Compositionality is a hallmark of human language and other symbolic systems: a finite set of meaningful elements can be systematically combined to convey an open-ended array of ideas. Compositionality is not uniformly distributed over expressions in a language or over individuals’ communicative behavior: at both levels, variation is observed. Here, we investigate the neural bases of interindividual variability by probing the relationship between intrinsic characteristics of brain networks and compositional behavior. We first collected functional resting-state and diffusion magnetic resonance imaging data from a large participant sample (N = 51). Subsequently, participants took part in two signaling games. They were instructed to learn and reproduce an auditory symbolic system of signals (tone sequences) associated with affective meanings (human faces expressing emotions). Signal-meaning mappings were artificial and had to be learned via repeated signaling interactions. We identified a temporoparietal network in which connection length was related to the degree of compositionality introduced in a signaling system by each player. Graph-theoretic analysis of resting-state functional connectivity revealed that, within that network, compositional behavior was associated with integration measures in 2 semantic hubs: the left posterior cingulate cortex and the left angular gyrus. Our findings link individual variability in compositional biases to variation in the anatomy of semantic networks and in the functional topology of their constituent units.

中文翻译：

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人脑连接的网络分析揭示了信号系统中组合性偏差的神经指纹

组合性是人类语言和其他符号系统的标志：一组有限的有意义的元素可以系统地组合起来，以传达一系列开放的思想。组合性在语言表达或个人交际行为上并不是均匀分布的：在这两个层面上，都可以观察到变化。在这里，我们通过探讨大脑网络的内在特征与组合行为之间的关系来研究个体间变异性的神经基础。我们首先从一个大型参与者样本（N = 51）中收集了功能性静息状态和弥散磁共振成像数据。随后，参与者参加了两个信号游戏。他们被要求学习和再现与情感意义（表达情感的人脸）相关的听觉符号系统（音调序列）。信号意义映射是人为的，必须通过重复的信号交互来学习。我们确定了一个颞顶网络，其中连接长度与每个玩家在信号系统中引入的组合程度有关。静息状态功能连通性的图论分析表明，在该网络中，组合行为与 2 个语义中心的整合测量相关：左后扣带皮层和左角回。我们的研究结果将组成偏差的个体差异与语义网络的解剖结构及其组成单元的功能拓扑结构的差异联系起来。