Intermuscular coherence (IMC) reveals that affective emotional pictures modulate neural control mechanisms during the initiation of arm pointing movements.
Bibliographic record
Abstract
Abstract Several studies in psychology suggest that relationships exist between emotional context and motor control. Such a claim is based mainly on behavioral investigations whereas the underlying neurophysiological processes remain little known. Using a classical paradigm in motor control, we tested the impacts of viewing standardized affective pictures during pointing movements performed from a standing position. The hand reaction and movement times were measured and ten muscle activities spread around the body were recorded to investigate the intermuscular coherence between muscle pairs of interest. The hand movement time increased when an emotional picture perceived as unpleasant appeared under the target to reach, compared to an emotional picture perceived as pleasant. When an unpleasant emotional picture appeared, the beta (β, 15-35 Hz) and gamma (γ, 35-60 Hz) intermuscular coherence decreased in the recorded pairs of postural muscles during the initiation of pointing movements. Moreover, a linear relationship between the magnitude of the intermuscular coherence in the pairs of posturo-focal muscles and the hand movement time appeared in the unpleasant scenarios. Our findings demonstrate that emotional stimuli induce modulation of the motor command sent by the central nervous system to muscles when performing voluntary goal-oriented movements.
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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 | 0.001 |
| 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".