The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Neurophysiological dynamics of phrase-structure building during sentence processing
Open accessAbstract
Significance: According to most linguists, the syntactic structure of sentences involves a tree-like hierarchy of nested phrases, as in the sentence [happy linguists] [draw [a diagram]]. Here, we searched for the neural implementation of this hypothetical construct. Epileptic patients volunteered to perform a language task while implanted with intracranial electrodes for clinical purposes. While patients read sentences one word at a time, neural activation in left-hemisphere language areas increased with each successive word but decreased suddenly whenever words could be merged into a phrase. This may be the neural footprint of “merge,” a fundamental tree-building operation that has been hypothesized to allow for the recursive properties of human language.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“They documented a gradual increase in high-gamma power during single word reading of sentences, which decreased at points where words could be merged into a constituent phrase.”
From Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension (2018) · cites this paper as Nelson et al. (2017)
In the citing paper
“While word-yoked gamma power was explained using computational models that estimated forward-entropy and word-surprisal, (similar to Willems et al., 2016), grammatically motivated predictions derived from a phrase-structure parser provided the best fit to gamma fluctuations.”
From Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension (2018) · cites this paper as Nelson et al., 2017
In the citing paper
“The fact that the degree to which prior context constrains future complements appears to mediate both Gamma (Nelson et al., 2017) and alpha (Rommers et al., 2017) activity suggests their interplay is crucial, and that coupled changes in alpha and gamma power may be a signature of efficient forward-looking predictions.”
From Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension (2018) · cites this paper as Nelson et al., 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Chomsky, N. (1957) · doi:10.1515/9783112316009 · Cited by 7,885
A multi-modal parcellation of human cerebral cortex
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., et al. (2016) · Nature · doi:10.1038/nature18933 · Cited by 5,111
Quantitative Analysis of Culture Using Millions of Digitized Books
Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., The Google Books Team, Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., et al. (2010) · Science · doi:10.1126/science.1199644 · Cited by 3,235
A cortical network for semantics: (de)constructing the N400
Lau, E. F., Phillips, C., & Poeppel, D. (2008) · Nature reviews. Neuroscience · doi:10.1038/nrn2532 · Cited by 1,779
On Broca, brain, and binding: A new framework
Hagoort, P. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.07.004 · Cited by 1,411
Chomsky, N. (2013) · Lingua · doi:10.1016/j.lingua.2012.12.003 · Cited by 1,284
Cortical tracking of hierarchical linguistic structures in connected speech
Ding, N., Melloni, L., Zhang, H., Tian, X., & Poeppel, D. (2016) · Nature Neuroscience · doi:10.1038/nn.4186 · Cited by 1,059
Different Origins of Gamma Rhythm and High-Gamma Activity in Macaque Visual Cortex
Ray, S., & Maunsell, J. H. R. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1000610 · Cited by 1,012
Phonetic Feature Encoding in Human Superior Temporal Gyrus
Mesgarani, N., Cheung, C., Johnson, K., & Chang, E. F. (2014) · Science · doi:10.1126/science.1245994 · Cited by 993
A Large-Scale Model of the Functioning Brain
Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., & Rasmussen, D. (2012) · Science · doi:10.1126/science.1225266 · Cited by 973
Research works printed but not linked: 2
- JA Fodor, TG Bever, MF Garrett The Psychology of Language: An Introduction to Psycholinguistics and Generative Grammar (McGraw-Hill, New York, 1974).
- P Smolensky, G Legendre The Harmonic Mind: Cognitive Architecture (MIT Press, Cambridge, MA, 2006).
Cited by
Sorted by citations
Discovering Event Structure in Continuous Narrative Perception and Memory
Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., & Norman, K. A. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.041 · Cited by 952
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1198 · Cited by 657
The neural architecture of language: Integrative modeling converges on predictive processing
Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105646118 · Cited by 517
The Cortical Organization of Syntax
Matchin, W., & Hickok, G. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhz180 · Cited by 370
A hierarchy of linguistic predictions during natural language comprehension
Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P., & de Lange, F. P. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2201968119 · Cited by 343
What you can cram into a single vector: Probing sentence embeddings for linguistic propertiesPreprint
Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.01070 · Cited by 426
Evidence of a predictive coding hierarchy in the human brain listening to speech
Caucheteux, C., Gramfort, A., & King, J.-R. (2023) · Nature Human Behaviour · doi:10.1038/s41562-022-01516-2 · Cited by 308
La gramàtica generativa i la facultat del llenguatge: descobriments, preguntes i desafiaments
Chomsky, N., Gallego, Á. J., & Ott, D. (2019) · Catalan Journal of Linguistics · doi:10.5565/rev/catjl.288 · Cited by 271
The neural basis of combinatory syntax and semantics
Liina Pylkkänen, A. (2019) · Science · doi:10.1126/science.aax0050 · Cited by 237
fMRI reveals language-specific predictive coding during naturalistic sentence comprehension
Shain, C., Blank, I. A., van Schijndel, M., Schuler, W., & Fedorenko, E. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107307 · Cited by 215
Symbols and mental programs: a hypothesis about human singularity
Dehaene, S., Roumi, F. A., Lakretz, Y., Planton, S., & Sablé-Meyer, M. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.06.010 · Cited by 216
Self-citationLanguage is primarily a tool for communication rather than thought
Fedorenko, E., Piantadosi, S. T., & Gibson, E. A. F. (2024) · Nature · doi:10.1038/s41586-024-07522-w · Cited by 190
Lack of selectivity for syntax relative to word meanings throughout the language network
Fedorenko, E., Blank, I. A., Siegelman, M., & Mineroff, Z. (2020) · Cognition · doi:10.1016/j.cognition.2020.104348 · Cited by 170
Hasson, U., Egidi, G., Marelli, M., & Willems, R. M. (2018) · Cognition · doi:10.1016/j.cognition.2018.06.018 · Cited by 157
Large-scale evidence for logarithmic effects of word predictability on reading time
Shain, C., Meister, C., Pimentel, T., Cotterell, R., & Lévy, R. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2307876121 · Cited by 173
Walenski, M., Europa, E., Caplan, D. N., & Thompson, C. K. (2019) · Human Brain Mapping · doi:10.1002/hbm.24523 · Cited by 159
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Kuncoro, A., Dyer, C., Hale, J., Yogatama, D., Clark, S. C., & Blunsom, P. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1132 · Cited by 134
Self-citationDing, N., Melloni, L., Yang, A., Wang, Y., Zhang, W., & Poeppel, D. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00481 · Cited by 122
(2019) · doi:10.18653/v1/n19-1002 · Cited by 118
Self-citationBrodbeck, C. J., Presacco, A., & Simon, J. Z. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.042 · Cited by 125
A Compositional Neural Architecture for Language
Martin, A. E. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01552 · Cited by 128
Hierarchical Encoding of Attended Auditory Objects in Multi-talker Speech Perception
O’Sullivan, J., Herrero, J. L., Smith, E. H., Schevon, C. A., McKhann, G. M., Sheth, S. A., Mehta, A. D., & Mesgarani, N. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.007 · Cited by 116
Hierarchical structure guides rapid linguistic predictions during naturalistic listening
Brennan, J., & Hale, J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0207741 · Cited by 125
Self-citationThe power of neural oscillations to inform sentence comprehension: A linguistic perspective
Prystauka, Y., & Lewis, A. (2019) · Language and Linguistics Compass · doi:10.1111/lnc3.12347 · Cited by 112
Chimpanzees produce diverse vocal sequences with ordered and recombinatorial properties
Girard‐Buttoz, C., Zaccarella, E., Bortolato, T., Friederici, A. D., Wittig, R. M., & Crockford, C. (2022) · Communications Biology · doi:10.1038/s42003-022-03350-8 · Cited by 105
Information flow across the cortical timescale hierarchy during narrative construction
Chang, C. H. C., Nastase, S. A., & Hasson, U. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209307119 · Cited by 92
Matchin, W., Brodbeck, C. J., Hammerly, C. M., & Lau, E. F. (2018) · Human Brain Mapping · doi:10.1002/hbm.24403 · Cited by 93
Building words and phrases in the left temporal lobe
Flick, G., Oseki, Y., Kaczmarek, A. R., Kaabi, M. A., Marantz, A. P., & Liina Pylkkänen, A. (2018) · Cortex · doi:10.1016/j.cortex.2018.06.004 · Cited by 84
Atonal Music: Can Uncertainty Lead to Pleasure?
Mencke, I., Omigie, D., Wald‐Fuhrmann, M., & Brattico, E. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00979 · Cited by 85
Geerligs, L., Gözükara, D., Oetringer, D., Campbell, K. L., van Gerven, M., & Güçlü, U. (2022) · eLife · doi:10.7554/elife.77430 · Cited by 78
Ding, N., Pan, X., Luo, C., Su, N., Zhang, W., & Zhang, J. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2606-17.2017 · Cited by 78
Hierarchy processing in human neurobiology: how specific is it?
Friederici, A. D. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2018.0391 · Cited by 77
Wehbe, L., Blank, I. A., Shain, C., Futrell, R., Levy, R., von der Malsburg, T., Smith, N., Gibson, E., & Fedorenko, E. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab065 · Cited by 72
Liina Pylkkänen, A. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0299 · Cited by 80
Anderson, A. J., Kiela, D., Binder, J. R., Fernandino, L., Humphries, C. J., Conant, L. L., Raizada, R. D. S., Grimm, S., & Lalor, E. C. (2021) · The Journal of Neuroscience · doi:10.1523/jneurosci.1152-20.2021 · Cited by 70
Language in Brains, Minds, and Machines
Tuckute, G., Kanwisher, N., & Fedorenko, E. G. (2024) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-120623-101142 · Cited by 78
Minimal Phrase Composition Revealed by Intracranial Recordings
Murphy, E., Woolnough, O., Rollo, P. S., Roccaforte, Z. J., Segaert, K., Peter Hagoort, A., & Tandon, N. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1575-21.2022 · Cited by 67
Hierarchical and sequential processing of language
Frank, S. L., & Christiansen, M. H. (2018) · Language Cognition and Neuroscience · doi:10.1080/23273798.2018.1424347 · Cited by 66
Single-neuronal elements of speech production in humans
Khanna, A. R., Muñoz, W., Kim, Y. J., Kfir, Y., Paulk, A. C., Jamali, M., Cai, J., Mustroph, M. L., Caprara, I., Hardstone, R., et al. (2024) · Nature · doi:10.1038/s41586-023-06982-w · Cited by 80
Self-citationNeurocomputational Models of Language Processing
Hale, J., Campanelli, L., Li, J., Bhattasali, S., Pallier, C., & Brennan, J. (2021) · Annual Review of Linguistics · doi:10.1146/annurev-linguistics-051421-020803 · Cited by 68
Self-citationDistributed Sensitivity to Syntax and Semantics throughout the Language Network
Shain, C., Kean, H., Casto, C., Lipkin, B., Affourtit, J., Siegelman, M., Mollica, F., & Fedorenko, E. G. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02164 · Cited by 69
Spatiotemporally distributed frontotemporal networks for sentence reading
Woolnough, O., Donos, C., Murphy, E., Rollo, P. S., Roccaforte, Z. J., Dehaene, S., & Tandon, N. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2300252120 · Cited by 63
Self-citationThe Cortical Maps of Hierarchical Linguistic Structures during Speech Perception
Sheng, J., Zheng, L., Lyu, B., Cen, Z., Qin, L., Tan, L. H., Huang, M., Ding, N., & Gao, J. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy191 · Cited by 68
Eye activity tracks task-relevant structures during speech and auditory sequence perception
Jin, P., Zou, J., Zhou, T., & Ding, N. (2018) · Nature Communications · doi:10.1038/s41467-018-07773-y · Cited by 65
Linguistic processing of task-irrelevant speech at a cocktail party
Yahav, P. H.-S., & Golumbic, E. Z. (2021) · eLife · doi:10.7554/elife.65096 · Cited by 66
What neural oscillations can and cannot do for syntactic structure building
Kazanina, N., & Tavano, A. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00659-5 · Cited by 59
Probabilistic language models in cognitive neuroscience: Promises and pitfalls
Armeni, K., Willems, R. M., & Frank, S. L. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.09.001 · Cited by 61
Localizing syntactic predictions using recurrent neural network grammars
Brennan, J. R., Dyer, C., Kuncoro, A., & Hale, J. T. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107479 · Cited by 58
Self-citationSequences of Intonation Units form a ~ 1 Hz rhythm
Inbar, M., Grossman, E., & Landau, A. N. (2020) · Scientific Reports · doi:10.1038/s41598-020-72739-4 · Cited by 58
Roumi, F. A., Marti, S., Wang, L., Amalric, M., & Dehaene, S. (2021) · Neuron · doi:10.1016/j.neuron.2021.06.009 · Cited by 66
Self-citationLe Petit Prince multilingual naturalistic fMRI corpus
Li, J., Bhattasali, S., Zhang, S., Franzluebbers, B., Luh, W., Spreng, R. N., Brennan, J., Yang, Y., Pallier, C., & Hale, J. (2022) · Scientific Data · doi:10.1038/s41597-022-01625-7 · Cited by 57
Self-citationA Faculdade da Linguagem: um objeto biológico, uma janela para a mente e uma ponte entre disciplinas
Gallego, Á. J., & Chomsky, N. (2020) · Revista Linguíʃtica · doi:10.31513/linguistica.2020.v16nesp.a39404 · Cited by 55
The measurement, evolution, and neural representation of action grammars of human behavior
Stout, D., Chaminade, T., Apel, J., Shafti, A., & Faisal, A. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-92992-5 · Cited by 57
A neuronal retuning hypothesis of sentence-specificity in Broca’s area
Matchin, W. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1377-6 · Cited by 55
A compositional neural code in high-level visual cortex can explain jumbled word reading
Agrawal, A., Hari, K. V. S., & Arun, S. P. (2020) · eLife · doi:10.7554/elife.54846 · Cited by 54
Milne, A. E., Petkov, C. I., & Wilson, B. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.06.059 · Cited by 55
The emergence of number and syntax units in LSTM language modelsPreprint
Lakretz, Y., Kruszewski, G., Desbordes, T., Hupkes, D., Dehaene, S., & Baroni, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.07435 · Cited by 69
Self-citationROSE: A neurocomputational architecture for syntax
Murphy, E. (2023) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2023.101180 · Cited by 57
Bhattasali, S., Fabre, M., Luh, W., Saied, H. A., Constant, M., Pallier, C., Brennan, J., Spreng, R. N., & Hale, J. (2018) · Language Cognition and Neuroscience · doi:10.1080/23273798.2018.1518533 · Cited by 51
Self-citationNo evidence of theory of mind reasoning in the human language network
Shain, C., Paunov, A. M., Chen, X. J., Lipkin, B., & Fedorenko, E. G. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac505 · Cited by 49
Hierarchical dynamic coding coordinates speech comprehension in the human brain
Gwilliams, L., Marantz, A. P., Poeppel, D., & King, J.-R. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2422097122 · Cited by 54
Effects of Structure and Meaning on Cortical Tracking of Linguistic Units in Naturalistic Speech
Coopmans, C. W., de Hoop, H., Peter Hagoort, A., & Martin, A. E. (2022) · Neurobiology of Language · doi:10.1162/nol_a_00070 · Cited by 47
Neural tracking of phrases in spoken language comprehension is automatic and task-dependent
ten Oever, S., Carta, S., Kaufeld, G., & Martin, A. E. (2022) · eLife · doi:10.7554/elife.77468 · Cited by 46
Glushko, A., Poeppel, D., & Steinhauer, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-18162-3 · Cited by 46
Frequency-specific brain dynamics related to prediction during language comprehension
Armeni, K., Willems, R. M., van den Bosch, A. P. J., & Schoffelen, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.083 · Cited by 45
SLING: A framework for frame semantic parsingPreprint
Ringgaard, M., Gupta, R., & Pereira, F. C. N. (2017) · arXiv (Cornell University) · doi:10.4230/oasics.iclp.2018.21 · Cited by 44
Delta-band neural activity primarily tracks sentences instead of semantic properties of words
Lu, Y., Jin, P., Pan, X., & Ding, N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118979 · Cited by 43
Natural language processing models reveal neural dynamics of human conversation
Cai, J., Hadjinicolaou, A. E., Paulk, A. C., Soper, D. J., Xia, T., Wang, A. F., Rolston, J. D., Richardson, R. M., Williams, Z. M., & Cash, S. S. (2025) · Nature Communications · doi:10.1038/s41467-025-58620-w · Cited by 47
Self-citationMaran, M., Friederici, A. D., & Zaccarella, E. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104881 · Cited by 42
Brain correlates of constituent structure in sign language comprehension
Moreno, A., Limousin, F., Dehaene, S., & Pallier, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.040 · Cited by 46
Self-citationPhase synchronization varies systematically with linguistic structure composition
Brennan, J., & Martin, A. E. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0305 · Cited by 43
Uddén, J., Hultén, A., Schoffelen, J., Lam, N. H. L., Harbusch, K., van den Bosch, A. P. J., Kempen, G., Petersson, K. M., & Peter Hagoort, A. (2022) · Neurobiology of Language · doi:10.1162/nol_a_00076 · Cited by 40
Chen, L., Goucha, T., Männel, C., Friederici, A. D., & Zaccarella, E. (2021) · Human Brain Mapping · doi:10.1002/hbm.25432 · Cited by 39
Left posterior temporal cortex is sensitive to syntax within conceptually matched Arabic expressions
Matar, S., Dirani, J., Marantz, A., & Liina Pylkkänen, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-86474-x · Cited by 39
Diverging neural dynamics for syntactic structure building in naturalistic speaking and listening
Giglio, L., Ostarek, M., Sharoh, D., & Peter Hagoort, A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2310766121 · Cited by 40
Word Frequency and Predictability Dissociate in Naturalistic Reading
Shain, C. (2024) · Open Mind · doi:10.1162/opmi_a_00119 · Cited by 43
Uddén, J., Martins, M. J. D., Zuidema, W. H., & Fitch, W. T. (2019) · Topics in Cognitive Science · doi:10.1111/tops.12442 · Cited by 38
Brain activity reflects the predictability of word sequences in listened continuous speech
Koskinen, M., Kurimo, M., Groß, J., Hyvärinen, A., & Hari, R. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116936 · Cited by 38
Modeling Structure‐Building in the Brain With CCG Parsing and Large Language Models
Stanojević, M., Brennan, J., Dunagan, D. G., Steedman, M. J., & Hale, J. (2023) · Cognitive Science · doi:10.1111/cogs.13312 · Cited by 39
Self-citationDesbordes, T., Lakretz, Y., Chanoine, V., Oquab, M., Badier, J., Trébuchon, A., Carron, R., Bénar, C., Dehaene, S., & King, J.-R. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1163-22.2023 · Cited by 38
Self-citationDelta-Band Neural Responses to Individual Words Are Modulated by Sentence Processing
Slaats, S., Weissbart, H., Schoffelen, J., Meyer, A. S., & Martin, A. E. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0964-22.2023 · Cited by 36
Vassileiou, B., Meyer, L., Beese, C., & Friederici, A. D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.008 · Cited by 35
Anderson, A. J., Lalor, E. C., Lin, F. V., Binder, J. R., Fernandino, L., Humphries, C., Conant, L. L., Raizada, R. D. S., Grimm, S., & Wang, X. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy110 · Cited by 39
Pronouns reactivate conceptual representations in human hippocampal neurons
Dijksterhuis, D. E., Self, M. W., Possel, J. K., Peters, J. C., van Straaten, E. C. W., Idema, S., Baayen, J. C., van der Salm, S. M. A., Aarnoutse, E. J., van Klink, N. E. C., et al. (2024) · Science · doi:10.1126/science.adr2813 · Cited by 36
Self-citationWeissbart, H., & Martin, A. E. (2024) · Nature Communications · doi:10.1038/s41467-024-53128-1 · Cited by 40
Distinguishing Syntactic Operations in the Brain: Dependency and Phrase-Structure Parsing
Lopopolo, A., van den Bosch, A. P. J., Petersson, K. M., & Willems, R. M. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00029 · Cited by 36
Discrete Sequential Information Coding: Heteroclinic Cognitive Dynamics
Rabinovich, M. I., & Varona, P. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00073 · Cited by 35
van der Burght, C. L., Numssen, O., Schlaak, B., Goucha, T., & Hartwigsen, G. (2022) · Human Brain Mapping · doi:10.1002/hbm.26086 · Cited by 32
Neural populations in the language network differ in the size of their temporal receptive windows
Regev, T. I., Casto, C., Hosseini, E. A., Adamek, M., Ritaccio, A. L., Willie, J. T., Brunner, P., & Fedorenko, E. G. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01944-2 · Cited by 31
What Limits Our Capacity to Process Nested Long-Range Dependencies in Sentence Comprehension?
Lakretz, Y., Dehaene, S., & King, J.-R. (2020) · Entropy · doi:10.3390/e22040446 · Cited by 34
Self-citationNeural dynamics of verbal working memory in auditory description naming
Kambara, T., Brown, E. C., Silverstein, B. H., Nakai, Y., & Asano, E. (2018) · Scientific Reports · doi:10.1038/s41598-018-33776-2 · Cited by 29
Linguistic structure across time: ERP responses to coordinated and uncoordinated noun phrases
Lau, E. F., & Liao, C.-H. (2017) · Language Cognition and Neuroscience · doi:10.1080/23273798.2017.1400081 · Cited by 29
Modularity and granularity across the language network-A primary progressive aphasia perspective
Mesulam, M. M., Coventry, C., Rader, B. M., Kuang, A., Sridhar, J., Martersteck, A., Zhang, H., Thompson, C. K., Weıntraub, S., & Rogalski, E. J. (2021) · Cortex · doi:10.1016/j.cortex.2021.05.002 · Cited by 29
Lu, Y., Jin, P., Ding, N., & Tian, X. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac354 · Cited by 33
How does inattention affect written and spoken language processing?
Cohen, L., Salondy, P., Pallier, C., & Dehaene, S. (2021) · Cortex · doi:10.1016/j.cortex.2021.02.007 · Cited by 29
Self-citationDeep learning models to study sentence comprehension in the human brain
Arana, S., Lerousseau, J. P., & Peter Hagoort, A. (2023) · Language Cognition and Neuroscience · doi:10.1080/23273798.2023.2198245 · Cited by 31
What’s Surprising About Surprisal
Slaats, S., & Martin, A. E. (2025) · Computational Brain & Behavior · doi:10.1007/s42113-025-00237-9 · Cited by 27
Language-specific neural dynamics extend syntax into the time domain
Coopmans, C. W., de Hoop, H., Tezcan, F., Peter Hagoort, A., & Martin, A. E. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3002968 · Cited by 26
Magnetoencephalography can reveal deep brain network activities linked to memory processes
López‐Madrona, V. J., Villalon, S. M., Badier, J., Trébuchon, A., Velmurugan, J., Bartoloméi, F., Carron, R., Barborică, A., Vulliémoz, S., Alario, F. ‐., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.25987 · Cited by 26
Metadata
- Authors
- Nelson, M. J., El Karoui, I., Giber, K., Yang, X., Cohen, L., Koopman, H., Cash, S. S., Naccache, L., Hale, J. T., Pallier, C., & Dehaene, S.
- Affiliations
- Inserm (FR)
- Institut National de la Santé et de la Recherche Médicale (INSERM) U1127, Centre National de la Recherche Scientifique (CNRS) UMR7225, Université Pierre et Marie Curie (UPMC) Paris 6, Institut du Cerveau et de la Moelle Épinière Research Center (ICM), 75013 Paris, France;
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114;
- Stanford University (US)
- Centre National de la Recherche Scientifique (FR)
- University of California, Los Angeles (US)
- Harvard University (US)
- Cornell University (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-04-17
- Volume
- 114
- Issue
- 18
- Pages
- E3669-E3678
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5422821
- OpenAlex
- W2606837722
- Cited by
- 319
- References
- 65
- Citations by year
- 2026: 25 · 2025: 36 · 2024: 37 · 2023: 41 · 2022: 33 · 2021: 39 · 2020: 32 · 2019: 34 · 2018: 29 · 2017: 13
- FWCI
- 16.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- EC | European Research Council (NeuroSyntax)
- Agence Nationale de la Recherche (140301)
- AXA Research Fund (Doctoral Fellowship)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- sentence processing, phrase structure, syntactic analysis, Merge operation, high-gamma power, superior temporal cortex, inferior frontal cortex, middle temporal gyrus, bottom-up parsing, intracranial recordings
- MeSH terms
- Brain, Female, Frontal Lobe, Humans, Male, Models, Neurological, Speech, Temporal Lobe
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
