This area is dedicated to understanding the development of social behavior across typical and atypical trajectories. The research focuses particularly on caregiver–child interactions, examining how behavioral and physiological synchrony emerges early in life. Using a multimethod approach, the lab combines neural, physiological, and observational measures to capture the complexity of early social exchanges. Special attention is given to atypical development, including autism, with the aim of identifying early markers and mechanisms that differentiate developmental pathways. 

This research line focuses on advancing the methodological approches used to study interpersonal synchrony. The lab develops computational models to improve the quantification of synchrony across behavioral, physiological, and neural signals. A key component is the optimization of fNIRS signal processing, addressing challenges such as noise reduction and signal interpretation. These efforts are focused on artificial intelligence and machine learning approaches, enabling more robust analysis of complex datasets.