Alexithymia modulates emotion concept activation during facial expression processing
Bibliographic record
Abstract
Alexithymia is characterized by difficulties in emotional information processing. However, the underlying reasons for emotional processing deficits in alexithymia are not fully understood. The present study aimed to investigate the mechanism underlying emotional deficits in alexithymia. Using the Toronto Alexithymia Scale-20, we recruited college students with high alexithymia (n = 24) or low alexithymia (n = 24) in this study. Participants judged the emotional consistency of facial expressions and contextual sentences while recording their event-related potentials. Behaviorally, the high alexithymia group showed longer response times versus the low alexithymia group in processing facial expressions. The event-related potential results showed that the high alexithymia group had more negative-going N400 amplitudes compared with the low alexithymia group in the incongruent condition. More negative N400 amplitudes are also associated with slower responses to facial expressions. Furthermore, machine learning analyses based on N400 amplitudes could distinguish the high alexithymia group from the low alexithymia group in the incongruent condition. Overall, these findings suggest worse facial emotion perception for the high alexithymia group, potentially due to difficulty in spontaneously activating emotion concepts. Our findings have important implications for the affective science and clinical intervention of alexithymia-related affective disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".