Cyber (motion) sickness in active stereoscopic 3D gaming
Bibliographic record
Abstract
Mass-market stereoscopic 3D gaming has recently become a reality on both gaming consoles and PCs. At the same time the success of devices such as the Nintendo Wii, Nintendo Wii Balance Board, Sony Move and Microsoft Kinect have made active movement of the head, limbs and body a key means of interaction in many games. We hypothesized that players may be more prone to cybersickness symptoms in stereoscopic 3D games based on active movement compared to similar games played with controllers or other devices, which do not require physical movement of the body with the exception of the hands and fingers. Two experimental games were developed to test this hypothesis while keeping other parameters as constant as possible. For the disorientation and oculomotor cybersickness subscales and the overall score of the Simulator Sickness Questionnaire, a significant interaction between display mode (S3D versus non-stereoscopic) and motion sickness susceptibility was found. However, contrary to our hypothesis, there was no indication that participants were particularly susceptible to cybersickness in S3D motion controller games.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".