Anodal motor cortex stimulation paired with movement repetition increases anterograde interference but not savings
Bibliographic record
Abstract
Retention of motor adaptation is evident in savings, where initial learning improves subsequent learning, and anterograde interference, where initial learning impairs subsequent learning. Previously, we proposed that use-dependent movement biases induced by movement repetition contribute to anterograde interference, but not to savings. Here, we evaluate this proposal by limiting or extending movement repetition while stimulating the motor cortex (M1) with anodal transcranial direct current stimulation (tDCS), a brain stimulation technique known to increase use-dependent plasticity when applied during movement repetition. Participants first adapted to a counterclockwise rotation of visual feedback imposed either abruptly (extended repetition) or gradually (limited repetition) in a first block (A1), during which either sham or anodal tDCS (2 mA) was applied over M1. Anterograde interference was then assessed in a second block (B) with a clockwise rotation, and savings in a third block (A2) with a counterclockwise rotation. Anodal M1 tDCS elicited more anterograde interference than sham stimulation with extended but not with limited movement repetition. Conversely, anodal M1 tDCS did not affect savings with either limited or extended repetition of the adapted movement. Crucially, the effect of anodal M1 tDCS on anterograde interference did not require large errors evoked by an abrupt perturbation schedule, as anodal M1 tDCS combined with extended movement repetition within a gradual perturbation schedule similarly increased anterograde interference but not savings. These findings demonstrate that use-dependent plasticity contributes to anterograde interference but not to savings.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".