Computational Modeling of Sequential Dependencies in Mother-Child Social Interaction and Associations to Empathic...

Understanding how early mother-child interactions are linked to children's social-cognitive processes requires methods capable of capturing the temporal structure of naturalistic behavior. This study introduces a computational framework based on Bayesian Network modeling to identify sequential dependencies among nonverbal behaviors (smiles, gaze, and social touch) exchanged during free play in mother-child dyads (n = 38; age 3 years). From each network, we derived the Order of Sequential Interaction (OSI), a compact index of interaction complexity. We then examined its associations with behavioral, physiological, and neural measures relevant to cognitive development. Although OSI was not associated with language or executive-function scores, analyses revealed links between OSI and prosocial behavior, facial EMG, and neural responses (rTPJ, lIFG) during prosocial-scene viewing. These findings suggest that OSI may capture aspects of interaction structure specifically connected to children's social and affective responsiveness. Building on this, the present framework demonstrates how probabilistic graphical models can structure complex interaction data and support future investigations into multimodal processes in early social cognition. SUMMARY: A Bayesian-network framework is proposed to model multivariate sequential dependencies in naturalistic mother-child interaction. The order of sequential interaction (OSI) quantifies interaction complexity from behavioral time-series data. Higher OSI is associated with greater prosocial behavior and with neural (rTPJ, lIFG) and physiological (facial EMG) responses during social processing. Interaction complexity is not associated with general cognitive or language measures, suggesting that it reflects a distinct dimension of social behavior. The proposed framework provides a basis for studying social-cognitive development from naturalistic interaction data.