Bibliographic record
Abstract
The perception of self-motion can be induced or enhanced by exposure to visual stimuli such as optic flow. It has also been shown that consistent stereoscopic information enhances visually-induced self-motion perception (vection). Conversely, does vection affect the observer’s ability to parse the flow? And if so, how does it interact with binocular stereopsis? To investigate, we presented participants a scene including a target, a fixation cross, floor, ceiling, and pillars to provide optic flow using a wide-field, stereoscopic, immersive display. Participants virtually moved forward or backward at 1.4 m/s, either while continuously viewing the scene to produce vection or when it was only displayed during the 500 ms trial (the no-vection condition). The target was presented initially at eye level, and moved obliquely upward in a sagittal-parallel plane. The target’s velocity in depth was adjusted by adaptive staircases to obtain the bias and sensitivity. The task was to indicate whether the target moved obliquely forward or backward in the scene. The stimuli were presented in three viewing conditions: stereoscopic condition, synoptic condition, and monocular condition, to explore the possible interaction between vection and stereoscopic information. While all participants verbally reported that they experienced more vection with the vection condition, the result showed that the bias was slightly but significantly (F(1,127)=5.0217, p<.027) higher with vection (1.279 m/s) than without vection (1.219 m/s). This means vection did not help on reducing the flow parsing bias. Furthermore, we did not find any significant interaction effect between vection conditions and viewing conditions.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".