Transcranial direct current stimulation applied to primary motor cortex does not enhance the learning benefits of self-controlled KR schedules
Bibliographic record
Abstract
A distinct learning advantage has been shown when participants control their knowledge-of-results (KR) scheduling during practice compared to when the same KR schedule is imposed on the learner without choice (i.e., yoked). Although this learning advantage is well-documented, the brain regions contributing to these advantages remain unknown. Using transcranial direct current stimulation (tDCS), which can increase (anodal) or decrease (cathodal) cortical excitability and thus modulate subsequent behaviour, we investigated whether increased primary motor cortex excitability mediates the learning advantages of self-controlled KR schedules. Participants practiced a waveform matching task in one of four groups using a factorial combination of choice (Self-Controlled versus Yoked) and tDCS (Anodal versus Sham). Testing occurred on two consecutive days with spatial and temporal accuracy measured on both days. Learning was assessed using 24-hour retention tests with and without KR, as well as a no-KR transfer test. All groups improved their performance across practice blocks; however, no significant group differences were found on either retention test (p's > .05). Greater temporal accuracy was found for the self-controlled groups compared to the yoked-KR groups on the transfer test (p = .001, ?2p = .26); thus, practicing with a self-controlled KR schedule, independent of tDCS, resulted in an enhanced ability to generalize one's learning to novel temporal demands. Although a trend for greater temporal accuracy in transfer was noted for those receiving anodal-tDCS relative to sham-tDCS (p = .24), this lack of a significant effect for tDCS suggests that primary motor cortex may not be strongly implicated in self-controlled KR learning benefits. Acknowledgments: Supported 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".