Combining computational controls with natural text reveals aspects of meaning composition

被引:20
作者
Toneva, Mariya [1 ,2 ,3 ]
Mitchell, Tom M. [1 ,2 ]
Wehbe, Leila [1 ,2 ,4 ]
机构
[1] Carnegie Mellon Univ, Machine Learning Dept, Pittsburgh, PA 15213 USA
[2] Carnegie Mellon Univ, Neurosci Inst, Pittsburgh, PA 15213 USA
[3] Max Planck Inst Software Syst, Saarbrucken, Germany
[4] Carnegie Mellon Univ, Dept Psychol, Pittsburgh, PA 15213 USA
来源
NATURE COMPUTATIONAL SCIENCE | 2022年 / 2卷 / 11期
基金
美国国家卫生研究院;
关键词
MEG; REPRESENTATION; COMPREHENSION; INTERFERENCE; FREQUENCY; SYSTEM; BOLD;
D O I
10.1038/s43588-022-00354-6
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
To study a core component of human intelligence-our ability to combine the meaning of words-neuroscientists have looked to linguistics. However, linguistic theories are insufficient to account for all brain responses reflecting linguistic composition. In contrast, we adopt a data-driven approach to study the composed meaning of words beyond their individual meaning, which we term 'supra-word meaning'. We construct a computational representation for supra-word meaning and study its brain basis through brain recordings from two complementary imaging modalities. Using functional magnetic resonance imaging, we reveal that hubs that are thought to process lexical meaning also maintain supra-word meaning, suggesting a common substrate for lexical and combinatorial semantics. Surprisingly, we cannot detect supra-word meaning in magnetoencephalography, which suggests that composed meaning might be maintained through a different neural mechanism than the synchronized firing of pyramidal cells. This sensitivity difference has implications for past neuroimaging results and future wearable neurotechnology.
引用
收藏
页码:745 / 757
页数:13
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