fNIRS 机器学习分类研究:陌生人对话中默认网络脑间同步与连接感
Making new connections: an fNIRS machine learning classification study of inter-brain synchrony in the default mode network
依据摘要,一项 fNIRS 研究让 70 对陌生人在浅谈或深谈条件下对话,同时测量脑活动,发现深谈组报告的社会连接感高于浅谈组。连接感更强与默认网络(DMN)脑间同步增强相关,其中右侧颞顶联合区(TPJ)子区域在回归分析与机器学习分类中均为最强的个体预测指标。
Little is known about the neurocognitive underpinnings of forming social connections. In this study, we tested whether default mode network (DMN) inter-brain synchrony (which prior work has linked to interpersonal alignment, or “seeing eye-to-eye”) predicts feelings of connection during conversations. Seventy pairs of strangers engaged in shallow or deep conversations while brain activity was measured with functional near-infrared spectroscopy (fNIRS). Stranger dyads in the deep condition felt more connected than those in the shallow condition. Greater feelings of connection were associated with increased DMN synchrony, with the right temporoparietal junction (TPJ) subregion of the DMN emerging as the strongest individual predictor across regression analyses and machine learning classification. These findings suggest that DMN synchrony in general and TPJ synchrony in particular may play a key role in strangers coming to “see eye-to-eye” during face-to-face interactions and feeling more socially connected.
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来源:Social Cognitive and Affective Neuroscience · academic.oup.com