Clarifying the relationship between alexithymia and subjective interoception
Bibliographic record
Abstract
The long-standing hypothesis that emotions rely on bodily states is back in the spotlight. This has led some researchers to suggest that alexithymia, a personality construct characterized by altered emotional awareness, reflects a general deficit in interoception. However, tests of this hypothesis have relied on heterogeneous assessment methods, leading to inconsistent results. To shed some light on this issue, we administered a battery of self-report questionnaires of interoception and alexithymia to three samples from Italy, the U.S., and Singapore (N = 814). Correlation and machine learning analyses showed that alexithymia was associated with deficits in both subjective interoceptive accuracy and attention. Alexithymics' interoceptive deficits were primarily related to difficulty identifying and describing feelings. Interoception showed a weaker association with externally-oriented thinking as operationalized by the Toronto Alexithymia Scale (TAS-20) and no association with the affective dimension of alexithymia later introduced by the Bermond-Vorst Alexithymia Questionnaire (BVAQ). We discuss our results with reference to the theoretical and psychometric differences between these two measures of alexithymia and their shortcomings. Overall, our results support the view that interoceptive deficits are a core component of alexithymia, although the latter cannot be reduced to the former.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".