More than a cool illusion? Functional significance of self-motion illusion (circular vection) for perspective switches
Bibliographic record
Abstract
Self-motion can facilitate perspective switches and "automatic spatial updating" and help reduce disorientation in applications like virtual reality (VR). However, providing physical motion through moving-base motion simulators or free-space walking areas comes with high cost and technical complexity. This study provides first evidence that merely experiencing an embodied illusion of self-motion ("circular vection") can provide similar behavioral benefits as actual self-motion: Blindfolded participants were asked to imagine facing new perspectives in a well-learned room, and point to previously learned objects. Merely imagining perspective switches while stationary yielded worst performance. When perceiving illusory self-rotation to the novel perspective, however, performance improved significantly and yielded performance similar to actual rotation. Circular vection was induced by combining rotating sound fields ("auditory vection") and biomechanical vection from stepping along a carrousel-like rotating floor platter. In sum, illusory self-motion indeed facilitated perspective switches and thus spatial orientation, similar to actual self-motion, thus providing first compelling evidence of the functional significance and behavioral relevance of vection. This could ultimately enable us to complement the prevailing introspective vection measures with behavioral indicators, and guide the design for more affordable yet effective VR simulators that intelligently employ multi-modal self-motion illusions to reduce the need for costly physical observer motion.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".