The Relationship between Immersion and Psychophysiological Indicators
Bibliographic record
Abstract
Abstract Psychophysiological indicators have garnered significant interest in the assessment of presence. However, despite this interest, the nature of the relationship between psychophysiological indicators and presence factors remains undetermined. Presence, the perceived realness of a mediated or virtual experience, is modulated by two factors: immersion and coherence. Immersion represents the extent and precision of the simulated sensory modalities, while coherence refers to the environment's ability to behave as expected by the user. To study the relationship between psychophysiological indicators and presence factors, we objectively manipulated immersion by altering three visual qualities. The visual qualities were set to values above, at, or below their functional threshold. These thresholds are defined as a perceptual boundary under which a sensory quality value should be considered functionally degraded. Sixty participants performed a driving task in a virtual environment under the aforementioned conditions, while we measured their cardiovascular and eye responses. We found that degraded immersion conditions yielded significantly different psychophysiological indicator results than the condition without degradation. However, we observed an effect of immersion degradation on our measured variables only when the visual conditions were set below the functional threshold. Manipulations of immersion below the functional threshold introduced unreasonable circumstances which modified our participants' behavior. Thus, our findings suggest a direct impact of immersion on coherence and highlight the sensitivity of psychophysiological indicators to the coherence of a virtual environment. These results have theoretical implications, as a presence concepts relationship model should include the direct impact of immersion on coherence.
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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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".