Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning
Open accessAbstract
Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge. Recent advances have begun to explain brain activation elicited by sentences using vector models of word meaning derived from patterns of word co-occurrence in text corpora. These studies have helped map out semantic representation across a distributed brain network spanning temporal, parietal, and frontal cortex. However, it remains unclear whether activation patterns within regions reflect unified representations of sentence-level meaning, as opposed to superpositions of context-independent component words. This is because models have typically represented sentences as “bags-of-words” that neglect sentence-level structure. To address this issue, we interrogated fMRI activation elicited as 240 sentences were read by 14 participants (9 female, 5 male), using sentences encoded by a recurrent deep artificial neural-network trained on a sentence inference task (InferSent). Recurrent connections and nonlinear filters enable InferSent to transform sequences of word vectors into unified “propositional” sentence representations suitable for evaluating intersentence entailment relations. Using voxelwise encoding modeling, we demonstrate that InferSent predicts elements of fMRI activation that cannot be predicted by bag-of-words models and sentence models using grammatical rules to assemble word vectors. This effect occurs throughout a distributed network, which suggests that propositional sentence-level meaning is represented within and across multiple cortical regions rather than at any single site. In follow-up analyses, we place results in the context of other deep network approaches (ELMo and BERT) and estimate the degree of unpredicted neural signal using an “experiential” semantic model and cross-participant encoding. SIGNIFICANCE STATEMENTA modern-day scientific challenge is to understand how the human brain transforms word sequences into representations of sentence meaning. A recent approach, emerging from advances in functional neuroimaging, big data, and machine learning, is to computationally model meaning, and use models to predict brain activity. Such models have helped map a cortical semantic information-processing network. However, how unified sentence-level information, as opposed to word-level units, is represented throughout this network remains unclear. This is because models have typically represented sentences as unordered “bags-of-words.” Using a deep artificial neural network that recurrently and nonlinearly combines word representations into unified propositional sentence representations, we provide evidence that sentence-level information is encoded throughout a cortical network, rather than in a single region.
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
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?
Cognitive operational functions are based on coordinated activity of key distributed neural networks rather than isolated brain regions.
“Nevertheless, cognitive operational functions are based on coordinated activity of key distributed neural networks rather than isolated brain regions (Anderson et al., 2021).”
From Enhancement of modern defense capabilities by convergence of cognitive neuroscience and artificial intelligence (2026) · cites this paper as Anderson et al., 2021
The Conclusion and ‘How the results relate to other brain imaging studies’ report that sentence-level meaning is represented across multiple cortical regions rather than confined to one site. The claim extends that finding to cognitive functions generally and asserts coordinated network activity; the paper says it cannot determine whether the regions construct representations through interacting hubs. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThese regions operate within dynamic large-scale networks, including salience and executive control networks, which underpin cognitive flexibility and situational readiness.
“Given their role in attentional allocation and multisensory integration, these regions operate within dynamic large-scale networks, including the salience and executive control networks, which underpin cognitive flexibility and situational readiness (Anderson et al., 2021; Menon, 2015).”
From Enhancement of modern defense capabilities by convergence of cognitive neuroscience and artificial intelligence (2026) · cites this paper as Anderson et al., 2021
The Cortical language network section describes distributed temporal, parietal, and frontal regions, supporting the claim that the regions form part of a large-scale network. But the paper does not place them in salience or executive control networks or link them to cognitive flexibility or situational readiness. In How the results relate to other brain imaging studies, it says the timing and interactions among regions remain unclear (page numbers unavailable). (LLM, conf=0.86)
Experimental — accuracy not yet measuredDefault mode, salience, and executive large-scale networks facilitate task switching, self-monitoring, and environmental scanning.
“Default mode, salience and executive large-scale networks facilitate task switching, self-monitoring (Morese et al., 2018), and environmental scanning (Anderson et al., 2021; Menon, 2015).”
From Enhancement of modern defense capabilities by convergence of cognitive neuroscience and artificial intelligence (2026) · cites this paper as Anderson et al., 2021
The Anderson et al. (2021) citation is attached to the environmental-scanning portion of the sentence. The Procedure and cortical-language-network passages describe sentence reading and models of sentence meaning, not environmental scanning. Searches for that term and for the named large-scale networks found no passage addressing the attributed function. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · 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) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900 · see citing works
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913 · see citing works
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741 · see citing works
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · see citing works
Ridge Regression: Biased Estimation for Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (2000) · Technometrics · doi:10.2307/1271436 · Cited by 6,553 · see citing works
Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282 · see citing works
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 5,882 · see citing works
Talairach, J., Tournoux, P., & Rayport, M. (1988) · Thieme eBooks · Cited by 5,058 · see citing works
Kutas, M., & Federmeier, K. D. (2011) · Annual Review of Psychology · doi:10.1146/annurev.psych.093008.131123 · Cited by 4,377 · see citing works
Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp055 · Cited by 4,060 · see citing works
Self-citationA large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377 · see citing works
Representational similarity analysis-connecting the branches of systems neuroscience
Kriegeskorte, N. (2008) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.004.2008 · Cited by 3,355 · see citing works
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.03771 · Cited by 3,347 · see citing works
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1070 · Cited by 2,049 · see citing works
Self-citationRidge Regression: Biased Estimation for Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.2307/1267351 · Cited by 1,978 · see citing works
Producing high-dimensional semantic spaces from lexical co-occurrence
Lund, K., & Burgess, C. (1996) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03204766 · Cited by 1,808 · see citing works
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 1,688 · see citing works
Lexical access during sentence comprehension: (Re)consideration of context effects
Swinney, D. (1979) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(79)90355-4 · Cited by 1,631 · see citing works
Character-Aware Neural Language Models
Kim, Y., Jernite, Y., Sontag, D. A., & Rush, A. M. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10362 · Cited by 1,546 · see citing works
Character-Aware Neural Language Models
Kim, Y., Jernite, Y., Sontag, D., & Rush, A. M. (2015) · arXiv · doi:10.48550/arxiv.1508.06615 · Cited by 1,530 · see citing works
Natural speech reveals the semantic maps that tile human cerebral cortex
Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016) · Nature · doi:10.1038/nature17637 · Cited by 1,504 · see citing works
Predicting Human Brain Activity Associated with the Meanings of Nouns
Mitchell, T. M., Shinkareva, S. V., Carlson, A., Chang, K.-M., Malave, V. L., Mason, R. A., & Just, M. A. (2008) · Science · doi:10.1126/science.1152876 · Cited by 1,180 · see citing works
What do we mean by prediction in language comprehension?
Kuperberg, G. R., & Jaeger, T. F. (2016) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2015.1102299 · Cited by 1,087 · see citing works
Composition in Distributional Models of Semantics
Mitchell, J., & Lapata, M. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2010.01106.x · Cited by 946 · see citing works
A large annotated corpus for learning natural language inferencePreprint
Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.05326 · Cited by 732 · see citing works
How does nature program neuron types?
Borst, A. (2008) · Frontiers in Neuroscience · doi:10.3389/neuro.01.016.2008 · Cited by 705 · see citing works
Language and simulation in conceptual processing
Barsalou, L. W., Santos, A., Simmons, W. K., & Wilson, C. (2008) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199217274.003.0013 · Cited by 703 · see citing works
New method for fMRI investigations of language: defining ROIs functionally in individual subjects
Fedorenko, E., Hsieh, P.-J., Nieto-Castañón, A., Whitfield-Gabrieli, S., & Kanwisher, N. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00032.2010 · Cited by 686 · see citing works
A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex
Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B., Gobbini, M. I., Hanke, M., & Ramadge, P. J. (2011) · Neuron · doi:10.1016/j.neuron.2011.08.026 · Cited by 645 · see citing works
How neurons make meaning: brain mechanisms for embodied and abstract-symbolic semantics
Pulvermüller, F. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.06.004 · Cited by 631 · see citing works
Embodiment as a unifying perspective for psychology
Glenberg, A. M. (2010) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.55 · Cited by 601 · see citing works
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 conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1198 · Cited by 572 · see citing works
Evidence for multiple stages in the processing of ambiguous words in syntactic contexts
Tanenhaus, M. K., Leiman, J. M., & Seidenberg, M. S. (1979) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(79)90237-8 · Cited by 561 · see citing works
Toward a brain-based componential semantic representation
Binder, J. R., Conant, L. L., Humphries, C., Fernandino, L., Simons, S. B., Aguilar, M., & Desai, R. H. (2016) · Cognitive Neuropsychology · doi:10.1080/02643294.2016.1147426 · Cited by 360 · see citing works
Self-citationVisual perception of rapidly presented word sequences of varying complexity
Forster, K. I. (1970) · Perception & Psychophysics · doi:10.3758/bf03210208 · Cited by 345 · see citing works
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 · see citing works
Toward a universal decoder of linguistic meaning from brain activation
Pereira, F., Lou, B., Pritchett, B., Ritter, S., Gershman, S. J., Kanwisher, N., Botvinick, M., & Fedorenko, E. (2018) · Nature Communications · doi:10.1038/s41467-018-03068-4 · Cited by 324 · see citing works
Neurophysiological dynamics of phrase-structure building during sentence processing
Nelson, M. J., El Karoui, I., Giber, K., Yang, X., Cohen, L., Koopman, H., Cash, S. S., Naccache, L., Hale, J. T., Pallier, C., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1701590114 · Cited by 319 · see citing works
The Hierarchical Cortical Organization of Human Speech Processing
de Heer, W. A., Huth, A. G., Griffiths, T. L., Gallant, J. L., & Theunissen, F. E. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3267-16.2017 · Cited by 281 · see citing works
Bemis, D. K., & Liina Pylkkänen, A. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.5003-10.2011 · Cited by 279 · see citing works
Wehbe, L., Murphy, B., Talukdar, P. P., Fyshe, A., Ramdas, A., & Mitchell, T. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112575 · Cited by 256 · see citing works
Time course of priming for associate and inference words in a discourse context
Till, R. E., Mross, E. F., & Kintsch, W. (1988) · Memory & Cognition · doi:10.3758/bf03197039 · Cited by 230 · see citing works
Deniz, F., Nunez-Elizalde, A. O., Huth, A. G., & Gallant, J. L. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.0675-19.2019 · Cited by 214 · see citing works
A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes
Just, M. A., Cherkassky, V. L., Aryal, S., & Mitchell, T. M. (2010) · PLoS ONE · doi:10.1371/journal.pone.0008622 · Cited by 211 · see citing works
Devereux, B. J., Clarke, A., Marouchos, A., & TYLER, L. K. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3809-13.2013 · Cited by 202 · see citing works
Neural correlate of the construction of sentence meaning
Fedorenko, E., Scott, T. L., Brunner, P., Coon, W. G., Pritchett, B., Schalk, G., & Kanwisher, N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1612132113 · Cited by 199 · see citing works
Not lost in translation: neural responses shared across languages
Honey, C. J., Thompson, C. R., Lerner, Y., & Hasson, U. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.1800-12.2012 · Cited by 192 · see citing works
A Systematic Study of Semantic Vector Space Model Parameters
Kiela, D., & Clark, S. C. (2014) · doi:10.3115/v1/w14-1503 · Cited by 177 · see citing works
Self-citationLack 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 · see citing works
An architecture for encoding sentence meaning in left mid-superior temporal cortex
Frankland, S., & Greene, J. D. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421236112 · Cited by 168 · see citing works
Syntactic processing is distributed across the language system
Blank, I., Balewski, Z. Z., Mahowald, K., & Fedorenko, E. G. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.069 · Cited by 164 · see citing works
Incorporating Context into Language Encoding Models for fMRI
Jain, S., & Huth, A. G. (2018) · doi:10.1101/327601 · Cited by 155 · cited 1× · see citing works
“…Honey, 2020); and also other studies that have begun to use deep network-based models to predict contextualized semantic responses in neural data (Jain and Huth, 2018; Gauthier and Levy, 2019; Sun et al., 2019; Toneva and Wehbe, 2019; Goldstein et al., 2020; Heilbron et al., 2020; Schrimpf et…”
BackgroundThe role of the left anterior temporal lobe in semantic composition vs. semantic memory
Westerlund, M., & Liina Pylkkänen, A. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.03.001 · Cited by 130 · see citing works
Evidence for conceptual combination in the left anterior temporal lobe
Baron, S. G., & Osherson, D. N. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.066 · Cited by 106 · see citing works
Organizational Principles of Abstract Words in the Human Brain
Wang, X., Wu, W. H., Ling, Z.-H., Xu, Y., Fang, Y., Wang, X., Binder, J. R., Men, W., Gao, J., & Bi, Y. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx283 · Cited by 106 · see citing works
Self-citationAnderson, A. J., Binder, J. R., Fernandino, L., Humphries, C., Conant, L. L., Aguilar, M., Wang, X., Doko, D., & Raizada, R. D. S. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw240 · Cited by 105 · see citing works
Self-citationWang, J., Cherkassky, V. L., & Just, M. A. (2017) · Human Brain Mapping · doi:10.1002/hbm.23692 · Cited by 99 · see citing works
Heteromodal Cortical Areas Encode Sensory-Motor Features of Word Meaning
Fernandino, L., Humphries, C., Conant, L. L., Seidenberg, M. S., & Binder, J. R. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.4095-15.2016 · Cited by 91 · see citing works
Self-citationConstructing and Forgetting Temporal Context in the Human Cerebral Cortex
Chien, H.-Y. S., & Honey, C. J. (2020) · Neuron · doi:10.1016/j.neuron.2020.02.013 · Cited by 86 · see citing works
Zhang, L., & Liina Pylkkänen, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.028 · Cited by 85 · see citing works
Anderson, A. J., Bruni, E., Lopopolo, A., Poesio, M., & Baroni, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.093 · Cited by 77 · see citing works
Self-citationAnderson, A. J., Kiela, D., Clark, S. C., & Poesio, M. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00043 · Cited by 76 · see citing works
Self-citationNo Training Required: Exploring Random Encoders for Sentence ClassificationPreprint
Wieting, J., & Kiela, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.10444 · Cited by 74 · see citing works
Self-citationLinking artificial and human neural representations of language
Gauthier, J., & Lévy, R. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1050 · Cited by 72 · see citing works
Neural dynamics of semantic composition
Lyu, B., Choi, H., Marslen‐Wilson, W. D., Clarke, A., Randall, B., & TYLER, L. K. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903402116 · Cited by 66 · see citing works
Chang, K.-M. K., Mitchell, T. M., & Just, M. A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.271 · Cited by 58 · see citing works
Anderson, A. J., Zinszer, B. D., & Raizada, R. D. S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.035 · Cited by 56 · see citing works
Self-citationPereira, F., Botvinick, M., & Detre, G. J. (2012) · Artificial Intelligence · doi:10.1016/j.artint.2012.06.005 · Cited by 55 · see citing works
Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machinesPreprint
Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A. R., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.02.403477 · Cited by 51 · see citing works
Abnar, S., Ahmed, R., Mijnheer, M., & Zuidema, W. H. (2018) · Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · doi:10.18653/v1/w18-0107 · Cited by 49 · see citing works
An Integrated Neural Decoder of Linguistic and Experiential Meaning
Anderson, A. J., Binder, J. R., Fernandino, L., Humphries, C., Conant, L. L., Raizada, R. D. S., Lin, F. V., & Lalor, E. C. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2575-18.2019 · Cited by 49 · see citing works
Self-citationLanguage processing in brains and deep neural networks: computational convergence and its limitsPreprint
Caucheteux, C., & King, J.-R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.03.186288 · Cited by 49 · see citing works
Anderson, A. J., Bruni, E., Bordignon, U., Poesio, M., & Baroni, M. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1202 · Cited by 42 · see citing works
Self-citationThe lexical semantics of adjective–noun phrases in the human brain
Fyshe, A., Sudre, G. P., Wehbe, L., Rafidi, N., & Mitchell, T. M. (2019) · Human Brain Mapping · doi:10.1002/hbm.24714 · Cited by 40 · see citing works
Towards Sentence-Level Brain Decoding with Distributed Representations
Sun, J., Wang, S., Zhang, J., & Zong, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017047 · Cited by 39 · see citing works
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 35 · see citing works
Self-citationWang, J., Cherkassky, V. L., Yang, Y., Chang, K.-M. K., Vargas, R., Diana, N., & Just, M. A. (2016) · Cognitive Neuropsychology · doi:10.1080/02643294.2016.1182480 · Cited by 33 · see citing works
Yang, Y., Wang, J., Bailer, C., Cherkassky, V. L., & Just, M. A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.029 · Cited by 33 · see citing works
Bulat, L., Clark, S. C., & Shutova, E. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1113 · Cited by 29 · see citing works
Decoding individual identity from brain activity elicited in imagining common experiences
Anderson, A. J., McDermott, K., Rooks, B., Heffner, K. L., Dodell‐Feder, D., & Lin, F. V. (2020) · Nature Communications · doi:10.1038/s41467-020-19630-y · Cited by 25 · see citing works
Self-citationComparing Transformers and RNNs on predicting human sentence processing data.Preprint
Merkx, D., & Frank, S. L. (2020) · arXiv (Cornell University) · Cited by 23 · see citing works
Evaluating semantic models with word-sentence relatednessPreprint
Glasgow, K., Roos, M. J., Haufler, A., Chevillet, M. A., & Wolmetz, M. E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07253 · Cited by 19 · see citing works
Fedorenko, E., Mineroff, Z., Siegelman, M., & Blank, I. (2018) · bioRxiv · doi:10.1101/477851 · Cited by 18 · see citing works
Djokic, V. G., Maillard, J., Bulat, L., & Shutova, E. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00307 · Cited by 14 · see citing works
Lopopolo, A., Schoffelen, J., van den Bosch, A. P. J., & Willems, R. M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.19.161190 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Subramanian S, Trischler A, Bengio Y, Pal CJ (2018) Learning general pur- pose distributed sentence representations via large scale multi-task learn- ing. In: Proceedings of the 2018 International Conference on Learning Representations (ICLR). Vancouver, Canada.
- Toneva M, Wehbe L (2019) Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). In: Advances in Neural Information Processing Systems, pp 14928- 14938. Vancouver: Curran.
Cited by
Sorted by citations
Deep language algorithms predict semantic comprehension from brain activity
Caucheteux, C., Gramfort, A., & King, J.-R. (2022) · Scientific Reports · doi:10.1038/s41598-022-20460-9 · Cited by 91 · see citing works
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 69 · see citing works
Neurocomputational 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 64 · see citing works
How Does the “Default Mode” Network Contribute to Semantic Cognition?
Fernandino, L., & Binder, J. R. (2024) · Brain and Language · doi:10.1016/j.bandl.2024.105405 · Cited by 59 · see citing works
Self-citationROSE: A neurocomputational architecture for syntax
Murphy, E. (2023) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2023.101180 · Cited by 51 · see citing works
Contextual feature extraction hierarchies converge in large language models and the brain
Mischler, G., Li, Y. A., Bickel, S., Mehta, A. D., & Mesgarani, N. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00925-4 · Cited by 58 · see citing works
Distributed 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 64 · see citing works
Antonello, R., & Huth, A. G. (2022) · Neurobiology of Language · doi:10.1162/nol_a_00087 · Cited by 49 · see citing works
He, R., Palominos, C., Zhang, H., Alonso-Sánchez, M. F., Palaniyappan, L. K., & Hinzen, W. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.115752 · Cited by 41 · see citing works
Computational Language Modeling and the Promise of In Silico Experimentation
Jain, S., Vo, V. A., Wehbe, L., & Huth, A. G. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00101 · Cited by 37 · see citing works
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 · see citing works
The ‘L-factor’: Language as a transdiagnostic dimension in psychopathology
Hinzen, W., & Palaniyappan, L. K. (2024) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2024.110952 · Cited by 29 · see citing works
Deep 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 28 · see citing works
Yu, S., Gu, C., Huang, K., & Li, P. (2024) · Science Advances · doi:10.1126/sciadv.adn7744 · Cited by 27 · see citing works
Generative language reconstruction from brain recordings
Ye, Z., Ai, Q., Liu, Y., de Rijke, M., Zhang, M., Lioma, C., & Ruotsalo, T. (2025) · Communications Biology · doi:10.1038/s42003-025-07731-7 · Cited by 22 · see citing works
Sentiment Classification Method Based on Blending of Emoticons and Short Texts
Zou, H., & Xiang, K. (2022) · Entropy · doi:10.3390/e24030398 · Cited by 20 · see citing works
Alonso-Sánchez, M. F., Hinzen, W., He, R., Gati, J. S., & Palaniyappan, L. K. (2024) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.240031 · Cited by 17 · see citing works
Qiu, Y., Liu, H., & Zhao, M. (2025) · Applied Sciences · doi:10.3390/app15010392 · Cited by 18 · see citing works
Carota, F., Nili, H., Kriegeskorte, N., & Friedemann Pulvermüller, A. (2023) · Language Cognition and Neuroscience · doi:10.1080/23273798.2023.2232481 · Cited by 14 · see citing works
Fernandino, L., & Binder, J. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-820480-1.00155-8 · Cited by 14 · see citing works
Self-citationAnderson, A. J., Davis, C., & Lalor, E. C. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012537 · Cited by 13 · see citing works
Self-citationPrediction errors drive dynamic changes in neural patterns that guide behavior
Möhring, L., & Gläscher, J. P. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112931 · Cited by 13 · see citing works
Modelling brain representations of abstract concepts
KAISER, D. H., Jacobs, A. M., & Cichy, R. M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009837 · Cited by 13 · see citing works
Graded sensitivity to structure and meaning throughout the human language networkPreprint
Shain, C., Kean, H., Casto, C., Lipkin, B., Affourtit, J., Siegelman, M., Mollica, F., & Fedorenko, E. G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.12.467812 · Cited by 12 · see citing works
Modeling Brain Representations of Words' Concreteness in Context Using GPT‐2 and Human Ratings
Bruera, A., Tao, Y., Anderson, A. J., Çokal, D., Haber, J., & Poesio, M. (2023) · Cognitive Science · doi:10.1111/cogs.13388 · Cited by 10 · see citing works
Self-citationLinguistic contextualization in the human hippocampusPreprint
Katlowitz, K. A., Belanger, J. L., Ismail, T., Chavez, A. G., Chericoni, A., Franch, M. C., Mickiewicz, E. A., Mathura, R. K., Paulo, D. L., Bartoli, E., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.23.661103 · Cited by 10 · see citing works
Angular gyrus: an anatomical case study for association cortex
Rockland, K. S. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02537-3 · Cited by 9 · see citing works
Fairhall, S. L. (2024) · Brain and Language · doi:10.1016/j.bandl.2024.105389 · Cited by 12 · see citing works
Do Machines Really Understand Meaning? (Again)
Perconti, P., & Plebe, A. (2023) · Journal of Artificial Intelligence and Consciousness · doi:10.1142/s2705078522500205 · Cited by 8 · see citing works
Disorganisation and depression: a re-examination of how we think and speak when depressed
Palaniyappan, L. K., Wang, Y. L., & Meister, F. (2025) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-025-01994-1 · Cited by 8 · see citing works
Cortical processing of reference in language revealed by computational modelsPreprint
Li, J., Wang, S., Luh, W., Liina Pylkkänen, A., Yang, Y., & Hale, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.24.396598 · Cited by 8 · see citing works
Neural encoding of semantic structures during sentence production
Giglio, L., Peter Hagoort, A., & Ostarek, M. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae482 · Cited by 7 · see citing works
Semantic Analysis Technology of English Translation Based on Deep Neural Network
Wang, Q. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1176943 · Cited by 7 · see citing works
Ormerod, M., del Rincón, J. M., & Devereux, B. J. (2023) · Computational Linguistics · doi:10.1162/coli_a_00495 · Cited by 6 · see citing works
Bruera, A., & Poesio, M. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.796793 · Cited by 5 · see citing works
The cortical architecture representing the linguistic hierarchy of the conversational speech
Aili, R., Zhou, S., Xu, X., He, X., & Lu, C. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121180 · Cited by 4 · see citing works
Cosic, K., Kuljanac, A., & Palermo, S. (2026) · Frontiers in psychology · doi:10.3389/fpsyg.2026.1821242 · Cited by 3 · see citing works
“Nevertheless, cognitive operational functions are based on coordinated activity of key distributed neural networks rather than isolated brain regions (Anderson et al., 2021).”
Zhou, F., Zhou, S., Long, Y., Flinker, A., & Lu, C. (2025) · Communications Biology · doi:10.1038/s42003-025-09377-x · Cited by 5 · see citing works
Frontal cortex organization supporting audiovisual processing during naturalistic viewing
Zhou, F., Khalilian-Gourtani, A., Dugan, P. C., Michalak, A. J., Devinsky, O., Rozman, P. A., Doyle, W., Friedman, D., & Flinker, A. (2026) · Nature Communications · doi:10.1038/s41467-026-73947-8 · Cited by 2 · see citing works
Neural decoding of autobiographical mental image features with a general semantic model
Anderson, A. J., Fernandino, L., Gross, W. L., Rey, H. G., & Binder, J. R. (2025) · Nature Communications · doi:10.1038/s41467-025-65541-1 · Cited by 2 · see citing works
Self-citationA decade of comparing brains to DNNs: Progress and perspectives
Schmitt, L., Dong, D. T., Çelik, E., de Lange, F. P., & Toneva, M. (2026) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2026.06.011 · Cited by 3 · see citing works
Jiang, J., Guoyu, Z. H. A. O., Yingbo, M. A., Ding, G., & Lanfang, L. I. U. (2023) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2023.01002 · Cited by 3 · see citing works
Skrill, D., & Norman-Haignere, S. (2023) · PubMed · Cited by 2 · see citing works
Zhou, F., Zhou, S., Long, Y., Flinker, A., & Lu, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.27.645665 · Cited by 2 · see citing works
Puffay, C., Mischler, G., Choudhari, V., Vanthornhout, J., Bickel, S., Mehta, A. D., Schevon, C. A., McKhann, G. M., hamme, H. V., Francart, T., et al. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1227 · Cited by 1 · see citing works
Semantic control regions actively generate rule-based semantic predictions
Belluzzi, A., & Fairhall, S. L. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121379 · Cited by 1 · see citing works
Fodor, J., Murawski, C., & Suzuki, S. (2026) · eLife · doi:10.1101/2025.07.19.665701 · Cited by 1 · see citing works
Modelling brain representations of abstract conceptsPreprint
KAISER, D. H., Jacobs, A. M., & Cichy, R. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.02.446744 · Cited by 1 · see citing works
Norman-Haignere, S., Keshishian, M., Devinsky, O., Doyle, W., McKhann, G. M., Schevon, C. A., Flinker, A., & Mesgarani, N. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.23.614358 · Cited by 1 · see citing works
Optimizing Language Model Embeddings to Voxel Activity Improves Brain Activity PredictionsPreprint
Negi, A., Tseng, C., Núñez-Elizalde, A. O., Gong, X. L., & Deniz, F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.18.676935 · Cited by 1 · see citing works
A Connection Access Mechanism of Distributed Network based on Block Chain
Zhou, X., Cheng, H., & Chen, F. (2022) · International Journal of Circuits Systems and Signal Processing · doi:10.46300/9106.2022.16.27
NAVIGATION PASSIVE PAR SCANNER ASSISTANT LA CHIRURGIE ENDONASALE CHEZ L'ENFANT
Ayari, S., Bossard, D., Disant, F., & Froehlich, P. (2008)
Context and Attention Shape Electrophysiological Correlates of Speech-to-Language TransformationPreprint
Anderson, A. J., Davis, C., & Lalor, E. C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.24.559177
Self-citationInvestigating Neural Fit Approaches for Sentence Embedding Model Paradigms
Balabin, H., Liuzzi, A. G., Sun, J., Dupont, P. J., Vandenberghe, R. R., & Moens, M. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230267
Krauß, P. (2023) · Springer eBooks · doi:10.1007/978-3-662-67179-5_21
Krauß, P. (2024) · Springer eBooks · doi:10.1007/978-3-662-68980-6_21
Fernandino, L., & Binder, J. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15754-7.00058-4
Self-citationImagined Speech Reconstruction with 3D Neural Metabolism and Large Language Model IntegrationPreprint
Rajagopal, G., Chaudhari, A., LaRocco, J., Xue, J., Zachariah, E., Varanasi, S., & Rothman, D. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.30.667805
Fodor, J., Murawski, C., & Suzuki, S. (2025) · eLife · doi:10.7554/elife.108442.1
Fodor, J., Murawski, C., & Suzuki, S. (2025) · eLife · doi:10.7554/elife.108442
Brain function in language and associated networks in non- or minimally verbal children
Montaña-Valverde, G., Linke, A. C., Slušná, D., Lopez, J. M., Rodríguez‐Fornells, A., Deco, G., & Hinzen, W. (2026) · Brain Communications · doi:10.1093/braincomms/fcag151
Identification of Neuro-semantic Representation of Concrete Nouns Using Cascaded Feature Selection
Ranjan, A., Rawat, A. S., Singh, V. P., Singh, A. K., & Singh, A. K. (2025) · SN Computer Science · doi:10.1007/s42979-025-04556-3
Medial temporal default mode network selectively encodes autobiographical visual imagery
Anderson, A. J., Turnbull, A. G., & Lin, F. V. (2026) · doi:10.21428/8e6ba8ef.70bfcf1d
Self-citationUsing computational semantics to study meaning in the brain
Fodor, J., & Suzuki, S. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106514
Semantic control regions actively generate rule-based semantic predictionsPreprint
Belluzzi, A. (2024) · OSF Preprints (OSF Preprints) · doi:10.17605/osf.io/jtkag
Human-like sequential sound-to-meaning transfer drives artificial speech comprehensionPreprint
Zhang, S., Li, S., Yang, R., Chen, G., Tian, X., Wang, Q., & Fang, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.13.723203
Medial temporal default mode network selectively encodes autobiographical visual imagery
Anderson, A. J., Turnbull, A. G., & Lin, F. V. (2026) · Science Advances · doi:10.1126/sciadv.aee4232
Self-citationFodor, J., Murawski, C., & Suzuki, S. (2026) · eLife · doi:10.7554/elife.108442.3
Action features dominate cortical representation during natural vision
Jung, H., Jain, H., Han, X., Feilong, M., Han, J., Shokeen, D., Uden, C. V., Hanson, I., Connolly, A. C., Wager, T. D., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2625018123
Xu, T., & Jiang, X. (2026) · Journal of Speech Language and Hearing Research · doi:10.1044/2026_jslhr-25-01029
- Authors
- 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.
- Affiliations
- University of Rochester Medicine (US)
- Meta (United States) (US)
- Medical College of Wisconsin (US)
- University of Rochester (US)
- ORCID
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2021-03-22
- Volume
- 41
- Issue
- 18
- Pages
- 4100-4119
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC8176751
- OpenAlex
- W3136656403
- Cited by
- 70
- References
- 91
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 17 · 2023: 13 · 2022: 8 · 2021: 3 · 2020: 1
- FWCI
- 5.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- IARPA (FA8650-14-C-7357)
- NSF (1652127)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- propositional representation, distributed cortical networks, semantic representation, fMRI decoding, recurrent neural networks, logical consequence, word embeddings, bag-of-words model, ELMo, BERT, temporal cortex, parietal cortex, frontal cortex
- MeSH terms
- Adult, Cerebral Cortex, Computer Simulation, Female, Humans, Language, Magnetic Resonance Imaging, Male, Middle Aged, Reading, Semantics, Neural Networks, Computer, Comprehension, Young Adult
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
