It's About Time – Breathing Dynamics Modulate Emotion and Cognition
Bibliographic record
Abstract
The breathing rate, phase, and amplitude have been shown to track changes in emotional states such as anxiety and cognitive performance in tasks that involve perception, attention, and short-term memory. It is common practice to characterize breathing by using a block average breathing rate, phase, or amplitude. While these features are useful for measuring the central tendencies of breathing, they do not capture the structure of the patterns of change in its activity over time (i.e., breathing dynamics) whose relationship with affective and cognitive processes remains unclear. To fill this knowledge gap, we characterized breathing dynamics by a set of measures which capture the breathing signal’s rate and amplitude central tendency, variability, complexity, entropy, and timescales. Then, we conducted a principal components analysis and demonstrated that these metrics capture similar, yet distinct features of the breathing rate and amplitude time series. Next, we showed that breathing dynamics change across rest and task conditions, suggesting they may be sensitive to changes in behavioral states. Finally, using multivariate analyses, we demonstrated that breathing complexity and entropy in the resting state are strongly and positively correlated with anxiety levels, while breathing variability in the task state is strongly and negatively associated with working memory performance. Our findings extend the current understanding of how breathing is associated with affective and cognitive processes by highlighting the key role of dynamics in that relationship
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".