Are crossed and uncrossed disparities processed by the same mechanism?
Bibliographic record
Abstract
Stereopsis allows us to judge whether objects are “in front” or “behind” each other. However, it is still not clear whether this ability to judge position in depth is harnessing the same mechanism whether the stimulus is “popping in” (crossed disparity) or “popping out” (uncrossed disparity). We used a paradigm called "2-by-2 forced-choice paradigm". This paradigm involved two intervals of presentations - the target stimulus and noise. Subjects would indicate in which interval the stimulus appeared and judge whether the stimulus was crossed or uncrossed. So, we could record when the subject sees the stimulus (detection) and recognizes the stimulus (discrimination). We observed great inconsistency among people: Some participants showed strong bias towards cross or uncrossed disparity, some didn’t reach 100% identification performance even at large disparities and, surprisingly, some showed better discrimination than detection performance. Such higher discrimination rate could be explained by a Thurstonian model in which all disparities, crossed and uncrossed would be represented on a same axis. As a qualitative task, this would reveal an unconscious identification of the stimuli.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".