The role of visuomotor synchrony on virtual full‐body illusions in children and adults
Bibliographic record
Abstract
The present study explored the effects of visuomotor synchrony in virtual reality during the embodiment of a full human avatar in children (aged 5-6 years) and adults. Participants viewed their virtual bodies from a first-person perspective while they moved the body during self-generated and structured movement. Embodiment was measured via questions and psychophysiological responses (skin conductance) to a virtual body-threat and during both movement conditions. Both children and adults had increased feelings of ownership and agency over a virtual body during synchronous visuomotor feedback (compared to asynchronous visuomotor feedback). Children had greater ownership compared to adults during synchronous movement but did not differ from adults on agency. There were no differences in SCRs (frequency or magnitude) between children and adults, between conditions (i.e., baseline or movement conditions) or visuomotor feedback. Collectively, the study highlights the importance of visuomotor synchrony for children's ratings of embodiment for a virtual avatar from at least 5 years old, and suggests adults and children are comparable in terms of psychophysiological arousal when moving (or receiving a threat to) a virtual body. This has important implications for our understanding of the development of embodied cognition and highlights the considerable promise of exploring visuomotor VR experiences in children.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".