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Localizing syntactic predictions using recurrent neural network grammars
Open accessAbstract
Brain activity in numerous perisylvian brain regions is modulated by the expectedness of linguistic stimuli. We leverage recent advances in computational parsing models to test what representations guide the processes reflected by this activity. Recurrent Neural Network Grammars (RNNGs) are generative models of (tree, string) pairs that use neural networks to drive derivational choices. Parsing with them yields a variety of incremental complexity metrics that we evaluate against a publicly available fMRI data-set recorded while participants simply listen to an audiobook story. Surprisal, which captures a word's un-expectedness, correlates with a wide range of temporal and frontal regions when it is calculated based on word-sequence information using a top-performing LSTM neural network language model. The explicit encoding of hierarchy afforded by the RNNG additionally captures activity in left posterior temporal areas. A separate metric tracking the number of derivational steps taken between words correlates with activity in the left temporal lobe and inferior frontal gyrus. This pattern of results narrows down the kinds of linguistic representations at play during predictive processing across the brain's language network.
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“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 [112]
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Metadata
- Authors
- Brennan, J. R., Dyer, C., Kuncoro, A., & Hale, J. T.
- Affiliations
- University of Michigan (US)
- Google DeepMind (United Kingdom) (GB)
- University of Oxford (GB)
- University of Georgia (US)
- Corresponding author
- Brennan, Jonathan R.
- ORCID
- Brennan, Jonathan R.
- Venue
- Neuropsychologia
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 146
- Pages
- 107479
- ISSN
- 0028-3932 (ISSN-L)
- OpenAlex
- W3024404575
- Cited by
- 58
- References
- 72
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 12 · 2023: 14 · 2022: 4 · 2021: 2 · 2020: 3
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuropsychologia (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Keywords
- syntactic analysis, predictive processing, language comprehension, functional MRI, inferior frontal gyrus, derivational complexity, linguistic representation
- MeSH terms
- Brain Mapping, Humans, Language, Linguistics, Magnetic Resonance Imaging, Temporal Lobe, Neural Networks, Computer
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