What Causes Specificity of Practice in a Manual Aiming Movement: Vision Dominance or Transformation Errors?
Bibliographic record
Abstract
The withdrawal of vision of the arm during a manual aiming task has been found to result in a large increase in aiming error, regardless of the amount of practice in normal vision before its withdrawal. In the present study, the authors investigated whether the increase in error reflects the domination of visual afferent information over the movement representation developed during practice to the detriment of other sources of afferent information or whether it reflects only transformation errors of the location of the target from an allocentric to an egocentric frame of reference. Participants (N = 40) performed aiming movements with their dominant or nondominant arm in a full-vision or target- only condition. The results of the present experiment supported both of those hypotheses. The data indicated that practice does not eliminate the need for visual information for optimizing movement accuracy and that learning is specific to the source or sources of afferent information more likely to ensure optimal accuracy during practice. In addition, the results indicated that movement planning in an allocentric frame of reference might require simultaneous vision of the arm and the target. Finally, practice in a target-only condition, with knowledge of results, was found to improve recoding of the target in an egocentric frame of reference.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".