Effects of robotic guidance on sensorimotor control: Planning vs. online control?
Bibliographic record
Abstract
BACKGROUND: Robotic guidance has been shown to facilitate motor skill acquisition, through altered sensorimotor control, in neurologically impaired and healthy populations. OBJECTIVE: To determine if robot-guided practice and online visual feedback availability primarily influences movement planning or online control mechanisms. METHODS: In this two-experiment study, participants first performed a pre-test involving reaches with or without vision, to obtain baseline measures. In both experiments, participants then underwent an acquisition phase where they either actively followed robot-guided trajectories or trained unassisted. Only in the second experiment, robot-guided or unassisted acquisition was performed either with or without online vision. Following acquisition, all participants completed a post-test that was the same as the pre-test. Planning and online control mechanisms were assessed through endpoint error and kinematic analyses. RESULTS: The robot-guided and unassisted groups generally exhibited comparable changes in endpoint accuracy and precision. Kinematic analyses revealed that only participants who practiced with the robot exhibited significantly reduced the proportion of movement time spent during the limb deceleration phase (i.e., time after peak velocity). This was true regardless of online visual feedback availability during training. CONCLUSION: The influence of robot-assisted motor skill acquisition is best explained by improved motor planning 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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".