Testing the correlation between the heartbeat counting task and anxiety, depression, and alexithymia scales: A behavioural study and meta-analysis
Bibliographic record
Abstract
A link between interoception, as measured by the heartbeat counting task (HCT), and psychological phenomena like anxiety, alexithymia, and depression has been proposed. Previous research using the HCT alongside popular questionnaires—the State-Trait Anxiety Inventory (STAI-T), Toronto Alexithymia Scale (TAS-20), and Beck Depression Inventory-II (BDI-II)—has shown inconsistent results. This inconsistency may stem from publication bias, methodological variations, or underpowered studies. To investigate this inconsistency, we conducted a behavioural study and a meta-analysis. Our findings showed no association between HCT scores and responses on the TAS-20 or BDI-II. The meta-analysis found only a weak correlation between HCT and STAI-T responses (r = 0.06), which was moderated by the specific task instructions given to participants. Whilst no evidence of publication bias was seen, the included studies were consistently underpowered. These results challenge the idea of a meaningful relationship between the HCT and these common psychological measures. We highlight key methodological issues that may be contributing to the lack of robust findings in this field and discuss limitations of the HCT itself.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.035 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".