The influence of emotional intelligence on facial expression processing in males and females with and without psychiatric illnesses
Bibliographic record
Abstract
Emotional intelligence (EI) is a critical skill for understanding and managing emotions, and navigating daily social interactions. Emotion recognition is a crucial aspect of EI; however, our understanding of the impact of EI on facial expression identification, while accounting for both sex and mental health symptoms (i.e., anxiety, depression, stress and loneliness), is limited. In this study, we examined the influence of EI on facial expression recognition of masked faces [i.e., accuracy and reaction time (RT)] via an online study in N = 469 adult males and females, while also assessing mental health symptoms. Females tended to exhibit higher EI scores than males; higher EI scores were found in individuals without vs. with a self-reported current psychiatric illness. Higher levels of loneliness and perceived stress were predictive of lower EI scores. Further, higher EI predicted greater accuracy to all faces and to happy faces, in particular. Females vs. males had greater accuracy in recognizing all faces and happy faces. Finally, being younger also predicted higher accuracy in recognizing masked faces overall. Our results demonstrate the influence of sex and mental health symptoms on EI, as well as how they influence emotion recognition ability. These results can help inform public health and training programs in the realms of education, the workplace and mental health settings.
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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 | 0.002 |
| 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) | 0.002 | 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".