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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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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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  Data: Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.
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  Data: <searchLink fieldCode="AR" term="%22Anderson%2C+Andrew+J%2E%22">Anderson, Andrew J.</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<i> andanderson@mcw.edu</i><br /><searchLink fieldCode="AR" term="%22Davis%2C+Chris%22">Davis, Chris</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lalor%2C+Edmund+C%2E%22">Lalor, Edmund C.</searchLink><relatesTo>4,6,7</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22PLoS+Computational+Biology%22">PLoS Computational Biology</searchLink>. 11/11/2024, Vol. 20 Issue 11, p1-27. 27p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=180806884
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        Value: 10.1371/journal.pcbi.1012537
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        Text: English
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      – TitleFull: Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.
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              Text: 11/11/2024
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