Knowing the good from the bad: Does being aware of KR content matter?
Bibliographic record
Abstract
Previous research suggests that providing KR after a series of successful (i.e., good trials) rather than unsuccessful (i.e., poor trials) trials enhances motor skill acquisition. However, there is a caveat. Performers in these previous investigations were unaware their KR content was based on either their good or poor trials. Based on the role of KR precision during motor skill learning, we expected that being aware of the KR content would prove superior for motor learning compared to being unaware, independent of the content in the KR display (i.e., good or poor trials). For the present experiment, participants were required to propel a slider along a confined linear pathway to a pre-determined spatial goal (133 cm). Participant's vision was occluded before, during and after their motor action. Similar to previous research, participants either received KR based on their three best (KR good) or three worst (KR poor) trials in a 6 trial block, and were either aware (good-aware; poor-aware) or unaware (good-unaware, poor-unaware) of content in their KR display for a total of 4 experimental conditions. The acquisition results revealed no between group differences. However, the retention results showed that indpendent of the KR content, the groups aware of their KR content demonstrated superior learning (indexed by CE and VE measures) to the groups unaware of their KR content. The theoretical and practical importance of KR precision during motor learning will be discussed.
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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".