Embodying a self-avatar with a larger leg: its impacts on motor control and dynamic stability
Bibliographic record
Abstract
Several studies have shown that users of immersive virtual reality can feel high levels of embodiment in self-avatars that have different morphological proportions than those of their actual bodies. Deformed and unrealistic morphological modifications are accepted by embodied users, underlying the adaptability of one's mental map of their body (body schema) in response to incoming sensory feedback. Before initiating a motor action, the brain uses the body schema to plan and sequence the necessary movements. Therefore, embodiment in a self-avatar with a different morphology, such as one with deformed proportions, could lead to changes in motor planning and execution. In this study, we aimed to measure the effects on movement planning and execution of embodying a self-avatar with an enlarged lower leg on one side. Thirty participants embodied an avatar without any deformations, and with an enlarged dominant or non-dominant leg, in randomized order. Two different levels of embodiment were induced, using synchronous or asynchronous visuotactile stimuli. In each condition, participants performed a gait initiation task. Their center of mass and center of pressure were measured, and the margin of stability (MoS) was computed from these values. Their perceived level of embodiment was also measured, using a validated questionnaire. Results show no significant changes on the biomechenical variables related to dynamic stability. Embodiment scores decreased with asynchronous stimuli, without impacting the measures related to stability. The body schema may not have been impacted by the larger virtual leg. However, deforming the self-avatar's morphology could have important implications when addressing individuals with impaired physical mobility by subtly influencing action execution during a rehabilitation protocol.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".