Bibliographic record
Abstract
Introduction. Visual saliency plays an important role in early vision, guiding both attention shifts and eye movements. Visual saliency thus forms a central role in many models of early visual processing (e.g. Itti, 2006, VisCog; Zhaoping & Snowden, 2006, VisCog; Wolfe & Horowitz, 2004, NatRevNeurosci). Using a novel psychophysical method to measure saliency, we derive perceptual fields of contextual modulation. Methods. The stimulus is a grid of right- or left-oblique red or green lines on a black background. Line luminance varies continuously over the image, which participants (N=9) adjust locally towards equisalience using a mouse. Assuming that systematic deviations from equiluminance are indicative of compensation for saliency, local luminance setting correlates negatively with local saliency. Results. Perceptually salient image regions are more heterogeneous in color and orientation, indicative of short-range iso-feature inhibition. Perceptual fields of context modulation are obtained by correlating image properties with local saliency. Specifically, certain combinations of features correlated with local saliency, the strength of which was dependent on the distance between items containing these features. Using this analysis, we show that: (1) color center-surround fields for different-color are stronger but operate over shorter ranges than for same-color, (2) parallel orientations are inhibited, but less so if continuous, and (3) orthogonal orientations are more salient when end-stopping another line rather than being end-stopped. On average, these perceptive fields predict luminance and account for 60% of the variance in the data. Discussion. These new results can be compared to predictions from current models of visual salience. Moreover, this new method is sensitive within the normal functioning range, where most current research methods produce ceiling effects and flat reaction time functions. Here, we used a simple stimulus to validate the method, but the method can be generalized to any stimulus (e.g. reading, visual textures, natural images).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.280 | 0.136 |
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".