Typical pain experience but underestimation of others’ pain: Emotion perception in self and others in autism spectrum disorder
Bibliographic record
Abstract
Difficulties in emotion perception are commonly observed in autism spectrum disorder. However, it is unclear whether these difficulties can be attributed to a general problem of relating to emotional states, or whether they specifically concern the perception of others' expressions. This study addressed this question in the context of pain, a sensory and emotional state with strong social relevance. We investigated pain evaluation in self and others in 16 male individuals with autism spectrum disorder and 16 age- and gender-matched individuals without autism spectrum disorder. Both groups had at least average intelligence and comparable levels of alexithymia and pain catastrophizing. We assessed pain reactivity by administering suprathreshold electrical pain stimulation at four intensity levels. Pain evaluation in others was investigated using dynamic facial expressions of shoulder patients experiencing pain at the same four intensity levels. Participants with autism spectrum disorder evaluated their own pain as being more intense than the pain of others, showing an underestimation bias for others' pain at all intensity levels. Conversely, in the control group, self- and other evaluations of pain intensity were comparable and positively associated. Results indicate that emotion perception difficulties in autism spectrum disorder concern the evaluation of others' emotional expressions, with no evidence for atypical experience of own emotional states.
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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 | 0.003 |
| 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) | 0.001 | 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".