Orientation- and frequency-modulated textures at low depths of modulation are processed by off-orientation and off-frequency texture mechanisms
Bibliographic record
Abstract
Intuitively it may seem likely that orientation-modulated (OM) and frequency-modulated (FM) textures are processed utilizing the first-order channels that are most responsive to the first-order (luminance) information contained within the textures. This assumption would imply that the detection or segmentation of orientation-modulated or frequency-modulated textures is accomplished by second-order mechanisms that receive their first-order input from neurons tuned to either the center, or to the peaks in the orientation and spatial frequency distribution of the texture. Here we show that at low depths of modulation this is not the case. Using an adaptation paradigm, we show that the first-order filters involved in the perception of OM and FM textures are those which maximize the differential response between the different texture regions. These filters show preferences for orientations and spatial frequencies that are very different from the center and peak orientation/spatial frequency contained in the stimulus. Our explanation of this result is similar to that made by Regan and Beverley (J. Opt. Soc. Am., 73, 1684 [1983], J. Opt. Soc. Am, A, 2, 147 [1985]) for simple grating stimuli. However, we show that whereas Regan and Beverley's results could be accounted for on the basis of the tuning functions of the putative mechanisms involved, our results can be explained in terms of the characteristics of the textures themselves. Our finding has some important implications. For example, the distribution of activity across texture mechanisms with differing first-order tuning functions may be used to determine the nature of a texture modulation.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".