The influence of scene rigidity and head tilt on vection.
Bibliographic record
Abstract
Changing head orientation with respect to gravity changes the dynamic sensitivity of the otoliths to linear accelerations (gravitational and inertial). We explored whether varying head orientation and optic flow direction relative to gravity affects the perception of visually induced self-motion (vection). We previously found that vection was enhanced when upright observers viewed lamellar flow that moved vertically relative to the head (i.e., simulating self motion along the spinal axis) compared to horizontal flow. We hypothesized that if this benefit was due to aligning the simulated self-motion with gravity, then inter-aural (as opposed to spinal) axis motion while laying on the side would provide a similar vection advantage. Alternatively, motion along the spinal axis could enhance vection regardless of head orientation relative to gravity. Observers stood and lay supine, prone, left and right side down, while viewing a translating random dot pattern that simulated observer motion along the spinal or inter-aural axis. Vection magnitude estimates, onset, and duration were recorded. The results showed that aligning the optic flow direction with gravity enhanced vection in side-laying observers, but when overlapping these signals was not possible—as in the supine and prone posture—spinal axis motion enhanced vection. However, perceived scene rigidity varied with head orientation (e.g., dots were seen as floating bubbles in some conditions). To examine the issue of scene rigidity, we compared vection during simulated motion with respect to two environments: a rigid pipe structure which looked like a complex arrangement of plumbing pipes, and a field of dots. The results of varying head and motion direction and perceived scene rigidity will be discussed, and may provide insight into whether self-motion perception is determined by a weighted summation of visual and vestibular inputs. Meeting abstract presented at VSS 2015
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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 | 0.000 | 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".