The Effect of Head Tracking on the Degree of Presence in Virtual Reality
Bibliographic record
Abstract
The performance of a virtual reality (VR) system can be assessed from two aspects in the human-VR interaction loop. One aspect is the degree of immersion, which objectively quantifies the performance of the VR system using metrics such as the display field of view or the refresh rate. The other aspect is presence, which measures the user response to the VR system. This article presents a study that compares the impact on presence by changing immersion through enabling and disabling of the head tracking ability on a VR headset. The study quantitatively assesses this change by taking objective measurements of posture and subjective ratings of the VR experience, in terms of presence and motion sickness, after participants have gone through two versions of a roller coaster simulation; one with head tracking on and the other with head tracking off. The results indicate that a loss of immersion, caused by turning the head tracking feature off, results in a significant reduction in postural sway. This loss of immersion also affected presence, as shown through the user surveys. The survey responses indicate that the simulation with head tracking off was less enjoyable and caused more motion sickness compared to when head tracking is kept on.
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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".