Inferring the nature of linguistic computations in the brain.
Sentences contain structure that determines their meaning beyond that of individual words. An influential study by Ding and colleagues (2016) used frequency tagging of phrases and sentences to show that the human brain is sensitive to structure by finding peaks of neural power at the rate at which s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010269 |
| _version_ | 1817998863959588864 |
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| author | Sanne Ten Oever Karthikeya Kaushik Andrea E Martin |
| author_facet | Sanne Ten Oever Karthikeya Kaushik Andrea E Martin |
| author_sort | Sanne Ten Oever |
| collection | DOAJ |
| description | Sentences contain structure that determines their meaning beyond that of individual words. An influential study by Ding and colleagues (2016) used frequency tagging of phrases and sentences to show that the human brain is sensitive to structure by finding peaks of neural power at the rate at which structures were presented. Since then, there has been a rich debate on how to best explain this pattern of results with profound impact on the language sciences. Models that use hierarchical structure building, as well as models based on associative sequence processing, can predict the neural response, creating an inferential impasse as to which class of models explains the nature of the linguistic computations reflected in the neural readout. In the current manuscript, we discuss pitfalls and common fallacies seen in the conclusions drawn in the literature illustrated by various simulations. We conclude that inferring the neural operations of sentence processing based on these neural data, and any like it, alone, is insufficient. We discuss how to best evaluate models and how to approach the modeling of neural readouts to sentence processing in a manner that remains faithful to cognitive, neural, and linguistic principles. |
| first_indexed | 2024-04-14T02:59:55Z |
| format | Article |
| id | doaj.art-0a3c8d6f8f4541c887048670044e06f8 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-14T02:59:55Z |
| publishDate | 2022-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-0a3c8d6f8f4541c887048670044e06f82022-12-22T02:15:56ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-07-01187e101026910.1371/journal.pcbi.1010269Inferring the nature of linguistic computations in the brain.Sanne Ten OeverKarthikeya KaushikAndrea E MartinSentences contain structure that determines their meaning beyond that of individual words. An influential study by Ding and colleagues (2016) used frequency tagging of phrases and sentences to show that the human brain is sensitive to structure by finding peaks of neural power at the rate at which structures were presented. Since then, there has been a rich debate on how to best explain this pattern of results with profound impact on the language sciences. Models that use hierarchical structure building, as well as models based on associative sequence processing, can predict the neural response, creating an inferential impasse as to which class of models explains the nature of the linguistic computations reflected in the neural readout. In the current manuscript, we discuss pitfalls and common fallacies seen in the conclusions drawn in the literature illustrated by various simulations. We conclude that inferring the neural operations of sentence processing based on these neural data, and any like it, alone, is insufficient. We discuss how to best evaluate models and how to approach the modeling of neural readouts to sentence processing in a manner that remains faithful to cognitive, neural, and linguistic principles.https://doi.org/10.1371/journal.pcbi.1010269 |
| spellingShingle | Sanne Ten Oever Karthikeya Kaushik Andrea E Martin Inferring the nature of linguistic computations in the brain. PLoS Computational Biology |
| title | Inferring the nature of linguistic computations in the brain. |
| title_full | Inferring the nature of linguistic computations in the brain. |
| title_fullStr | Inferring the nature of linguistic computations in the brain. |
| title_full_unstemmed | Inferring the nature of linguistic computations in the brain. |
| title_short | Inferring the nature of linguistic computations in the brain. |
| title_sort | inferring the nature of linguistic computations in the brain |
| url | https://doi.org/10.1371/journal.pcbi.1010269 |
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