Binocular contributions to linear vertical vection
Bibliographic record
Abstract
Compelling illusions of self-motion, known as vection, can be produced in a stationary observer by visual stimulation alone. The role of binocular vision and stereopsis in these illusions was explored in a series of three experiments. Previous research had provided evidence of stereoscopic enhancements for linear vection in depth (e.g., Palmisano, 1996, 2002). Here we examined for the first time the effects of binocular vision and stereopsis on linear vertical vection. Vertical vection was induced by the upward or downward translation of large stereoscopic surfaces. These surfaces were horizontally oriented depth corrugations produced by disparity modulation of patterns of persistent or short lifetime dot elements. We found that binocular viewing of such surfaces significantly increased the magnitudes and decreased the onset delays of vertical vection. Experiments utilizing short lifetime dot stereograms demonstrated that these particular binocular enhancements of vection were due to the motion of stereoscopically defined features.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".