[1] Liberman A M, Cooper F S, Shankweiler D P, et al. Perception of the speech code[J]. Psychological review, 1967, 74(6): 431.
[2] Wang S, Zhang J, Zong C. Learning multimodal word representation via dynamic fusion methods[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2018: 5973-5980.
[3] Wang S, Zhang J, Zong C. Learning sentence representation with guidance of human attention[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017: 4137-4143.
[4] Wang S, Zhang J, Zong C. Associative multichannel autoencoder for multimodal word representation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2018: 115-124.
[5] Sun J, Wang S, Zhang J, et al. Distill and replay for continual language learning[C]//Proceedings of the 28th International Conference on Computational Linguistics. 2020: 3569-3579.
[6] Zhang J, Zhou L, Zhao Y, et al. Synchronous bidirectional inference for neural sequence generation[J]. Artificial Intelligence, 2020, 281: 103234.
[7] Wang S, Zhang J, Wang H, et al. Fine-grained neural decoding with distributed word representations[J]. Information Sciences, 2020, 507: 256-272.
[8] Sun J, Wang S, Zhang J, et al. Towards sentence-level brain decoding with distributed representations[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33(01): 7047-7054.
[9] Sun J, Wang S, Zhang J, et al. Neural encoding and decoding with distributed sentence representations[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(2): 589-603.
[10] Arnon I, Snider N. More than words: Frequency effects for multi-word phrases[J]. Journal of Memory and Language, 2010, 62(1): 67-82.
[11] Ding N, Melloni L, Zhang H, et al. Cortical tracking of hierarchical linguistic structures in connected speech[J]. Nature Neuroscience, 2016, 19(1): 158-164.
[12] Fernandino L, Binder J R, Desai R H, et al. Concept representation reflects multimodal abstraction: A framework for embodied semantics[J]. Cerebral Cortex, 2016, 26(5): 2018-2034.
[13] Binder J R, Conant L L, Humphries C J, et al. Toward a brain-based componential semantic representation[J]. Cognitive Neuropsychology, 2016, 33(3-4): 130-174.
[14] Mitchell T M, Shinkareva S V, Carlson A, et al. Predicting human brain activity associated with the meanings of nouns[J]. Science, 2008, 320(5880): 1191-1195.
[15] Huth A G, De Heer W A, Griffiths T L, et al. Natural speech reveals the semantic maps that tile human cerebral cortex[J]. Nature, 2016, 532(7600): 453-458.
[16] Wang X, Wu W, Ling Z, et al. Organizational principles of abstract words in the human brain[J]. Cerebral Cortex, 2018, 28(12): 4305-4318.
[17] Wehbe L, Murphy B, Talukdar P, et al. Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses[J]. PloS One, 2014, 9(11): e112575.
[18] Keitel A, Gross J, Kayser C. Perceptually relevant speech tracking in auditory and motor cortex reflects distinct linguistic features[J]. PLoS Biology, 2018, 16(3): e2004473.
[19] Ding N, Melloni L, Yang A, et al. Characterizing neural entrainment to hierarchical linguistic units using electroencephalography (EEG)[J]. Frontiers in Human Neuroscience, 2017, 11: 481.
[20] Meyer L, Sun Y, Martin A E. “Entraining” to speech, generating language?[J]. Language, Cognition and Neuroscience, 2020, 35(9): 1138-1148.
[21] Kazanina N, Bowers J S, Idsardi W. Phonemes: Lexical access and beyond[J]. Psychonomic Bulletin and Review, 2018, 25(2): 560-585.
[22] Anderson A J, Kiela D, Binder J R, et al. Deep artificial neural networks reveal a distributed cortical network encoding propositional sentence-level meaning[J]. Journal of Neuroscience, 2021, 41(18): 4100-4119.
[23] Kumar A A. Semantic memory: A review of methods, models, and current challenges[J]. Psychonomic Bulletin and Review, 2021, 28(1): 40-80.
[24] Fedorenko E, Behr M K, Kanwisher N. Functional specificity for high-level linguistic processing in the human brain[J]. Proceedings of the National Academy of Sciences, 2011, 108(39): 16428-16433.
[25] Fedorenko E, Duncan J, Kanwisher N. Broad domain generality in focal regions of frontal and parietal cortex[C]//Proceedings of the National Academy of Sciences, 2013, 110(41): 16616-16621.
[26] Fedorenko E, Duncan J, Kanwisher N. Language-selective and domain-general regions lie side by side within Broca’s area[J]. Current Biology, 2012, 22(21): 2059-2062.
[27] Frankland S M, Greene J D. An architecture for encoding sentence meaning in left mid-superior temporal cortex[C]//Proceedings of the National Academy of Sciences, 2015, 112(37): 11732-11737.
[28] Blank I, Balewski Z, Mahowald K, et al. Syntactic processing is distributed across the language system[J]. NeuroImage, 2016, 127: 307-323.
[29] Thompson C K, Bonakdarpour B, Fix S C, et al. Neural correlates of verb argument structure processing[J]. Journal of Cognitive Neuroscience, 2007, 19(11): 1753-1767.
[30] Yu X, Law S P, Han Z, et al. Dissociative neural correlates of semantic processing of nouns and verbs in Chinese—A language with minimal inflectional morphology[J]. NeuroImage, 2011, 58(3): 912-922.
[31] Chen E, West W C, Waters G, et al. Determinants of BOLD signal correlates of processing object-extracted relative clauses[J]. Cortex, 2006, 42(4): 591-604.
[32] Dapretto M, Bookheimer S Y. Form and content: dissociating syntax and semantics in sentence comprehension[J]. Neuron, 1999, 24(2): 427-432.
[33] Fedorenko E, Nieto-Castanon A, Kanwisher N. Lexical and syntactic representations in the brain: an fMRI investigation with multi-voxel pattern analyses[J]. Neuropsychologia, 2012, 50(4): 499-513.
[34] Humphries C, Binder J R, Medler D A, et al. Syntactic and semantic modulation of neural activity during auditory sentence comprehension[J]. Journal of Cognitive Neuroscience, 2006, 18(4): 665-679.
[35] Reddy A J, Wehbe L. Syntactic representations in the human brain: beyond effort-based metrics[J]. BioRxiv, 2021: 2020.06. 16.155499.
[36] Wang S, Zhang J, Lin N, et al. Probing brain activation patterns by dissociating semantics and syntax in sentences[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(05): 9201-9208.
[37] Toneva M, Mitchell T M, Wehbe L. Combining computational controls with natural text reveals new aspects of meaning composition[J]. BioRxiv, 2020.
[38] Zhang Y, Han K, Worth R, et al. Connecting concepts in the brain by mapping cortical representations of semantic relations[J]. Nature Communications, 2020, 11(1): 1-13.
[39] Anderson A J, Lalor E C, Lin F, et al. Multiple regions of a cortical network commonly encode the meaning of words in multiple grammatical positions of read sentences[J]. Cerebral Cortex, 2019, 29(6): 2396-2411.
[40] Anderson A J, Kiela D, Clark S, et al. Visually grounded and textual semantic models differentially decode brain activity associated with concrete and abstract nouns[J]. Transactions of the Association for Computational Linguistics, 2017, 5: 17-30.
[41] Mollica F, Siegelman M, Diachek E, et al. Composition is the core driver of the language-selective network[J]. Neurobiology of Language, 2020, 1(1): 104-134.
[42] Asyraff A, Lemarchand R, Tamm A, et al. Stimulus-independent neural coding of event semantics: Evidence from cross-sentence fMRI decoding[J]. NeuroImage, 2021, 236: 118073.
[43] Henderson J M, Choi W, Lowder M W, et al. Language structure in the brain: A fixation-related fMRI study of syntactic surprisal in reading[J]. NeuroImage, 2016, 132: 293-300.
[44] Lopopolo A, Frank S L, Van den Bosch A, et al. Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain[J]. PloS One, 2017, 12(5): e0177794.
[45] Pylkknen L. Neural basis of basic composition: what we have learned from the red-boat studies and their extensions[J]. Philosophical Transactions of the Royal Society B, 2020, 375(1791): 20190299.
[46] Bemis D K, Pylkknen L. Simple composition: A magnetoencephalography investigation into the comprehension of minimal linguistic phrases[J]. Journal of Neuroscience, 2011, 31(8): 2801-2814.
[47] Ding N, Pan X, Luo C, et al. Attention is required for knowledge-based sequential grouping: insights from the integration of syllables into words[J]. Journal of Neuroscience, 2018, 38(5): 1178-1188.
[48] Matchin W, Brodbeck C, Hammerly C, et al. The temporal dynamics of structure and content in sentence comprehension: Evidence from fMRI‐constrained MEG[J]. Human Brain Mapping, 2019, 40(2): 663-678.
[49] Nelson M J, El Karoui I, Giber K, et al. Neurophysiological dynamics of phrase-structure building during sentence processing[C]//Proceedings of the National Academy of Sciences, 2017, 114(18): E3669-E3678.
[50] Fedorenko E, Scott T L, Brunner P, et al. Neural correlate of the construction of sentence meaning[C]//Proceedings of the National Academy of Sciences, 2016, 113(41): E6256-E6262.
[51] Hauk O, Davis M H, Ford M, et al. The time course of visual word recognition as revealed by linear regression analysis of ERP data[J]. NeuroImage, 2006, 30(4): 1383-1400.
[52] Kamide Y, Altmann G T M, Haywood S L. The time-course of prediction in incremental sentence processing: Evidence from anticipatory eye movements[J]. Journal of Memory and language, 2003, 49(1): 133-156.
[53] Rastle K, Davis M H, Marslen-Wilson W D, et al. Morphological and semantic effects in visual word recognition: A time-course study[J]. Language and Cognitive Processes, 2000, 15(4-5): 507-537.
[54] Marslen-Wilson W, Tyler L K. The temporal structure of spoken language understanding[J]. Cognition, 1980, 8(1): 1-71.
[55] Chen Y, Jin P, Ding N. The influence of linguistic information on cortical tracking of words[J]. Neuropsychologia, 2020, 148: 107640.
[56] Luo C, Ding N. Cortical encoding of acoustic and linguistic rhythms in spoken narratives[J]. Elife, 2020, 9: e60433.
[57] Wang Y, Zhang J, Zou J, et al. Prior knowledge guides speech segregation in human auditory cortex[J]. Cerebral Cortex, 2019, 29(4): 1561-1571.
[58] Law R, Pylkknen L. Lists with and without syntax: A new approach to measuring the neural processing of syntax[J]. Journal of Neuroscience, 2021, 41(10): 2186-2196.
[59] Flick G, Pylkknen L. Isolating syntax in natural language: MEG evidence for an early contribution of left posterior temporal cortex[J]. Cortex, 2020, 127: 42-57.
[60] Pylkknen L. The neural basis of combinatory syntax and semantics[J]. Science, 2019, 366(6461): 62-66.
[61] Lyu B, Choi H S, Marslen-Wilson W D, et al. Neural dynamics of semantic composition[C]//Proceedings of the National Academy of Sciences, 2019, 116(42): 21318-21327.
[62] Broderick M P, Anderson A J, Di Liberto G M, et al. Electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech[J]. Current Biology, 2018, 28(5): 803-809. e3.
[63] Teng X, Ma M, Yang J, et al. Constrained structure of ancient Chinese poetry facilitates speech content grouping[J]. Current Biology, 2020, 30(7): 1299-1305. e7.
[64] Li J, Pylkknen L. Disentangling semantic composition and semantic association in the left temporal lobe[J]. Journal of Neuroscience, 2021, 41(30): 6526-6538.
[65] Fyshe A, Sudre G, Wehbe L, et al. The lexical semantics of adjective-noun phrases in the human brain[J]. Human Brain Mapping, 2019, 40(15): 4457-4469.
[66] Wehbe L, Vaswani A, Knight K, et al. Aligning context-based statistical models of language with brain activity during reading[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2014: 233-243.
[67] Jain S, Vo V, Mahto S, et al. Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech[C]// Proceedings of the 34th Conference on Neural Information Processing Systems, 2020: 13738-13749.
[68] Jain S, Huth A. Incorporating context into language encoding models for fMRI[C]//Proceedings of the 32nd Annual Conference on Neural Information Processing Systems, 2018: 6629-6638.
[69] Deniz F, Nunez-Elizalde A O, Huth A G, et al. The representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality[J]. Journal of Neuroscience, 2019, 39(39): 7722-7736.
[70] Brennan J R, Stabler E P, Van Wagenen S E, et al. Abstract linguistic structure correlates with temporal activity during naturalistic comprehension[J]. Brain and Language, 2016, 157: 81-94.
[71] Brennan J, Nir Y, Hasson U, et al. Syntactic structure building in the anterior temporal lobe during natural story listening[J]. Brain and Language, 2012, 120(2): 163-173.
[72] Gauthier J, Levy R. Linking artificial and human neural representations of language[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing.2019: 529-539.
[73] Schrimpf M, Blank I, Tuckute G, et al. The neural architecture of language: Integrative reverse-engineering converges on a model for predictive processing[J]. BioRxiv, 2020.
[74] Willems R M, Frank S L, Nijhof A D, et al. Prediction during natural language comprehension[J]. Cerebral Cortex, 2016, 26(6): 2506-2516.
[75] Hashemzadeh M, Kaufeld G, White M, et al. From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?[J]. ArXiv preprint arXiv:2010.07435, 2020.
[76] Shain C, Blank I A, van Schijndel M, et al. fMRI reveals language-specific predictive coding during naturalistic sentence comprehension[J]. Neuropsychologia, 2020, 138: 107307.
[77] Li J, Wang S, Luh W M, et al. Cortical processing of reference in language revealed by computational models[J]. BioRxiv, 2021: 2020.11. 24.396598.
[78] Hale J, Dyer C, Kuncoro A, et al. Finding syntax in human encephalography with beam search[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018: 2727-2736.
[79] Martin A E, Doumas L A A. A mechanism for the cortical computation of hierarchical linguistic structure[J]. PLoS Biology, 2017, 15(3): e2000663.
[80] Chien H Y S, Honey C J. Constructing and forgetting temporal context in the human cerebral cortex[J]. Neuron, 2020, 106(4): 675-686. e11.
[81] Pereira F, Lou B, Pritchett B, et al. Toward a universal decoder of linguistic meaning from brain activation[J]. Nature Communications, 2018, 9(1): 1-13.
[82] Brennan J R, Hale J T. Hierarchical structure guides rapid linguistic predictions during naturalistic listening[J]. PloS One, 2019, 14(1): e0207741.
[83] Bhattasali S, Brennan J, Luh W M, et al. The Alice Datasets: fMRI & EEG observations of natural language comprehension[C]//Proceedings of the 12th Language Resources and Evaluation Conference. 2020: 120-125.
[84] Nastase S A, Liu Y F, Hillman H, et al. Narratives: fMRI data for evaluating models of naturalistic language comprehension. preprint[J]. Neuroscience, 2020: 2020.06.
[85] Li J, Bhattasali S, Zhang S, et al. Le Petit Prince: A multilingual fMRI corpus using ecological stimuli[J]. BioRxiv, 2021.
[86] Schoffelen J M, Oostenveld R, Lam N H L, et al. A 204-subject multimodal neuroimaging dataset to study language processing[J]. Scientific Data, 2019, 6(1): 1-13.
[87] Hanke M, Baumgartner F J, Ibe P, et al. A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie[J]. Scientific Data, 2014, 1(1): 1-18.
[88] Ayyash D, Malik-Moraleda S, Gallée J, et al. The universal language network: A cross-linguistic investigation spanning 45 languages and 11 language families[J]. BioRxiv, 2021.
[89] Poeppel D, Embick D. Defining the relation between linguistics and neuroscience[J]. 21st century psycholinguistics: Four cornerstones, 2005, 1: 103-118.