EEG theta dynamics for error processing during online movement control
Bibliographic record
Abstract
To ensure optimal visuomotor feedback control during manual tracking, the brain must continuously monitor the error between the hand and the target. Modulations in the theta band (3-8 Hz) are related to error processing, but this has been mainly shown in cognitive control contexts. Hence, their relationship with hand-target errors during online control remains unclear. Here we assessed the impact of motor error processing on EEG theta-band activity in 29 healthy participants while they performed continuous tracking of a moving target with their dominant (right) hand. Two conditions were used to manipulate error processing demands: 1) in the Repeated condition, the same target trajectory was presented 80 times, allowing participants to implicitly learn the pattern and reduce tracking errors; 2) in the Random condition, 80 different trajectories were used, inducing persistent high tracking errors. Behavioral analyses confirmed that tracking errors were significantly higher in the Random than in the Repeated condition. Importantly, EEG theta power was also significantly higher in the Random condition, with a peak difference occurring at electrodes overlaying the left sensorimotor regions. This effect was selective to theta activity, as there was no modulation in alpha- (8-12 Hz) and beta-band (15-30 Hz) activity. Overall, this study extends the role of theta oscillations to online error processing in the context of motor control. It is possible that theta modulations reflected cortical activity mediating the communication and integration of information within sensorimotor circuits including the motor, premotor and parietal cortex, which are known to mediate online movement control.
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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 | 0.002 |
| 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".