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Prediction During Natural Language Comprehension
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The notion of prediction is studied in cognitive neuroscience with increasing intensity. We investigated the neural basis of 2 distinct aspects of word prediction, derived from information theory, during story comprehension. We assessed the effect of entropy of next-word probability distributions as well as surprisal A computational model determined entropy and surprisal for each word in 3 literary stories. Twenty-four healthy participants listened to the same 3 stories while their brain activation was measured using fMRI. Reversed speech fragments were presented as a control condition. Brain areas sensitive to entropy were left ventral premotor cortex, left middle frontal gyrus, right inferior frontal gyrus, left inferior parietal lobule, and left supplementary motor area. Areas sensitive to surprisal were left inferior temporal sulcus ("visual word form area"), bilateral superior temporal gyrus, right amygdala, bilateral anterior temporal poles, and right inferior frontal sulcus. We conclude that prediction during language comprehension can occur at several levels of processing, including at the level of word form. Our study exemplifies the power of combining computational linguistics with cognitive neuroscience, and additionally underlines the feasibility of studying continuous spoken language materials with fMRI.
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“While word-yoked gamma power was explained using computational models that estimated forward-entropy and word-surprisal, (similar to Willems et al., 2016), grammatically motivated predictions derived from a phrase-structure parser provided the best fit to gamma fluctuations.”
From Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension (2018) · cites this paper as Willems et al., 2016
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Cites
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
Brain magnetic resonance imaging with contrast dependent on blood oxygenation.
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.24.9868 · Cited by 6,483
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578
SRILM - an extensible language modeling toolkit
Stolcke, A. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-303 · Cited by 4,385
Kutas, M., & Federmeier, K. D. (2011) · Annual Review of Psychology · doi:10.1146/annurev.psych.093008.131123 · Cited by 4,377
Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp055 · Cited by 4,060
A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data
Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.034 · Cited by 4,033
Expectation-based syntactic comprehension
Lévy, R. (2007) · Cognition · doi:10.1016/j.cognition.2007.05.006 · Cited by 2,345
Region of interest analysis using an SPM toolbox
Brett, M. (2002) · NeuroImage · Cited by 2,273
Flagged first, then by citations
Research works printed but not linked: 1
- Hale JT . 2001. A probabilistic early parser as a psycholinguistic model. In: Proceedings of the Second Meeting of the North American Chapter of the Association for Computational Linguistics. Pittsburgh, PA: Association for Computational Linguistics. p. 159–166.
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Items 101–200 of 288
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Reddy, A. J., & Wehbe, L. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.16.155499 · Cited by 26
What’s in the brain that ink may character ….
Jacobs, A. M., Schuster, S., Xue, S., & Lüdtke, J. (2017) · Scientific Study of Literature · doi:10.1075/ssol.7.1.02jac · Cited by 28
Russo, A. G., Martino, M. D., Mancuso, A., İaconetta, G., Manara, R., Elia, A., Laudanna, A., Salle, F. D., & Esposito, F. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117281 · Cited by 23
Söderström, P., Horne, M. A., Mannfolk, P., van Westen, D., & Roll, M. (2017) · Brain and Language · doi:10.1016/j.bandl.2017.08.004 · Cited by 23
Kapteijns, B., & Hintz, F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254546 · Cited by 22
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Schubert, J., Schmidt, F., Gehmacher, Q., Bresgen, A., & Weisz, N. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhac528 · Cited by 22
Gastaldon, S., Busan, P., Arcara, G., & Peressotti, F. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad004 · Cited by 23
Szewczyk, J., & Wodniecka, Z. (2020) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/xlm0000835 · Cited by 22
Goldstein, A., Ham, E., Nastase, S. A., Zada, Z., Grinstein-Dabush, A., Aubrey, B., Schain, M., Gazula, H., Feder, A., Doyle, W., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.11.499562 · Cited by 22
Li, X., & Qu, Q. (2023) · Psychonomic Bulletin & Review · doi:10.3758/s13423-023-02348-5 · Cited by 26
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Schuster, S., Himmelstoß, N. A., Hutzler, F., Richlan, F., Kronbichler, M., & Hawelka, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117687 · Cited by 22
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Brennan, J. R., Lajiness-O'Neill, R., Bowyer, S., Kovelman, I., & Hale, J. T. (2019) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2018.1560483 · Cited by 19
Wu, W., Morales, M., Patel, T., Pickering, M. J., & Hoffman, P. (2022) · Cortex · doi:10.1016/j.cortex.2022.08.002 · Cited by 22
Zhang, T., Zhou, S., Bai, X., Zhou, F., Zhai, Y., Long, Y., & Lu, C. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120400 · Cited by 19
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Cong, Y. (2024) · Computer Speech & Language · doi:10.1016/j.csl.2024.101700 · Cited by 22
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Heilbron, M., Ehinger, B., Peter Hagoort, A., & de Lange, F. P. (2019) · MPG.PuRe (Max Planck Society) · Cited by 19
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Tuckute, G., Sathe, A., Srikant, S., Taliaferro, M., Wang, M., Schrimpf, M., Kay, K., & Fedorenko, E. G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.16.537080 · Cited by 20
Benetti, S., Ferrari, A., & Pavani, F. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1108354 · Cited by 22
Strijkers, K., Chanoine, V., Munding, D., Dubarry, A., Trébuchon, A., Badier, J., & Alario, F. ‐. (2019) · Scientific Reports · doi:10.1038/s41598-019-41376-x · Cited by 22
Toward Computational Models of Multilingual Sentence Processing
Frank, S. L. (2020) · Language Learning · doi:10.1111/lang.12406 · Cited by 20
Self-citationHuber, E., Sauppe, S., Isasi-Isasmendi, A., Bornkessel‐Schlesewsky, I., Merlo, P., & Bickel, B. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00121 · Cited by 21
Goldstein, A., Ham, E., Schain, M., Nastase, S. A., Aubrey, B., Zada, Z., Grinstein-Dabush, A., Gazula, H., Feder, A., Doyle, W., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65518-0 · Cited by 58
Speaker Recognition through Deep Learning Techniques
Shome, N., Sarkar, A., Ghosh, A. K., Laskar, R. H., & Kashyap, R. (2023) · Periodica Polytechnica Electrical Engineering and Computer Science · doi:10.3311/ppee.20971 · Cited by 17
Decoding the Real-Time Neurobiological Properties of Incremental Semantic Interpretation
Choi, H., Marslen‐Wilson, W. D., Lyu, B., Randall, B., & TYLER, L. K. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa222 · Cited by 16
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Momenian, M., Vaghefi, M., Sadeghi, H., Momtazi, S., & Meyer, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-57426-y · Cited by 15
Event-related responses reflect chunk boundaries in natural speech
Anurova, I., Vetchinnikova, S., Dobrego, A., Williams, N., Mikušová, N., Suni, A., Mauranen, A., & Palva, S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119203 · Cited by 17
Su, Y., MacGregor, L. J., Olasagasti, I., & Giraud, A. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002046 · Cited by 17
Semantic integration by pattern priming: experiment and cortical network model
Lavigne, F., Longrée, D., Mayaffre, D., & Mellet, S. (2016) · Cognitive Neurodynamics · doi:10.1007/s11571-016-9410-4 · Cited by 16
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Yu, L., Dugan, P. C., Doyle, W., Devinsky, O., Friedman, D. J., & Flinker, A. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115677 · Cited by 15
Chen, A. C. (2019) · Language Learning · doi:10.1111/lang.12340 · Cited by 14
Lau, E. F., & Namyst, A. M. (2019) · Brain and Language · doi:10.1016/j.bandl.2019.104697 · Cited by 15
Russo, A. G., Ciarlo, A., Ponticorvo, S., Salle, F. D., Tedeschi, G., & Esposito, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-21782-4 · Cited by 16
Matar, S., Liina Pylkkänen, A., & Marantz, A. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107230 · Cited by 14
Cassani, G., Bianchi, F., Attanasio, G., Marelli, M., & Günther, F. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/b45ys · Cited by 19
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Lopopolo, A., & Rabovsky, M. (2024) · Neurobiology of Language · doi:10.1162/nol_a_00134 · Cited by 14
He, Y., Shao, X., Liu, C., Fan, C., Jefferies, E., Zhang, M., & Li, X. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1404-24.2024 · Cited by 13
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Zhang, X., Li, J., Li, Z., Hong, B., Diao, T., Ma, X., Nolte, G., Engel, A. K., & Zhang, D. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120404 · Cited by 11
Beier, E. J., Chantavarin, S., Rehrig, G., Ferreira, F., & Miller, L. M. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01676 · Cited by 11
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Lopukhina, A., Александрович, Л. К., & Laurinavichyute, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0246133 · Cited by 8
Klimovich‐Gray, A., Barrena, A., Agirre, E., & Molinaro, N. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab071 · Cited by 11
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Schönmann, I., Szewczyk, J., de Lange, F. P., & Heilbron, M. (2025) · eLife · doi:10.7554/elife.106543 · Cited by 16
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Kherif, F., & Muller, S. (2020) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026620666200302111130 · Cited by 8
Skipper, J. I., & Hasson, U. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/139550 · Cited by 8
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Cuevas, P., He, Y., Steines, M., & Straube, B. (2022) · Schizophrenia Bulletin Open · doi:10.1093/schizbullopen/sgac026 · Cited by 7
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Szalárdy, O., Tóth, B., Farkas, D., Orosz, G., Honbolygó, F., & Winkler, I. (2020) · Psychophysiology · doi:10.1111/psyp.13547 · Cited by 7
Huebner, P. A., & Willits, J. A. (2017) · doi:10.31234/osf.io/qhwv5 · Cited by 7
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Shain, C., & Schuler, W. (2019) · doi:10.31234/osf.io/whvk5 · Cited by 6
The unique contribution of uncertainty reduction during naturalistic language comprehension
Ming, S., Wang, J., & Cai, Q. (2024) · Cortex · doi:10.1016/j.cortex.2024.09.007 · Cited by 6
Liedtke, N., Boeltzig, M., Mecklenbrauck, F., Siestrup, S., & Schubotz, R. I. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121194 · Cited by 6
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Bentum, M., ten Bosch, L., van den Bosch, A. P. J., & Ernestus, M. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2741 · Cited by 6
Günther, T., Kirschenkern, A., Mayer, A., Steinke, F., & Cholewa, J. (2023) · Journal of Speech Language and Hearing Research · doi:10.1044/2023_jslhr-23-00100 · Cited by 6
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Willems, R. M., & van Gerven, M. A. J. (2018) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198786825.013.42 · Cited by 5
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Hörberg, T., & Jaeger, T. F. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.674202 · Cited by 4
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Olson, H. A., Johnson, K. T., Nishith, S., Frosch, I. R., Gabrieli, J. D. E., & D’Mello, A. M. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00339 · Cited by 4
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Matar, S., & Marantz, A. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.0781-24.2024 · Cited by 3
Osorio, S., & Assaneo, M. F. (2025) · PLoS ONE · doi:10.1371/journal.pone.0320519 · Cited by 5
Zhou, F., Zhou, S., Long, Y., Flinker, A., & Lu, C. (2025) · Communications Biology · doi:10.1038/s42003-025-09377-x · Cited by 5
Söderström, P. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009161114 · Cited by 3
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Russo, A. G., Martino, M. D., Elia, A., Salle, F. D., & Esposito, F. (2022) · Cortex · doi:10.1016/j.cortex.2022.07.005 · Cited by 3
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Weber, K., Micheli, C., Ruigendijk, E., & Rieger, J. W. (2019) · Brain and Behavior · doi:10.1002/brb3.1308 · Cited by 3
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Metadata
- Authors
- Willems, R. M., Frank, S. L., Nijhof, A. D., Hagoort, P., & van den Bosch, A.
- Affiliations
- Max Planck Institute for Psycholinguistics (NL)
- Radboud University Nijmegen (NL)
- Donders Institute for Brain, Cognition and Behaviour (NL)
- Corresponding author
- Willems, Roel M.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-04-22
- Volume
- 26
- Issue
- 6
- Pages
- 2506-2516
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W1969221307
- Cited by
- 288
- References
- 94
- Citations by year
- 2026: 17 · 2025: 39 · 2024: 29 · 2023: 38 · 2022: 23 · 2021: 35 · 2020: 27 · 2019: 28 · 2018: 17 · 2017: 17 · 2016: 13 · 2015: 3
- FWCI
- 10.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cerebral Cortex (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- the European Union Seventh Framework Programme (334028)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Keywords
- word prediction, natural language understanding, entropy (information theory), information content, narrative comprehension, ventral premotor cortex, inferior frontal gyrus, visual word form area, amygdala, computational models of language, spoken language processing
- MeSH terms
- Acoustic Stimulation, Adolescent, Adult, Brain Mapping, Computer Simulation, Female, Humans, Language, Magnetic Resonance Imaging, Male, Models, Neurological, Models, Psychological, Neuropsychological Tests, Speech Perception, Comprehension, Young Adult, Anticipation, Psychological
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