Predicting Preschoolers' Externalizing Problems with Mother-Child Interaction Dynamics and Deep Learning

TL;DR

This study demonstrates that analyzing mother-child interaction dynamics with deep learning models can effectively predict preschoolers' externalizing problems six months later, highlighting the importance of maternal support behaviors.

Contribution

The paper introduces a novel deep learning approach, ASBIM, that improves prediction accuracy of externalizing problems by incorporating interaction dynamics and child inhibitory control.

Findings

Mother's autonomy support reduces externalizing problems.

Deep learning models outperform traditional methods.

Including child inhibitory control enhances prediction accuracy.

Abstract

Objective: Predicting children's future levels of externalizing problems helps to identify children at risk and guide targeted prevention. Existing studies have shown that mothers providing support in response to children's dysregulation was associated with children's lower levels of externalizing problems. The current study aims to evaluate and improve the accuracy of predicting children's externalizing problems with mother-child interaction dynamics. Method: This study used mother-child interaction dynamics during a challenging puzzle task to predict children's externalizing problems six months later (N=101, 46 boys, Mage=57.41 months, SD=6.58). Performance of the Residual Dynamic Structural Equation Model (RDSEM) was compared with the Attention-based Sequential Behavior Interaction Modeling (ASBIM) model, developed using the deep learning techniques. Results: The RDSEM revealed that children whose mothers provided more autonomy support after increases of child defeat had lower levels of externalizing problems. Five-fold cross-validation showed that the RDSEM had good prediction accuracy. The ASBIM model further improved prediction accuracy, especially after including child inhibitory control as a personalized individual feature. Conclusions: The dynamic process of mother-child interaction provides important information for predicting children's externalizing problems, especially maternal autonomy supportive response to child defeat. The deep learning model is a useful tool to further improve prediction accuracy.