Binder, J. R. et al. Human brain language areas identified by functional magnetic resonance imaging. J. Neurosci. 17, 353–362 (1997).
Fedorenko, E., Hsieh, P.-J., Nieto-Castañón, A., Whitfield-Gabrieli, S. & Kanwisher, N. New method for fMRI investigations of language: defining ROIs functionally in individual subjects. J. Neurophysiol. 104, 1177–1194 (2010).
Fedorenko, E. & Thompson-Schill, S. L. Reworking the language network. Trends Cogn. Sci. 18, 120–126 (2014).
Lipkin, B. et al. Probabilistic atlas for the language network based on precision fMRI data from >800 individuals. Sci. Data 9, 529 (2022).
MacSweeney, M. et al. Neural systems underlying British Sign Language and audio-visual English processing in native users. Brain J. Neurol. 125, 1583–1593 (2002).
Deniz, F., Nunez-Elizalde, A. O., Huth, A. G. & Gallant, J. L. The representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality. J. Neurosci. 39, 7722–7736 (2019).
Hu, J. et al. Precision fMRI reveals that the language-selective network supports both phrase-structure building and lexical access during language production. Cereb. Cortex 33, 4384–4404 (2022).
Malik-Moraleda, S. et al. An investigation across 45 languages and 12 language families reveals a universal language network. Nat. Neurosci. 25, 1014–1019 (2022).
Fedorenko, E. & Blank, I. A. Broca’s area is not a natural kind. Trends Cogn. Sci. 24, 270–284 (2020).
Bautista, A. & Wilson, S. M. Neural responses to grammatically and lexically degraded speech. Lang. Cogn. Neurosci. 31, 567–574 (2016).
Fedorenko, E., Blank, I. A., Siegelman, M. & Mineroff, Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition 203, 104348 (2020).
Mesulam, M.-M. Primary progressive aphasia. Ann. Neurol. 49, 425–432 (2001).
Wilson, S. M. et al. Language mapping in aphasia. J. Speech Lang. Hear. Res. 62, 3937–3946 (2019).
Radford, A., Narasimhan, K., Salimans, T. & Sutskever, I. Improving Language Understanding by Generative Pre-training Technical Report (OpenAI, 2018).
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. NAACL-HLT 2019 (eds Burstein, J. et al.) 4171–4186 (Association for Computational Linguistics, 2019); https://doi.org/10.18653/v1/N19-1423
Wilcox, E. G., Gauthier, J., Hu, J., Qian, P. & Levy, R. On the predictive power of neural language models for human real-time comprehension behavior. In Proc. 42nd Annual Meeting of the Cognitive Science Society (eds Denison, S. et al.) 1707–1713 (Cognitive Science Society, 2020).
Shain, C., Meister, C., Pimentel, T., Cotterell, R. & Levy, R. P. Large-scale evidence for logarithmic effects of word predictability on reading time. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/4hyna (2022).
Toneva, M. & Wehbe, L. Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). In Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (eds Wallach, H. et al.) 14954–14964 (Curran Associates, Inc., 2019).
Gauthier, J. & Levy, R. Linking artificial and human neural representations of language. In Proc. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (eds Inui, K. et al.) 529–539 (Association for Computational Linguistics, 2019); https://doi.org/10.18653/v1/D19-1050
Schrimpf, M. et al. The neural architecture of language: integrative modeling converges on predictive processing. Proc. Natl Acad. Sci. USA 118, e2105646118 (2021).
Caucheteux, C. & King, J.-R. Brains and algorithms partially converge in natural language processing. Commun. Biol. 5, 134 (2022).
Goldstein, A. et al. Shared computational principles for language processing in humans and deep language models. Nat. Neurosci. 25, 369–380 (2022).
Caucheteux, C., Gramfort, A. & King, J.-R. Evidence of a predictive coding hierarchy in the human brain listening to speech. Nat. Hum. Behav. 7, 430–441 (2023).
Bashivan, P., Kar, K. & DiCarlo, J. J. Neural population control via deep image synthesis. Science 364, eaav9436 (2019).
Ponce, C. R. et al. Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences. Cell 177, 999–1009 (2019).
Fedorenko, E., Behr, M. K. & Kanwisher, N. Functional specificity for high-level linguistic processing in the human brain. Proc. Natl Acad. Sci. USA 108, 16428–16433 (2011).
Blank, I., Kanwisher, N. & Fedorenko, E. A functional dissociation between language and multiple-demand systems revealed in patterns of BOLD signal fluctuations. J. Neurophysiol. 112, 1105–1118 (2014).
Paunov, A. M., Blank, I. A. & Fedorenko, E. Functionally distinct language and Theory of Mind networks are synchronized at rest and during language comprehension. J. Neurophysiol. 121, 1244–1265 (2019).
Blank, I. A. & Fedorenko, E. No evidence for differences among language regions in their temporal receptive windows. NeuroImage 219, 116925 (2020).
Prince, J. S. et al. Improving the accuracy of single-trial fMRI response estimates using GLMsingle. eLife 11, e77599 (2022).
Allen, E. J. et al. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat. Neurosci. 25, 116–126 (2022).
Duncan, J. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends Cogn. Sci. 14, 172–179 (2010).
Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1–38 (2008).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
Lerner, Y., Honey, C. J., Silbert, L. J. & Hasson, U. Topographic mapping of a hierarchy of temporal receptive windows using a narrated story. J. Neurosci. 31, 2906–2915 (2011).
Honey, C. J., Thompson, C. R., Lerner, Y. & Hasson, U. Not lost in translation: neural responses shared across languages. J. Neurosci. 32, 15277–15283 (2012).
Blank, I. A. & Fedorenko, E. Domain-general brain regions do not track linguistic input as closely as language-selective regions. J. Neurosci. 37, 9999–10011 (2017).
Nieto-Castañón, A. & Fedorenko, E. Subject-specific functional localizers increase sensitivity and functional resolution of multi-subject analyses. NeuroImage 63, 1646–1669 (2012).
Braga, R. M., DiNicola, L. M., Becker, H. C. & Buckner, R. L. Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks. J. Neurophysiol. 124, 1415–1448 (2020).
Demberg, V. & Keller, F. Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition 109, 193–210 (2008).
Smith, N. J. & Levy, R. The effect of word predictability on reading time is logarithmic. Cognition 128, 302–319 (2013).
Brothers, T. & Kuperberg, G. R. Word predictability effects are linear, not logarithmic: implications for probabilistic models of sentence comprehension. J. Mem. Lang. 116, 104174 (2021).
Willems, R. M., Frank, S. L., Nijhof, A. D., Hagoort, P. & van den Bosch, A. Prediction during natural language comprehension. Cereb. Cortex 26, 2506–2516 (2016).
Henderson, J. M., Choi, W., Lowder, M. W. & Ferreira, F. Language structure in the brain: a fixation-related fMRI study of syntactic surprisal in reading. NeuroImage 132, 293–300 (2016).
Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P. & de Lange, F. P. A hierarchy of linguistic predictions during natural language comprehension. Proc. Natl Acad. Sci. USA 119, e2201968119 (2022).
Shain, C., Blank, I. A., van Schijndel, M., Schuler, W. & Fedorenko, E. fMRI reveals language-specific predictive coding during naturalistic sentence comprehension. Neuropsychologia 138, 107307 (2020).
Michaelov, J. A., Bardolph, M. D., Van Petten, C. K., Bergen, B. K. & Coulson, S. Strong prediction: language model surprisal explains multiple N400 effects. Neurobiol. Lang. https://doi.org/10.1162/nol_a_00105 (2023).
Rayner, K. & Duffy, S. A. Lexical complexity and fixation times in reading: effects of word frequency, verb complexity, and lexical ambiguity. Mem. Cogn. 14, 191–201 (1986).
Brysbaert, M., Warriner, A. B. & Kuperman, V. Concreteness ratings for 40 thousand generally known English word lemmas. Behav. Res. Methods 46, 904–911 (2014).
Arfé, B., Delatorre, P. & Mason, L. Effects of negative emotional valence on readers’ text processing and memory for text: an eye-tracking study. Read. Writ. 36, 1743–1768 (2022).
Kuchinke, L. et al. Incidental effects of emotional valence in single word processing: an fMRI study. NeuroImage 28, 1022–1032 (2005).
Binder, J. R., Westbury, C. F., McKiernan, K. A., Possing, E. T. & Medler, D. A. Distinct brain systems for processing concrete and abstract concepts. J. Cogn. Neurosci. 17, 905–917 (2005).
Ferstl, E. C. & von Cramon, D. Y. Time, space and emotion: fMRI reveals content-specific activation during text comprehension. Neurosci. Lett. 427, 159–164 (2007).
Lau, J. H., Clark, A. & Lappin, S. Grammaticality, acceptability, and probability: a probabilistic view of linguistic knowledge. Cogn. Sci. 41, 1202–1241 (2017).
Hu, J., Gauthier, J., Qian, P., Wilcox, E. & Levy, R. P. A systematic assessment of syntactic generalization in neural language models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (eds Jurafsky, D. et al.) 1725–1744 (Association for Computational Linguistics, 2020).
Kauf, C. et al. Event knowledge in large language models: the gap between the impossible and the unlikely. Cogn. Sci. 47, e13386 (2023).
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. Nature 532, 453–458 (2016).
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. Cortex 29, 2396–2411 (2019).
Baron-Cohen, S., Wheelwright, S., Spong, A., Scahill, V. & Lawson, J. Are intuitive physics and intuitive psychology independent? A test with children with Asperger syndrome. J. Dev. Learn. Disord. 5, 47–78 (2001).
Jack, A. I. et al. fMRI reveals reciprocal inhibition between social and physical cognitive domains. NeuroImage 66, 385–401 (2013).
Pallier, C. & Devauchelle, A.-D. Cortical representation of the constituent structure of sentences. Proc. Natl Acad. Sci. USA 108, 2522–2527 (2011).
Diachek, E., Blank, I., Siegelman, M., Affourtit, J. & Fedorenko, E. The domain-general multiple demand (MD) network does not support core aspects of language comprehension: a large-scale fMRI investigation. J. Neurosci. 40, 4536–4550 (2020).
Wehbe, L. et al. Incremental language comprehension difficulty predicts activity in the language network but not the multiple demand network. Cereb. Cortex 31, 4006–4023 (2021).
Mellem, M. S., Jasmin, K. M., Peng, C. & Martin, A. Sentence processing in anterior superior temporal cortex shows a social-emotional bias. Neuropsychologia 89, 217–224 (2016).
Redcay, E., Velnoskey, K. R. & Rowe, M. L. Perceived communicative intent in gesture and language modulates the superior temporal sulcus. Hum. Brain Mapp. 37, 3444–3461 (2016).
Wehbe, L. et al. Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses. PLoS ONE 9, e112575 (2014).
Jain, S. & Huth, A. G. Incorporating context into language encoding models for fMRI. In Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (eds Bengio, S., et al.) 6628–6637 (Curran Associates, Inc., 2018).
Toneva, M., Mitchell, T. M. & Wehbe, L. Combining computational controls with natural text reveals aspects of meaning composition. Nat. Comput. Sci. 2, 745–757 (2022).
Kozachkov, L., Kastanenka, K. V. & Krotov, D. Building transformers from neurons and astrocytes. Proc. Natl Acad. Sci. USA 120, e2219150120 (2023).
Jang, J., Ye, S. & Seo, M. Can large language models truly understand prompts? A case study with negated prompts. In Proc. 1st Transfer Learning for Natural Language Processing Workshop (eds Albalak A. et al.) 52–62 (PMLR, 2023).
Michaelov, J. A. & Bergen, B. K. Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifiers. In Findings of the Association for Computational Linguistics: ACL 2023 (eds Rogers, A. et al.) 14162–14174 (Association for Computational Linguistics, 2023).
Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A. & Konkle, T. What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines? Preprint at bioRxiv https://doi.org/10.1101/2022.03.28.485868 (2023).
DiCarlo, J. J., Zoccolan, D. & Rust, N. C. How does the brain solve visual object recognition? Neuron 73, 415–434 (2012).
Wang, X. & Bi, Y. Idiosyncratic Tower of Babel: individual differences in word-meaning representation increase as word abstractness increases. Psychol. Sci. 32, 1617–1635 (2021).
Cohen, L., Salondy, P., Pallier, C. & Dehaene, S. How does inattention affect written and spoken language processing? Cortex 138, 212–227 (2021).
Gratton, C. & Braga, R. M. Editorial overview: deep imaging of the individual brain: past, practice, and promise. Curr. Opin. Behav. Sci. 40, iii–vi (2021).
Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160, 106–154 (1962).
Tenney, I., Das, D. & Pavlick, E. BERT rediscovers the classical NLP pipeline. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (eds Korhonen, A. et al.) 4593–4601 (Association for Computational Linguistics, 2019).
Li, B. Z., Nye, M. & Andreas, J. Implicit representations of meaning in neural language models. In Proc. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Vol. 1: Long Papers) (eds Zong, C. et al.) 1813–1827 (Association for Computational Linguistics, 2021); https://doi.org/10.18653/v1/2021.acl-long.143
Unger, L. & Fisher, A. V. The emergence of richly organized semantic knowledge from simple statistics: a synthetic review. Dev. Rev. 60, 100949 (2021).
Keller, T. A., Carpenter, P. A. & Just, M. A. The neural bases of sentence comprehension: a fMRI examination of syntactic and lexical processing. Cereb. Cortex 11, 223–237 (2001).
Regev, T. I. et al. Neural populations in the language network differ in the size of their temporal receptive windows. Preprint at bioRxiv https://doi.org/10.1101/2022.12.30.522216 (2023).
Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV). In International Conference on Machine Learning (ICML 2018) (eds Dy, J. & Krause, A.) 2673–2682 (Proceedings of Machine Learning Research, 2018).
Saxe, R. & Kanwisher, N. People thinking about thinking people: the role of the temporo-parietal junction in ‘theory of mind’. NeuroImage 19, 1835–1842 (2003).
Baldassano, C., Hasson, U. & Norman, K. A. Representation of real-world event schemas during narrative perception. J. Neurosci. 38, 9689–9699 (2018).
Deen, B. & Freiwald, W. A. Parallel systems for social and spatial reasoning within the cortical apex. Preprint at bioRxiv https://doi.org/10.1101/2021.09.23.461550 (2022).
Jain, S., Vo, V. A., Wehbe, L. & Huth, A. G. Computational language modeling and the promise of in silico experimentation. Neurobiol. Lang. https://doi.org/10.1162/nol_a_00101 (2023).
Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Wolf, T. et al. Transformers: state-of-the-art natural language processing. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (eds Liu, Q. & Schlangen, D.) 38–45 (Association for Computational Linguistics, 2020); https://doi.org/10.18653/v1/2020.emnlp-demos.6
Oldfield, R. C. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9, 97–113 (1971).
Nieto-Castanon, A. Handbook of Functional Connectivity Magnetic Resonance Imaging Methods in CONN (Hilbert, 2020); https://doi.org/10.56441/hilbertpress.2207.6598
Ashburner, J. & Friston, K. J. Unified segmentation. NeuroImage 26, 839–851 (2005).
Rokem, A. & Kay, K. Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. GigaScience 9, giaa133 (2020).
Vázquez-Rodríguez, B. et al. Gradients of structure–function tethering across neocortex. Proc. Natl Acad. Sci. USA 116, 21219–21227 (2019).
Mahowald, K. & Fedorenko, E. Reliable individual-level neural markers of high-level language processing: a necessary precursor for relating neural variability to behavioral and genetic variability. NeuroImage 139, 74–93 (2016).
Hale, J. A probabilistic Earley parser as a psycholinguistic model. In 2nd Meeting of the North American Chapter of the Association for Computational Linguistics (Association for Computational Linguistics, 2001).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means. R package version 1.8.4-1 (2023).
Friston, K., Ashburner, J., Kiebel, S., Nichols, T. & Penny, W. Statistical Parametric Mapping: The Analysis of Functional Brain Images (Elsevier, 2006).
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 9, 179–194 (1999).