Project Description
Paying attention to facial emotion is an essential building block of social attention that develops dramatically in early childhood and lays the foundation for later socioemotional and prosocial behavior. The purpose of the Emotion Project is to investigate the development of emotion processing in the first few years of life. Specifically, we are interested in how emotion processing changes from infancy to childhood and how it may be related to other cognitive domains, temperament, and physiology in children. In the lab, we measure brain activity and eye movements while young kids watch pictures of people displaying different emotions.
Recent & Ongoing Studies
One recent study examined the event-related potential (ERP) and cortical source responses of infants to happy, fearful, and angry faces at 5, 7, and 12 months of age (Xie, McCormick, Westerlund, Bowman, & Nelson, 2019). Reconstructed source activities in major cortical components (ROIs) of the face and emotion networks were estimated and compared between different emotion conditions. The findings of the study offer convergent evidence that 5 to 7 months is a critical period for development of the “fear-bias” in infants and provides novel insight into the development of the neural bases of infant social attention. We are currently extending these ERP and cortical source analyses with the infants’ data to their longitudinal data collected at 3 years of age.
Could children’s (infants and 3 years old) brain responses to emotional expressions explain their looking behaviors in the eye-tracking task showing these emotions? This question is being tested in an ongoing study, in which the relation between children’s “ERP and behavioral fear-bias” is analyzed.
Do children’s looking behaviors to emotional expressions also differ with their temperament traits? My colleague (Joe Bathelt) and I are investigating whether data-driven subgroups defined by community clustering of children’s temperament traits show differences in their looking behaviours in the eye-tracking task. Please see Bathelt et al. (2018, JAACAP) as a reference for these machine learning and graph theory techniques. Preliminary results and programs (written in Python) for the community clustering analysis could be found here.
