Feasibility of the heaviness perception test as an assessment of interoception
Bibliographic record
Abstract
BACKGROUND: Interoception refers to the multisensory integration and perception of the body's internal state within the central nervous system, which involves learning, memory, emotions, and experiences. Interoceptive dysfunction has been associated with alexithymia and alexisomia. Despite growing academic interest in interoception, standardized evaluation methods have yet to be established. The widely used Heartbeat Counting Task (HCT), a representative method for assessing interoceptive accuracy, has limitations owing to the potential influence of knowledge of heart rate, time perception, and tactile sensations. Therefore, more reliable assessment methods are needed. This study focused on the feasibility of the heaviness perception test as a method for assessing interoceptive accuracy and investigates its relationship with other interoceptive indices. METHODS: A total of 41 healthy volunteers (19 female; mean age 19.1 ± 0.8 years) participated in the study. The heaviness perception test was conducted using an approach similar to the method of adjustment applied to psychophysical measurements, and the absolute error scores were calculated. Other interoceptive indices investigated in this study include the HCT, Body Perception Questionnaire-Body Awareness Very Short Form (BPQ-VSF), the 20-item Toronto Alexithymia Scale (TAS-20), and Shitsu-Taikan-Sho-Scale (STSS) for alexisomia. RESULTS: Interoception accuracy assessed using the heaviness perception test showed a significant positive correlation with the BPQ-VSF score (r = .504, p < .01) and a negative correlation with the TAS-20 and STSS scores (TAS-20: r = -.342, p < .05; STSS: r = -.353, p < .05). However, there was no correlation between the heaviness perception test score and the absolute error score on the HCT. CONCLUSIONS: The results suggest that the heaviness perception test is a feasible and useful method for assessing interoceptive accuracy and that it may be useful as an evaluation tool.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".