Perception of Materials in Virtual Reality based on their Audiovisual Properties
Bibliographic record
Abstract
Material perception requires integration of information from multiple senses. In virtual reality (VR) simulations, good agreement between sensory inputs leads to better accuracy, however, the effect of conflicting inputs is less understood. Here, we evaluated the effects of cue conflicts between auditory and visual material information in a virtual environment. To create the auditory stimuli, impact sounds were recorded in a controlled environment with a mechanized rod hitting a panel made of glass, wood, plastic, or metal. We rendered panels made of these same materials mounted on a stand, presented in a virtual room. During testing we combined the visual material textures with each of the recorded impact sounds, to create sixteen different conditions that were interleaved randomly and viewed using a VIVE Pro VR headset. On each trial the target object was presented and struck with a rod to produce an impact sound. The participants then classified the target material, and we recorded their responses and response time. To study the effect of agency, on half the trials, the participant observed an agent striking the target (agent-interaction trials), and in the remaining trials the participant struck the target themselves (self-interaction trials). Our results show that most participants classify materials based on their auditory properties. Further, there was no difference in the classification response between the agent-interaction trials and the self-interaction trials. Interestingly, in one of the sixteen conditions, we observed a potential audiovisual illusion - when observing a wooden target paired with a plastic impact sound, participants predominantly responded ‘metallic’. In sum, attention needs to be paid to incorporating auditory cues in VR, as discordant signals can distort perceived material properties.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".