Neuroadaptive Bayesian Optimisation to Identify which Combination of Gaze and Emotion in the Parent Face Maximises Attention in the Individual Infant

Infants’ motivation to engage with the social world depends on the interplay between individual brain’s characteristics and previous exposure to social cues such as the parent’s smile or eye contact. Different hypotheses about why specific combinations of emotional expressions and gaze direction engage children have been tested with group-level approaches rather than focusing on individual differences in the social brain development. Here, a novel Artificial Intelligence-enhanced brain-imaging approach, Neuroadaptive Bayesian Optimisation (NBO), was applied to infant electro-encephalography (EEG) to understand how selected neural signals encode social cues in individual infants.
EEG data was acquired from 42 6- to 9-month-old infants looking at images of their parent’s face, analysed in real-time and selected by a Bayesian Optimisation algorithm to identify which combination of gaze and emotional expression of the parent’s face produces the strongest brain activation in the child. This individualised approach supported the theory that the infant’s brain is maximally engaged by communicative cues with a negative valence such as direct gaze and angry facial expressions. Moreover, we evaluated whether results also capture individual differences in behaviour. We found that infants attending preferentially to faces with direct gaze had increased positive affectivity and decreased negative affectivity compared to infants preferentially attending to faces with averted gaze.
This work supports the idea that infants’ attentional preferences for social cues are heterogeneous and lays the foundation for the development of neuroimaging-informed personalized experiments to study diversity in neurodevelopmental trajectories of social skills.