Creating Non-Visual Non-Verbal Social Interactions in Virtual Reality
Bibliographic record
Abstract
Although virtual reality (VR) was originally conceived of as a multi-sensory experience, most developers of the technology have focused on its visual aspects to the detriment of other senses such as hearing. This paper presents design patterns to make virtual reality fully accessible to non-visual users, including totally blind users, especially with non-verbal social interactions. Non-visual VR has been present in the blindness audio game community since the early 2000s, but the conventions from those interfaces have never been described to a sighted audience, outside of a few limited sonification interface papers. This paper presents non-visual design patterns created by five of the top English-speaking audio game developers through a three round Delphi method, encompassing 29 non-verbal social interactions grouped into 12 categories in VR, including movement, emotes, and self-expression. This paper will be useful to developers of VR experiences who wish to represent non-verbal social information to their users through non-visual conventions. These methods have only been rigorously tested through the commercial market, and not through scientific approaches. These design patterns can serve as the foundation for future investigation in exploring non-visual non-verbal social interactions in VR.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".