Structural sparseness and spatial phase alignment in natural scenes
Bibliographic record
Abstract
The Fourier phase spectrum plays a central role regarding where in an image contours occur, thereby defining the spatial relationship between those structures in the overall scene. Only a handful of studies have demonstrated psychophysically the relevance of the Fourier phase spectrum with respect to human visual processing, and none have demonstrated the relative amount of local cross-scale spatial phase alignment needed to perceptually extract meaningful structure from an image. We investigated the relative amount of spatial phase alignment needed for humans to perceptually match natural scene image structures at three different spatial frequencies [3, 6, and 12 cycles per degree (cpd)] as a function of the number of structures within the image (i.e., "structural sparseness"). The results showed that (1) the amount of spatial phase alignment needed to match structures depends on structural sparseness, with a bias for matching structures at 6 cpd and (2) the ability to match partially phase-randomized images at a given spatial frequency is independent of structural sparseness at other spatial frequencies. The findings of the current study are discussed in terms of a network of feature integrators in the human visual system.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".