Visual after-effect of perceived regularity
Bibliographic record
Abstract
Aim: Regular repeating patterns are prominent features in a visual scene. Here I consider whether regularity is an adaptable feature that produces a subsequent after-effect and whether a first- or second-order process mediates that after-effect. Method: Stimuli consisted of a 7 by 7 arrangement of elements on a baseline grid. The position of each element was randomly jittered from its baseline position by an amount that determined its degree of pattern irregularity. The elements of the pattern consisted of dark Gaussian blobs (GB), difference of Gaussians (DOG) or random binary patterns (RBP). Observers adapted for 60 seconds to a pair of patterns above and below fixation with a different degree of regularity, then adjusted the relative degree of regularity of two subsequently presented test patterns. The size of the after-effect at the point of subjective equality (PSE) was given by the baseline removed difference in regularity at the PSE or log ratio of the physical element jitter of the two test patterns at the PSE. Results: PSEs revealed that regularity is an adaptable feature that produces a unidirectional after-effect; specifically that adaptation only causes test patterns to appear less regular. The after-effect displayed transfer from GB adaptors to both DOG and RB test patterns and from DOG and (RBP) adaptors to GB patterns. Conclusion: Pattern regularity is an adaptable feature in vision, which produces a novel unidirectional after-effect I have termed Regularity After-Effect, or RAE. I propose second-order spatial-frequency channels as candidate mechanisms of regularity processing.
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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 | 0.002 |
| 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) | 0.003 | 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".