Contemplative/emotion training reduces negative emotional behavior and promotes prosocial responses.
Bibliographic record
Abstract
Contemplative practices are believed to alleviate psychological problems, cultivate prosocial behavior and promote self-awareness. In addition, psychological science has developed tools and models for understanding the mind and promoting well-being. Additional effort is needed to combine frameworks and techniques from these traditions to improve emotional experience and socioemotional behavior. An 8-week intensive (42 hr) meditation/emotion regulation training intervention was designed by experts in contemplative traditions and emotion science to reduce "destructive enactment of emotions" and enhance prosocial responses. Participants were 82 healthy female schoolteachers who were randomly assigned to a training group or a wait-list control group, and assessed preassessment, postassessment, and 5 months after training completion. Assessments included self-reports and experimental tasks to capture changes in emotional behavior. The training group reported reduced trait negative affect, rumination, depression, and anxiety, and increased trait positive affect and mindfulness compared to the control group. On a series of behavioral tasks, the training increased recognition of emotions in others (Micro-Expression Training Tool), protected trainees from some of the psychophysiological effects of an experimental threat to self (Trier Social Stress Test; TSST), appeared to activate cognitive networks associated with compassion (lexical decision procedure), and affected hostile behavior in the Marital Interaction Task. Most effects at postassessment that were examined at follow-up were maintained (excluding positive affect, TSST rumination, and respiratory sinus arrhythmia recovery). Findings suggest that increased awareness of mental processes can influence emotional behavior, and they support the benefit of integrating contemplative theories/practices with psychological models and methods of emotion regulation.
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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 | 0.000 |
| 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) | 0.003 | 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".