Multisensory Effects on Illusory Self-Motion (Vection): the Role of Visual, Auditory, and Tactile Cues
Bibliographic record
Abstract
A critical component to many immersive experiences in virtual reality (VR) is vection, defined as the illusion of self-motion. Traditionally, vection has been described as a visual phenomenon, but more recent research suggests that vection can be influenced by a variety of senses. The goal of the present study was to investigate the role of multisensory cues on vection by manipulating the availability of visual, auditory, and tactile stimuli in a VR setting. To achieve this, 24 adults (Mage = 25.04) were presented with a rotating stimulus aimed to induce circular vection. All participants completed trials that included a single sensory cue, a combination of two cues, or all three cues presented together. The size of the field of view (FOV) was manipulated across four levels (no-visuals, small, medium, full). Participants rated vection intensity and duration verbally after each trial. Results showed that all three sensory cues induced vection when presented in isolation, with visual cues eliciting the highest intensity and longest duration. The presence of auditory and tactile cues further increased vection intensity and duration compared to conditions where these cues were not presented. These findings support the idea that vection can be induced via multiple types of sensory inputs and can be intensified when multiple sensory inputs are combined.
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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 | 0.001 |
| 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".