A systematic review and meta-analysis of empathy in autism: The influence of measures
Bibliographic record
Abstract
Empathy deficits are considered a core attribute of autism and are scored in standardized autism diagnostic instruments. However, empirical evidence concerning empathy in autism is contradictory. This systematic review, which included 226 studies, thus offers a comprehensive overview of empathy in autism. It additionally examined the impact of the chosen empathy measure and the effect of several moderators. The results reveal a large effect size for cognitive empathy (g = -0.85) and unidimensional empathy (g = -1.70), but only a small effect size for affective empathy (g = -0.17), which became non-significant when limiting analyses to high-quality studies. Meta-regressions suggest that publication year, study quality, alexithymia, verbal IQ, and age do not moderate empathy, whereas sex specifically moderates unidimensional empathy. Critically, there were notable differences in effect sizes obtained across empathy measures and even between subscales of the same measure. For instance, results for the affective empathy subscales of the Interpersonal Reactivity Index reveal lower empathic concern (g = -0.59) but increased personal distress (g = 0.67) in autistic relative to typical participants. A qualitative review of ecological and neuroimaging tasks mostly demonstrated minimal autistic versus non-autistic differences. This meta-analysis thus suggests that measuring empathy as a unidimensional construct may both distort and increase the notion of an empathy deficit in 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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.025 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".