Mixed Reality Alters Motor Planning and Control
Bibliographic record
Abstract
Compared to physical unmediated reality (UR), mixed reality technologies, such as Virtual (VR) and Augmented (AR) Reality, entail perturbations across multiple sensory modalities (visual, haptic, etc.) that could alter how actors move within the different environments. Because of the mediated nature, goal-directed movements in VR and AR may rely on planning and control processes that are different from movements in UR, resulting in less efficient motor control. The current study involved participants performing manual pointing movements on Müller-Lyer illusion stimuli to examine the relative contributions of movement planning and online control in UR, VR, and AR. Compared to UR, movements in VR were slower but were equally variable with a comparable level of online control, whereas movements in AR showed comparable speed but exhibited higher variability and less online control. Further, movements in VR and AR demonstrated a greater illusory effect in endpoint accuracy relative to UR. These findings suggested that participants in VR adopted an active compensation strategy to overcome the impact of less efficient online control, whereas participants in AR did not. The findings that movement planning and execution in VR and AR are fundamentally different from those in UR provide valuable insights into the potential neural systems engaged during movements in different realities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| 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".