Mirror-Touch Synaesthesia Is Not Associated with Heightened Empathy, and Can Occur with Autism
Bibliographic record
Abstract
Research has linked Mirror-Touch (MT) synaesthesia with enhanced empathy. We test the largest sample of MT synaesthetes to date to examine two claims that have been previously made: that MT synaesthetes (1) have superior empathy; and (2) only ever experience their MT synaesthesia in response to viewing a person being touched. Given that autism has been suggested to involve deficits in cognitive empathy, we also test two predictions: that MT synaesthetes should (3) be less likely than general population individuals without MT synaesthesia to have an autism spectrum condition (ASC), if MT is characterized by superior empathy; and (4) have fewer autistic traits. We selected three groups: a pure MT synaesthesia group (N = 46), a pure grapheme-colour (GC) synaesthesia group (N = 36), and a typical control group without synaesthesia (N = 46). Participants took three measures of empathy and one measure of autistic traits. MT synaesthetes did not show enhanced empathy. In addition, 30% of all MT synaesthetes recruited into this study (N = 135) reported also having ASC, and MT synaesthetes showed higher autistic trait scores than controls. Finally, some MT experiences were reported in response to viewing objects being touched. Our findings dispute the views that MT synaesthesia is linked with enhanced empathy, is less likely to occur with ASC or elevated autistic traits, and is specific to seeing a person being touched.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".