Neural markers of movement preparation and outcome during motor adaptation and de novo learning tasks
Bibliographic record
Abstract
In motor learning, people must process movement errors when either adapting well-known movements or acquiring new motor skills (de novo learning). While these two motor learning types are distinct behaviorally, we understand less about their underlying neural mechanisms. Here, we investigated event-related potentials (ERPs) during movement preparation and outcome in reaching movements. We distinguished the two motor learning types by having participants (N = 32) train with two perturbations: a 30° visuomotor rotation (adaptation) and a mirror reversal (de novo learning). Participants learned to compensate for both perturbation types. For movement outcome, we time-locked to feedback onset at the end of the movement. We found a negative-going ERP before feedback onset, with perturbed reaches showing more negativity than aligned baseline reaches. However, the ERP amplitude did not scale with perturbation type nor error magnitude. Furthermore, we found a larger P3 component, or more attention allocation, after feedback onset in the perturbed conditions. For movement preparation, we time-locked to the go signal onset before the movement. We found a Readiness Potential that was dependent on the participant’s intended movement direction. We then quantified changes in movement preparation per perturbation type across learning, using a Lateralized Readiness Potential. However, we found no LRP differences between the fixed rotation and mirror reversed reaches. Thus, although the ERPs we investigated represent movement preparation and outcome error processing, these markers are unable to distinguish between motor adaptation and de novo learning. Other markers in relation to these two motor learning types warrant further investigation.
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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 | 0.004 |
| 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".