An external focus of attention does not always benefit motor learning: evidence from visuomotor adaptation
Bibliographic record
Abstract
We asked if the benefits of adopting an external focus of attention, as observed in the skill acquisition literature, extend to visuomotor adaptation. Participants reached in a virtual environment where a cursor (1) accurately represented participants’ hand motion (48 trials), (2) was rotated 40° clockwise relative to participants’ hand motion (160 trials), and (3) was removed such that participants reached in the absence of cursor feedback (24 trials) to assess explicit adaptation. Participants were divided into 3 groups: External focus (EXT, n=30), Internal focus (INT, n=30) and Control (CTL, n=30). The EXT group was instructed to focus on the path taken by the cursor, while the INT group was instructed to focus on the path taken by their hand. The CTL group was not provided with attentional focus instructions. Post-experiment reports indicated that participants followed the attentional focus instructions, such that when asked to draw “the path your hand made”, participants in the EXT group drew trajectories that more closely represented the path the cursor took compared to the INT group. As for reaching performance, analyses of angular errors at peak velocity revealed that participants in the INT and CTL groups were able to adapt their reaches to a similar extent across mid-to-late rotated reach training trials. Surprisingly, participants in the EXT group adapted significantly less across reach training and exhibited less explicit adaptation compared to the INT and CTL groups. Together, these results suggest that an external focus of attention can interfere with strategic reaching in visuomotor adaptation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".