Physiological measurement of emotion from infancy to preschool: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Emotion regulation, the ability to regulate emotional responses to environmental stimuli, develops in the first years of life and plays an important role in the development of personality, social competence, and behavior. Substantial literature suggests a relationship between emotion regulation and cardiac physiology; specifically, heart rate changes in response to positive or negative emotion-eliciting stimuli. METHOD: This systematic review and meta-analysis provide an in-depth examination of research that has measured physiological responding during emotional-evoking tasks in children from birth to 4 years of age. RESULTS: The review had three main findings. First, meta-regressions resulted in an age-related decrease in baseline and task-related heart rate (HR) and increases in baseline and task-related respiratory sinus arrhythmia (RSA). Second, meta-analyses suggest task-related increases in HR and decreases in RSA and heart rate variability (HRV), regardless of emotional valence of the task. Third, associations between physiological responding and observed behavioral regulation are not consistently present in children aged 4 and younger. The review also provides a summary of the various methodology used to measure physiological reactions to emotional-evoking tasks, including number of sensors used and placement, various baseline and emotional-evoking tasks used, methods for extracting RSA, as well as percentage of loss and reasons for loss for each study. CONCLUSION: Characterizing the physiological reactivity of typically developing children is important to understanding the role emotional regulation plays in typical and atypical development.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".