Do you see what I see? Sex differences in the discrimination of facial emotions during adolescence.
Bibliographic record
Abstract
During adolescence social relationships become increasingly important. Establishing and maintaining these relationships requires understanding of emotional stimuli, such as facial emotions. A failure to adequately interpret emotional facial expressions has previously been associated with various mental disorders that emerge during adolescence. The current study examined sex differences in emotional face processing during adolescence. Participants were adolescents (n = 1951) with a target age of 14, who completed a forced-choice emotion discrimination task. The stimuli used comprised morphed faces that contained a blend of two emotions in varying intensities (11 stimuli per set of emotions). Adolescent girls showed faster and more sensitive perception of facial emotions than boys. However, both adolescent boys and girls were most sensitive to variations in emotion intensity in faces combining happiness and sadness, and least sensitive to changes in faces comprising fear and anger. Furthermore, both sexes overidentified happiness and anger. However, the overidentification of happiness was stronger in boys. These findings were not influenced by individual differences in the level of pubertal maturation. These results indicate that male and female adolescents differ in their ability to identify emotions in morphed faces containing emotional blends. The findings provide information for clinical studies examining whether sex differences in emotional processing are related to sex differences in the prevalence of psychiatric disorders within this age group.
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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) | 0.001 | 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".