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Predicting Dyadic Synchrony: A Theory-driven Machine Learning Approach.

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Predicting Dyadic Synchrony: A Theory-driven Machine Learning Approach.

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Published on: March 25, 2014

Predicting Dyadic Synchrony: A Theory-Driven Machine Learning Approach.

Michel Sfeir^1,2, Fabiola Silletti^3,4,5, Hung-Chu Lin^5,6

¹Department of Clinical Psychology, University of Mons, Mons, Belgium.

Infancy : the Official Journal of the International Society on Infant Studies

|May 15, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Predicting mother-child synchrony is complex. Machine learning reveals synchrony depends on the interplay of parenting stress and child affect, not just additive factors, offering new insights into early socioemotional development.

Keywords:

development emotions family relationships machine learning synchrony

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Published on: March 25, 2014

Area of Science:

Developmental Psychology
Computational Social Science

Background:

Dyadic synchrony, the coordination of emotional and behavioral signals between mother and child, is crucial for early socioemotional development.
Predicting dyadic synchrony is challenging due to complex, context-sensitive interactions between relational and psychological factors, often not captured by linear models.

Purpose of the Study:

To apply a theory-guided machine learning approach to identify predictors of observed dyadic synchrony in mother-child dyads.
To compare the predictive performance of machine learning models (Random Forest) with traditional linear regression.

Main Methods:

Utilized data from 204 mother-child dyads in the PEACE Study (collected Nov 2021-Aug 2022).
Coded dyadic synchrony and affective behaviors from free-play interactions using the Coding Interactive Behavior system.

Employed machine learning (Random Forest) and linear regression, with SHAP and ALE for model interpretation, examining maternal anxiety, resilience, parenting stress, and affective expressions.

Main Results:

While linear regression showed slightly stronger predictive performance, Random Forest models identified complex non-linear and interaction-based patterns.
Dyadic synchrony decreased with imbalances between parenting stress and child positive affect.
Affective mismatch demonstrated a non-monotonic association with synchrony, highlighting context-dependent emergence.

Conclusions:

Dyadic synchrony emerges from configurations of stress, affect, and emotional alignment, rather than simple additive effects.
Interpretable machine learning offers a valuable complement to traditional statistical models for exploring complex relational processes in early development.
Findings underscore the context-dependent nature of dyadic coordination in supporting socioemotional development.