Test Tube: On the Sensorimotor Costs of Virtual Environments
Bibliographic record
Abstract
Virtual environments offer multiple advantages for training (e.g., risk reduction) but also include potential weaknesses. For example, adding short visual delays between a limb movement and its virtual motion representation can significantly impair visuomotor performance (Smith, 1972). Also, considering the principles of multisensory integration (Ernst & Bulthoff, 2004), it is relevant to compare visual-proprioceptive contributions to upper-limb control during virtual vs. real limb motion. The currently study implemented visual and proprioceptive perturbations during upper-limb trajectories in real or virtual aiming environments. Participants performed reaches under a half-silvered mirror (mean movement time = 336 ms; amplitude = 25 cm; index of difficulty = 5.65) with direct vision of their hand (real) or of a cursor representing their fingertip (virtual). Further, vision of the hand or cursor was either available prior to movement onset (prior) or throughout the trajectory (prior and during). As well, simultaneous agonist-antagonist tendon vibration was present before half of the trials. Participants exhibited significantly longer movement times and more errorful movements (i.e., increased constant and variable error) when reaching in the virtual than the real environment. Furthermore, the effect of between-trial tendon vibration yielded some mitigated evidence suggesting proprioception contributed less to upper-limb control in the virtual environment. Finally, the aforementioned differences were best explained by online control mechanisms because the sensory perturbations yielded longer limb deceleration durations. Overall, the current study indicates that virtual environments can incur temporal and spatial costs to limb control, which appear to be linked to online sensorimotor processes.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.005 |
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".