Depth magnitude and binocular disparity: a closer look at patent vs. qualitative stereopsis
Bibliographic record
Abstract
Ogle (1952; 1953) used measurements of perceived depth as a function of disparity to divide human stereopsis into patent (quantitative) and qualitative categories. Patent depth percepts result from a range of disparities within and outside Panum's fusional zone, while qualitative percepts result only from very large disparities well beyond the fusional limit. While this dichotomy is widely recognized, it is not clear if it is merely descriptive, or if it reflects an underlying neural dichotomy. If the latter is true, then patent and qualitative depth percepts should be associated with other distinguishing properties. In this series of experiments we evaluate the possibility that the 1st /2nd -order dichotomy proposed by Hess & Wilcox (1994) maps onto Ogle's patent/qualitative distinction. We used a magnitude estimation technique to evaluate the amount of depth perceived from test disparities within and beyond the fusable range. In separate blocks of trials we used stimuli designed to activate either the luminance-based 1st-order or the contrast-based 2nd-order system. The stimuli were windowed, 1D luminance noise patches that were presented either as correlated or uncorrelated stereopairs which activated 1st and 2nd-order stereopsis respectively. As anticipated, we find that at small disparities our 1st-order stimuli provide patent depth percepts that follow geometric predictions. However, our data also reveal that quantitative depth percepts are provided by 2nd-order stereopsis at small disparities, but the amount of depth is less than predicted by viewing geometry. Further, depth percepts become qualitative as the stimuli become diplopic and are mediated by solely 2nd-order mechanisms. Our results show that Ogle's qualitative stereopsis reflects the operation of a distinct neural mechanism designed to provide crude depth estimates for diplopic stimuli. The situation for stimuli within Panum's area is not as straightforward, as both 1st and 2nd-order mechanisms provide quantitative depth information in this range.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".