Integration of first- and second-order orientation
Bibliographic record
Abstract
The problem of how visual information such as orientation is combined across space bears on key visual abiities, such as texture perception. Orientation signals can be derived from both luminance and contrast, but it is not well understood how such information is pooled or how these different orientation signals interact in the integration process. We measured orientation discrimination thresholds for arrays of equivisible first-order and second-order Gabors. Thresholds were measured as the orientation variability in the arrays increased, and we estimated the number of samples (or efficiency) and internal noise of the mechanism being used. Observers were able to judge the mean orientation of arrays of either first- or second-order Gabors. For arrays of first-order and arrays of second-order Gabors, estimates of the number of samples used increased as the number of Gabors increased. When judging the orientation of arrays of either order, observers were able to ignore randomly oriented Gabors of the opposite order. If observers did not know which Gabor type carried the more useful orientation information, they tended to use the information from first-order Gabors (even when this was poorer information). Observers were unable to combine information from first- and second-order Gabors, though this would have improved their performance. The visual system appears to have separate integrators for combining local orientation across space for luminance- and contrast-defined features.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".