Effector-dependent improvements in action prediction in left-handed individuals after short-term physical practice
Bibliographic record
Abstract
Research has established the influence of short-term motor practice for enhancing action prediction in right-handed (RH) individuals. In addition to benefits of action training for later assessed perceptual skills, effector-specific interference has been shown through action-incongruent secondary tasks. This interference is presumed to be due to the inability of the motor system to be engaged during action prediction of other’s actions, although it may be a result of more generalized motor processes, thought to be lateralized to the left-hemisphere of the brain. Here we investigated this experience-driven facilitation of action predictions and effector-specific interference in left-handed (LH) novices (N=43), before and after practicing a dart throwing task. Participants watched either RH (n=19) or LH (n=24) videos of temporally occluded dart throws, across four secondary task conditions: control, tone-monitoring, RH or LH isometric force task. These conditions were completed before and after physical practice throwing with the LH. Significantly greater improvement in prediction accuracy was shown post-practice for the LH- versus RH-video group. Consistent with previous work, effector-specific interference was shown, exclusive to the LH-video group. These results suggest that practice leads to effector-specific motor representations which facilitate action prediction.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".