Cognitive Empathy as Imagination: Evidence From Reading the Mind in the Eyes in Autism and Schizotypy
Bibliographic record
Abstract
How is cognitive empathy related to sociality, imagination, and other psychological constructs? How is it altered in disorders of human social cognition? We leveraged a large data set (1,168 students, 62% female) on the Reading the Mind in the Eyes test (RMET), the Autism Quotient (AQ), and the Schizotypal Personality Questionnaire (SPQ-BR) to test the hypotheses that the RMET, as a metric of cognitive empathy, reflects mainly social abilities, imagination, or both. RMET showed the expected female bias in performance, though only for eyes that expressed emotions and not for neutral expressions. RMET performance was significantly, and more strongly, associated with the AQ and SPQ subscales that reflect aspects of imagination (AQ-Imagination and SPQ-Magical Ideation) than aspects of social abilities (AQ-Social, AQ-Communication, and SPQ-Interpersonal subscales). These results were confirmed with multiple regression analysis, which also implicated increased attention (AQ-Attention Switching and, marginally non-significantly, AQ-Attention to Detail) in RMET performance. The two imagination-related correlates of RMET performance also show the strongest sex biases for the AQ and SPQ: male biased in AQ-Imagination, and female biased in SPQ-Magical Ideation, with small to medium effect sizes. Taken together, these findings suggest that cognitive empathy, as quantified by the RMET, centrally involves imagination, which is underdeveloped (with a male bias) on the autism spectrum and overdeveloped (with a female bias) on the schizotypy spectrum, with optimal emotion-recognition performance intermediate between the two. The results, in conjunction with previous studies, implicate a combination of optimal imagination and focused attention in enhanced RMET performance.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".