Reexamining empathy in autism: Empathic disequilibrium as a novel predictor of autism diagnosis and autistic traits
Bibliographic record
Abstract
A large body of research showed that autistic people have intact emotional (affective) empathy alongside reduced cognitive empathy. However, there are mixed findings and these call for a more subtle understanding of empathy in autism. Empathic disequilibrium refers to the imbalance between emotional and cognitive empathy and is associated with a higher number of autistic traits in the typical population. Here we examined whether empathic disequilibrium predicts both the number of autistic traits and autism diagnosis. In a large sample of autistic (N = 1905) and typical individuals (N = 3009), we examined empathic disequilibrium and empathy as predictors of autistic traits and autism diagnosis, using a polynomial regression with response surface analysis. Empathy and autistic traits were measured using validated self-report questionnaires. Both empathic disequilibrium and empathy predicted linearly and non-linearly autism diagnosis and autistic traits. Specifically, a tendency towards higher emotional than cognitive empathy (empathic disequilibrium towards emotional empathy) predicted both autism diagnosis and the social domain of autistic traits, while higher cognitive than emotional empathy was associated with the non-social domain of autism. Empathic disequilibrium was also more prominent in autistic females. This study provides evidence that beyond empathy as was measured thus far, empathic disequilibrium offers a novel analytical approach for examining the role of empathy. Empathic disequilibrium allows for a more nuanced understanding of the links between empathy and autism. LAY SUMMARY: Many autistic individuals report feelings of excessive empathy, yet their experience is not reflected by most of the current literature, typically suggesting that autism is characterized by intact emotional and reduced cognitive empathy. To fill this gap, we looked at both ends of the imbalance between these components, termed empathic disequilibrium. We show that, like empathy, empathic disequilibrium is related to autism diagnosis and traits, and thus may provide a more nuanced understanding of empathy and its link with autism.
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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 | 0.001 | 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 | 0.000 |
| 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".