Investigating error detection capabilities in a novel sensorimotor task as a function of athletic experience
Bibliographic record
Abstract
The ability to detect movement error is an essential cognitive ability underlying skilled motor performance. An identified gap in the literature is whether the error detection capabilities in a well-learned motor task transfer to a novel, but similar sensorimotor task. The purpose of this experiment was to examine whether previous athletic experience in a routine sport (i.e. Cheerleading) would affect the participant's error-detection accuracy during the acquisition of a novel motor skill. Twenty-four subjects (n = 12 routine athletes, n = 12 non-routine athletes) participated in an alternating isometric elbow flexion and extension task. All participants completed 15 acquisition trials alternating between 46% flexion and 38% extension of their maximal voluntary contraction. After each acquisition trial, participants self-reported their perceived overall flexion and extension force prior to receiving KR regarding their approximation of the flexion and extension isometric goals. Participants completed a 2-day retention test that replicated the acquisition protocol but without KR. The results from the two-day retention test showed the routine athletes' error detection improved from block one (M=8.08, SD= 5.78) to block two (M= 7.25, SD= 5.53). However, there were no between group differences. The Movement-Specific Reinvestment Scale revealed that the routine athletes (M= 4.45, SD= 0.24) scored significantly higher (p = 0.02) than the non-routine athletes (M= 3.54, SD= 0.24) suggesting they had greater movement self-consciousness. Thus, previous sensorimotor experience did not differentially impact the error-detection accuracy of routine athletes during the acquisition of a novel force production task.
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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 | 0.006 |
| 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) | 0.001 | 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".