Bibliographic record
Abstract
Visual saliency plays an important role in early vision. Humans automatically orient to salient information, via covert attentional shifts and overt eye movements. Here, we measured saliency using a novel psychophysical method. The stimulus was a grid of colored oriented lines. Line luminance varied continuously over the image. Using a mouse, participants adjusted line luminance at locations in the image, until all lines appeared homogeneously luminant. Participants tended to increase (or decrease) luminance of lines where perceptually salient information was absent (or present), thus line luminance setting correlates with perceived saliency. Perceptually less salient image regions were more homogenous in color and orientation, consistent with iso-feature suppression. Perceptual fields of contextual modulation are derived, showing increased saliency surrounding color and/or orientation changes, increased saliency for collinear and end-stopping lines, and a nonlinear integration of saliencies across dimensions. It took 3 or more surround items identical to a target to generate a measurable inhibitory effect, beyond which every additional identical item had a monotonic effect. These novel findings allow a revision of current models of visual saliency. In particular, we found evidence of sustained saliency. Moreover, this new method is sensitive within the normal functioning range, unlike most current research methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".