Virtual Vistas: Exploring the Demographics of the Virtual Reality User Base
Bibliographic record
Abstract
Research on the use and effects of virtual reality (VR) has increased considerably over the past decade as technological advancements continued to enhance the accessibility of VR equipment and software. A significant amount of VR research has been conducted by psychologists examining a variety of topics such as developing unique VR experiences for gaming and cinema, investigating social interactions in virtual environments, and how VR impacts cognition and perception. Previous research has aptly focused on the effects of VR use, but no studies have examined the foundational aspects of VR adoption by providing a comprehensive demographic overview. In this research we aspire to provide a nuanced understanding of the current landscape of VR users, thereby laying the groundwork for more contextually relevant and impactful investigations utilizing virtual reality. Across two studies, we recruited hundreds of VR users in Canada, The United States, and the United Kingdom to assess their demographic backgrounds (Sample A N = 638, Sample B N = 642). Males and females had similar response patterns regarding their VR experiences and participants reported using VR in a variety of contexts, such as solitarily or with friends or family. Participants from Sample A, who owned VR headsets, generally had longer and more enjoyable VR experiences than those in Sample B, with a notable finding that feelings of novelty persisted even with frequent VR use and headset ownership. The current research highlights several demographic indicators that are important to consider for sample selection or potentially controlled for in future VR research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".