Different computational relations in language are captured by distinct brain systems. (24th March 2022)

• Record Type: Journal Article
• Authors: Fu, Ze; Wang, Xiaosha; Wang, Xiaoying; Yang, Huichao; Wang, Jiahuan; Wei, Tao; Liao, Xuhong; Liu, Zhiyuan; Chen, Huimin; Bi, Yanchao
• Is Part Of: Cerebral cortex. Volume 33:Number 4(2023)
• Page Start: 997
• Page End: 1013
• Publication Date: 2022-03-24
• Subjects: semantics -- language computation models -- fMRI -- graph theory -- neural network model; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhac117
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: 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.