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
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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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