A hierarchy of linguistic predictions during natural language comprehension
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Abstract
Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.
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The human language network is sensitive to abstract hierarchical rules across isolated phrases, naturalistic narratives, and syntactically well-formed but semantically empty stimuli.
“it is sensitive to abstract hierarchical rules in isolated phrases and sentences [31, 108, 109, 110], in naturalistic narratives [111, 112, 113, 114], and in syntactically well-formed but semantically empty (”
From Dissociating language and thought in large language models (2024) · cites this paper as [113]
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- Authors
- Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P., & de Lange, F. P.
- Affiliations
- Radboud University Nijmegen (NL)
- Donders Institute, Radboud University, 6525 EN Nijmegen, The Netherlands
- Corresponding author
- Heilbron, Micha
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2022-08-03
- Volume
- 119
- Issue
- 32
- Pages
- e2201968119-e2201968119
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC9371745
- OpenAlex
- W4289638300
- Cited by
- 343
- References
- 80
- Citations by year
- 2026: 70 · 2025: 95 · 2024: 72 · 2023: 78 · 2022: 21 · 2021: 7
- FWCI
- 34.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- European Commission (HORIZON2020)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Vidi‐452‐13‐016, 864.14.011, 024.001.006, 406.17.516, 452-13-016)
- H2020 European Research Council (678286)
- 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 › Developmental and Educational Psychology › Language Development and Disorders
- Keywords
- predictive processing, natural language understanding, spoken language, hierarchical representation, phoneme prediction, semantic prediction, parts of speech, word prediction, GPT-2, audiobook listening
- MeSH terms
- Brain, Humans, Language, Linguistics, Semantics, Speech Perception, Neural Networks, Computer, Comprehension
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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