Mechanisms underlying global stereopsis in fovea and periphery
Bibliographic record
Abstract
To better understand the pooling properties underlying global stereopsis we examined the relationship between carrier luminance spatial frequency and modulator disparity spatial frequency. Thresholds for detecting global sinusoidal disparity corrugations of spatially band-pass noise were measured as a function of modulator disparity spatial frequency for both centrally and peripherally located stimuli using a standard 2-IFC task. We found a characteristic relationship that depended on modulator disparity spatial frequency. At high modulator disparity spatial frequencies (>1c/d), there is an optimal ratio of around 2.6, whereas at low modulator disparity spatial frequencies, there is an optimal absolute carrier luminance spatial frequency (i.e., 3c/d). In the periphery, vision is restricted to modulator disparity spatial frequencies below 1c/d and, as a consequence, following the above rule, there is an optimum absolute carrier luminance spatial frequency that reduces in spatial frequency with increasing eccentricity. This finding is consistent with there being more than one channel processing global stereo that is subsequently confirmed using a 2×2 AFC detection/discrimination paradigm. Furthermore, because of the different carrier/modulator relationships in central and peripheral vision, peripheral global stereo cannot be simply related to central global stereo by a scaling factor and thus cannot be simply due to cortical magnification, as originally thought.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".