Toward a universal decoder of linguistic meaning from brain activation

Previous work decoding linguistic meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode meanings of semantically diverse new sent...

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Autores principales:	Francisco Pereira, Bin Lou, Brianna Pritchett, Samuel Ritter, Samuel J. Gershman, Nancy Kanwisher, Matthew Botvinick, Evelina Fedorenko
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4eab538b394d41aca61a5cfdc4d5c391
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spelling	oai:doaj.org-article:4eab538b394d41aca61a5cfdc4d5c3912021-12-02T15:33:55ZToward a universal decoder of linguistic meaning from brain activation10.1038/s41467-018-03068-42041-1723https://doaj.org/article/4eab538b394d41aca61a5cfdc4d5c3912018-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-03068-4https://doaj.org/toc/2041-1723Previous work decoding linguistic meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode meanings of semantically diverse new sentences with topics not encountered during training.Francisco PereiraBin LouBrianna PritchettSamuel RitterSamuel J. GershmanNancy KanwisherMatthew BotvinickEvelina FedorenkoNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-13 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Francisco Pereira Bin Lou Brianna Pritchett Samuel Ritter Samuel J. Gershman Nancy Kanwisher Matthew Botvinick Evelina Fedorenko Toward a universal decoder of linguistic meaning from brain activation
description	Previous work decoding linguistic meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode meanings of semantically diverse new sentences with topics not encountered during training.
format	article
author	Francisco Pereira Bin Lou Brianna Pritchett Samuel Ritter Samuel J. Gershman Nancy Kanwisher Matthew Botvinick Evelina Fedorenko
author_facet	Francisco Pereira Bin Lou Brianna Pritchett Samuel Ritter Samuel J. Gershman Nancy Kanwisher Matthew Botvinick Evelina Fedorenko
author_sort	Francisco Pereira
title	Toward a universal decoder of linguistic meaning from brain activation
title_short	Toward a universal decoder of linguistic meaning from brain activation
title_full	Toward a universal decoder of linguistic meaning from brain activation
title_fullStr	Toward a universal decoder of linguistic meaning from brain activation
title_full_unstemmed	Toward a universal decoder of linguistic meaning from brain activation
title_sort	toward a universal decoder of linguistic meaning from brain activation
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/4eab538b394d41aca61a5cfdc4d5c391
work_keys_str_mv	AT franciscopereira towardauniversaldecoderoflinguisticmeaningfrombrainactivation AT binlou towardauniversaldecoderoflinguisticmeaningfrombrainactivation AT briannapritchett towardauniversaldecoderoflinguisticmeaningfrombrainactivation AT samuelritter towardauniversaldecoderoflinguisticmeaningfrombrainactivation AT samueljgershman towardauniversaldecoderoflinguisticmeaningfrombrainactivation AT nancykanwisher towardauniversaldecoderoflinguisticmeaningfrombrainactivation AT matthewbotvinick towardauniversaldecoderoflinguisticmeaningfrombrainactivation AT evelinafedorenko towardauniversaldecoderoflinguisticmeaningfrombrainactivation
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Toward a universal decoder of linguistic meaning from brain activation

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