Papers

Preprints

Ergin, I., Kries, J., Gupta, S., & Gwilliams, L. (2024). Measuring Naturalistic Speech Comprehension in Real Time. https://doi.org/10.31234/osf.io/93a75 [pdf]

Gwilliams, L., Marantz, A., Poeppel, D., & King, J. R. (2024). Hierarchical dynamic coding coordinates speech comprehension in the brain. bioRxiv. https://doi.org/10.1101/2024.04.19.590280 [pdf]

Kries, J., De Clercq, P., Vandermosten, M., & Gwilliams, L. (2024). The spatio-temporal dynamics of phoneme encoding in aging and aphasia. bioRxiv, 2024-10. https://doi.org/10.1101/2024.10.21.619562 [pdf]

Journal Publications

Abrams, E. B., Marantz, A., Krementsov, I., & Gwilliams, L. (2025). Dynamics of pitch perception in the auditory cortex. Journal of Neuroscience, e1111242025. https://doi.org/10.1523/JNEUROSCI.1111-24.2025 [pdf]

Gwilliams, L., Bhaya-Grossman, I., Zhang, Y., Scott, T., Harper, S., & Levy, D. (2025). Computational Architecture of Speech Comprehension in the Human Brain. Annual Review of Linguistics, 11. https://doi.org/10.1146/annurev-linguistics-031120-111245 [pdf]

Reilly, J., Shain, C., Borghesani, V., Kuhnke, P., Vigliocco, G., Peelle, J. E., ... & Vinson, D. (2024). What we mean when we say semantic: Toward a multidisciplinary semantic glossary. Psychonomic bulletin & review, 1-38.https://doi.org/10.3758/s13423-024-02556-7 [pdf]

Degano, G., Donhauser, P. W., Gwilliams, L., Merlo, P., & Golestani, N. (2024). Speech prosody enhances the neural processing of syntax. Communications Biology, 7(1), 1-10. https://doi.org/10.1038/s42003-024-06444-7 [pdf]

Zuanazzi, A., Ripollés, P., Lin, W. M., Gwilliams, L., King, J. R., & Poeppel, D. (2024). Negation mitigates rather than inverts the neural representations of adjectives. Plos Biology, 22(5), e3002622.https://doi.org/10.1371/journal.pbio.3002622 [pdf]

*Gwilliams, L., *Leonard, M. K., Sellers, K. K., Chung, J. E., Xu, D., Mischler, G., ... & Chang, E. F. (2024). Large-scale single-neuron speech sound encoding across the depth of human cortex. Nature, 626(7999), 593-602.https://doi.org/10.1038/s41586-023-06839-2 [pdf]

Gwilliams, L., Flick, G., Marantz, A., Pylkkänen, L., Poeppel, D., & King, J. R. (2023). Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing. Scientific data, 10(1), 862. https://doi.org/10.1038/s41597-023-02752-5 [pdf]

Gwilliams, L., Marantz, A., Poeppel, D., & King, J. R. (2023). Top-down information shapes lexical processing when listening to continuous speech. Language, Cognition and Neuroscience, 39(8), 1045-1058. https://doi.org/10.1080/23273798.2023.2171072 [pdf]

*Chung, J. E., *Sellers, K. K., Leonard, M. K., Gwilliams, L., Xu, D., Dougherty, M. E., ... & Chang, E. F. (2022). High-density single-unit human cortical recordings using the Neuropixels probe. Neuron, 110(15), 2409-2421. https://doi.org/10.1016/j.neuron.2022.05.007 [pdf]

Gwilliams, L., King, J. R., Marantz, A., & Poeppel, D. (2022). Neural dynamics of phoneme sequences reveal position-invariant code for content and order. Nature communications, 13(1), 6606. https://doi.org/10.1038/s41467-022-34326-1 [pdf]

Iemi, L., Gwilliams, L., Samaha, J., Auksztulewicz, R., Cycowicz, Y. M., King, J. R., ... & Haegens, S. (2022). Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability. NeuroImage, 247, 118746. https://doi.org/10.1016/j.neuroimage.2021.118746 [pdf]

*Gwilliams, L., *Blanco-Elorrieta, E., Marantz, A., & Pylkkänen, L. (2021). Adaptation to mis-pronounced speech: evidence for a prefrontal-cortex repair mechanism. Scientific reports, 11(1), 97. https://doi.org/10.1038/s41598-020-79640-0 [pdf]

Gwilliams, L., & King, J. R. (2020). Recurrent processes support a cascade of hierarchical decisions. Elife, 9, e56603. https://doi.org/10.7554/eLife.56603 [pdf]

Dikker, S., Assaneo, M. F., Gwilliams, L., Wang, L., & Kösem, A. (2020). Magnetoencephalography and language. Neuroimaging Clinics, 30(2), 229-238. https://doi.org/10.1016/j.nic.2020.01.004

Gwilliams, L. (2020). Hierarchical oscillators in speech comprehension: A commentary on Meyer, Sun, and Martin (2019). Language, Cognition and Neuroscience, 35(9), 1114-1118. https://doi.org/10.1080/23273798.2020.1740749 [pdf]

Gwilliams, L. (2020). How the brain composes morphemes into meaning. Philosophical Transactions of the Royal Society B, 375(1791), 20190311. https://doi.org/10.1098/rstb.2019.0311 [pdf]

Stockall, L., Manouilidou, C., Gwilliams, L., Neophytou, K., & Marantz, A. (2019). Prefix stripping re-re-revisited: MEG investigations of morphological decomposition and recomposition. Frontiers in Psychology, 10, 1964. https://doi.org/10.3389/fpsyg.2019.01964 [pdf]

Gwilliams, L., Poeppel, D., Marantz, A., & Linzen, T. (2018). Phonological (un) certainty weights lexical activation. In 8th Workshop on Cognitive Modeling and Computational Linguistics, CMCL 2018 (pp. 29-34). Association for Computational Linguistics (ACL). [pdf]

Gwilliams, L., Linzen, T., Poeppel, D., & Marantz, A. (2018). In spoken word recognition, the future predicts the past. Journal of Neuroscience, 38(35), 7585-7599. https://doi.org/10.1523/JNEUROSCI.0065-18.2018 [pdf]

Gwilliams, L., & Marantz, A. (2018). Morphological representations are extrapolated from morpho-syntactic rules. Neuropsychologia, 114, 77-87. https://doi.org/10.1016/j.neuropsychologia.2018.04.015 [pdf]

Brodbeck, C., Gwilliams, L., & Pylkkänen, L. (2016). Language in context: MEG evidence for modality-general and-specific responses to reference resolution. ENeuro, 3(6). https://doi.org/10.1523/ENEURO.0145-16.2016 [pdf]

Gwilliams, L., Lewis, G. A., & Marantz, A. (2016). Functional characterisation of letter-specific responses in time, space and current polarity using magnetoencephalography. Neuroimage, 132, 320-333. https://doi.org/10.1016/j.neuroimage.2016.02.057 [pdf]

Brodbeck, C., Gwilliams, L., & Pylkkänen, L. (2015). EEG can track the time course of successful reference resolution in small visual worlds. Frontiers in psychology, 6, 1787. https://doi.org/10.3389/fpsyg.2015.01787 [pdf]

Gwilliams, L., & Marantz, A. (2015). Non-linear processing of a linear speech stream: The influence of morphological structure on the recognition of spoken Arabic words. Brain and language, 147, 1-13. https://doi.org/10.1016/j.bandl.2015.04.006 [pdf]

Gwilliams, L. E., Monahan, P. J., & Samuel, A. G. (2015). Sensitivity to morphological composition in spoken word recognition: Evidence from grammatical and lexical identification tasks. Journal of experimental psychology: Learning, memory, and cognition, 41(6), 1663. https://doi.org/10.1037/xlm0000130 [pdf]

Gwilliams, L., & Fontaine, L. (2015). Indeterminacy in process type classification. Functional Linguistics, 2, 1-19. https://doi.org/10.1186/s40554-015-0021-x [pdf]

Politzer-Ahles, S., & Gwilliams, L. (2015). Involvement of prefrontal cortex in scalar implicatures: evidence from magnetoencephalography. Language, Cognition and Neuroscience, 30(7), 853-866. https://doi.org/10.1080/23273798.2015.1027235 [pdf]

Book Chapters

Gwilliams, L., & Marantz, A. (2023). Neural Processing of Morphological Structure in Speech Production, Listening, and Reading. In Linguistic Morphology in the Mind and Brain (pp. 137-151). Routledge.

Stockall, L. & Gwilliams, L. (2023). Distributed morphology and neurolinguistics. In The Cambridge Handbook of Distributed Morphology.

Dikker, S., Mech, E. N., Gwilliams, L., West, T., Dumas, G., & Federmeier, K. D. (2022). Exploring age-related changes in inter-brain synchrony during verbal communication. In Psychology of Learning and Motivation (Vol. 77, pp. 29-68). Academic Press. https://doi.org/10.1016/bs.plm.2022.08.003

Gwilliams, L., Davis, M.H. (2022). Extracting Language Content from Speech Sounds: The Information Theoretic Approach. In: Holt, L.L., Peelle, J.E., Coffin, A.B., Popper, A.N., Fay, R.R. (eds) Speech Perception. Springer Handbook of Auditory Research, vol 74. Springer, Cham. https://doi.org/10.1007/978-3-030-81542-4_5

King, JR., Gwilliams, L., Holdgraf, C., Sassenhagen, J., Barachant, A., Engemann, D., Larson, E. & Gramfort, A. (2020). Encoding and Decoding Framework to Uncover the Algorithms of Cognition. In The Cognitive Neurosciences.