Efficient Coding of Spatial Frequency in Natural Images: Cross-frequency Dependence
Bibliographic record
Abstract
Abstract Research suggests that spatial frequency (SF) channels in the visual system operate with a degree of independence. However, the independence model has been questioned by evidence of non-additive effects in compound gratings, indicating complex interactions between SF channels. These studies, however, typically employ artificial stimuli, leaving questions about SF processing in natural images. Efficient Coding hypothesis, which posits that the visual system minimizes redundancy and retains relevant information, predicts a dependence between HSF and LSF. In this study, we examined interactions between LSF and HSF using natural and phase-scrambled images to explore SF integration during perception. Participants completed an SF identification task, using both natural and scrambled images to isolate the role of phase alignment. Our results indicate that HSF and LSF interact primarily in phase-aligned conditions, with phase scrambling driving independent processing of two SFs and reducing error rates. These findings suggest that phase alignment enhances perceptual efficiency, facilitating a trade-off between accuracy and redundancy reduction in natural scene processing.
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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 | 0.000 | 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) | 0.001 | 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".