The Effects of Hand Representation on Experience and Performance for 3D Interactions in Virtual Reality Games
Bibliographic record
Abstract
In Virtual Reality (VR), natural 3D interactions are performed with hand representations - the visualizations and interactors used for manipulating objects. Hand representations in VR games range from abstract shapes, to graphical versions of input controllers, to realistic human-like hands. Hand representations have been shown to have an important effect on play experience and performance. However, previous work has only considered them for individual 3D interactions or an entire game, giving designers little information about how a representation might perform and be experienced across different 3D interactions (like picking up and rotating objects, or opening a container). In this work, we compare three hand representations across 12 different 3D interactions and in a longer game experience in a study of 45 participants. We find that while representation did not affect performance, representations were overall experienced differently across 3D interactions. Our work provides a deeper understanding for VR game designers about how hand representations can be used to shape play experiences.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".