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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009138 |
| _version_ | 1818931645997121536 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T04:19:54Z |
| format | Article |
| id | doaj.art-e6170cee28ff4db48eed978f4dedba68 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-20T04:19:54Z |
| publishDate | 2021-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-e6170cee28ff4db48eed978f4dedba682022-12-21T19:53:40ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-06-01176e100913810.1371/journal.pcbi.1009138Behavioral correlates of cortical semantic representations modeled by word vectors.Satoshi NishidaAntoine BlancNaoya MaedaMasataka KadoShinji NishimotoThe 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.https://doi.org/10.1371/journal.pcbi.1009138 |
| spellingShingle | Satoshi Nishida Antoine Blanc Naoya Maeda Masataka Kado Shinji Nishimoto Behavioral correlates of cortical semantic representations modeled by word vectors. PLoS Computational Biology |
| title | 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_short | Behavioral correlates of cortical semantic representations modeled by word vectors. |
| title_sort | behavioral correlates of cortical semantic representations modeled by word vectors |
| url | https://doi.org/10.1371/journal.pcbi.1009138 |
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