PDF(Pubmed)
Abstract：
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 from 42 6- to 9-month-old infants looking at images of their parent\'s face were analysed in real-time and used by a Bayesian Optimisation algorithm to identify which combination of the parent\'s gaze/head direction and emotional expression 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 (angry faces with direct gaze). Infants attending preferentially to faces with direct gaze had increased positive affectivity and decreased negative affectivity. This work confirmed that infants\' attentional preferences for social cues are heterogeneous and shows the NBO\'s potential to study diversity in neurodevelopmental trajectories.
摘要：
婴儿参与社会世界的动机取决于个体大脑的特征与先前暴露于社会线索（如父母的微笑或眼神交流）之间的相互作用。关于为什么情绪表达和注视方向的特定组合会吸引儿童的不同假设已通过小组水平的方法进行了测试，而不是关注社交大脑发育中的个体差异。这里,一种新的人工智能增强脑成像方法，神经自适应贝叶斯优化(NBO)，应用于婴儿脑电图（EEG），以了解选定的神经信号如何编码个体婴儿的社交线索。对42名6至9个月大的婴儿的EEG数据进行了实时分析，并通过贝叶斯优化算法进行了分析，以确定父母的注视/头部方向和情感表达的组合在孩子中产生了最强的大脑激活。这种个性化的方法支持了这样一种理论，即婴儿的大脑最大程度地通过具有负价的交流线索（带有直视的愤怒面孔）来参与。优先注视直视面部的婴儿的积极情感增加，消极情感减少。这项工作证实了婴儿对社交线索的注意偏好是异质的，并显示了NBO研究神经发育轨迹多样性的潜力。
