Dynamic Neural Deactivation Bridges Direct and Competitive Inhibition Processes
Bibliographic record
Abstract
Inhibition is an important concept in cognitive neuroscience. Direct inhibition, characterized by the active suppression of stimuli, and competition-induced inhibition, which involves ignoring irrelevant stimuli by prioritizing relevant ones, have traditionally been considered distinct and studied separately. Although their spatial neural overlap has been highlighted, the temporal dimension-the development of neural activities over time-remains largely unexplored. Using multimodal neuroimaging and behavioral experiments in the auditory and visual domains, in addition to conjunction analyses that capture their neural commonalities, we observed that both inhibition types exhibit a shared deactivation temporal dynamic. It is characterized by a progressive reduction in frontoparietal activation and increased deactivation in sensory regions, a pattern that is positively correlated with improved inhibition performance and whose causal disruption contributes to reduced inhibitory effect. Furthermore, this deactivation-dominant pattern is consistent across different sensory modalities and generalizes to various low-processing demand scenarios, whether actively induced or passively experienced. In addition, functional blurring in information clarity during inhibition is found. Overall, the findings reveal that diverse inhibitory processes for modulating information input converge on a shared neural substrate characterized by dynamic feedforward signal attenuation, thereby bridging previously disconnected domains of inhibition research and offering new perspectives of neural deactivation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".