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Mirroring and beyond: coupled dynamics as a generalized framework for modelling social interactions

机译:镜像及其他:耦合动力学作为建模社会交互的通用框架

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摘要

When people observe one another, behavioural alignment can be detected at many levels, from the physical to the mental. Likewise, when people process the same highly complex stimulus sequences, such as films and stories, alignment is detected in the elicited brain activity. In early sensory areas, shared neural patterns are coupled to the low-level properties of the stimulus (shape, motion, volume, etc.), while in high-order brain areas, shared neural patterns are coupled to high-levels aspects of the stimulus, such as meaning. Successful social interactions require such alignments (both behavioural and neural), as communication cannot occur without shared understanding. However, we need to go beyond simple, symmetric (mirror) alignment once we start interacting. Interactions are dynamic processes, which involve continuous mutual adaptation, development of complementary behaviour and division of labour such as leader–follower roles. Here, we argue that interacting individuals are dynamically coupled rather than simply aligned. This broader framework for understanding interactions can encompass both processes by which behaviour and brain activity mirror each other (neural alignment), and situations in which behaviour and brain activity in one participant are coupled (but not mirrored) to the dynamics in the other participant. To apply these more sophisticated accounts of social interactions to the study of the underlying neural processes we need to develop new experimental paradigms and novel methods of data analysis
机译:当人们彼此观察时,可以从身体到精神的许多层面检测到行为的一致性。同样,当人们处理相同的高度复杂的刺激序列(例如电影和故事)时,在诱发的大脑活动中会检测到对齐。在早期的感觉区域中,共享的神经模式与刺激的低级属性(形状,运动,体积等)耦合,而在高级大脑区域中,共享的神经模式与刺激的高阶方面耦合。刺激等含义。成功的社交互动需要这种结合(在行为和神经方面),因为没有共同的理解就无法进行交流。但是,一旦开始交互,我们就需要超越简单的对称(镜像)对齐方式。互动是动态过程,涉及不断的相互适应,互补行为的发展和分工(例如领导者与跟随者的角色)。在这里,我们认为互动的个体是动态耦合的,而不是简单地对齐的。这种用于理解交互的更广泛的框架可以涵盖行为和大脑活动相互反映(神经对齐)的过程,以及一个参与者的行为和大脑活动与另一参与者的动态耦合(但不反映)的情况。为了将这些更复杂的社交互动说明应用到基础神经过程的研究中,我们需要开发新的实验范例和新颖的数据分析方法

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