Contour sparseness and the interactions in the visual processing of local phase alignment of natural scene contours
Bibliographic record
Abstract
The phase spectra of natural scene imagery play a central role regarding where contours occur, thereby defining the spatial relationship between those features in the formation of image structure. Thus, we were interested in 1) measuring the relative amount of local spatial phase alignment needed by humans to extract contours from an image, and 2) determine if those measurements depended on the contour “sparseness” at different spatial frequencies (SF). We examined this with a match-to-sample task that used either natural scene images or noise images possessing naturalistic contours, grouped with respect to their level of sparseness. Phase alignment in the stimuli was controlled by band-pass filtering the phase spectra, where phase angles falling within the filter's pass-band were preserved, and everything else randomized. Filter widths were varied (0.3 octave steps) about one of three central SFs (3, 6, 12cpd). On any given trial, following a 250ms presentation of a partially phase-randomized image, participants were simultaneously shown (2sec) four images and asked which one corresponded to the previously viewed, partially phase-randomized image. Results indicated that 1) the bandwidth of local spatial phase alignment needed to match image contours depended on the relative sparseness of the original image; 2) for contours falling within the 6cpd central SF filter, less phase alignment was needed as compared to the other central SFs; 3) contour sparseness outside of a given filter's central SF was not found to interfere with the amount of phase alignment needed to match image contours.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".