Loading…
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...
Saved in:
Published in: | PLoS computational biology 2022-07, Vol.18 (7), p.e1010269 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c661t-592b5d06c114cdcb150c8f9684281b46feffe3b0d6afa92c90e976242f01a8293 |
---|---|
cites | cdi_FETCH-LOGICAL-c661t-592b5d06c114cdcb150c8f9684281b46feffe3b0d6afa92c90e976242f01a8293 |
container_end_page | |
container_issue | 7 |
container_start_page | e1010269 |
container_title | PLoS computational biology |
container_volume | 18 |
creator | Ten Oever, Sanne Kaushik, Karthikeya Martin, Andrea E |
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. |
doi_str_mv | 10.1371/journal.pcbi.1010269 |
format | article |
fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2703188743</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A712354433</galeid><doaj_id>oai_doaj_org_article_0a3c8d6f8f4541c887048670044e06f8</doaj_id><sourcerecordid>A712354433</sourcerecordid><originalsourceid>FETCH-LOGICAL-c661t-592b5d06c114cdcb150c8f9684281b46feffe3b0d6afa92c90e976242f01a8293</originalsourceid><addsrcrecordid>eNqVkl1rFDEUhgdRbF39B6ID3ujFrvmeBEQoxY-FouDHdchkkmmWmWSbZET_vdnutHSkN5KLhJPnfZP3cKrqOQQbiBv4dhem6NWw2evWbSCAADHxoDqFlOJ1gyl_eOd8Uj1JaQdAOQr2uDrBVAAgGnJavdt6a2J0vq_zpam9ylM0dbD1UEqTS9npWodxP2WVXfCpdv4abKNy_mn1yKohmWfzvqp-fvzw4_zz-uLrp-352cVaMwbzmgrU0g4wDSHRnW4hBZpbwThBHLaEWWOtwS3omLJKIC2AEQ1DBFkAFUcCr6qXR9_9EJKcgyeJGoAh5w3BhdgeiS6ondxHN6r4Rwbl5HUhxF6qWLIMRgKFNe-Y5ZZQAnXRA8JZAwAhBpRy8Xo_vza1o-m08TmqYWG6vPHuUvbhlxQYY0QPn3k9G8RwNZmU5eiSNsOgvAlT-TcTrLSfF3ZVvfoHvT_dTPWqBHDehvKuPpjKswYiTEmBCrW5hyqrM6PTwRvrSn0heLMQFCab37lXU0py-_3bf7Bfliw5sjqGlKKxt72DQB5m9yakPMyunGe3yF7c7fut6GZY8V-yXuf1</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2703188743</pqid></control><display><type>article</type><title>Inferring the nature of linguistic computations in the brain</title><source>Publicly Available Content Database</source><source>PubMed Central</source><creator>Ten Oever, Sanne ; Kaushik, Karthikeya ; Martin, Andrea E</creator><contributor>Bush, Daniel</contributor><creatorcontrib>Ten Oever, Sanne ; Kaushik, Karthikeya ; Martin, Andrea E ; Bush, Daniel</creatorcontrib><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.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1010269</identifier><identifier>PMID: 35900974</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Biology and Life Sciences ; Brain ; Brain - physiology ; Brain Mapping ; Cognitive ability ; Computer and Information Sciences ; Engineering and Technology ; Head ; Humans ; Information processing ; Language ; Linguistics ; Neural networks ; Semantics ; Sentences ; Social Sciences ; Structural hierarchy</subject><ispartof>PLoS computational biology, 2022-07, Vol.18 (7), p.e1010269</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Ten Oever et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Ten Oever et al 2022 Ten Oever et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c661t-592b5d06c114cdcb150c8f9684281b46feffe3b0d6afa92c90e976242f01a8293</citedby><cites>FETCH-LOGICAL-c661t-592b5d06c114cdcb150c8f9684281b46feffe3b0d6afa92c90e976242f01a8293</cites><orcidid>0000-0001-7547-5842 ; 0000-0002-3395-7234</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2703188743/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2703188743?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35900974$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Bush, Daniel</contributor><creatorcontrib>Ten Oever, Sanne</creatorcontrib><creatorcontrib>Kaushik, Karthikeya</creatorcontrib><creatorcontrib>Martin, Andrea E</creatorcontrib><title>Inferring the nature of linguistic computations in the brain</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><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.</description><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Brain</subject><subject>Brain - physiology</subject><subject>Brain Mapping</subject><subject>Cognitive ability</subject><subject>Computer and Information Sciences</subject><subject>Engineering and Technology</subject><subject>Head</subject><subject>Humans</subject><subject>Information processing</subject><subject>Language</subject><subject>Linguistics</subject><subject>Neural networks</subject><subject>Semantics</subject><subject>Sentences</subject><subject>Social Sciences</subject><subject>Structural hierarchy</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqVkl1rFDEUhgdRbF39B6ID3ujFrvmeBEQoxY-FouDHdchkkmmWmWSbZET_vdnutHSkN5KLhJPnfZP3cKrqOQQbiBv4dhem6NWw2evWbSCAADHxoDqFlOJ1gyl_eOd8Uj1JaQdAOQr2uDrBVAAgGnJavdt6a2J0vq_zpam9ylM0dbD1UEqTS9npWodxP2WVXfCpdv4abKNy_mn1yKohmWfzvqp-fvzw4_zz-uLrp-352cVaMwbzmgrU0g4wDSHRnW4hBZpbwThBHLaEWWOtwS3omLJKIC2AEQ1DBFkAFUcCr6qXR9_9EJKcgyeJGoAh5w3BhdgeiS6ondxHN6r4Rwbl5HUhxF6qWLIMRgKFNe-Y5ZZQAnXRA8JZAwAhBpRy8Xo_vza1o-m08TmqYWG6vPHuUvbhlxQYY0QPn3k9G8RwNZmU5eiSNsOgvAlT-TcTrLSfF3ZVvfoHvT_dTPWqBHDehvKuPpjKswYiTEmBCrW5hyqrM6PTwRvrSn0heLMQFCab37lXU0py-_3bf7Bfliw5sjqGlKKxt72DQB5m9yakPMyunGe3yF7c7fut6GZY8V-yXuf1</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Ten Oever, Sanne</creator><creator>Kaushik, Karthikeya</creator><creator>Martin, Andrea E</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7547-5842</orcidid><orcidid>https://orcid.org/0000-0002-3395-7234</orcidid></search><sort><creationdate>20220701</creationdate><title>Inferring the nature of linguistic computations in the brain</title><author>Ten Oever, Sanne ; Kaushik, Karthikeya ; Martin, Andrea E</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c661t-592b5d06c114cdcb150c8f9684281b46feffe3b0d6afa92c90e976242f01a8293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Analysis</topic><topic>Biology and Life Sciences</topic><topic>Brain</topic><topic>Brain - physiology</topic><topic>Brain Mapping</topic><topic>Cognitive ability</topic><topic>Computer and Information Sciences</topic><topic>Engineering and Technology</topic><topic>Head</topic><topic>Humans</topic><topic>Information processing</topic><topic>Language</topic><topic>Linguistics</topic><topic>Neural networks</topic><topic>Semantics</topic><topic>Sentences</topic><topic>Social Sciences</topic><topic>Structural hierarchy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ten Oever, Sanne</creatorcontrib><creatorcontrib>Kaushik, Karthikeya</creatorcontrib><creatorcontrib>Martin, Andrea E</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer science database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Biological Science Journals</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ten Oever, Sanne</au><au>Kaushik, Karthikeya</au><au>Martin, Andrea E</au><au>Bush, Daniel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inferring the nature of linguistic computations in the brain</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2022-07-01</date><risdate>2022</risdate><volume>18</volume><issue>7</issue><spage>e1010269</spage><pages>e1010269-</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35900974</pmid><doi>10.1371/journal.pcbi.1010269</doi><tpages>e1010269</tpages><orcidid>https://orcid.org/0000-0001-7547-5842</orcidid><orcidid>https://orcid.org/0000-0002-3395-7234</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1553-7358 |
ispartof | PLoS computational biology, 2022-07, Vol.18 (7), p.e1010269 |
issn | 1553-7358 1553-734X 1553-7358 |
language | eng |
recordid | cdi_plos_journals_2703188743 |
source | Publicly Available Content Database; PubMed Central |
subjects | Analysis Biology and Life Sciences Brain Brain - physiology Brain Mapping Cognitive ability Computer and Information Sciences Engineering and Technology Head Humans Information processing Language Linguistics Neural networks Semantics Sentences Social Sciences Structural hierarchy |
title | Inferring the nature of linguistic computations in the brain |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-30T01%3A19%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Inferring%20the%20nature%20of%20linguistic%20computations%20in%20the%20brain&rft.jtitle=PLoS%20computational%20biology&rft.au=Ten%20Oever,%20Sanne&rft.date=2022-07-01&rft.volume=18&rft.issue=7&rft.spage=e1010269&rft.pages=e1010269-&rft.issn=1553-7358&rft.eissn=1553-7358&rft_id=info:doi/10.1371/journal.pcbi.1010269&rft_dat=%3Cgale_plos_%3EA712354433%3C/gale_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c661t-592b5d06c114cdcb150c8f9684281b46feffe3b0d6afa92c90e976242f01a8293%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2703188743&rft_id=info:pmid/35900974&rft_galeid=A712354433&rfr_iscdi=true |