Brains and algorithms partially converge in natural language processing

Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models t...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Communications biology Ročník 5; číslo 1; s. 134 - 10
Hlavní autori:	Caucheteux, Charlotte, King, Jean-Rémi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 16.02.2022 Nature Publishing Group Nature Portfolio
Predmet:	631/378/116/1925 631/378/116/2395 631/378/2649/1594 Algorithms Artificial Intelligence Biology Biomedical and Life Sciences Brain Brain - diagnostic imaging Brain - physiology Brain mapping Cognitive science Computation and Language Computational neuroscience Computer Science Deep learning Functional magnetic resonance imaging Humans Language Learning algorithms Life Sciences Machine Learning Magnetic Resonance Imaging Magnetoencephalography Natural Language Processing Neural networks Neuroimaging Neuroscience Magneto-encephalography Encoding Functional Magnetic Resonance Imaging Natural Language Processing
ISSN:	2399-3642, 2399-3642
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models to identify the computational principles that lead them to generate brain-like representations of sentences. Specifically, we analyze the brain responses to 400 isolated sentences in a large cohort of 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where and when each of these algorithms maps onto the brain responses. Finally, we estimate how the architecture, training, and performance of these models independently account for the generation of brain-like representations. Our analyses reveal two main findings. First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing. Charlotte Caucheteux and Jean-Rémi King examine the ability of transformer neural networks trained on word prediction tasks to fit representations in the human brain measured with fMRI and MEG. Their results provide further insight into the workings of transformer language models and their relevance to brain responses.
AbstractList	Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models to identify the computational principles that lead them to generate brain-like representations of sentences. Specifically, we analyze the brain responses to 400 isolated sentences in a large cohort of 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where and when each of these algorithms maps onto the brain responses. Finally, we estimate how the architecture, training, and performance of these models independently account for the generation of brain-like representations. Our analyses reveal two main findings. First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing. Charlotte Caucheteux and Jean-Rémi King examine the ability of transformer neural networks trained on word prediction tasks to fit representations in the human brain measured with fMRI and MEG. Their results provide further insight into the workings of transformer language models and their relevance to brain responses. Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models to identify the computational principles that lead them to generate brain-like representations of sentences. Specifically, we analyze the brain responses to 400 isolated sentences in a large cohort of 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where and when each of these algorithms maps onto the brain responses. Finally, we estimate how the architecture, training, and performance of these models independently account for the generation of brain-like representations. Our analyses reveal two main findings. First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing. Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models to identify the computational principles that lead them to generate brain-like representations of sentences. Specifically, we analyze the brain responses to 400 isolated sentences in a large cohort of 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where and when each of these algorithms maps onto the brain responses. Finally, we estimate how the architecture, training, and performance of these models independently account for the generation of brain-like representations. Our analyses reveal two main findings. First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing. Charlotte Caucheteux and Jean-Rémi King examine the ability of transformer neural networks trained on word prediction tasks to fit representations in the human brain measured with fMRI and MEG. Their results provide further insight into the workings of transformer language models and their relevance to brain responses. Abstract Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models to identify the computational principles that lead them to generate brain-like representations of sentences. Specifically, we analyze the brain responses to 400 isolated sentences in a large cohort of 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where and when each of these algorithms maps onto the brain responses. Finally, we estimate how the architecture, training, and performance of these models independently account for the generation of brain-like representations. Our analyses reveal two main findings. First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing. Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models to identify the computational principles that lead them to generate brain-like representations of sentences. Specifically, we analyze the brain responses to 400 isolated sentences in a large cohort of 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where and when each of these algorithms maps onto the brain responses. Finally, we estimate how the architecture, training, and performance of these models independently account for the generation of brain-like representations. Our analyses reveal two main findings. First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing.Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models to identify the computational principles that lead them to generate brain-like representations of sentences. Specifically, we analyze the brain responses to 400 isolated sentences in a large cohort of 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where and when each of these algorithms maps onto the brain responses. Finally, we estimate how the architecture, training, and performance of these models independently account for the generation of brain-like representations. Our analyses reveal two main findings. First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing.
ArticleNumber	134
Author	Caucheteux, Charlotte King, Jean-Rémi
Author_xml	– sequence: 1 givenname: Charlotte surname: Caucheteux fullname: Caucheteux, Charlotte email: ccaucheteux@fb.com organization: Facebook AI Research, Université Paris-Saclay, Inria, CEA – sequence: 2 givenname: Jean-Rémi orcidid: 0000-0002-2121-170X surname: King fullname: King, Jean-Rémi email: jeanremi@fb.com organization: Facebook AI Research, École normale supérieure, PSL University, CNRS
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/35173264$$D View this record in MEDLINE/PubMed https://hal.science/hal-03361439$$DView record in HAL
BookMark	eNp9Uk1v1DAUtFARLaV_gAOKxAUOAX_FSS5IpYK20kpc4GzZznPWq6y92MlK_fd1Ni20e-jJT88z8-bZ8xad-OABofcEfyGYNV8TpxizElNaYoaZKMkrdEZZ25ZMcHrypD5FFyltMMakbVvB-Bt0yipSMyr4Gbr-HpXzqVC-K9TQh-jG9TYVOxVHp4bhrjDB7yH2UDhfeDVOUQ3FoHw_qdzbxWAgJef7d-i1VUOCi4fzHP35-eP31U25-nV9e3W5Ko3geCy5qA23utJ1LTgICp3SGHjLOe4s1Y2wtrIU85pQDTXoznAzO6eWWq44Z-fodtHtgtrIXXRbFe9kUE4eGiH2crZuBpDCNsowlsmi5toozYHhXNZVZUxHWdb6tmjtJr2FzoAf83bPRJ_feLeWfdjLpqmwIDQLfF4E1ke0m8uVnHuYMUE4a_ckYz89DIvh7wRplFuXDAz5LSFMSVJB20Zkf02GfjyCbsIUfX7WA4rkHSqcUR-euv83__FvM6BZACaGlCJYadyoRhfmZdwgCZZzkuSSJJmTJA9JkrNZekR9VH-RxBZSymDfQ_xv-wXWPZg52VI
CitedBy_id	crossref_primary_10_1093_scan_nsaf022 crossref_primary_10_1098_rsta_2022_0048 crossref_primary_10_1038_s41593_023_01304_9 crossref_primary_10_1371_journal_pbio_3002622 crossref_primary_10_7554_eLife_96217_3 crossref_primary_10_1162_tacl_a_00698 crossref_primary_10_1080_23273798_2023_2198245 crossref_primary_10_1016_j_neuropsychologia_2024_108962 crossref_primary_10_3389_frai_2023_1062230 crossref_primary_10_1007_s12551_023_01074_5 crossref_primary_10_1038_s41467_023_43713_1 crossref_primary_10_1162_nol_a_00116 crossref_primary_10_1073_pnas_2410196121 crossref_primary_10_1038_s41467_024_52541_w crossref_primary_10_3389_fncom_2024_1352685 crossref_primary_10_1038_s41586_024_07522_w crossref_primary_10_1038_s41583_023_00705_w crossref_primary_10_1038_s43588_025_00863_0 crossref_primary_10_1016_j_brainres_2025_149841 crossref_primary_10_1093_psyrad_kkaf007 crossref_primary_10_1162_nol_a_00125 crossref_primary_10_1016_j_tics_2022_06_010 crossref_primary_10_7554_eLife_96217 crossref_primary_10_1111_cogs_70013 crossref_primary_10_1038_s41597_023_02752_5 crossref_primary_10_1371_journal_pcbi_1013476 crossref_primary_10_1038_s42256_024_00925_4 crossref_primary_10_1111_cogs_13312 crossref_primary_10_1016_j_nlp_2025_100163 crossref_primary_10_1016_j_isci_2024_111401 crossref_primary_10_3390_e24050665 crossref_primary_10_32604_cmes_2023_030512 crossref_primary_10_1145_3766067 crossref_primary_10_1016_j_tics_2023_08_003 crossref_primary_10_1016_j_inffus_2025_103349 crossref_primary_10_1016_j_neuron_2024_06_025 crossref_primary_10_1038_s41467_024_51887_5 crossref_primary_10_1016_j_tics_2024_01_011 crossref_primary_10_1038_s42003_023_05040_5 crossref_primary_10_7554_eLife_89311_3 crossref_primary_10_1145_3735632 crossref_primary_10_1016_j_cognition_2024_105971 crossref_primary_10_1038_s41467_024_44801_6 crossref_primary_10_1038_s41598_022_20460_9 crossref_primary_10_3389_frai_2025_1509338 crossref_primary_10_1016_j_isci_2025_112229 crossref_primary_10_1088_1741_2552_ad593c crossref_primary_10_3390_arts11050083 crossref_primary_10_1038_s41467_025_62306_8 crossref_primary_10_1162_nol_a_00101 crossref_primary_10_1162_imag_a_00072 crossref_primary_10_7554_eLife_89311 crossref_primary_10_1038_s41467_024_49173_5 crossref_primary_10_1007_s12671_024_02316_7 crossref_primary_10_1038_s41467_025_58620_w crossref_primary_10_1038_s43588_022_00354_6 crossref_primary_10_1038_s41467_024_46631_y crossref_primary_10_1093_cercor_bhae155 crossref_primary_10_1093_pnasnexus_pgad161 crossref_primary_10_1088_1741_2552_addd4a crossref_primary_10_1007_s10462_023_10401_x crossref_primary_10_1016_j_neuroimage_2022_119723 crossref_primary_10_1038_s41598_024_84530_w crossref_primary_10_1038_s41597_025_04951_8 crossref_primary_10_1016_j_asoc_2024_111443 crossref_primary_10_1007_s10670_024_00909_1 crossref_primary_10_1016_j_cortex_2022_08_002 crossref_primary_10_3389_fnins_2024_1372257 crossref_primary_10_1038_s41539_025_00337_y crossref_primary_10_1073_pnas_2219150120 crossref_primary_10_1016_j_cub_2025_01_024 crossref_primary_10_1111_tops_12771 crossref_primary_10_1007_s10579_025_09843_2 crossref_primary_10_1162_imag_a_00170 crossref_primary_10_3758_s13428_024_02569_z crossref_primary_10_1038_s42003_024_05909_z crossref_primary_10_1162_imag_a_00575 crossref_primary_10_1038_s42256_023_00714_5 crossref_primary_10_1038_s42256_023_00718_1 crossref_primary_10_1080_23273798_2023_2171072 crossref_primary_10_1093_scan_nsae032 crossref_primary_10_1016_j_device_2024_100411 crossref_primary_10_1038_s41562_023_01783_7 crossref_primary_10_3758_s13428_025_02774_4 crossref_primary_10_1016_j_neuroimage_2023_119980 crossref_primary_10_1016_j_tins_2022_12_008 crossref_primary_10_1063_5_0134156 crossref_primary_10_1371_journal_pbio_3002046 crossref_primary_10_1162_nol_a_00137 crossref_primary_10_1038_s42003_025_08706_4 crossref_primary_10_1016_j_eswa_2023_122700 crossref_primary_10_1111_cogs_70066 crossref_primary_10_1146_annurev_neuro_120623_101142 crossref_primary_10_1523_JNEUROSCI_0288_24_2024 crossref_primary_10_1016_j_conb_2024_102969 crossref_primary_10_1038_s42003_025_07731_7 crossref_primary_10_1111_cogs_13388 crossref_primary_10_1016_j_bandl_2024_105389 crossref_primary_10_1111_cogs_13386 crossref_primary_10_1016_j_inffus_2025_103650 crossref_primary_10_1111_mila_12523 crossref_primary_10_1016_j_jneumeth_2024_110347 crossref_primary_10_3390_biology12101330 crossref_primary_10_1109_TTS_2025_3556355 crossref_primary_10_1017_psa_2024_33 crossref_primary_10_3389_fnins_2022_928841 crossref_primary_10_1007_s11023_023_09622_4 crossref_primary_10_1038_s41598_024_69568_0 crossref_primary_10_1038_s41562_022_01516_2
Cites_doi	10.1101/407007 10.1016/j.tins.2020.03.003 10.1101/2020.12.03.410399 10.3389/neuro.06.004.2008 10.32470/CCN.2018.1237-0 10.1016/j.neuroimage.2010.06.010 10.1038/s41592-018-0235-4 10.1145/502512.502546 10.1523/JNEUROSCI.5023-14.2015 10.1016/j.cub.2018.10.042 10.18653/v1/W18-5413 10.18653/v1/D16-1064 10.1016/j.cognition.2020.104348 10.1162/tacl_a_00051 10.1023/A:1010933404324 10.1162/nol_a_00003 10.3115/v1/D14-1162 10.1038/nrn2113 10.1111/j.1749-6632.2010.05444.x 10.1101/2021.04.20.440622 10.1038/s41583-020-00395-8 10.1016/j.neuron.2018.03.044 10.1038/nn.4244 10.3389/fninf.2014.00014 10.1038/nature17637 10.18653/v1/P18-1254 10.1016/j.neuroimage.2007.04.042 10.1111/cogs.12445 10.1038/nature12935 10.1073/pnas.1719397115 10.1017/CBO9780511791222 10.1038/nrn2787 10.1016/j.tics.2014.01.002 10.1016/j.neuroimage.2012.01.021 10.1016/j.tics.2011.04.003 10.3115/1557769.1557821 10.1152/jn.00032.2010 10.1146/annurev-vision-082114-035447 10.1007/978-3-030-04182-3_1 10.1073/pnas.1403112111 10.1073/pnas.1612132113 10.18653/v1/W18-0107 10.1038/s41562-020-00982-w 10.1038/s41467-020-14578-5 10.1073/pnas.2105646118 10.18653/v1/P19-1472 10.1101/2020.12.02.408765 10.1093/cercor/bhy110 10.3115/v1/D14-1030 10.1016/j.neuron.2018.10.003 10.31219/osf.io/fq6gd 10.1371/journal.pcbi.1003915 10.1038/s41597-019-0020-y 10.1016/j.neuroimage.2005.06.058 10.1101/2020.12.02.403477 10.1016/j.neuroimage.2016.10.001 10.1126/science.aax0289 10.1073/pnas.1907367117 10.1126/science.1152876 10.7554/eLife.64972 10.1016/j.neuroimage.2013.10.027 10.1371/journal.pcbi.1003963 10.18653/v1/2021.findings-emnlp.308 10.1109/ICCV.2019.00481 10.1073/pnas.1018711108 10.1017/S0140525X16001837
ContentType	Journal Article
Copyright	The Author(s) 2022. corrected publication 2023 2022. The Author(s). The Author(s) 2022. corrected publication 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Distributed under a Creative Commons Attribution 4.0 International License The Author(s) 2022
Copyright_xml	– notice: The Author(s) 2022. corrected publication 2023 – notice: 2022. The Author(s). – notice: The Author(s) 2022. corrected publication 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Distributed under a Creative Commons Attribution 4.0 International License – notice: The Author(s) 2022
DBID	C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7XB 88I 8FE 8FH 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M2P M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 1XC VOOES 5PM DOA
DOI	10.1038/s42003-022-03036-1
DatabaseName	Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection Biological Sciences Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	CrossRef MEDLINE MEDLINE - Academic Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Biology Computer Science
EISSN	2399-3642
EndPage	10
ExternalDocumentID	oai_doaj_org_article_6f8ac33012674bcab4e30674755ccd23 PMC8850612 oai:HAL:hal-03361439v1 35173264 10_1038_s42003_022_03036_1
Genre	Research Support, Non-U.S. Gov't Journal Article
GroupedDBID	0R~ 53G 88I AAJSJ ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV AFKRA AJTQC ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BBNVY BCNDV BENPR BHPHI C6C CCPQU DWQXO EBLON EBS GNUQQ GROUPED_DOAJ HCIFZ HYE M2P M7P M~E NAO O9- OK1 PGMZT PIMPY RNT RPM SNYQT AASML AAYXX AFFHD CITATION PHGZM PHGZT PQGLB CGR CUY CVF ECM EIF NPM 3V. 7XB 8FE 8FH 8FK AARCD LK8 PKEHL PQEST PQQKQ PQUKI PRINS Q9U 7X8 1XC VOOES 5PM
ID	FETCH-LOGICAL-c640t-467c4fb5b7764e62edab0e49440df2b86ff5f204712be7ebdc4c00012f2f4a443
IEDL.DBID	BENPR
ISICitedReferencesCount	144
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000756886800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2399-3642
IngestDate	Fri Oct 03 12:50:41 EDT 2025 Tue Nov 04 01:55:37 EST 2025 Tue Oct 14 20:28:40 EDT 2025 Sun Nov 09 09:03:32 EST 2025 Wed Aug 13 06:31:23 EDT 2025 Thu Jan 02 22:41:17 EST 2025 Sat Nov 29 04:16:55 EST 2025 Tue Nov 18 21:30:30 EST 2025 Fri Feb 21 02:40:06 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Magneto-encephalography Encoding Functional Magnetic Resonance Imaging Natural Language Processing
Language	English
License	2022. The Author(s). Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c640t-467c4fb5b7764e62edab0e49440df2b86ff5f204712be7ebdc4c00012f2f4a443
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-2121-170X 0000-0002-3965-3143
OpenAccessLink	https://www.proquest.com/docview/2629167450?pq-origsite=%requestingapplication%
PMID	35173264
PQID	2629167450
PQPubID	4669726
PageCount	10
ParticipantIDs	doaj_primary_oai_doaj_org_article_6f8ac33012674bcab4e30674755ccd23 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8850612 hal_primary_oai_HAL_hal_03361439v1 proquest_miscellaneous_2629860678 proquest_journals_2629167450 pubmed_primary_35173264 crossref_citationtrail_10_1038_s42003_022_03036_1 crossref_primary_10_1038_s42003_022_03036_1 springer_journals_10_1038_s42003_022_03036_1
PublicationCentury	2000
PublicationDate	2022-02-16
PublicationDateYYYYMMDD	2022-02-16
PublicationDate_xml	– month: 02 year: 2022 text: 2022-02-16 day: 16
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: England
PublicationTitle	Communications biology
PublicationTitleAbbrev	Commun Biol
PublicationTitleAlternate	Commun Biol
PublicationYear	2022
Publisher	Nature Publishing Group UK Nature Publishing Group Nature Portfolio
Publisher_xml	– name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio
References	RadfordALanguage models are unsupervised multitask learnersOpenAI Blog201919 DestrieuxCFischlBDaleAHalgrenEAutomatic parcellation of human cortical gyri and sulci using standard anatomical nomenclatureNeuroimage20105311510.1016/j.neuroimage.2010.06.01020547229 Nastase, S. A. et al. Narratives: fmri data for evaluating models of naturalistic language comprehension. Trends in neurosciences43, 271–273 (2020). CadieuCFDeep neural networks rival the representation of primate it cortex for core visual object recognitionPLoS Comput. Biol.201410e100396310.1371/journal.pcbi.1003963255212944270441 Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 3111–3119 (MIT Press, 2013). Marcus, G. Deep learning: a critical appraisal. Preprint at https://arXiv.org/1801.00631 (2018). Bojanowski, P., Grave, E., Joulin, A. & Mikolov, T. Enriching Word Vectors with Subword Information. In Transactions of the Association for Computational Linguistics (2016). Mitchell, T. M. et al. Predicting human brain activity associated with the meanings of nouns. Science320, 1191–1195 (2008). KingJ-RDehaeneSCharacterizing the dynamics of mental representations: the temporal generalization methodTrends Cogn. Sci.20141820321010.1016/j.tics.2014.01.002245939825635958 KellAJEYaminsDLKShookENNorman-HaignereSVMcDermottJHA task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchyNeuron201898630–64410.1016/j.neuron.2018.03.044 Frankle, J. & Carbin, M. The lottery ticket hypothesis: finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635 (2018). DehaeneSCohenLThe unique role of the visual word form area in readingTrends Cogn. Sci.20111525426210.1016/j.tics.2011.04.00321592844 Reddy Oota, S., Manwani, N. & Raju S, B. fMRI semantic category decoding using linguistic encoding of word embeddings. In International Conference on Neural Information Processing (Springer, Cham, 2018). Attardi, G. Wikiextractor. https://github.com/attardi/wikiextractor (2015). Caucheteux, C., Gramfort, A. & King, J.-R. Disentangling syntax and semantics in the brain with deep networks. ICML 2021-38th International Conference on Machine Learning (2021). LeeCSAlyMBaldassanoCAnticipation of temporally structured events in the braineLife202110e649721:CAS:528:DC%2BB3MXitlOgs7bP10.7554/eLife.64972338849538169103 Bengio, Y., Ducharme, R. & Vincent, P. in Advances in Neural Information Processing Systems (eds. Leen, T. K. et al.) vol. 13, 932–938 (MIT Press, 2003). Kell, A., Yamins, D., Shook, E., Norman-Haignere, S. & McDermott, J. A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron98, 630–644 (2018). Baek, J. et al. What is wrong with scene text recognition model comparisons? dataset and model analysis. In Proceedings of the IEEE International Conference on Computer Vision, 4715–4723 https://github.com/clovaai/deep-text-recognition-benchmark (2019). EstebanOfmriprep: a robust preprocessing pipeline for functional mriNat. Methods2019161111161:CAS:528:DC%2BC1cXisVyhurnN10.1038/s41592-018-0235-430532080 Gauthier, J. & Ivanova, A. Does the brain represent words? an evaluation of brain decoding studies of language understanding. Preprint at https://arXiv.org/1806.00591 (2018). Kriegeskorte, N., Mur, M. & Bandettini, P. A. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci.2, 4 (2008). Reddy, A. J. & Wehbe, L. Syntactic representations in the human brain: beyond effort-based metrics. Preprint at bioRXiv (2021). PallierCDevauchelleA-DDehaeneSCortical representation of the constituent structure of sentencesProc. Natl Acad. Sci.2011108252225271:CAS:528:DC%2BC3MXitFWns7o%3D10.1073/pnas.1018711108212244153038732 Schoffelen, J. -M. et al. A 204-subject multimodal neuroimaging dataset to study language processing. Sci. Data6, 1–13 (2019). BrodbeckCHongLESimonJZRapid transformation from auditory to linguistic representations of continuous speechCurr. Biol.201828397639831:CAS:528:DC%2BC1cXitlKmu7jF10.1016/j.cub.2018.10.042305036206339854 Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P. & de Lange, F. P. A hierarchy of linguistic predictions during natural language comprehension. bioRxiv https://doi.org/10.1101/2020.12.03.410399 (2020). Schrimpf, M. et al. Brain-score: which artificial neural network for object recognition is most brain-like? Preprint at bioRXiv (2018). Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P. & de Lange, F. P. A hierarchy of linguistic predictions during natural language comprehension. Preprint at bioRXiv (2020). Jain, S. & Huth, A. in Advances in Neural Information Processing Systems (eds Bengio, S. et al.) vol. 31, 6628–6637 (Curran Associates, Inc., 2018). Woolnough, O. et al. Spatiotemporal dynamics of orthographic and lexical processing in the ventral visual pathway. Nat. Hum. Behav.5, 389–398 (2021). CoganGBSensory–motor transformations for speech occur bilaterallyNature201450794981:CAS:528:DC%2BC2cXjs1Ght7o%3D10.1038/nature12935244295204000028 Hale, J., Dyer, C., Kuncoro, A. & Brennan, J. R. Finding syntax in human encephalography with beam search. Preprint at https://arXiv.org/1806.04127 (2018). Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature532, 453–458 (2016). Fedorenko, E., Blank, I., Siegelman, M. & Mineroff, Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition203, 104348 (2020). Toneva, M. & Wehbe, L. Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). Advances in Neural Information Processing Systems 32 (2019). YaminsDLPerformance-optimized hierarchical models predict neural responses in higher visual cortexProc. Natl Acad. Sci.2014111861986241:CAS:528:DC%2BC2cXnslWnsb4%3D10.1073/pnas.1403112111248121274060707 AbrahamAMachine learning for neuroimaging with scikit-learnFront. Neuroinform.201481410.3389/fninf.2014.00014246003883930868 KriegeskorteNDeep neural networks: a new framework for modeling biological vision and brain information processingAnnu. Rev. Vis. Sci.2015141744610.1146/annurev-vision-082114-03544728532370 FischlBFreesurferNeuroimage20126277478110.1016/j.neuroimage.2012.01.02122248573 Lakretz, Y. et al. The emergence of number and syntax units in LSTM language models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (2019). Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT (2019). Millet, J. & King, J.-R. Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech. Preprint at https://arXiv.org/2103.01032 [cs, eess, q-bio] (2021). Hale, J. T. et al. Neuro-computational models of language processing. Saxe, A., Nelli, S. & Summerfield, C. If deep learning is the answer, what is the question? Nat. Rev. Neurosci.22, 1–13 (2020). Hickok, G. & Poeppel, D. The Cortical Organization of Speech Processing vol. 8, 393–402 (Nature Publishing Group, 2007). TangHRecurrent computations for visual pattern completionProc. Natl Acad. Sci.2018115883588401:CAS:528:DC%2BC1cXitVCkur%2FJ10.1073/pnas.1719397115301043636126774 Van EssenDCA population-average, landmark-and surface-based (pals) atlas of human cerebral cortexNeuroimage20052863566210.1016/j.neuroimage.2005.06.05816172003 Caucheteux, C., Gramfort, A. & King, J.-R. GPT-2’s Activations Predict the Degree of Semantic Comprehension in the Human Brain (Cold Spring Harbor Laboratory Section: New Results, 2021). Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A. & Choi, Y. Hellaswag: can a machine really finish your sentence? Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019). Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behavioral and brain sciences 40 (2017). Pennington, J., Socher, R. & Manning, C. D. Glove: global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP) Conference 1532–1543 (2014). Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U. & Levy, O. Emergent linguistic structure in artificial neural networks trained by self-supervision. Proc. Natl Acad. Sci.117, 30046–30054 (2020). Abnar, S., Ahmed, R., Mijnheer, M. & Zuidema, W. H. Experiential, distributional and dependency-based word embeddings have complementary roles in decoding brain activity. In Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018), (2018). YaminsDLDiCarloJJUsing goal-driven deep learning models to understand sensory cortexNat. Neurosci.2016193561:CAS:528:DC%2BC28XjtVOjt7k%3D10.1038/nn.424426906502 Dehaene, S., Yann, L. & Girardon, J. La plus belle histoire de l’intelligence: des origines aux neurones artificiels: vers une nouvelle étape de l’évolution (Robert Laffont, 2018). Devlin, J., Chang, M., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (2019). Sassenhagen, J. & Fiebach, C. J. Traces of meaning itself: Encoding distributional word vectors in brain activity. Neurobiology of Language 1.1, 54–76 (2020). Athanasiou, N., Iosif, E. & Potamianos, A. Neural activation semantic models: computational lexical semantic models of localized neural activations. In Proceedings of the 27 S Dehaene (3036_CR39) 2011; 15 3036_CR13 A Radford (3036_CR82) 2019; 1 3036_CR14 M Eickenberg (3036_CR54) 2017; 152 3036_CR15 3036_CR59 3036_CR16 3036_CR17 3036_CR18 3036_CR19 C Destrieux (3036_CR88) 2010; 53 3036_CR93 DL Yamins (3036_CR55) 2016; 19 3036_CR94 3036_CR95 3036_CR10 3036_CR11 3036_CR12 3036_CR56 N Kriegeskorte (3036_CR52) 2015; 1 F Pedregosa (3036_CR49) 2011; 12 S-M Khaligh-Razavi (3036_CR51) 2014; 10 3036_CR46 AJE Kell (3036_CR71) 2018; 98 JR Brennan (3036_CR35) 2017; 41 3036_CR47 B Fischl (3036_CR86) 2012; 62 3036_CR48 CJ Price (3036_CR64) 2010; 1191 3036_CR83 3036_CR84 3036_CR41 CF Cadieu (3036_CR58) 2014; 10 3036_CR85 3036_CR44 3036_CR45 J-R King (3036_CR60) 2014; 18 DC Van Essen (3036_CR87) 2005; 28 3036_CR80 3036_CR81 C Pallier (3036_CR65) 2011; 108 3036_CR79 3036_CR36 3036_CR37 E Fedorenko (3036_CR43) 2016; 113 3036_CR38 GB Keller (3036_CR73) 2018; 100 DL Yamins (3036_CR42) 2014; 111 3036_CR1 3036_CR72 3036_CR30 3036_CR74 3036_CR5 3036_CR31 3036_CR75 3036_CR4 3036_CR32 3036_CR3 C Brodbeck (3036_CR24) 2018; 28 3036_CR33 3036_CR2 3036_CR34 3036_CR78 3036_CR9 3036_CR8 P Hagoort (3036_CR40) 2019; 366 3036_CR7 A Abraham (3036_CR91) 2014; 8 3036_CR6 3036_CR70 CS Lee (3036_CR76) 2021; 10 K Friston (3036_CR77) 2010; 11 H Tang (3036_CR50) 2018; 115 U Güçlü (3036_CR53) 2015; 35 3036_CR68 3036_CR25 3036_CR69 3036_CR26 D Hermes (3036_CR62) 2017; 27 3036_CR27 M Minsky (3036_CR57) 1969 O Esteban (3036_CR89) 2019; 16 3036_CR28 Y Behzadi (3036_CR90) 2007; 37 3036_CR29 E Fedorenko (3036_CR66) 2010; 104 U Cohen (3036_CR61) 2020; 11 3036_CR63 3036_CR20 A Gramfort (3036_CR92) 2014; 86 3036_CR21 3036_CR22 3036_CR23 GB Cogan (3036_CR67) 2014; 507 37041229 - Commun Biol. 2023 Apr 11;6(1):396. doi: 10.1038/s42003-023-04776-4
References_xml	– reference: EickenbergMGramfortAVaroquauxGThirionBSeeing it all: convolutional network layers map the function of the human visual systemNeuroImage201715218419410.1016/j.neuroimage.2016.10.00127777172 – reference: YaminsDLDiCarloJJUsing goal-driven deep learning models to understand sensory cortexNat. Neurosci.2016193561:CAS:528:DC%2BC28XjtVOjt7k%3D10.1038/nn.424426906502 – reference: FristonKThe free-energy principle: a unified brain theory?Nat. Rev. Neurosci.2010111271381:CAS:528:DC%2BC3cXksFGktw%3D%3D10.1038/nrn278720068583 – reference: PallierCDevauchelleA-DDehaeneSCortical representation of the constituent structure of sentencesProc. Natl Acad. Sci.2011108252225271:CAS:528:DC%2BC3MXitFWns7o%3D10.1073/pnas.1018711108212244153038732 – reference: Mitchell, T. M. et al. Predicting human brain activity associated with the meanings of nouns. Science320, 1191–1195 (2008). – reference: Baek, J. et al. What is wrong with scene text recognition model comparisons? dataset and model analysis. In Proceedings of the IEEE International Conference on Computer Vision, 4715–4723 https://github.com/clovaai/deep-text-recognition-benchmark (2019). – reference: Millet, J. & King, J.-R. Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech. Preprint at https://arXiv.org/2103.01032 [cs, eess, q-bio] (2021). – reference: LeeCSAlyMBaldassanoCAnticipation of temporally structured events in the braineLife202110e649721:CAS:528:DC%2BB3MXitlOgs7bP10.7554/eLife.64972338849538169103 – reference: KellerGBMrsic-FlogelTDPredictive processing: a canonical cortical computationNeuron20181004244351:CAS:528:DC%2BC1cXitVSisrnF10.1016/j.neuron.2018.10.003303596066400266 – reference: Hale, J., Dyer, C., Kuncoro, A. & Brennan, J. R. Finding syntax in human encephalography with beam search. Preprint at https://arXiv.org/1806.04127 (2018). – reference: AbrahamAMachine learning for neuroimaging with scikit-learnFront. Neuroinform.201481410.3389/fninf.2014.00014246003883930868 – reference: TangHRecurrent computations for visual pattern completionProc. Natl Acad. Sci.2018115883588401:CAS:528:DC%2BC1cXitVCkur%2FJ10.1073/pnas.1719397115301043636126774 – reference: PriceCJThe anatomy of language: a review of 100 fmri studies published in 2009Ann. N. Y. Acad. Sci.20101191628810.1111/j.1749-6632.2010.05444.x20392276 – reference: Schrimpf, M. et al. Brain-score: which artificial neural network for object recognition is most brain-like? Preprint at bioRXiv (2018). – reference: Frankle, J. & Carbin, M. The lottery ticket hypothesis: finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635 (2018). – reference: Sassenhagen, J. & Fiebach, C. J. Traces of meaning itself: Encoding distributional word vectors in brain activity. Neurobiology of Language 1.1, 54–76 (2020). – reference: BrodbeckCHongLESimonJZRapid transformation from auditory to linguistic representations of continuous speechCurr. Biol.201828397639831:CAS:528:DC%2BC1cXitlKmu7jF10.1016/j.cub.2018.10.042305036206339854 – reference: Hale, J. T. et al. Neuro-computational models of language processing. – reference: Wang, L. Dynamic predictive coding across the left fronto-temporal language hierarchy: evidence from MEG, EEG and fMRI29. – reference: Reddy Oota, S., Manwani, N. & Raju S, B. fMRI semantic category decoding using linguistic encoding of word embeddings. In International Conference on Neural Information Processing (Springer, Cham, 2018). – reference: Reddy, A. J. & Wehbe, L. Syntactic representations in the human brain: beyond effort-based metrics. Preprint at bioRXiv (2021). – reference: Attardi, G. Wikiextractor. https://github.com/attardi/wikiextractor (2015). – reference: Van EssenDCA population-average, landmark-and surface-based (pals) atlas of human cerebral cortexNeuroimage20052863566210.1016/j.neuroimage.2005.06.05816172003 – reference: Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT (2019). – reference: BehzadiYRestomKLiauJLiuTTA component based noise correction method (compcor) for bold and perfusion based fmriNeuroimage2007379010110.1016/j.neuroimage.2007.04.04217560126 – reference: Vaswani, A. et al. Attention is all you need. In Proceedings on NIPS (Cornell University, 2017). – reference: Bojanowski, P., Grave, E., Joulin, A. & Mikolov, T. Enriching Word Vectors with Subword Information. In Transactions of the Association for Computational Linguistics (2016). – reference: PedregosaFScikit-learn: machine learning in pythonJ. Mach. Learn. Res.20111228252830 – reference: Marcus, G. Deep learning: a critical appraisal. Preprint at https://arXiv.org/1801.00631 (2018). – reference: Wehbe, L., Vaswani, A., Knight, K. & Mitchell, T. Aligning context-based statistical models of language with brain activity during reading. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 233–243 (Association for Computational Linguistics, 2014). – reference: Bingham, E. & Mannila, H. Random projection in dimensionality reduction: applications to image and text data. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 245–250 (ACM, 2001). – reference: Caucheteux, C., Gramfort, A. & King, J.-R. GPT-2’s Activations Predict the Degree of Semantic Comprehension in the Human Brain (Cold Spring Harbor Laboratory Section: New Results, 2021). – reference: Seydell-Greenwald, A., Wang, X., Newport, E., Bi, Y. & Striem-Amit, E. Spoken language comprehension activates the primary visual cortex. Preprint at bioRxiv (2020). – reference: KingJ-RDehaeneSCharacterizing the dynamics of mental representations: the temporal generalization methodTrends Cogn. Sci.20141820321010.1016/j.tics.2014.01.002245939825635958 – reference: Kell, A., Yamins, D., Shook, E., Norman-Haignere, S. & McDermott, J. A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron98, 630–644 (2018). – reference: Abnar, S., Ahmed, R., Mijnheer, M. & Zuidema, W. H. Experiential, distributional and dependency-based word embeddings have complementary roles in decoding brain activity. In Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018), (2018). – reference: Schoffelen, J. -M. et al. A 204-subject multimodal neuroimaging dataset to study language processing. Sci. Data6, 1–13 (2019). – reference: Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P. & de Lange, F. P. A hierarchy of linguistic predictions during natural language comprehension. Preprint at bioRXiv (2020). – reference: GüçlüUvan GervenMADeep neural networks reveal a gradient in the complexity of neural representations across the ventral streamJ. Neurosci.201535100051001410.1523/JNEUROSCI.5023-14.2015261570006605414 – reference: FischlBFreesurferNeuroimage20126277478110.1016/j.neuroimage.2012.01.02122248573 – reference: YaminsDLPerformance-optimized hierarchical models predict neural responses in higher visual cortexProc. Natl Acad. Sci.2014111861986241:CAS:528:DC%2BC2cXnslWnsb4%3D10.1073/pnas.1403112111248121274060707 – reference: Saxe, A., Nelli, S. & Summerfield, C. If deep learning is the answer, what is the question? Nat. Rev. Neurosci.22, 1–13 (2020). – reference: CoganGBSensory–motor transformations for speech occur bilaterallyNature201450794981:CAS:528:DC%2BC2cXjs1Ght7o%3D10.1038/nature12935244295204000028 – reference: Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A. & Choi, Y. Hellaswag: can a machine really finish your sentence? Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019). – reference: Kriegeskorte, N., Mur, M. & Bandettini, P. A. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci.2, 4 (2008). – reference: Turing, A. M. Parsing the Turing Test 23–65 (Springer, 2009). – reference: Devlin, J., Chang, M., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (2019). – reference: Brown, T. B. et al. Language models are few-shot learners. In Advances in Neural Information Processing Systems (2020). – reference: Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 3111–3119 (MIT Press, 2013). – reference: Koehn, P. et al. Moses: ppen source toolkit for statistical machine translation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions 177–180 (Association for Computational Linguistics, 2007). – reference: Goldstein, A. et al. Thinking ahead: prediction in context as a keystone of language in humans and machines. Preprint at bioRxiv (2020). – reference: KellAJEYaminsDLKShookENNorman-HaignereSVMcDermottJHA task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchyNeuron201898630–64410.1016/j.neuron.2018.03.044 – reference: Khaligh-RazaviS-MKriegeskorteNDeep supervised, but not unsupervised, models may explain it cortical representationPLoS Comput. Biol.201410e100391510.1371/journal.pcbi.1003915253751364222664 – reference: Chomsky, N. Language and Mind (Cambridge University Press, 2006). – reference: MinskyMPapertSPerceptrons: An Introduction to Computational Geometry1969MIT Press – reference: Athanasiou, N., Iosif, E. & Potamianos, A. Neural activation semantic models: computational lexical semantic models of localized neural activations. In Proceedings of the 27th International Conference on Computational Linguistics 2867–2878 (Association for Computational Linguistics, 2018). – reference: DestrieuxCFischlBDaleAHalgrenEAutomatic parcellation of human cortical gyri and sulci using standard anatomical nomenclatureNeuroimage20105311510.1016/j.neuroimage.2010.06.01020547229 – reference: HermesDElectrophysiological responses in the ventral temporal cortex during reading of numerals and calculationCereb. Cortex20172756757526503267 – reference: Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature532, 453–458 (2016). – reference: Toneva, M. & Wehbe, L. Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). Advances in Neural Information Processing Systems 32 (2019). – reference: Jain, S. & Huth, A. in Advances in Neural Information Processing Systems (eds Bengio, S. et al.) vol. 31, 6628–6637 (Curran Associates, Inc., 2018). – reference: Loula, J., Baroni, M. & Lake, B. M. Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks. In BlackboxNLP@ EMNLP (2018). – reference: Bengio, Y., Ducharme, R. & Vincent, P. in Advances in Neural Information Processing Systems (eds. Leen, T. K. et al.) vol. 13, 932–938 (MIT Press, 2003). – reference: BrennanJRPylkkänenLMeg evidence for incremental sentence composition in the anterior temporal lobeCogn. Sci.2017411515153110.1111/cogs.1244527813182 – reference: HagoortPThe neurobiology of language beyond single-word processingScience201936655581:CAS:528:DC%2BC1MXhvFWlurzM10.1126/science.aax028931604301 – reference: CohenUChungSLeeDDSompolinskyHSeparability and geometry of object manifolds in deep neural networksNat. Commun.20201111310.1038/s41467-020-14578-5 – reference: EstebanOfmriprep: a robust preprocessing pipeline for functional mriNat. Methods2019161111161:CAS:528:DC%2BC1cXisVyhurnN10.1038/s41592-018-0235-430532080 – reference: Woolnough, O. et al. Spatiotemporal dynamics of orthographic and lexical processing in the ventral visual pathway. Nat. Hum. Behav.5, 389–398 (2021). – reference: Ruan, Y. -P., Ling, Z. -H. & Hu, Y. Exploring semantic representation in brain activity using word embeddings. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 669–679 (Association for Computational Linguistics, 2016). – reference: Lakretz, Y. et al. The emergence of number and syntax units in LSTM language models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (2019). – reference: Dehaene, S., Yann, L. & Girardon, J. La plus belle histoire de l’intelligence: des origines aux neurones artificiels: vers une nouvelle étape de l’évolution (Robert Laffont, 2018). – reference: Gauthier, J. & Ivanova, A. Does the brain represent words? an evaluation of brain decoding studies of language understanding. Preprint at https://arXiv.org/1806.00591 (2018). – reference: Schrimpf, M. et al. The neural architecture of language: Integrative modeling converges on predictive processing. In Proceedings of the National Academy of Sciences (2021). – reference: Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U. & Levy, O. Emergent linguistic structure in artificial neural networks trained by self-supervision. Proc. Natl Acad. Sci.117, 30046–30054 (2020). – reference: Pennington, J., Socher, R. & Manning, C. D. Glove: global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP) Conference 1532–1543 (2014). – reference: Caucheteux, C., Gramfort, A. & King, J.-R. Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects. In EMNLP 2021—Conference on Empirical Methods in Natural Language Processing (2021). – reference: Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P. & de Lange, F. P. A hierarchy of linguistic predictions during natural language comprehension. bioRxiv https://doi.org/10.1101/2020.12.03.410399 (2020). – reference: CadieuCFDeep neural networks rival the representation of primate it cortex for core visual object recognitionPLoS Comput. Biol.201410e100396310.1371/journal.pcbi.1003963255212944270441 – reference: Hickok, G. & Poeppel, D. The Cortical Organization of Speech Processing vol. 8, 393–402 (Nature Publishing Group, 2007). – reference: Breiman, L. Random forests. Mach. Learn.45, 5–32 (2001). – reference: Lample, G. & Conneau, A. Cross-lingual language model pretraining. In Adv. Neural Inf. Process. Syst. (2019). – reference: Lake, B. M. & Murphy, G. L. Word meaning in minds and machines. Psychol. Rev. (2021). – reference: Fedorenko, E., Blank, I., Siegelman, M. & Mineroff, Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition203, 104348 (2020). – reference: FedorenkoENeural correlate of the construction of sentence meaningProc. Natl Acad. Sci.2016113E6256E62621:CAS:528:DC%2BC28XhsFKjsLzM10.1073/pnas.1612132113276716425068329 – reference: KriegeskorteNDeep neural networks: a new framework for modeling biological vision and brain information processingAnnu. Rev. Vis. Sci.2015141744610.1146/annurev-vision-082114-03544728532370 – reference: GramfortAMne software for processing meg and eeg dataNeuroImage20148644646010.1016/j.neuroimage.2013.10.02724161808 – reference: Caucheteux, C., Gramfort, A. & King, J.-R. Disentangling syntax and semantics in the brain with deep networks. ICML 2021-38th International Conference on Machine Learning (2021). – reference: Nastase, S. A. et al. Narratives: fmri data for evaluating models of naturalistic language comprehension. Trends in neurosciences43, 271–273 (2020). – reference: Anderson, A. J. et al. Multiple regions of a cortical network commonly encode the meaning of words in multiple grammatical positions of read sentences. Cereb. Cortex29, 2396–2411 (2019). – reference: Mikolov, T., Chen, K., Corrado, G. & Dean, J. Efficient estimation of word representations in vector space. Preprint at https://arxiv.org/1301.3781 (2013). – reference: Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behavioral and brain sciences 40 (2017). – reference: FedorenkoEHsiehP-JNieto-CastañónAWhitfield-GabrieliSKanwisherNNew method for fmri investigations of language: defining rois functionally in individual subjectsJ. Neurophysiol.20101041177119410.1152/jn.00032.2010204103632934923 – reference: DehaeneSCohenLThe unique role of the visual word form area in readingTrends Cogn. Sci.20111525426210.1016/j.tics.2011.04.00321592844 – reference: Ramsauer, H. et al. Hopfield networks is all you need. Preprint at https://arXiv.org/2008.02217 [cs, stat] (2021). – reference: RadfordALanguage models are unsupervised multitask learnersOpenAI Blog201919 – ident: 3036_CR12 – ident: 3036_CR7 – ident: 3036_CR70 doi: 10.1101/407007 – ident: 3036_CR3 – ident: 3036_CR72 doi: 10.1016/j.tins.2020.03.003 – ident: 3036_CR83 – ident: 3036_CR34 doi: 10.1101/2020.12.03.410399 – ident: 3036_CR59 doi: 10.3389/neuro.06.004.2008 – ident: 3036_CR25 doi: 10.32470/CCN.2018.1237-0 – volume: 53 start-page: 1 year: 2010 ident: 3036_CR88 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2010.06.010 – volume: 16 start-page: 111 year: 2019 ident: 3036_CR89 publication-title: Nat. Methods doi: 10.1038/s41592-018-0235-4 – ident: 3036_CR94 doi: 10.1145/502512.502546 – ident: 3036_CR32 – volume: 35 start-page: 10005 year: 2015 ident: 3036_CR53 publication-title: J. Neurosci. doi: 10.1523/JNEUROSCI.5023-14.2015 – ident: 3036_CR93 – volume: 28 start-page: 3976 year: 2018 ident: 3036_CR24 publication-title: Curr. Biol. doi: 10.1016/j.cub.2018.10.042 – ident: 3036_CR78 – ident: 3036_CR8 – ident: 3036_CR9 doi: 10.18653/v1/W18-5413 – ident: 3036_CR23 doi: 10.18653/v1/D16-1064 – volume: 12 start-page: 2825 year: 2011 ident: 3036_CR49 publication-title: J. Mach. Learn. Res. – ident: 3036_CR13 – ident: 3036_CR38 doi: 10.1016/j.cognition.2020.104348 – ident: 3036_CR4 – ident: 3036_CR16 doi: 10.1162/tacl_a_00051 – ident: 3036_CR48 doi: 10.1023/A:1010933404324 – ident: 3036_CR19 doi: 10.1162/nol_a_00003 – ident: 3036_CR69 – ident: 3036_CR15 doi: 10.3115/v1/D14-1162 – ident: 3036_CR41 doi: 10.1038/nrn2113 – volume: 1191 start-page: 62 year: 2010 ident: 3036_CR64 publication-title: Ann. N. Y. Acad. Sci. doi: 10.1111/j.1749-6632.2010.05444.x – ident: 3036_CR20 doi: 10.1101/2021.04.20.440622 – ident: 3036_CR75 – ident: 3036_CR56 doi: 10.1038/s41583-020-00395-8 – ident: 3036_CR46 doi: 10.1016/j.neuron.2018.03.044 – volume: 19 start-page: 356 year: 2016 ident: 3036_CR55 publication-title: Nat. Neurosci. doi: 10.1038/nn.4244 – volume-title: Perceptrons: An Introduction to Computational Geometry year: 1969 ident: 3036_CR57 – volume: 8 start-page: 14 year: 2014 ident: 3036_CR91 publication-title: Front. Neuroinform. doi: 10.3389/fninf.2014.00014 – ident: 3036_CR44 doi: 10.1038/nature17637 – ident: 3036_CR36 doi: 10.18653/v1/P18-1254 – ident: 3036_CR14 – volume: 37 start-page: 90 year: 2007 ident: 3036_CR90 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2007.04.042 – volume: 41 start-page: 1515 year: 2017 ident: 3036_CR35 publication-title: Cogn. Sci. doi: 10.1111/cogs.12445 – volume: 507 start-page: 94 year: 2014 ident: 3036_CR67 publication-title: Nature doi: 10.1038/nature12935 – volume: 115 start-page: 8835 year: 2018 ident: 3036_CR50 publication-title: Proc. Natl Acad. Sci. doi: 10.1073/pnas.1719397115 – ident: 3036_CR2 doi: 10.1017/CBO9780511791222 – volume: 11 start-page: 127 year: 2010 ident: 3036_CR77 publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn2787 – ident: 3036_CR5 – ident: 3036_CR33 – volume: 18 start-page: 203 year: 2014 ident: 3036_CR60 publication-title: Trends Cogn. Sci. doi: 10.1016/j.tics.2014.01.002 – volume: 62 start-page: 774 year: 2012 ident: 3036_CR86 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.01.021 – ident: 3036_CR10 – volume: 15 start-page: 254 year: 2011 ident: 3036_CR39 publication-title: Trends Cogn. Sci. doi: 10.1016/j.tics.2011.04.003 – ident: 3036_CR81 – ident: 3036_CR84 doi: 10.3115/1557769.1557821 – volume: 104 start-page: 1177 year: 2010 ident: 3036_CR66 publication-title: J. Neurophysiol. doi: 10.1152/jn.00032.2010 – volume: 1 start-page: 417 year: 2015 ident: 3036_CR52 publication-title: Annu. Rev. Vis. Sci. doi: 10.1146/annurev-vision-082114-035447 – ident: 3036_CR21 doi: 10.1007/978-3-030-04182-3_1 – volume: 111 start-page: 8619 year: 2014 ident: 3036_CR42 publication-title: Proc. Natl Acad. Sci. doi: 10.1073/pnas.1403112111 – volume: 113 start-page: E6256 year: 2016 ident: 3036_CR43 publication-title: Proc. Natl Acad. Sci. doi: 10.1073/pnas.1612132113 – ident: 3036_CR22 doi: 10.18653/v1/W18-0107 – ident: 3036_CR63 doi: 10.1038/s41562-020-00982-w – ident: 3036_CR28 – volume: 11 start-page: 1 year: 2020 ident: 3036_CR61 publication-title: Nat. Commun. doi: 10.1038/s41467-020-14578-5 – ident: 3036_CR27 doi: 10.1073/pnas.2105646118 – ident: 3036_CR80 doi: 10.18653/v1/P19-1472 – volume: 1 start-page: 9 year: 2019 ident: 3036_CR82 publication-title: OpenAI Blog – ident: 3036_CR45 doi: 10.1101/2020.12.02.408765 – ident: 3036_CR18 doi: 10.1093/cercor/bhy110 – ident: 3036_CR95 – ident: 3036_CR26 doi: 10.3115/v1/D14-1030 – volume: 100 start-page: 424 year: 2018 ident: 3036_CR73 publication-title: Neuron doi: 10.1016/j.neuron.2018.10.003 – volume: 98 start-page: 630–644 year: 2018 ident: 3036_CR71 publication-title: Neuron doi: 10.1016/j.neuron.2018.03.044 – ident: 3036_CR11 – ident: 3036_CR47 doi: 10.31219/osf.io/fq6gd – ident: 3036_CR6 – volume: 10 start-page: e1003915 year: 2014 ident: 3036_CR51 publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1003915 – ident: 3036_CR37 doi: 10.1038/s41597-019-0020-y – volume: 28 start-page: 635 year: 2005 ident: 3036_CR87 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2005.06.058 – ident: 3036_CR30 doi: 10.1101/2020.12.02.403477 – volume: 152 start-page: 184 year: 2017 ident: 3036_CR54 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2016.10.001 – volume: 366 start-page: 55 year: 2019 ident: 3036_CR40 publication-title: Science doi: 10.1126/science.aax0289 – ident: 3036_CR68 doi: 10.1073/pnas.1907367117 – ident: 3036_CR1 – ident: 3036_CR17 doi: 10.1126/science.1152876 – ident: 3036_CR31 – ident: 3036_CR74 doi: 10.1101/2020.12.03.410399 – volume: 27 start-page: 567 year: 2017 ident: 3036_CR62 publication-title: Cereb. Cortex – volume: 10 start-page: e64972 year: 2021 ident: 3036_CR76 publication-title: eLife doi: 10.7554/eLife.64972 – volume: 86 start-page: 446 year: 2014 ident: 3036_CR92 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.10.027 – volume: 10 start-page: e1003963 year: 2014 ident: 3036_CR58 publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1003963 – ident: 3036_CR29 doi: 10.18653/v1/2021.findings-emnlp.308 – ident: 3036_CR85 doi: 10.1109/ICCV.2019.00481 – volume: 108 start-page: 2522 year: 2011 ident: 3036_CR65 publication-title: Proc. Natl Acad. Sci. doi: 10.1073/pnas.1018711108 – ident: 3036_CR79 doi: 10.1017/S0140525X16001837 – reference: 37041229 - Commun Biol. 2023 Apr 11;6(1):396. doi: 10.1038/s42003-023-04776-4
SSID	ssj0001999634
Score	2.611378
Snippet	Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the... Abstract Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those...
SourceID	doaj pubmedcentral hal proquest pubmed crossref springer
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	134
SubjectTerms	631/378/116/1925 631/378/116/2395 631/378/2649/1594 Algorithms Artificial Intelligence Biology Biomedical and Life Sciences Brain Brain - diagnostic imaging Brain - physiology Brain mapping Cognitive science Computation and Language Computational neuroscience Computer Science Deep learning Functional magnetic resonance imaging Humans Language Learning algorithms Life Sciences Machine Learning Magnetic Resonance Imaging Magnetoencephalography Natural Language Processing Neural networks Neuroimaging Neuroscience
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB2hCiQuiG8CBQXEDazGzsROji2i9FBVHEDqzbIdu7vSkl1ttpX67xk72aWhAi5cbcd2xmO_sT1-A_DeBUPAZBwjNA8MuTKMthGGcVNKMkcsgX5i1z9VZ2f1-Xnz9Uaor-gTNtADD4I7kKE2jjbdXEiF1hmLPlq5qKrKuVYknk-yem5sptLpSrTjSxxfyRRlfdBjcsOKzutFXLYZnyBRIuwnfJlFd8jbtuZtl8nf7k0THB0_hAejHZkfDv1_BHd89xjuDZElr5_Al6MY-qHPTdfmZnGxXM83sx99vor_axaL6zx5m68vfD7v8kTuSZVtzy7z1fB6gBp-Ct-PP3_7dMLGmAnMSSw2jNY9h8FWVimJXgrfGlt4bBCLNghbyxCqIAqCJGG98rZ16NJpVBABDWL5DPa6ZedfQF43BjnVoYyUKFsSaQihlWhoeTcVdxnwrfy0GwnFY1yLhU4X22WtB5lrkrlOMtc8gw-7b1YDncZfSx_FYdmVjFTYKYEURI8Kov-lIBm8o0Gd1HFyeKpjWlGWZJ2UzRW1tL8dcz1O4l4LKZr4SKMqMni7y6bpF-9UTOeXl0OZWkbIz-D5oCK7psqKK7KOMQM1UZ5JX6Y53XyWKL7rSCTIRQYft2r2q1t_ltfL_yGvV3BfpFkiGJf7sLdZX_rXcNddbeb9-k2aZj8BwIsnaQ priority: 102 providerName: Directory of Open Access Journals
Title	Brains and algorithms partially converge in natural language processing
URI	https://link.springer.com/article/10.1038/s42003-022-03036-1 https://www.ncbi.nlm.nih.gov/pubmed/35173264 https://www.proquest.com/docview/2629167450 https://www.proquest.com/docview/2629860678 https://hal.science/hal-03361439 https://pubmed.ncbi.nlm.nih.gov/PMC8850612 https://doaj.org/article/6f8ac33012674bcab4e30674755ccd23
Volume	5
WOSCitedRecordID	wos000756886800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: DOA dateStart: 20180101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: M~E dateStart: 20180101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: M7P dateStart: 20220101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: BENPR dateStart: 20220101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: PIMPY dateStart: 20220101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2399-3642 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999634 issn: 2399-3642 databaseCode: M2P dateStart: 20220101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwED-xFiRe-P4IjCog3iBaYjt2-oRWtDGkrYoQSOXJchy7rVTS0nST9t9zdpJWZWIvvPghcRwnv_Pd5Xz5HcB7bRUaJqUjtOY2YolQEX5GqChRlKM7UqDR9-z652I8ziaTYd4G3Oo2rbLTiV5Rl0vtYuRHhJOhy5hP40-r35GrGuV2V9sSGgfQd0xlrAf90ck4_7aLsjh_nrL2b5mYZkc18-lYLok9duo7SvYskifuRzszc2mRN33Om6mTf-2ferN0-vB_H-gRPGgd0vC4kaDHcMdUT-BeU6Ly-il8GbkaEnWoqjJUiykOsJn9qsOVkzi1WFyHPm19PTXhvAo9SygO1gVBw1XzGwLO_Bn8OD35_vksaosvRJqzeBOhAtXMFmkhBGeGE1OqIjZsyFhcWlJk3NrUkhhtGymMMEWpmfZhLUssU4zR59CrlpV5CWE2VCzBMYTinPESMbHWlpwptBMqTXQASQeA1C0zuSuQsZB-h5xmsgFNImjSgyaTAD5sr1k1vBy39h45XLc9Hae2P7BcT2W7RCW3mdIUFR5BVAqtCmbc9xQTaap1SWgA71Aq9sY4Oz6X7lhMKbo5dHiFdzrs0JatNqjlDuoA3m5P4zp2mzOqMsvLpk_Gne8QwItGxra3omki0M1mAYg96duby_6Zaj7zXOGZYyRMSAAfOzndTevf7-vV7U_xGu4Tv4BIlPBD6G3Wl-YN3NVXm3m9HsCBmGSDdiUOfJAD2wuSu1Zg28-_XuQ__wAB6zyj
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3Nb9MwFH8aAzQufA8CAwKCE0RLbMdJDghtwOi0Uu0wpN2M49htpZKWphvqP8XfyLOTtAoTu-3A1XYcf_zel_38HsBrZSQKJqkClOYmYFEiAzQjZBBJylEdyVHou-j6_WQwSE9Ps-MN-N2-hbFulS1PdIy6mCp7Rr5LOMmsx3wcfpj9DGzWKHu72qbQqGFxpJe_0GSr3h9-wv19Q8jB55OPvaDJKhAozsJFgJxBMZPHeZJwpjnRhcxDzTLGwsKQPOXGxIaEyLRJrhOdF4opd15jiGGSMYr9XoPrqEaQ1LkKHq_PdKz1QFnzNiek6W7FnPOXdZkPrbAIoo78c2kCUKqNrBPmRQ33oqPmX7e1Tgge3Pnflu8u3G7UbX-vpo97sKHL-3CzTsC5fABf9m2GjMqXZeHLyRAHvBj9qPyZpSc5mSx955Q_H2p_XPouBip21h7x-rP6kQWu1EP4diWz2IbNclrqx-CnmWQR9pFIzhkvEAPGmIIziVJQxpHyIGo3XKgm7rpN_zER7v6fpqIGiUCQCAcSEXnwdvXNrI46cmnrfYujVUsbMdwVTOdD0TAgwU0qFUV2ThAFuZI509ZaZEkcK1UQ6sErRGGnj95eX9iykFJU4mh2jn_aadElGl5XiTW0PHi5qkYuZa-eZKmnZ3WblFvNyINHNaZXv6JxlKARwTxIOmjvjKVbU45HLhJ6auMtRsSDdy1drIf17_V6cvksXsBW7-RrX_QPB0dP4RZxxEuCiO_A5mJ-pp_BDXW-GFfz5476ffh-1fTyBy8Ck20
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Brains+and+algorithms+partially+converge+in+natural+language+processing&rft.jtitle=Communications+biology&rft.au=Caucheteux%2C+Charlotte&rft.au=King%2C+Jean-R%C3%A9mi&rft.date=2022-02-16&rft.eissn=2399-3642&rft.volume=5&rft.issue=1&rft.spage=134&rft_id=info:doi/10.1038%2Fs42003-022-03036-1&rft_id=info%3Apmid%2F35173264&rft.externalDocID=35173264
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2399-3642&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2399-3642&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2399-3642&client=summon