Self-other distinction and individual differences in interoception, empathy, and alexithymia
Bibliographic record
Abstract
Theoretical models have argued for a complex interaction between self-other distinction, interoception, and social cognition, despite inconsistent evidence. To examine the associations between self-other distinction and individual differences in interoception, empathy, and alexithymia. 51 college students (21.82 years; 47.1% male) completed the Imitation-Inhibition Task to assess self-other distinction and an online survey with questionnaires to evaluate empathy (Questionnaire of Cognitive and Affective Empathy), alexithymia (Toronto Alexithymia Scale), interoceptive attention (Body Perception Questionnaire - BPQ) and accuracy (Interoceptive Accuracy Scale). Zero-order correlations, t-tests, and hierarchical linear regressions were implemented for statistical analysis (preregistered at https://osf.io/w4qt6). Self-other distinction (indexed by imitation-inhibition scores) was marginally related to affective empathy, although this association was not significant when controlling for sex (male subjects displayed enhanced self-other distinction and worst affective empathy) and baseline reaction times. Imitation-inhibition was not associated with any other variable of interest. Findings regarding interoceptive attention should be interpreted with caution as only 18 subjects correctly interpreted the BPQ. Despite the reduced sample size, the current findings suggest that self-other distinction, as measured by the Imitation-Inhibition Task, is not associated with individual differences in interoception, empathy, or alexithymia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".