Investigating Presence Across Rendering Style and Ratio of Virtual to Real Content in Mixed Reality
Bibliographic record
Abstract
We investigate how the amount and rendering style of virtual content impact self-reported presence and subjective preference in an extended reality environment. In a within-subjects experiment, we vary the ratio of virtual to real content across three conditions: low (mostly real with some virtual elements), medium (a balanced mix of both), and high (mostly virtual with no real visual elements). For each ratio, we use two different rendering styles for virtual content: realistic and stylized (cartoon-like), evaluating presence through standardized questionnaires. Our results suggest that different ratios of virtual to real content minimally affect presence, with realistic renderings evoking stronger presence than stylized ones. Participants preferred higher amounts of virtual content and realistic virtual content over stylized versions. These findings imply that coherence and quality of virtual content may contribute more to presence in mixed reality settings than amount of virtual content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".