to the thresholds when plotted as a function of the total stimulus contrast energy, which suggests that age-related changes in summation and suppression can be modeled as reduced sensitivity to contrast energy. The summation parameter scaled according to stimulus spatial frequency in younger observers only. Suppression strength decreased as a function of spatial frequency in both age groups. Our findings provide additional evidence for age-related changes in summation and suppression mechanisms and suggest that the total contrast energy in the stimulus plays an important role in determining sensitivity to motion direction in both younger and older adults.
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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 | 0.000 | 0.001 |
| 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".