Bibliographic record
Abstract
Detecting and correcting motor execution errors is a challenging process as the underlying source of errors is often ambiguous and difficult to assign. This difficulty results from having to rely on noisy and delayed sensory information. Feedback from an external source like a coach can aid assignment processes. However, the scheduling of this feedback has a strong influence on the motor learning process. Over the last 25 years, it has been shown that allowing the learner to decide when they receive feedback during practice, termed self-controlled feedback, is an effective scheduling technique compared to externally-imposed yoked schedules and gold-standard schedules. Currently, discrepancies exist regarding whether self-controlled practice conditions are advantageous for motor learning due to motivational or informational factors. We have posited, from an information-processing perspective, that these learning advantages arise from participants engaging in performance-contingent strategies such as error estimation, which enhance feedback processing and reduce uncertainties about response outcomes. My presentation will highlight the use of behavioural manipulations to gain insight into the underlying processes contributing to self-controlled learning advantages. These studies highlight how error estimation processes on delayed tests of learning are influenced by the timing of the feedback decision and the processing of response produced feedback during the feedback-delay interval. I will also discuss various limitations and outstanding issues in the self-controlled literature. Taken together, this work suggests that although motivational factors may contribute to the learning advantages, information-processing activities related to the development of independent error detection and correction abilities seems to be more influential.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".