Alexithymia, but not autism spectrum disorder, may be related to the production of emotional facial expressions
Bibliographic record
Abstract
BACKGROUND: A prominent diagnostic criterion of autism spectrum disorder (ASD) relates to the abnormal or diminished use of facial expressions. Yet little is known about the mechanisms that contribute to this feature of ASD. METHODS: We showed children with and without ASD emotionally charged video clips in order to parse out individual differences in spontaneous production of facial expressions using automated facial expression analysis software. RESULTS: Using hierarchical multiple regression, we sought to determine whether alexithymia (characterized by difficulties interpreting one's own feeling states) contributes to diminished facial expression production. Across groups, alexithymic traits-but not ASD traits, IQ, or sex-were associated with quantity of facial expression production. CONCLUSIONS: These results accord with a growing body of research suggesting that many emotion processing abnormalities observed in ASD may be explained by co-occurring alexithymia. Developmental and clinical considerations are discussed, and it is argued that alexithymia is an important but too often ignored trait associated with ASD that may have implications for subtyping individuals on the autism spectrum.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".