The coarse vs. fine dichotomy in stereopsis: A matter of scale
Bibliographic record
Abstract
Is disparity processing subserved by a single mechanism that spans all disparities or by two distinct mechanisms that operate over different disparity ranges? Data from depth pedestal experiments have proven divided on this issue, likely due to the wide range of stimuli and tasks used, and to variables such as stimulus size and retinal eccentricity. In an effort to resolve this issue we exploit a result from Wilcox and Hess (1995) who showed that the coarse (2nd-order) system dominates processing for diplopic targets. Further, 2nd-order stereopsis depends critically on scale, as the upper limits for stereopsis increase with increasing stimulus width. Here we used a 1AFC method of constant stimuli to measure discrimination thresholds for a test bar relative to a reference bar positioned at depth pedestals ranging from 0 to 1 deg (n=5). The stimulus parameters were selected so that the target and reference would become diplopic within the test range. Not surprisingly, discrimination performance was degraded as pedestal disparity was increased. This occurred over a large range of disparities within the fusable range. However, outside the fusable range we find a dramatically different pattern of results; discrimination performance plateaus, remaining constant in spite of increasing diplopia. Our data echo those of Ogle (1952) and others, but with important differences that are tied to the test disparity relative to the stimulus width. We argue that these data reflect a neural dichotomy between coarse and fine processing that is tightly tied to scale rather than absolute disparity.
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".