Regional gray matter correlates of perceived emotional intelligence
Bibliographic record
Abstract
Coping with stressful life events requires a degree of skill in the ability to attend to, comprehend, label, communicate and regulate emotions. Individuals vary in the extent to which these skills are developed, with the term 'alexithymia' often applied in the clinical and personality literature to those individuals most compromised in these skills. Although a frontal lobe model of alexithymia is emerging, it is unclear whether such a model satisfactorily reflects brain-related patterns associated with perceived emotional intelligence at the facet level. To determine whether these trait meta-mood facets (ability to attend to, have clarity of and repair emotions) have unique gray matter volume correlates, a voxel-based morphometry study was conducted in 30 healthy adults using the Trait Meta Mood Scale while co-varying for potentially confounding sociodemographic variables. Poorer Attention to Emotion was associated with lower gray matter volume in clusters distributed primarily throughout the frontal lobe, with peak correlation in the left medial frontal gyrus. Poorer Mood Repair was related to lower gray matter volume in three clusters in frontal and inferior parietal areas, with peak correlation in the left anterior cingulate. No significant volumetric correlations emerged for the Clarity of Emotion facet. We discuss the localization of these areas in the context of cortical circuits known to be involved in processes of self-reflection and cognitive 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 | 0.001 | 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".