Non‐uniform phase sensitivity in spatial frequency maps of the human visual cortex
Bibliographic record
Abstract
KEY POINTS: Just as a portrait painting can come from a collection of coarse and fine details, natural vision can be decomposed into coarse and fine components. Previous studies have shown that the early visual areas in the brain represent these components in a map-like fashion. Other studies have shown that these same visual areas can be sensitive to how coarse and fine features line up in space. We found that the brain actually jointly represents both the scale of the feature (fine, medium, or coarse) and the alignment of these features in space. The results suggest that the visual cortex has an optimized representation particularly for the alignment of fine details, which are crucial in understanding the visual scene. ABSTRACT: Complex natural scenes can be decomposed into their oriented spatial frequency (SF) and phase relationships, both of which are represented locally at the earliest stages of cortical visual processing. The SF preference map in the human cortex, obtained using synthetic stimuli, is orderly and correlates strongly with eccentricity. In addition, early visual areas show sensitivity to the phase information that describes the relationship between SFs and thereby dictates the structure of the image. Taken together, two possibilities arise for the joint representation of SF and phase: either the entirety of the cortical SF map is uniformly sensitive to phase, or a particular set of SFs is selectively phase sensitive - for example, greater phase sensitivity for higher SFs that define fine-scale edges in a complex scene. To test between these two possibilities, we constructed a novel continuous natural scene video whereby phase information was maintained in one SF band but scrambled elsewhere. By shifting the central frequency of the phase-aligned band in time, we mapped the phase-sensitive SF preference of the visual cortex. Using functional magnetic resonance imaging, we found that phase sensitivity in early visual areas is biased toward higher SFs. Compared to a SF map of the same scene obtained using linear-filtered stimuli, a much larger patch of areas V1 and V2 is sensitive to the phase alignment of higher SFs. The results of early areas cannot be explained by attention. Our results suggest non-uniform sensitivity to phase alignment in population-level SF representations, with phase alignment being particularly important for fine-scale edge representations of natural scenes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".