Beyond the Eye: Multisensory Contributions to the Sensation of Illusory Self-Motion (Vection)
Bibliographic record
Abstract
Vection is typically defined as the embodied illusion of self-motion in the absence of real physical movement through space. Vection can occur in real-life situations (e.g., 'train illusion') and in virtual environments and simulators. The vast majority of vection research focuses on vection caused by visual stimulation. Even though visually induced vection is arguably the most compelling type of vection, the role of nonvisual sensory inputs, such as auditory, biomechanical, tactile, and vestibular cues, have recently gained more attention. Non-visual cues can play an important role in inducing vection in two ways. First, nonvisual cues can affect the occurrence and strength of vection when added to corresponding visual information. Second, nonvisual cues can also elicit vection in the absence of visual information, for instance when observers are blindfolded or tested in darkness. The present paper provides a narrative review of the literature on multimodal contributions to vection. We will discuss both the theoretical and applied relevance of multisensory processing as related to the experience of vection and provide design considerations on how to enhance vection in various contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".