Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex
Bibliographic record
Abstract
Successful self-control requires the ability to stop unwanted actions or thoughts. Stopping is regarded as a central function of inhibitory control, a mechanism enabling the suppression of diverse mental content, and strongly associated with the prefrontal cortex. A domain-general inhibitory control capacity, however, would require the region or regions implementing it to dynamically shift top-down inhibitory connectivity to diverse target regions in the brain. Here we show that stopping unwanted thoughts and stopping unwanted actions engage common regions in the right anterior dorsolateral and right ventrolateral prefrontal cortex, and that both areas exhibit this dynamic targeting capacity. Within each region, pattern classifiers trained to distinguish stopping actions from making actions also could identify when people were suppressing their thoughts (and vice versa) and could predict which people successfully forgot thoughts after inhibition. Effective connectivity analysis revealed that both regions contributed to action and thought stopping, by dynamically shifting inhibitory connectivity to motor area M1 or to the hippocampus, depending on the goal, suppressing task-specific activity in those regions. These findings support the existence of a domain-general inhibitory control mechanism that contributes to self-control and establish dynamic inhibitory targeting as a key mechanism enabling these abilities.
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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) | 0.001 | 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".