气候变化领域集成服务门户(CCPortal): The neural architecture of language: Integrative modeling converges on predictive processing

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DOI	10.1073/pnas.2105646118
	The neural architecture of language: Integrative modeling converges on predictive processing
	Schrimpf M.; Blank I.A.; Tuckute G.; Kauf C.; Hosseini E.A.; Kanwisher N.; Tenenbaum J.B.; Fedorenko E.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:45
英文摘要	The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechanisms in the target domain. We here present a systematic study taking this approach to higher-level cognition: Human language processing, our species' signature cognitive skill. We find that the most powerful "transformer" models predict nearly 100% of explainable variance in neural responses to sentences and generalize across different datasets and imaging modalities (functional MRI and electrocorticography). Models' neural fits ("brain score") and fits to behavioral responses are both strongly correlated with model accuracy on the next-word prediction task (but not other language tasks). Model architecture appears to substantially contribute to neural fit. These results provide computationally explicit evidence that predictive processing fundamentally shapes the language comprehension mechanisms in the human brain. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Artificial neural networks; Computational neuroscience; Deep learning; Language comprehension; Neural recordings (fMRI and ECoG)
语种	英语
scopus关键词	article; artificial neural network; brain; comprehension; deep learning; electrocorticography; functional magnetic resonance imaging; human; human experiment; language processing; language test; nerve potential; neuroscience; prediction; skill
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238762
作者单位	Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; Department of Psychology, University of California, Los Angeles, CA 90095, United States
推荐引用方式 GB/T 7714	Schrimpf M.,Blank I.A.,Tuckute G.,et al. The neural architecture of language: Integrative modeling converges on predictive processing[J],2021,118(45).
APA	Schrimpf M..,Blank I.A..,Tuckute G..,Kauf C..,Hosseini E.A..,...&Fedorenko E..(2021).The neural architecture of language: Integrative modeling converges on predictive processing.Proceedings of the National Academy of Sciences of the United States of America,118(45).
MLA	Schrimpf M.,et al."The neural architecture of language: Integrative modeling converges on predictive processing".Proceedings of the National Academy of Sciences of the United States of America 118.45(2021).