Why do shape aftereffects increase with eccentricity?
Bibliographic record
Abstract
Studies have shown that spatial aftereffects increase with eccentricity. Here, we demonstrate that the shape-frequency and shape-amplitude aftereffects, which describe the perceived shifts in the shape of a sinusoidal-shaped contour following adaptation to a slightly different sinusoidal-shaped contour, also increase with eccentricity. Why does this happen? We first demonstrate that the perceptual shift increases with eccentricity for stimuli of fixed sizes. These shifts are not attenuated by variations in stimulus size; in fact, at each eccentricity the degree of perceptual shift is scale-independent. This scale independence is specific to the aftereffect because basic discrimination thresholds (in the absence of adaptation) decrease as size increases. Structural aspects of the displays were found to have a modest effect on the degree of perceptual shift; the degree of adaptation depends modestly on distance between stimuli during adaptation and post-adaptation testing. There were similar temporal rates of decline of adaptation across the visual field and higher post-adaptation discrimination thresholds in the periphery than in the center. The observed results are consistent with greater sensitivity reduction in adapted mechanisms following adaptation in the periphery or an eccentricity-dependent increase in the bandwidth of the shape-frequency- and shape-amplitude-selective mechanisms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".