Combining computational controls with natural text reveals aspects of meaning composition

被引:0
作者
Mariya Toneva
Tom M. Mitchell
Leila Wehbe
机构
[1] Carnegie Mellon University,Machine Learning Department
[2] Neuroscience Institute,Department of Psychology
[3] Carnegie Mellon University,undefined
[4] Max Planck Institute for Software Systems,undefined
[5] Carnegie Mellon University,undefined
来源
Nature Computational Science | 2022年 / 2卷
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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.
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页码:745 / 757
页数:12
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