<scp>fMRI</scp> reveals overlapping and <scp>non‐overlapping</scp> neural bases of <scp>domain‐general</scp> and emotional conflict control
Bibliographic record
Abstract
The present study uses functional magnetic resonance image (fMRI) to examine the overlapping and specific neural correlates of contextualized emotional conflict control and domain-general conflict control. During a performance on emotional and domain-general conflict tasks, conjunction analyses showed that neural areas distributed in the frontoparietal network were engaged in both processes, supporting the notion that similar neural mechanisms are implemented in these two types of control. Importantly, disjunction analyses revealed a broader neural recruitment of emotional conflict control compared to domain-general conflict control as shown by the possible lateralization of the lateral prefrontal cortex (lPFC), such that emotional conflict control significantly involved the left lPFC while domain-general conflict control seemly involved the right lPFC. Results of generalized psychophysiological interaction (gPPI) analyses further demonstrated that emotional conflict control, compared to domain-general conflict control, elicited broader synergistic activities in individuals' brain networks. Together, these findings offer novel and compelling neural evidence that furthers our understanding of the complex relationship between domain-general and emotional conflict control.
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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 | 0.001 |
| 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".