Error estimation abilities and self-controlled feedback schedules
Bibliographic record
Abstract
The learning advantages of self-controlled feedback schedules are often attributed to the development of a more sensitive error detection and correction mechanism. That is, during practice participants engage in error estimation processes upon movement completion, which guides the feedback decision. Indeed, past research has shown that self-controlled feedback schedules lead to more accurate error estimation in retention and transfer relative to yoked feedback schedules (e.g., Carter et al. 2014). A limitation of these experiments is that no baseline of one's error detection and correction mechanism was collected. Thus, it is difficult to rule out participants in the self-controlled group naturally having more accurate error estimation abilities. Here, we addressed this limitation by randomly assigning half of the self-controlled participants to error estimate after each trial in a no-feedback pre-test. On Day 1, participants practiced an aiming task involving a rapid 40-deg extension movement in exactly 225 ms. Participants returned 24-hrs later to complete no-feedback retention and transfer tests, and all participants were asked to error estimate after each trial. Participants had less accurate error estimations in pre-test as compared to retention, and retention was more accurate than transfer. Self-controlled error feedback schedules (e.g., -24-deg) lead to more accurate estimations compared to self-controlled graded feedback schedules (e.g., too short). These data, along with previous findings, suggest that self-controlled feedback is effective for training error estimation abilities. However, this benefit may be restricted to feedback that provides both magnitude and direction information rather than only direction information.Acknowledgments: Funded by NSERC
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
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".