Comparing errorful and error-free visuomotor adaptation to test for unintentional after-effects in observers
Bibliographic record
Abstract
One proposition for how we learn from watching is via simultaneous covert activation of our motor system. There is conflicting evidence about the mechanisms that drive how observed errors impact subsequent movement. In visuomotor adaptation (VMA) paradigms where participants practice moving to targets with a rotation applied to their feedback, unintentional after-effects in the direction of the rotation is a robust effect. Among observers, although of the rotation occurs, after-effects are not shown. In an exception to this, compensatory after-effects in observers were evidenced when observers watched a confederate continuously miss (in the absence of a rotation). Therefore, we tested for the presence of after-effects among 3 groups (n=14/gp). A Rotation+Hit group observed an actor perform accurate reaches to a target with 30° rotated cursor feedback; a No-Rotation+Miss group observed errorful performance, where the actor consistently missed by 30° (so visual errors were matched). Group 3 did not observe. We compared performance in a normal environment, without vision in pre- and post-tests. Additionally, we tested for learning in the rotated environment. Despite evidence for direct effects of watching accurate reaches in Rotation+Hit group compared to the control group, there was no evidence of after-effects or implicit/motor based adaptation. Moreover, the Miss group showed no compensatory (or directional) after-effects. These data support other work showing that observational practice does not result in implicit adaptation of internal models for aiming, despite the fact that it is a useful way of acquiring new skills, arguably through more explicit, strategic means.Acknowledgments: The third author would like to acknowledge Discovery grant funding from NSERC (Natural Sciences & Engineering Council of Canada) for this research.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".