Delta-Band Oscillations in Motor Regions Predict Hand Selection for Reaching
Bibliographic record
Abstract
Current models hold that action selection is achieved by competitive interactions between co-existing motor representations associated with each potential action. Critically, selection via competition requires biasing signals to enable one of these alternatives to be selected. This study tested the hypothesis that selection is related to the prestimulus excitability of neuronal ensembles in which movements are encoded, as assessed through the phase of delta-band oscillations (2-4 Hz). Electroencephalography was recorded while participants performed speeded reaches toward appearing visual targets using the hand of their choice. The target locations were controlled such that only targets for which the left and right hands were selected equally often were used for analysis. Results revealed that hand selection as well as reach reaction times strongly depended upon the instantaneous phase of delta at the moment of target onset. This effect was maximal over contralateral motor regions, and occurred in the absence of prestimulus alpha- (8-12 Hz) and beta-band (15-30 Hz) amplitude modulations. These findings demonstrate that the excitability of motor regions acts as a modulatory factor for hand choice during reaching. They extend current models by showing that action selection is related to the underlying brain state independently of previously known decision variables.
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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) | 0.001 | 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".