Electrophysiological correlates of motor sequence learning
Bibliographic record
Abstract
BACKGROUND: The Error-related negativity (ERN) is a component of the event-related brain potentials elicited by error commission. The ERN is thought to reflect cognitive control processes aiming to improve performance. As previous studies showed a modulation of the ERN amplitude throughout the execution of a learning task, this study aims to follow the ERN amplitude changes from early to late learning blocks in relation with concomitant motor sequence learning using a serial reaction time (SRT) task. Twenty-two healthy participants completed a SRT task during which continuous EEG activity was recorded. The SRT task consists of series of stimulus-response pairs and involves motor learning of a repeating sequence. Learning was computed as the difference in mean response time between the last sequence block and the last random blocks that immediately follows it (sequence-specific learning). Event-related potentials were analysed to measure ERN amplitude elicited by error commission. RESULTS: Mean ERN amplitude difference between the first four learning blocks and the last four learning blocks of the SRT task correlated significantly with motor sequence learning as well as with overall response time improvement, such that those participants whose ERN amplitude most increased through learning blocks were also those who exhibited most SRT task improvements. In contrast, neither sequence-specific learning nor overall response time improvement across learning blocks were found to be related to averaged ERN amplitude from all learning blocks. CONCLUSION: Findings from the present study suggest that the ERN amplitude changes from early to late learning blocks occurring over the course of the SRT task, as opposed to the averaged ERN amplitude from all learning blocks, is more closely associated with learning of a motor sequence. These findings propose an improved electrophysiological marker to index change in cognitive control efficiency during motor sequence learning.
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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 | 0.003 |
| 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) | 0.002 | 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".