Physiological measures of mother–infant interactional synchrony
Bibliographic record
Abstract
Mother-infant interactional synchrony has been hypothesized to be crucial for the development of many key neurodevelopmental behaviors in infants, including speech and language. Assessing synchrony is challenging because many interactive behaviors may be subtlety, if at all, observable in overt behaviors. Physiological measures, therefore, may provide valuable physiological/biological markers of mother-infant synchrony. We have developed a multilevel measurement platform to assess physiological synchrony, attention, and vocal congruency during dynamic face-to-face mother-infant interactions. The present investigation was designed to provide preliminary data on its application in a group of 10 mother-infant dyads (20 subjects) ranging in age from 7 to 8.5 months at the time of the experimentation. Respiratory kinematics, heart rate, and vocalization were recorded simultaneously from mothers and infants during nonstructured, face-to-face interactions. Novel statistical methods were used to identify reliable moments of synchrony from cross-correlated, mother-infant respiration and to tag infant attention from heart rate deceleration. Results revealed that attention, vocal contingency, and respiratory synchrony are temporally clustered within the dyad interaction. This temporal alignment is consistent with the notion that biological synchrony provides a supportive platform for infant attention and mother-infant contingent vocalization.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".