S3D depth-axis interaction for video games: performance and engagement
Bibliographic record
Abstract
Game developers have yet to embrace and explore the interactive stereoscopic 3D medium. They typically view stereoscopy as a separate mode that can be disabled throughout the design process and rarely develop game mechanics that take advantage of the stereoscopic 3D medium. What if we designed games to be S3D-specific and viewed traditional 2D viewing as a separate mode that can be disabled? The design choices made throughout such a process may yield interesting and compelling results. Furthermore, we believe that interaction within a stereoscopic 3D environment is more important than the visual experience itself and therefore, further exploration is needed to take into account the interactive affordances presented by stereoscopic 3D displays. Stereoscopic 3D displays allow players to perceive objects at different depths, thus we hypothesize that designing a core mechanic to take advantage of this viewing paradigm will create compelling content. In this paper, we describe Z-Fighter a game that we have developed that requires the player to interact directly along the stereoscopic 3D depth axis. We also outline an experiment conducted to investigate the performance, perception, and enjoyment of this game in stereoscopic 3D vs. traditional 2D viewing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".