Changes in Movement Control Processes Following Visuomotor Adaptation
Bibliographic record
Abstract
Goal-directed reaches are modified based on previous errors experienced (i.e., offline control) and current errors experienced during movement execution (i.e., online control). It is well documented that the control processes (i.e., offline and online control) underlying well learned movements change based on the time available to complete an action, such that offline control processes are engaged to a greater extent when movements are completed in a faster movement time (MT). Here, we asked if the underlying movement control processes governing newly acquired movements also change under varying MT constraints. Sixteen participants adapted their movements to a visuomotor distortion. Following reach training trials, participants reached under Long (800-1000 ms) and Short (400-500 ms) MT constraints. Results indicate that movement errors when reaching with the rotated cursor were reduced online under the Long MT constraint compared to the Short MT constraint. Thus, the contributions of offline and online movement control processes engaged in newly acquired movements can be adjusted with changes in temporal demands.
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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 | 0.002 |
| 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".