What’s limbs got to do with it? Real-world movement correlates with feelings of ownership over virtual arms during object interactions in virtual reality
Bibliographic record
Abstract
Abstract Humans will initially move awkwardly so that the end-state of their movement is comfortable. But, what is comfortable? We might assume it refers to a particular physical body posture, however, humans have been shown to move a computer cursor on a screen with an out-of-sight hand less efficiently (curved) such that the visual representation appears more efficient (straight). This suggests that movement plans are made in large part to satisfy the demands of their visual appearance, rather than their physical movement properties. So, what determines if a body movement is comfortable—how it feels or how it looks? We translated an object-interaction task from the real-world into immersive virtual reality (IVR) to dissociate a movement from its visual appearance. Participants completed at least 20 trials in two conditions: Controllers—where participants saw a visual representation of the hand-held controllers and Arms—where they saw a set of virtual limbs. We found participants seeing virtual limbs moved in a less biomechanically efficient manner to make the limbs look similar to if they were interacting with a real-world object. These movement changes correlated with an increase in self-reported feelings of ownership over the limbs as compared to the controllers. Overall this suggests we plan our movements to provide optimal visual feedback, even at the cost of being less efficient. Moreover, we speculate that a detailed measurement of how people move in IVR may provide a new tool for assessing their degree of embodiment. There is something about seeing a set of limbs in front of you, doing your actions, that affects your moving, and in essence, your thinking.
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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 | 0.006 |
| 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 | 0.001 |
| 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".