Prolonged exposure to mixed reality alters task performance in the unmediated environment
Bibliographic record
Abstract
The popularity of mixed reality (MR) technologies, including virtual (VR) and augmented (AR) reality, have advanced many training and skill development applications. If successful, these technologies could be valuable for high-impact professional training, like medical operations or sports, where the physical resources could be limited or inaccessible. Despite MR's potential, it is still unclear whether repeatedly performing a task in MR would affect performance in the same or related tasks in the physical environment. To investigate this issue, participants executed a series of visually-guided manual pointing movements in the physical world before and after spending one hour in VR or AR performing similar movements. Results showed that, due to the MR headsets' intrinsic perceptual geometry, movements executed in VR were shorter and movements executed in AR were longer than the veridical Euclidean distance. Crucially, the sensorimotor bias in MR conditions also manifested in the subsequent post-test pointing task; participants transferring from VR initially undershoot whereas those from AR overshoot the target in the physical environment. These findings call for careful consideration of MR-based training because the exposure to MR may perturb the sensorimotor processes in the physical environment and negatively impact performance accuracy and transfer of training from MR to UR.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".