Modulation of intracortical circuits in primary motor cortex during automatic action tendencies
Bibliographic record
Abstract
Humans display automatic action tendencies toward emotional stimuli, showing faster automatic behavior (i.e., approaching a positive stimulus and avoiding a negative stimulus) than regulated behavior (i.e., avoiding a positive stimulus and approaching a negative stimulus). Previous studies have shown that the primary motor cortex is involved in the processing of automatic actions, with higher motor evoked potential amplitudes during automatic behavior elicited by single-pulse transcranial magnetic stimulation. However, it is unknown how intracortical circuits are involved with automatic action tendencies. Here, we measured short-interval intracortical inhibition and intracortical facilitation within the primary motor cortex by using paired-pulse transcranial magnetic stimulation protocols during a manikin task, which has been widely used to explore approaching and avoiding behavior. Results showed that intracortical facilitation was stronger during automatic behavior than during regulated behavior. Moreover, there was a significant negative correlation between reaction times and intracortical facilitation effect during automatic behavior: individuals with short reaction times had stronger faciliatory activity, as shown by higher intracortical facilitation. By contrast, no significant difference was found for short-interval intracortical inhibition between automatic behavior and regulated behavior. The results indicated that the intracortical facilitation circuit, mediated by excitatory glutamatergic neurons, in the primary motor cortex, plays an important role in mediating automatic action tendencies. This finding further supports the link between emotional perception and the action system.
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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 | 0.000 |
| 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".