Abstract
Understanding what someone says requires relating words in a sentence to one another as instructed by the grammatical rules of a language. In recent years, the neurophysiological basis for this process has become a prominent topic of discussion in cognitive neuroscience. Current proposals about the neural mechanisms of syntactic structure building converge on a key role for neural oscillations in this process, but they differ in terms of the exact function that is assigned to them. In this Perspective, we discuss two proposed functions for neural oscillations — chunking and multiscale information integration — and evaluate their merits and limitations taking into account a fundamentally hierarchical nature of syntactic representations in natural languages. We highlight insights that provide a tangible starting point for a neurocognitive model of syntactic structure building.
Similar content being viewed by others
References
Chomsky, N. Syntactic Structures (Mouton, 1957).
Adger, D. Language Unlimited: The Science Behind Our Most Creative Power (Oxford Univ. Press, 2019).
Jackendoff, R. Foundations of Language (Oxford Univ. Press, 2002).
Adger, D. Syntax. WIREs Cogn. Sci. 6, 131–147 (2015).
Crocker, M. W. Computational Psycholinguistics: An Interdisciplinary Approach to the Study of Language (Kluwer Academic, 1996).
Hale, J. T. What a rational parser would do. Cogn. Sci. 35, 399–443 (2011).
Hale, J. T. Automaton Theories of Human Sentence Comprehension (CSLI, 2014).
Ding, N., Melloni, L., Zhang, H., Tian, X. & Poeppel, D. Cortical tracking of hierarchical linguistic structures in connected speech. Nat. Neurosci. 19, 158–164 (2016).
Ghitza, O. Acoustic-driven delta rhythms as prosodic markers. Lang. Cogn. Neurosci. 32, 545–561 (2017).
Kaufeld, G. et al. Linguistic structure and meaning organize neural oscillations into a content-specific hierarchy. J. Neurosci. 40, 9467–9475 (2020).
Keitel, A., Gross, J. & Kayser, C. Perceptually relevant speech tracking in auditory and motor cortex reflects distinct linguistic features. PLoS Biol. 16, e2004473 (2018).
Meyer, L. The neural oscillations of speech processing and language comprehension: state of the art and emerging mechanisms. Eur. J. Neurosci. 48, 2609–2621 (2017).
Meyer, L., Sun, Y. & Martin, A. E. Synchronous, but not entrained: exogenous and endogenous cortical rhythms of speech and language processing. Lang. Cogn. Neurosci. 35, 1089–1099 (2019).
Benítez-Burraco, A. & Murphy, E. Why brain oscillations are improving our understanding of language. Front. Behav. Neurosci. 13, 190 (2019).
Murphy, E. The brain dynamics of linguistic computation. Front. Psychol. 6, 1515 (2015).
Murphy, E. The Oscillatory Nature of Language (Cambridge Univ. Press, 2020).
Calmus, R., Wilson, B., Kikuchi, Y. & Petkov, C. I. Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190304 (2020).
Martin, A. E. & Doumas, L. A. A mechanism for the cortical computation of hierarchical linguistic structure. PLoS Biol. 15, e2000663 (2017).
Martin, A. E. & Doumas, L. A. Predicate learning in neural systems: using oscillations to discover latent structure. Curr. Opin. Behav. Sci. 29, 77–83 (2019).
Boeckx, C. & Theofanopoulou, C. in Language, Syntax, and the Natural Sciences (eds Gallego, A. J. & Martin, R.) 295–315 (Cambridge Univ. Press, 2018).
Giraud, A. L. Oscillations for all ¯\_(ツ)_/¯? A commentary on Meyer, Sun & Martin (2020). Lang. Cogn. Neurosci. 35, 1106–1113 (2020).
Doelling, K. B. & Assaneo, F. M. Neural oscillations are a start toward understanding brain activity rather than the end. PLoS Biol. 19, e3001234 (2021).
Obleser, J., Henry, M. J. & Lakatos, P. What do we talk about when we talk about rhythm? PLoS Biol. 15, e2002794 (2017).
Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I. & Schroeder, C. E. Entrainment of neuronal oscillations as a mechanism of attentional selection. Science 320, 110–113 (2008).
Lakatos, P., Gross, J. & Thut, G. A new unifying account of the roles of neuronal entrainment. Curr. Biol. 29, R890–R905 (2019).
Schroeder, C. E. & Lakatos, P. Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci. 32, 9–18 (2009).
Giraud, A. L. & Poeppel, D. Cortical oscillations and speech processing: emerging computational principles and operations. Nat. Neurosci. 15, 511–517 (2012).
Ghitza, O. Linking speech perception and neurophysiology: speech decoding guided by cascaded oscillators locked to the input rhythm. Front. Psychol. 2, 130 (2011).
Ding, N. et al. Temporal modulations in speech and music. Neurosci. Biobehav. Rev. 81, 181–187 (2017).
Pellegrino, F., Coupé, C. & Marsico, E. Across-language perspective on speech information rate. Language 87, 539–558 (2011).
Norcia, A. M., Appelbaum, L. G. G., Ales, J. M. J. M., Cottereau, B. R. B. R. & Rossion, B. The steady-state visual evoked potential in vision research: a review. J. Vis. 15, 1–46 (2015).
Glushko, A., Poeppel, D. & Steinhauer, K. Overt and implicit prosody contribute to neurophysiological responses previously attributed to grammatical processing. Sci. Rep. 12, 1459 (2022).
Kalenkovich, E., Shestakova, A. & Kazanina, N. Frequency tagging of syntactic structure or lexical properties; a registered MEG study. Cortex 146, 24–38 (2022).
Burroughs, A., Kazanina, N. & Houghton, C. Grammatical category and the neural processing of phrases. Sci. Rep. 11, 2446 (2021).
Makov, S. et al. Sleep disrupts high-level speech parsing despite significant basic auditory processing. J. Neurosci. 37, 7772–7781 (2017).
Ding, N. et al. Characterizing neural entrainment to hierarchical linguistic units using electroencephalography (EEG). Front. Hum. Neurosci. 11, 481 (2017).
Marcus, M. P., Santorini, B. & Marcinkiewicz, M. A. Building a large annotated corpus of English: the Penn Treebank. Comput. Linguist. 19, 313–330 (1993).
Bird, S., Klein, E. & Loper, E. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit (O’Reilly Media, 2009).
Gwilliams, L. Hierarchical oscillators in speech comprehension: a commentary on Meyer, Sun, and Martin (2019). Lang. Cogn. Neurosci. 35, 1114–1118 (2020).
Ghitza, O. & Greenberg, S. On the possible role of brain rhythms in speech perception: intelligibility of time-compressed speech with periodic and aperiodic insertions of silence. Phonetica 66, 113–126 (2009).
Ghitza, O. “Acoustic-driven oscillators as cortical pacemaker”: a commentary on Meyer, Sun & Martin (2019). Lang. Cogn. Neurosci. 35, 1100–1105 (2020).
Honey, C. J. et al. Slow cortical dynamics and the accumulation of information over long timescales. Neuron 76, 423–434 (2012).
Hasson, U., Yang, E., Vallines, I., Heeger, D. J. & Rubin, N. A hierarchy of temporal receptive windows in human cortex. J. Neurosci. 28, 2539–2550 (2008).
Meyer, L., Sun, Y. & Martin, A. E. “Entraining” to speech, generating language? Lang. Cogn. Neurosci. 35, 1138–1148 (2020).
Crocker, M. W. in Perspectives on Sentence Processing (eds Clifton, C., Frazier, L. & Rayner, K.) 245–266 (L. Erlbaum Associates, 1994).
Sturt, P. & Lombardo, V. Processing coordinated structures: incrementality and connectedness. Cogn. Sci. 29, 291–305 (2005).
Sturt, P. & Crocker, M. W. Monotonic syntactic processing: a cross-linguistic study of attachment and reanalysis. Lang. Cogn. Process. 11, 449–494 (1996).
Schroeder, C. E., Wilson, D. A., Radman, T., Scharfman, H. & Lakatos, P. Dynamics of active sensing and perceptual selection. Curr. Opin. Neurobiol. 20, 172–176 (2010).
Morillon, B., Arnal, L. H., Schroeder, C. E. & Keitel, A. Prominence of delta oscillatory rhythms in the motor cortex and their relevance for auditory and speech perception. Neurosci. Biobehav. Rev. 107, 136–142 (2019).
Morillon, B. & Baillet, S. Motor origin of temporal predictions in auditory attention. Proc. Natl Acad. Sci. USA 114, E8913–E8921 (2017).
Zalta, A., Petkoski, S. & Morillon, B. Natural rhythms of periodic temporal attention. Nat. Commun. 11, 1051 (2020).
Wilson, M. & Wilson, T. P. An oscillator model of the timing of turn-taking. Psychon. Bull. Rev. 12, 957–968 (2005).
Scott, S. K., McGettigan, C. & Eisner, F. A little more conversation, a little less action—candidate roles for the motor cortex in speech perception. Nat. Rev. Neurosci. 10, 295–302 (2009).
Keitel, A., Ince, R. A. A., Gross, J. & Kayser, C. Auditory cortical delta-entrainment interacts with oscillatory power in multiple fronto-parietal networks. Neuroimage 147, 32–42 (2017).
Park, H., Ince, R. A. A., Schyns, P. G., Thut, G. & Gross, J. Frontal top-down signals increase coupling of auditory low-frequency oscillations to continuous speech in human listeners. Curr. Biol. 25, 1649–1653 (2015).
Kimball, J. Seven principles of surface structure parsing in natural language. Cognition 2, 15–47 (1973).
Frazier, L. & Clifton Jr, C. Construal (MIT Press, 1996).
Frazier, L. & Fodor, J. D. The sausage machine: a new two-stage parsing model. Cognition 6, 291–325 (1978).
Fodor, J. D. Learning to parse? J. Psycholinguist. Res. 27, 285–319 (1998).
Milner, P. M. A model for visual shape recognition. Psychol. Rev. 81, 521–535 (1974).
von der Malsburg, C. Nervous structures with dynamical links. Ber. Bunsenges. 89, 703–710 (1985).
von der Malsburg, C. The Correlation Theory of Brain Function. Internal report 81–82 (Max Planck Institute for Biophysical Chemistry, 1981).
Gray, C. M. & Singer, W. Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl Acad. Sci. USA 86, 1698–1702 (1989).
Gray, C. M., König, P., Engel, A. K. & Singer, W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989).
Singer, W. Binding by synchrony. Scholarpedia 2, 1657 (2007).
Perez-Orive, J. et al. Oscillations and sparsening of odor representations in the mushroom body. Science 297, 359–365 (2002).
Busch, N. A. & VanRullen, R. Spontaneous EEG oscillations reveal periodic sampling of visual attention. Proc. Natl Acad. Sci. USA 107, 16048–16053 (2010).
Dugué, L., McLelland, D., Lajous, M. & VanRullen, R. Attention searches nonuniformly in space and in time. Proc. Natl Acad. Sci. USA 112, 15214–15219 (2015).
Fries, P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. 9, 474–480 (2005).
Fries, P., Nikolić, D. & Singer, W. The gamma cycle. Trends Neurosci. 30, 309–316 (2007).
O’Keefe, J. & Dostrovsky, J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34, 171–175 (1971).
O’Keefe, J. & Recce, M. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317–330 (1993).
Bose, A. & Recce, M. Phase precession and phase-locking of hippocampal pyramidal cells. Hippocampus 11, 204–215 (2001).
Skaggs, W. E., McNaughton, B. L., Wilson, M. A. & Barnes, C. A. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus 6, 149–172 (1996).
Drieu, C. & Zugaro, M. Hippocampal sequences during exploration: mechanisms and functions. Front. Cell Neurosci. 13, 1–22 (2019).
Gupta, A. S., van der Meer, M. A. A., Touretzky, D. S. & Redish, A. D. Segmentation of spatial experience by hippocampal theta sequences. Nat. Neurosci. 15, 1032–1039 (2012).
Jensen, O. & Lisman, J. E. Hippocampal sequence-encoding driven by a cortical multi-item working memory buffer. Trends Neurosci. 28, 67–72 (2005).
Friederici, A. D. & Singer, W. Grounding language processing on basic neurophysiological principles. Trends Cogn. Sci. 19, 329–338 (2015).
King, C., Recce, M. & O’keefe, J. The rhythmicity of cells of the medial septum/diagonal band of Broca in the awake freely moving rat: relationships with behaviour and hippocampal theta. Eur. J. Neurosci. 10, 464–477 (1998).
Heusser, A. C., Poeppel, D., Ezzyat, Y. & Davachi, L. Episodic sequence memory is supported by a theta-gamma phase code. Nat. Neurosci. 19, 1374–1380 (2016).
Lisman, J. E. & Jensen, O. The theta-gamma neural code. Neuron 77, 1002–1016 (2013).
Boeckx, C. & Benítez-Burraco, A. The shape of the human language-ready brain. Front. Psychol. 5, 1–23 (2014).
Murphy, E. in The Talking Species: Perspectives on the Evolutionary, Neuronal and Cultural Foundations of Language (eds Luef, E. & Manuela, M.) 251–269 (Unipress Graz, 2018).
Doumas, L. A. A., Hummel, J. E. & Sandhofer, C. M. A theory of the discovery and predication of relational concepts. Psychol. Rev. 115, 1–43 (2008).
Hummel, J. E. & Holyoak, K. J. Distributed representations of structure: a theory of analogical access and mapping. Psychol. Rev. 104, 427–466 (1997).
Martin, A. E. A compositional neural architecture for language. J. Cogn. Neurosci. 32, 1407–1427 (2020).
Chomsky, N. Lectures on Government and Binding: The Pisa Lectures (Foris, 1981).
Chomsky, N. Aspects of the Theory of Syntax (MIT Press, 1965).
Joshi, A. K., Levy, L. S. & Takahashi, M. Tree adjunct grammars. J. Comput. Syst. Sci. 10, 136–163 (1975).
Shieber, S. M. An Introduction to Unification-Based Approaches to Grammar (Microtome, 2003).
Chomsky, N. The Minimalist Program (MIT Press, 1995).
Plate, T. A. Holographic reduced representations. IEEE Trans. Neural Netw. 6, 623–641 (1995).
Carpenter, A. F., Baud-Bovy, G., Georgopoulos, A. P. & Pellizzer, G. Encoding of serial order in working memory: neuronal activity in motor, premotor, and prefrontal cortex during a memory scanning task. J. Neurosci. 38, 4912–4933 (2018).
Petrides, M. Functional specialization within the dorsolateral frontal cortex for serial order memory. Proc. R. Soc. Lond. B Biol. Sci. 246, 299–306 (1991).
Long, N. M. & Kahana, M. J. Hippocampal contributions to serial-order memory. Hippocampus 29, 252–259 (2019).
Friederici, A. D., Fiebach, C. J., Schlesewsky, M., Bornkessel, I. D. & von Cramon, D. Y. Processing linguistic complexity and grammaticality in the left frontal cortex. Cereb. Cortex 16, 1709–1717 (2006).
Lisman, J. E. & Idiart, M. A. P. Storage of 7±2 short-term memories in oscillatory subcycles. Science 267, 1512–1515 (1995).
Bader, M. & Lasser, I. in Perspectives on Sentence Processing (eds Clifton, C., Frazier, L. & Reiner, K.) 225–242 (L. Erlbaum Associates, 1994).
Inoue, A. & Fodor, J. D. in Japanese Sentence Processing (eds Mazuka, R & Nagai, N.) 9–63 (L. Erlbaum Associates, 1995).
Mazuka, R. & Itoh, K. In Japanese Sentence Processing (eds Mazuka, R. & Nagai, N.) 295–329 (L. Erlbaum Associates, 1995).
Miyamoto, E. T. Case markers as clause boundary inducers in Japanese. J. Psycholinguist. Res. 31, 307–347 (2002).
Tabor, W., Galantucci, B. & Richardson, D. Effects of merely local syntactic coherence on sentence processing. J. Mem. Lang. 50, 355–370 (2004).
Altmann, G. T. M. & Mirković, J. Incrementality and prediction in human sentence processing. Cogn. Sci. 33, 583–609 (2009).
Bransford, J. D. & Johnson, M. K. Contextual prerequisites for understanding: some investigations of comprehension and recall. J. Verbal Learn. Verbal Behav. 11, 717–726 (1972).
Nelson, M. J. et al. Neurophysiological dynamics of phrase-structure building during sentence processing. Proc. Natl Acad. Sci. USA 114, E3669–E3678 (2017).
Uddén, J., de Jesus Dias Martins, M., Zuidema, W. & Tecumseh Fitch, W. Hierarchical structure in sequence processing: how to measure it and determine its neural implementation. Top. Cogn. Sci. 12, 910–924 (2020).
Carnie, A. Syntax: A Generative Introduction (Blackwell, 2002).
Berger, H. Über das Elektroenkephalogramm des Menschen. Arch. Psychiatr. Nervenkr. 87, 527–570 (1929).
Nunez, P. L. & Srinivasan, R. Electroencephalogram. Scholarpedia 2, 1348 (2007).
Rodin, E. & Funke, M. Cerebral electromagnetic activity in the subdelta range. J. Clin. Neurophysiol. 23, 238–244 (2006).
Buzsaki, G. & Watson, B. O. Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues Clin. Neurosci. 14, 345–367 (2012).
Klimesch, W. The frequency architecture of brain and brain body oscillations: an analysis. Eur. J. Neurosci. 48, 2431–2453 (2018).
Breska, A. & Deouell, L. Y. Neural mechanisms of rhythm-based temporal prediction: delta phase-locking reflects temporal predictability but not rhythmic entrainment. PLoS Biol. 15, e2001665 (2017).
Pikovsky, A., Kurths, J., Rosenblum, M. & Kurths, J. Synchronization: A Universal Concept in Nonlinear Sciences (Cambridge Univ. Press, 2003).
Strogatz, S. H. Nonlinear Dynamics and Chaos with Student Solutions Manual: With Applications to Physics, Biology, Chemistry, and Engineering (CRC, 2018).
Kopell, N., Ermentrout, G. B., Whittington, M. A. & Traub, R. D. Gamma rhythms and beta rhythms have different synchronization properties. Proc. Natl Acad. Sci. USA 97, 1867–1872 (2000).
Buzsáki, G. & Draguhn, A. Neuronal oscillations in cortical networks. Science 304, 1926–1929 (2004).
Herreras, O. Local field potentials: myths and misunderstandings. Front. Neural Circuits 10, 101 (2016).
Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents-EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420 (2012).
Doelling, K. B., Florencia Assaneo, M., Bevilacqua, D., Pesaran, B. & Poeppel, D. An oscillator model better predicts cortical entrainment to music. Proc. Natl Acad. Sci. USA 116, 10113–10121 (2019).
Helfrich, R. F., Breska, A. & Knight, R. T. Neural entrainment and network resonance in support of top-down guided attention. Curr. Opin. Psychol. 29, 82–89 (2019).
Obleser, J., Herrmann, B. & Henry, M. J. Neural oscillations in speech: don’t be enslaved by the envelope. Front. Hum. Neurosci. 6, 2008–2011 (2012).
Doelling, K. B., Arnal, L. H., Ghitza, O. & Poeppel, D. Acoustic landmarks drive delta-theta oscillations to enable speech comprehension by facilitating perceptual parsing. Neuroimage 85, 761–768 (2014).
van Rullen, R. Perceptual cycles. Trends Cogn. Sci. 20, 723–735 (2016).
Shamma, S. A., Elhilali, M. & Micheyl, C. Temporal coherence and attention in auditory scene analysis. Trends Neurosci. 34, 114–123 (2011).
Acknowledgements
The authors are very grateful to E. Lau and M. Yoshida for their vast input and feedback. They also thank J. Mitchell for help with corpus data, J. Bowers for his comments and S. Brendecke for help with illustrations. N.K. acknowledges the support of the International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, Higher School of Economics, Russian Federation (grant 075-15-2022-1037). A.T. acknowledges the support of the Max Planck Society.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Reviews Neuroscience thanks M. F. Assaneo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Related links
Natural Language Toolkit: https://www.nltk.org/
Supplementary information
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kazanina, N., Tavano, A. What neural oscillations can and cannot do for syntactic structure building. Nat Rev Neurosci 24, 113–128 (2023). https://doi.org/10.1038/s41583-022-00659-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41583-022-00659-5
- Springer Nature Limited
This article is cited by
-
When linguistic dogma rejects a neuroscientific hypothesis
Nature Reviews Neuroscience (2023)
-
Periodic fluctuations in reading times reflect multi-word-chunking
Scientific Reports (2023)
-
Reply to ‘What oscillations can do for syntax depends on your theory of structure building’
Nature Reviews Neuroscience (2023)
-
Low-frequency neural parsing of hierarchical linguistic structures
Nature Reviews Neuroscience (2023)
-
Reply to ‘When linguistic dogma rejects a neuroscientific hypothesis’
Nature Reviews Neuroscience (2023)