State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.
Cereb Cortex. 2023 Feb 7;33(4):997-1013. doi: 10.1093/cercor/bhac117.
A critical way for humans to acquire information is through language, yet whether and how language experience drives specific neural semantic representations is still poorly understood. We considered statistical properties captured by 3 different computational principles of language (simple co-occurrence, network-(graph)-topological relations, and neural-network-vector-embedding relations) and tested the extent to which they can explain the neural patterns of semantic representations, measured by 2 functional magnetic resonance imaging experiments that shared common semantic processes. Distinct graph-topological word relations, and not simple co-occurrence or neural-network-vector-embedding relations, had unique explanatory power for the neural patterns in the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were relatively specific to language: they were not explained by sensory-motor similarities and the same computational relations of visual objects (based on visual image database) showed effects in the visual cortex in the picture naming experiment. That is, different topological properties within language and the same topological computations (common-neighbors) for language and visual inputs are captured by different brain regions. These findings reveal the specific neural semantic representations along graph-topological properties of language, highlighting the information type-specific and statistical property-specific manner of semantic representations in the human brain.
人类获取信息的一个关键途径是通过语言，但语言经验是否以及如何驱动特定的神经语义表示仍知之甚少。我们考虑了语言的 3 种不同计算原则（简单共现、网络（图）拓扑关系和神经网络向量嵌入关系）所捕获的统计特性，并测试了它们在多大程度上可以解释通过 2 个共享共同语义过程的功能磁共振成像实验测量的语义表示的神经模式。独特的图拓扑词关系，而不是简单的共现或神经网络向量嵌入关系，对前颞叶（捕获图常见邻居）、下额叶和后中/下颞叶的神经模式具有独特的解释力（捕获图最短路径）。这些结果相对特定于语言：它们不能用感觉运动相似性来解释，视觉对象的相同计算关系（基于视觉图像数据库）在图片命名实验中显示出视觉皮层的效果。也就是说，语言内部的不同拓扑性质以及语言和视觉输入的相同拓扑计算（共同邻居）是由不同的大脑区域来捕获的。这些发现揭示了沿着语言图拓扑性质的特定神经语义表示，突出了大脑中语义表示的信息类型特异性和统计性质特异性方式。
