The mechanisms of crossed and uncrossed disparities in coarse stereopsis
Bibliographic record
Abstract
Stereopsis, our ability to perceive depth, is a fundamental aspect of vision. It allows us to judge whether objects are “in front of” or “behind” each other. High variability in the perception of stereopsis has been reported in the population. Previous studies suggest that this variability may results from different mechanisms subserving the perception of crossed and uncrossed stereopsis. In our previous study, we focused on fine stereopsis. Here we focused on coarse stereopsis. We investigated the difference between crossed and uncrossed stereopsis mechanisms using an identification-at-threshold paradigm. We used a 2-by-2 forced choice procedure where participants had to both report the interval with the stimulus and judge whether the stimulus was crossed or uncrossed. The stimulus consisted of a gaussian bump (size) in a filtered noise texture (0.7 cycles per degree). Preliminary data revealed that typical observers were able to consistently achieve perfect discrimination. However, at extremely large disparities, some individuals could not discern polarity, probably because they could not fuse anymore. As observed before for fine stereopsis, these results suggest that crossed and uncrossed disparities are mediated by 2 different channels for coarse stereopsis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".