After-effect of perceived regularity
Bibliographic record
Abstract
Aim: Regularity is a fundamental characteristic of the visual environment. Here we consider whether regularity is an adaptable feature, specifically whether one can obtain an after-effect (AE) in perceived regularity. Method: Stimuli consisted of a 7 by 7 arrangement of dark Gaussian blobs arranged on a baseline grid. The position of each blob was randomly jittered from its baseline position by an amount that determined the degree of pattern irregularity. Observers adapted for 60 sec to a pair of patterns above and below fixation with a different amount of regularity, then adjusted the relative regularity of two test patterns to obtain the PSE. The size of the AE was given by the difference in regularity at the PSE. Results: PSEs were significantly different from zero, indicating that regularity is an adaptable feature. Additional experiments indicated that the regularity AE was not due to a) luminance spatial frequency adaptation, b) local positional adaptation or c) local orientation adaptation. Experiments using single adaptors revealed that the AE is unidirectional, specifically that adaptation only causes test patterns to appear less regular. Conclusion: Pattern regularity is an adaptable feature in vision, but the functional significance of regularity adaptation is not yet clear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".