Learning under self-controlled feedback conditions: Does the impact of sport-related concussion matter?
Bibliographic record
Abstract
Previous research on sport-related concussion has revealed evidence of cognitive deficits that influence oculo- and manual-motor function for one or more years post-injury. Proprioceptive impairment to these systems offers a potential challenge to motor skill acquisition of the concussed athlete. Previous research suggests that allowing participants (without concussion) to self-control their KR (knowledge of results) schedule enhances motor skill acquisition; however, it is unclear whether these learning advantages would be influenced following concussion. Participants (18 – 22 years) with and without a concussion history performed a novel spatial-motor task in conditions with or without self-control of KR. During acquisition, participants in both groups requested KR on a similar proportion of trials, and reported a preference for KR after perceived 'good' trials. Further, those participants who reported a preference for KR after perceived good trials exhibited more accurate performance compared to no-KR trials: a result that was consistent across concussed and non-concussed groups. These results extend previous research and provide initial evidence that previous concussion history does not negatively influence the utility of a self-controlled KR context during the acquisition of a novel spatial-motor task.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".