Implicit processes do not contribute to learning to reach in small mirror reversed environments
Bibliographic record
Abstract
Abstract Learning to reach with a small visuomotor rotation (VR; a rotation of visual feedback relative to hand motion) has been shown to arise unconsciously (i.e., implicitly). Whether the same processes support learning in a small mirror reversal (MR), where feedback is reflected across the body midline, remains unknown. To address this gap, we asked whether implicit processes contribute to learning in a small MR. Forty-two right-handed participants reached to targets located 10° to the left and right of body midline using a Kinarm exoskeleton robot. Half of the participants experienced a VR distortion (VR group), which consisted of a 20° clockwise or counterclockwise cursor rotation. The remaining participants experienced a 20° MR distortion (MR group), where cursor feedback was reflected across body midline (y-axis). Following reaches with a VR or MR distortion, participants completed assessment trials in which they reached in the absence of cursor feedback to assess implicit learning. Analysis of angular errors (AE) revealed that all participants in the VR group learned to reach with the VR distortion, however, only 55% of MR participants learned to reach with the MR distortion. AEs on the no-cursor trials revealed that only the VR group engaged in implicit learning. These findings demonstrate that MR learning, even when small MR distortions are introduced, is not supported by implicit learning. The absence of implicit learning in MR provides evidence that MR is a different form of learning (i.e., skill acquisition) compared to VR learning (i.e., motor adaptation).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".