Taking time to feel our body: Steady increases in heartbeat perception accuracy and decreases in alexithymia over 9 months of contemplative mental training
Bibliographic record
Abstract
The ability to accurately perceive signals from the body has been shown to be important for physical and psychological health as well as understanding one's emotions. Despite the importance of this skill, often indexed by heartbeat perception accuracy (HBPa), little is known about its malleability. Here, we investigated whether contemplative mental practice can increase HBPa. In the context of a 9-month mental training study, the ReSource Project, two matched cohorts (n = 77 and n = 79) underwent three training modules of 3 months' duration that targeted attentional and interoceptive abilities (Presence module), socio-affective (Affect module), and socio-cognitive (Perspective module) abilities. A third cohort (n = 78) underwent 3 months of practice (Affect module) and a retest control group (n = 84) did not undergo any training. HBPa was measured with a heartbeat tracking task before and after each training module. Emotional awareness was measured by the Toronto Alexithymia Scale (TAS). Participants with TAS scores > 60 at screening were excluded. HBPa was found to increase steadily over the training, with significant and small- to medium-sized effects emerging after 6 months (Cohen's d = .173) and 9 months (d = .273) of mental training. Changes in HBPa were concomitant with and predictive of changes in emotional awareness. Our results suggest that HBPa can indeed be trained through intensive contemplative practice. The effect takes longer than the 8 weeks of typical mindfulness courses to reach meaningful magnitude. These increments in interoceptive accuracy and the related improvements in emotional awareness point to opportunities for improving physical and psychological health through contemplative mental training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".