The Phi Angle: A Theoretical Essay on Sense of Presence, Human Factors, and Performance in Virtual Reality
Bibliographic record
Abstract
Abstract The question of the relationship between the sense of presence and performance in virtual reality is fundamental for anyone wishing to use the tool methodologically. Indeed, if the sense of presence can modify performance per se, then individual factors affecting the human–computer interaction might have repercussions on performance, despite being unrelated to it. After a discussion on the sense of presence and the particularities it provokes, this work studies the psychophysiology of virtual reality. This in virtuo experience is understood according to a constitutive and reciprocal relationship with the subject's cognitive profile, made up of all the human, contextual, and motivational factors impacting the processing of immersion. The role and importance of performance in virtual reality is described in this framework in such a way as to be studied methodologically. The presence–performance relationship is discussed based on previous works and analyzed in terms of attentional resources. Finally, the degree of ecological validity of the performance is described as the factor modulating the relationship between the sense of presence and performance (the Phi Angle). Limitations, applications, and test hypotheses of the model are presented. This work not only aims to help explain the conceptualization of virtual reality, but also to improve its methodological framework.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".