Emotional recognition across the adult lifespan: Effects of age, sex, cognitive empathy, alexithymia traits, and amygdala subnuclei volumes
Bibliographic record
Abstract
The ability to recognize others' emotions is vital to everyday life. The goal of this study was to assess which emotions show age-related decline in recognition accuracy of facial emotional expressions across the entire adult lifespan and how this process is related to cognitive empathy (Theory of Mind [ToM]), alexithymia traits, and amygdala subnuclei volumes in a large cohort of healthy individuals. We recruited 140 healthy participants 18-85 years old. Facial affect processing was assessed with the Penn Emotion Recognition task (ER40) that contains images of the five basic emotions: Neutral, Happy, Sad, Angry, and Fearful. Structural magnetic resonance imaging (MRI) datasets were acquired on a 4.7T MRI system. Structural equation modeling was used to test the relationship between studied variables. We found that while both sexes demonstrated age-related reduction in recognition of happy emotions and preserved recognition of sadness, male participants showed age-related reduction in recognition of fear, while in female participants, age-related decline was linked to recognition of neutral and angry facial expressions. In both sexes, accurate recognition of sadness negatively correlated with alexithymia traits. On the other hand, better ToM capabilities in male participants were associated with improvement in recognition of positive and neutral emotions. Finally, none of the observed age-related reductions in emotional recognition were related to amygdala and its subnuclei volumes. In contrast, both global volume of amygdala and its cortical and centromedial subnuclei had significant direct effects on recognition of sad images.
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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 | 0.000 |
| 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".