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Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

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Bibliographic Details
Title:	Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.
Authors:	Anderson, Andrew J.^1,2,3,4 (AUTHOR) andanderson@mcw.edu, Davis, Chris⁵ (AUTHOR), Lalor, Edmund C.^4,6,7 (AUTHOR)
Source:	PLoS Computational Biology. 11/11/2024, Vol. 20 Issue 11, p1-27. 27p.
Database:	Academic Search Ultimate
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Description
ISSN:	1553734X
DOI:	10.1371/journal.pcbi.1012537