The site of orientation integration
Bibliographic record
Abstract
Purpose. We wanted to know if the site of orientation integration (Dakin, JOSA 2001) was at an early or late stage in the visual pathway relative to the site of binocular integration. Methods. We used a task in which subjects had to judge the mean orientation of an array of oriented Gabors. The Gabor orientations were samples from a Gaussian orientation distribution of variable bandwidth and mean that was left or right of vertical. The internal noise and number of samples were estimated from fitting a standard summation-variance model to the data. These orientation samples were either presented to one eye or to both eyes under dichoptic viewing. When presented to both eyes they could be in the same disparity plane or in different disparity planes. In some cases, signals of random orientation (termed noise) were added to the signal orientations in one or other of the above conditions. Results. Performance on this task depended on whether the signal and noise were presented in different disparity planes. Furthermore similar results were obtained for dichoptic and monoptic viewing conditions. Interestingly, noise significantly (p<0.05) disrupts performance when it is presented to the dominant eye, leading to higher thresholds, higher internal noise, and decreased sampling efficiency. Conclusions. Our results suggest that the site of orientation integration is not only after the site of binocular integration but also after the site where disparity is encoded. The finding that the effectiveness of noise depends on the eye to which it is presented, even though this information is not known to the subject, suggests that there are also monocular pathways through which this type of integration can occur although their sensitivity must be reduced compared with their binocular counterpart.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".