Vergence eye movements are not essential for stereoscopic depth
Bibliographic record
Abstract
The brain receives disparate retinal input owing to the separation of the eyes, yet we usually perceive a single fused world. This is because of complex interactions between sensory and oculomotor processes that quickly act to reduce excessive retinal disparity. This implies a strong link between depth perception and fusion, but it is well established that stereoscopic depth percepts are also obtained from stimuli that produce double images. Surprisingly, the nature of depth percepts from such diplopic stimuli remains poorly understood. Specifically, despite long-standing debate it is unclear whether depth under diplopia is owing to the retinal disparity (directly), or whether the brain interprets signals from fusional vergence responses to large disparities (indirectly). Here, we addressed this question using stereoscopic afterimages, for which fusional vergence cannot provide retinal feedback about depth. We showed that observers could reliably recover depth sign and magnitude from diplopic afterimages. In addition, measuring vergence responses to large disparity stimuli revealed that that the sign and magnitude of vergence responses are not systematically related to the target disparity, thus ruling out an indirect explanation of our results. Taken together, our research provides the first conclusive evidence that stereopsis is a direct process, even for diplopic targets.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".