Behavioral correlates of cortical semantic representations modeled by word vectors.

The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful...

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Autores principales:	Satoshi Nishida, Antoine Blanc, Naoya Maeda, Masataka Kado, Shinji Nishimoto
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e6170cee28ff4db48eed978f4dedba68
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spelling	oai:doaj.org-article:e6170cee28ff4db48eed978f4dedba682021-11-25T05:40:35ZBehavioral correlates of cortical semantic representations modeled by word vectors.1553-734X1553-735810.1371/journal.pcbi.1009138https://doaj.org/article/e6170cee28ff4db48eed978f4dedba682021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009138https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations in the brain revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled brain semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives in the brain estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of brain word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings.Satoshi NishidaAntoine BlancNaoya MaedaMasataka KadoShinji NishimotoPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 6, p e1009138 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Satoshi Nishida Antoine Blanc Naoya Maeda Masataka Kado Shinji Nishimoto Behavioral correlates of cortical semantic representations modeled by word vectors.
description	The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations in the brain revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled brain semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives in the brain estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of brain word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings.
format	article
author	Satoshi Nishida Antoine Blanc Naoya Maeda Masataka Kado Shinji Nishimoto
author_facet	Satoshi Nishida Antoine Blanc Naoya Maeda Masataka Kado Shinji Nishimoto
author_sort	Satoshi Nishida
title	Behavioral correlates of cortical semantic representations modeled by word vectors.
title_short	Behavioral correlates of cortical semantic representations modeled by word vectors.
title_full	Behavioral correlates of cortical semantic representations modeled by word vectors.
title_fullStr	Behavioral correlates of cortical semantic representations modeled by word vectors.
title_full_unstemmed	Behavioral correlates of cortical semantic representations modeled by word vectors.
title_sort	behavioral correlates of cortical semantic representations modeled by word vectors.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e6170cee28ff4db48eed978f4dedba68
work_keys_str_mv	AT satoshinishida behavioralcorrelatesofcorticalsemanticrepresentationsmodeledbywordvectors AT antoineblanc behavioralcorrelatesofcorticalsemanticrepresentationsmodeledbywordvectors AT naoyamaeda behavioralcorrelatesofcorticalsemanticrepresentationsmodeledbywordvectors AT masatakakado behavioralcorrelatesofcorticalsemanticrepresentationsmodeledbywordvectors AT shinjinishimoto behavioralcorrelatesofcorticalsemanticrepresentationsmodeledbywordvectors
_version_	1718414551558389760

Behavioral correlates of cortical semantic representations modeled by word vectors.

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