The duration discrimination respiratory task: A new test to measure respiratory interoceptive accuracy
Bibliographic record
Abstract
Interoception, which refers to the perception of body's internal state, is implicated in emotional processes and psychopathological disorders. Over the last decades, different tools have been developed to measure interoceptive accuracy, or the ability to accurately perceive physiological signals. Most of these tools have focused on cardiac interoception, but respiratory interoception has been less investigated due to the more complex and less portable equipment required. In this study, we suggest a new duration discrimination respiratory (DDR) task that does not require complex equipment. Using an adaptive staircase procedure, this task aims to determine an individual's ability to detect exhalation longer than their resting reference duration. One hundred and twenty-three healthy subjects completed the DDR task, an interoceptive task of heart rate discrimination, and filled out questionnaires on interoceptive awareness (Multidimensional Assessment of Interoceptive Awareness), alexithymia (Toronto Alexithymia Scale [TAS]), affects (Positive and Negative Affect Scale [PANAS]), and anamnestic. Results demonstrated a good internal consistency (Cronbach's alpha = .93) of the DDR task. On average, subjects needed 99.22% (SD = 36.38) of their reference exhalation time in addition to reference exhalation to detect a prolonged exhalation. Higher self-reported fitness levels, not counting during the DDR task and lower difficulty in describing feelings (TAS subscale), predicted higher respiratory discrimination duration. In conclusion, this study demonstrates the utility of the DDR task as a valid measure of interoception.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".