Priming the primary motor cortex with transcranial direct current stimulation: Effect on learning the golf putt
Bibliographic record
Abstract
BACKGROUND: Priming the primary motor cortex (M1) with transcranial direct current stimulation (tDCS) prior to motor practice modulates post-synaptic activity, thereby impacting learning of a motor skill. This effect has been shown for the acquisition of simple motor skills. It is not clear whether priming tDCS can impact the learning/retention of a more naturalistic motor task. OBJECTIVE/HYPOTHESIS: We investigated the effects of priming M1 with tDCS on the performance on a golf putting task. We hypothesized that participants who receive tDCS with the cathode over M1 (C-M1) would show better skill acquisition and retention performance, relative to participants who receive tDCS with the anode over M1 (A-M1) or sham tDCS. METHODS: Thirty-six participants were randomized into three groups: C-M1, A-M1, and sham tDCS. Participants received tDCS (1mA, 20 minutes) prior to practicing golf putting across two days. Performance (error) was measured for each putt. Participants returned on the third day for a retention test. RESULTS: After accounting for baseline performance, the C-M1 group performed significantly better compared to A-M1 [p = 0.02] and sham tDCS [p = 0.01] at the retention test. There was no difference in retention performance between A-M1 and sham tDCS. CONCLUSION: Our findings partially support the Bienenstock-Cooper-Munro rule of metaplasticity. C-M1 tDCS priming enhanced motor learning, while A-M1 tDCS priming had no effect, relative to sham.
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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) | 0.003 | 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".