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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
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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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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