Using error estimation to better understand the advantages of self-controlled practice
Bibliographic record
Abstract
Exercising choice during practice (self-controlled group) consistently leads to increased learning compared to being denied these choice opportunities (yoked group). Wulf and Lewthwaite (2016) proposed a motivational mechanism for self-controlled learning advantages, emphasizing autonomy-support, perceived competency, and enhanced expectancies. Others however, have advocated a greater relative contribution of informational factors such as error estimation and feedback processing (Carter & Ste-Marie, 2017). Here, we contrasted these two perspectives using three groups: a self-controlled (SC), a yoked (YK), and a yoked with error-estimation (YK+EE) group that estimated their movement time (MT) prior to receiving KR. Participants practiced a spatiotemporal motor task with a MT goal of 900 ms and completed motivation questionnaires after blocks one and six. Learning was inferred using 24-hour no-KR retention and transfer (new MT goal) tests, which included participants estimating their MT after each trial. No group differences were found during acquisition or for measures of motivation; however, there was a trend for less MT |CE| in retention and transfer for the SC (M=102.29 & 156.27ms) and YK+EE (M=102.79 & 125.67ms) groups compared to the YK group (M=186.77 & 221.59ms). A similar trend for more accurate estimations in retention and transfer were noted for the SC and YK+EE groups relative to the YK group. Although our findings were in the expected direction and suggest the detrimental effects of practicing in a yoked group can be attenuated through error estimation, the lack of significance prevents us from strongly asserting this conclusion.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".