Narrative and Gaming Experience Interact to Affect Presence and Cybersickness in Virtual Reality
Bibliographic record
Abstract
Abstract Research has established a link between presence and cybersickness in virtual environments, but there is significant disagreement regarding the directionality of the relationship (positive or negative) between the two factors, and if the relationship is modulated by other top-down influences. Several studies have revealed a negative association between the factors, highlighting the prospect that manipulating one factor might affect the other. Here we examined if a top-down factor (narrative context) enhances presence, and whether this effect is associated with a decrease in cybersickness. We analyzed the association between responses to questionnaire measures of cybersickness and presence, as well as the degree to which their relationship was affected by the administration of an ‘enriched’ or ‘minimal’ verbal narrative context. The results of the first experiment, conducted in a controlled laboratory environment, revealed that enriched narrative was associated with increased presence, but that the reductive effect of narrative on cybersickness depended on video gaming experience. We also observed the expected negative association between presence and cybersickness, but only in the enriched narrative group. In a second experiment, conducted with a diverse sample at a public museum, we confirmed our previous finding that presence and cybersickness are negatively correlated, specifically when participants experienced an enriched narrative. We also confirmed the interaction between narrative and gaming experience with respect to cybersickness. These results highlight the complexity of the presence-cybersickness relationship, and confirm that both factors can be modulated in a beneficial manner for virtual reality users by means of top-down interventions.
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 | 0.006 |
| 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 | 0.000 |
| 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".