The effect of robotic guidance on the use of visual information during a pointing task
Bibliographic record
Abstract
Robotic guidance has been shown to be effective for rehabilitation although fundamental research suggests guidance can be detrimental to performance due to the decreased need for an efferent command. However, physical guidance does provide added proprioceptive feedback which could influence how visual information is used. We sought to compare the influence of robot-guided vs. active upper limb pointing on the use of visual feedback. Participants completed a training phase comprised of 210 trials with vision to 3 target amplitudes (18, 20, 22 cm) during which participants were either guided by a robot, or actively aimed to each target (Control). We also included pre- and post-tests of 20 trials each (10 vision, 10 no vision) using the 20 cm amplitude. A 2 group (robot, control) by 2 phase (pre, post) by 2 vision (vision, no vision) mixed ANOVA was done on all accuracy and movement time variables. Overall, participants exhibited lower variability and were more accurate with vision compared to no vision. A group by phase interaction demonstrated that the control group exhibited a shorter reaching amplitude than the robot group after training, regardless of vision condition. This suggest that the altered efferent requirements, and the additional proprioceptive feedback presented during robotic guidance does not negatively impact the sensorimotor representation of a reaching task.Acknowledgments: This research was supported by NSERC
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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".