Visuomotor adaptation to a small mirror reversal perturbation
Bibliographic record
Abstract
Previous research has shown that implicit processes underlie reach adaptation to a visuomotor distortion that results in a consistent change in movement direction (e.g., cursor feedback is rotated 10° clockwise to hand motion, regardless of which target is displayed). The current research looked to determine if implicit processes play a role in visuomotor adaptation in response to a mirror reversal distortion, where visual feedback of hand motion is reversed across the y-axis and hence changes in movement direction are required for different targets. Participants reached towards a left and right target positioned 5° to the left and right of body midline with (i) aligned cursor feedback and (ii) mirror reversed cursor feedback, such that reaching to the left target caused the visual cursor to shift 5° to the right of the midline, and reaching to the right target caused the cursor to shift 5° to the left of the midline, creating a 10° mirror reversal. Following both aligned and mirror reversed trials, participants reached without a cursor to assess implicit adaptation. Angular errors at peak velocity early in the mirror reversed trials showed evidence of adaptation, such that reaches were adjusted to aim towards the right side of the midline when reaching to the left target and vice versa when reaching to the right target. These errors continued in the no cursor trials, providing evidence of implicit adaptation. Thus, our results indicate a role for implicit processes in visuomotor adaptation to a small mirror reversal.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".