Field (In)dependence Flexibility Following a Virtual Immersion Is Associated With Cybersickness and Sense of Presence
Bibliographic record
Abstract
Field dependence–independence (FDI) is a psychological construct determining an individual’s approach of the perception–cognition coupling. In virtual reality (VR) context, several studies suggest that an individual’s perceptive style is susceptible to shift toward a more FI mode through down-weighting of conflicting visual cues. The present study proposes to investigate the potential flexible nature of FDI following a virtual immersion and to assess if this flexibility might be associated with the subjective experience of VR. 86 participants explored a real-world–like virtual environment for approximately 10 min. FDI levels were measured before and after the VR exposure using the rod-and-frame test. Their subjective experience of VR was measured a posteriori (cybersickness and sense of presence) and used in order to build two experimental groups via a cluster analysis. The results showed that only participants with a poor subjective experience of VR (i.e., a low level of sense of presence associated with a high level of cybersickness) significantly shifted to a more FI mode, which is discussed as a sensory re-weighting mechanism. Pragmatical applications are discussed, and future studies are outlined, based on the conclusion that FDI might be more flexible than we thought, which could shed light on the psychophysiology of VR.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".