National Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China.
Cereb Cortex. 2018 Dec 1;28(12):4305-4318. doi: 10.1093/cercor/bhx283.
words constitute nearly half of the human lexicon and are critically associated with human abstract thoughts, yet little is known about how they are represented in the brain. We tested the neural basis of 2 classical cognitive notions of abstract meaning representation: by linguistic contexts and by semantic features. We collected fMRI BOLD responses for 360 abstract words and built theoretical representational models from state-of-the-art corpus-based natural language processing models and behavioral ratings of semantic features. Representational similarity analyses revealed that both linguistic contextual and semantic feature similarity affected the representation of abstract concepts, but in distinct neural levels. The corpus-based similarity was coded in the high-level linguistic processing system, whereas semantic feature information was reflected in distributed brain regions and in the principal component space derived from whole-brain activation patterns. These findings highlight the multidimensional organization and the neural dissociation between linguistic contextual and featural aspects of abstract concepts.
单词构成了人类词汇的近一半,与人类的抽象思维密切相关,但人们对它们在大脑中的表现知之甚少。我们测试了 2 种经典的抽象意义表示认知概念的神经基础:通过语言语境和语义特征。我们为 360 个抽象词收集了 fMRI BOLD 反应,并从最先进的基于语料库的自然语言处理模型和语义特征的行为评分中构建了理论表示模型。表示相似性分析表明,语言语境和语义特征相似性都影响了抽象概念的表示,但在不同的神经水平上。基于语料库的相似性被编码在高级语言处理系统中,而语义特征信息则反映在分布式脑区以及基于全脑激活模式的主成分空间中。这些发现强调了抽象概念的语言语境和特征方面的多维组织和神经分离。