Orientation gradient detection exhibits variable coupling between first- and second-stage filtering mechanisms
Bibliographic record
Abstract
We investigated sensitivity to orientation modulation using visual stimuli with bandpass filtered noise carriers. We characterized the relationship between the spatial parameters of the modulator and the carrier using a 2-AFC detection task. The relationship between these two parameters is potentially informative of the underlying coupling between first- and second-stage filtering mechanisms, which, in turn, may bear on the interrelationship between striate and extrastriate cortical processing. Our previous experiments on analogous motion stimuli found an optimum sensitivity when the ratio of the carrier and modulator spatial frequency parameters (r) was approximately ten. The current results do not exhibit an optimum sensitivity at a given value of the ratio r. Previous experiments involving second-order modulation sensitivity show an inconsistent range of estimates of optimum sensitivity at values of r between 5 and 50. Our results, using a complementary approach, confirm these discrepancies, demonstrating that the coupling between carrier and modulator frequency parameters depends on a number of stimulus-specific factors, such as contrast sensitivity, stimulus eccentricity, and absolute values of the carrier and modulator spatial frequency parameters. We show that these observations are true for a stimulus limited in eccentricity and that this orientation-modulated stimulus does not exhibit scale invariance. Such processing can not be modeled by a generic filter-rectify-filter model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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) | 0.000 | 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".