How Dynamic Brain Networks Tune Social Behavior in Real Time
How Dynamic Brain Networks Tune Social Behavior in Real Time
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DOI：
10.1177/0963721418773362
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发表时间：
2018-10
影响因子：
7.2
通讯作者：
B. Silston;D. Bassett;D. Mobbs
中科院分区：
文献类型：
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作者：
B. Silston;D. Bassett;D. Mobbs
在社交互动中，大脑有一项巨大的任务，那就是解释那些转瞬即逝的、微妙的、上下文相关的、抽象的、经常是模棱两可的信号。尽管信号很复杂，但人类的大脑已经进化成在社交场景中非常成功。在这里，我们认为人脑通过积累、整合和预测来理解嘈杂的动态信号，从而产生对社会世界的连贯表示。我们认为，成功的社交互动在很大程度上依赖于一组高度连接的核心枢纽，这些枢纽动态地积累和整合复杂的社交信息，并在这样做的过程中促进社交话语的即时调整。因此，成功的互动需要由高度集成的中枢组成的神经电路产生的适应性灵活性，这些中枢协调与上下文相适应的反应。神经底物的适应性属性，包括预测和自适应编码，以及神经重用，以及感知、推理和动机输入，为指导我们的社交互动的柔韧、分层的预测模型提供了成分。
During social interaction, the brain has the enormous task of interpreting signals that are fleeting, subtle, contextual, abstract, and often ambiguous. Despite the signal complexity, the human brain has evolved to be highly successful in the social landscape. Here, we propose that the human brain makes sense of noisy dynamic signals through accumulation, integration, and prediction, resulting in a coherent representation of the social world. We propose that successful social interaction is critically dependent on a core set of highly connected hubs that dynamically accumulate and integrate complex social information and, in doing so, facilitate social tuning during moment-to-moment social discourse. Successful interactions, therefore, require adaptive flexibility generated by neural circuits composed of highly integrated hubs that coordinate context-appropriate responses. Adaptive properties of the neural substrate, including predictive and adaptive coding, and neural reuse, along with perceptual, inferential, and motivational inputs, provide the ingredients for pliable, hierarchical predictive models that guide our social interactions.