Stereoscopic 3D video games and their effects on engagement
Bibliographic record
Abstract
With television manufacturers developing low-cost stereoscopic 3D displays, a large number of consumers will undoubtedly have access to 3D-capable televisions at home. The availability of 3D technology places the onus on content creators to develop interesting and engaging content. While the technology of stereoscopic displays and content generation are well understood, there are many questions yet to be answered surrounding its effects on the viewer. Effects of stereoscopic display on passive viewers for film are known, however video games are fundamentally different since the viewer/player is actively (rather than passively) engaged in the content. Questions of how stereoscopic viewing affects interaction mechanics have previously been studied in the context of player performance but very few have attempted to quantify the player experience to determine whether stereoscopic 3D has a positive or negative influence on their overall engagement. In this paper we present a preliminary study of the effects stereoscopic 3D have on player engagement in video games. Participants played a video game in two conditions, traditional 2D and stereoscopic 3D and their engagement was quantified using a previously validated self-reporting tool. The results suggest that S3D has a positive effect on immersion, presence, flow, and absorption.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".