Rapid Online Correction Is Selectively Suppressed During Movement With a Visuomotor Transformation
Bibliographic record
Abstract
Reaching movements to visual targets are under fast feedback control, which can rapidly correct an ongoing movement for errors. This study investigates how this online correction is affected by the application of a new visuomotor transformation. Thirty-two subjects made planar pointing movements to visual targets. Vision of the arm was prevented, and hand position was represented by a cursor displayed in the movement plane. In some trials, the target abruptly changed location at the onset of arm movement, which required a rapid correction of movement direction. After performing baseline trials, some subjects were required to adapt to a mirror-image transformation that inverted the visual feedback of their hand position across the body midline, whereas others were not familiarized with the transformation. Afterward, subjects' online correction was tested with target jumps in the presence of the mirror transformation. Results show that after short-term motor adaptation to the mirror transformation there was a selective suppression of the rapid non-mirror correction in the direction of visual target displacement but no mirror reversal. The suppression occurred within the first few trials after the introduction of the mirror transformation, and it was strongest for the movements in which the transformation caused the largest dissociation between the target location and hand movement. Finally, whether or not the short-latency non-mirror correction was suppressed in a given trial, the mirror correction occurred at the same latency as the onset time of voluntary correction in subjects who had not experienced the mirror transformation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".