Enhancing Executive Control: Attention to Balance, Breath, and the Speed Versus Accuracy Tradeoff
Bibliographic record
Abstract
Malleability of executive control and its enhancement through yoga training is unclear. In Study 1, participants (yoga group; n = 27, mean = 23.27 years) were tested on executive control tasks pre and post 8 weeks of yoga training. The training focused on attention to postural control during yoga asanas and respiratory control during pranayama-breathing (30 min each of postural and breath-control training, biweekly). Yoga training was assessed via performance ratings as to how well a posture was executed and by examining errors that reflected inattention/failures in postural and breath control. We also explored whether attentional demands on motor and respiratory control were associated with three components of executive control (working memory, cognitive flexibility, and inhibition) during nine executive control tasks. Partial correlation results revealed that the three components of executive control might be differentially impacted by postural and breath control, and selectively associated with either speed or accuracy (except for cognitive flexibility). Attentional demands influenced the link between postural, breath, and cognitive control. In Study 2, comparisons between a yoga group and a gender-matched control group (control group; n = 27, mean = 23.33 years) pointed towards higher working memory accuracy and a better speed-accuracy tradeoff in inhibitory control in the yoga group. A ceiling-practice effect was addressed by examining yoga practice learning (i.e., practice-induced change in postural and breath control reflected in ratings and errors) on executive control performance across two sets of tasks: repeatedly tested (pre and post-8 weeks) and non-repeatedly tested (post-8 weeks). Attention to motor and respiratory control during yoga might be considered as a potential mechanism through which specific components of executive control in young adults might be enhanced potentially via altering of speed-accuracy tradeoff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".