The role of spatial phase in texture segmentation and contour integration
Bibliographic record
Abstract
It has been recently argued that the visual system possesses just two phase "detector" mechanisms, namely, +cosine and -cosine (P. C. Huang, F. A. Kingdom, & R. F. Hess, 2006). This suggests rather limited access to the rich distribution of receptive field phase that neurophysiologists tell us are represented in the different response profiles of striate simple cells. Whereas that study has suggested that striate receptive field phase is not directly available to perception for the detection/discrimination of localized stimuli, here we investigate whether such information might be used in more integrative striate or extrastriate functions such as texture segregation or contour integration. Specifically, given that simple cells have different local absolute phase response profiles, we ask whether a network of simple cells with similar phase preferences interact in such a way as to extract textures, contours, or both based on phase alone. Two novel texture segmentation experiments and one contour integration experiment were carried out with the intention of providing an answer to the question of how useful is local absolute spatial phase for texture segmentation and contour integration. The results support the possibility of two phase mechanisms (+/-cosine) for global texture segmentation, as well as for contour integration, when the elements that make up a given contour are orthogonal to contour paths.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".