The Influence of Immersion on Situational Awareness in a Virtual Environment
Bibliographic record
Abstract
Abstract Researchers have pointed out the need to find an alternative to subjective questionnaires to measure presence in a virtual environment. Situational awareness has been proposed to objectively measure the concept of presence. However, the link between situational awareness and specific factors of presence has not been established. To study this relationship, 60 participants executed a driving task in a virtual environment under different visual conditions while we measured their situational awareness with the situational awareness global assessment technique (SAGAT), and their presence with the presence questionnaire (PQ). During the driving task, we objectively and meaningfully manipulated immersion, a factor of presence, by varying our participants' contrast sensitivity, size of the field of view, and visual acuity. The meaningful manipulation of presence also allowed us to evaluate the functional thresholds of the three aforementioned visual qualities for a driving task, which were previously measured from a multidirectional selection test. Our results indicated a significant positive correlation between SAGAT and PQ. They also showed that SAGAT was sensitive to an immersion's degradation and brought a good diagnosticity on the effect of an immersion's manipulation. Consequently, we conclude that it could represent an objective alternative to subjective questionnaires to measure presence in a virtual environment. Moreover, our assessment of the functional thresholds allowed us to confirm that they were context dependent. Our results indicated that only the contrast sensitivity functional threshold was valid in both a multidirectional selection test and a driving task.
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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 | 0.010 |
| 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) | 0.001 | 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".