Online reach adjustments induced by real-time movement sonification
Bibliographic record
Abstract
Movement sonification can improve motor control in both healthy subjects (e.g., learning or refining a sport skill) and those with sensorimotor deficits (e.g., stroke patients and deafferented individuals). It is not known whether improved motor control and learning from movement sonification are driven by feedback-based real-time ("online") trajectory adjustments, adjustments to internal models over multiple trials, or both. We searched for evidence of online trajectory adjustments (muscle twitches) in response to movement sonification feedback by comparing the kinematics and error of reaches made with online (i.e., real-time) and terminal sonification feedback. We found that reaches made with online feedback were significantly more jerky than reaches made with terminal feedback, indicating increased muscle twitching (i.e., online trajectory adjustment). Using a between-subject design, we found that online feedback was associated with improved motor learning of a reach path and target over terminal feedback; however, using a within-subjects design, we found that switching participants who had learned with online sonification feedback to terminal feedback was associated with a decrease in error. Thus, our results suggest that, with our task and sonification, movement sonification leads to online trajectory adjustments which improve internal models over multiple trials, but which themselves are not helpful online corrections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".