Identification of Visual Functional Thresholds for Immersion Assessment in Virtual Reality
Bibliographic record
Abstract
Abstract We consider that to objectively measure immersion, one needs to assess how each sensory quality is reproduced in a virtual environment. In this perspective, we introduce the concept of functional threshold which corresponds to the value at which a sensory quality can be degraded without being noticed by the user of a virtual environment. We suggest that the perceived realism of a virtual experience can potentially be evoked for sensory qualities values ranging from the perceptual threshold to the functional threshold. Thus, the identification of functional thresholds values allows us to constrain immersion. To lay the foundation for the identification of functional thresholds, we applied a modified version of the method of limits. We measured the value at which 30 participants were able to identify the degradation of their field of view (FOV), visual acuity, and contrast sensitivity while executing a multidirectional selection test. This enabled us to identify functional perceptual thresholds of 96.6 degrees for FOV, 12.2 arcmin for visual acuity, and 25.6% for contrast sensitivity.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".