ArticleNature communications2025
Incremental accumulation of linguistic context in artificial and biological neural networks.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Social communication and brain network properties: cognitive predictors and modulation by autistic traits.Cognitive neurodynamics · 2026Article
- Competing speech streams are simultaneously represented in the human cortex during attention switching.PLoS biology · 2026Article
- Event segmentation applications in large language model enabled automated recall assessments.Communications psychology · 2025Article
- Predicting whole-brain neural dynamics from prefrontal cortex functional near-infrared spectroscopy signal during movie-watching.Social cognitive and affective neuroscience · 2025Article
- Uncovering a Timescale Hierarchy by Studying the Brain in a Natural Context.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Generative language reconstruction from brain recordings.Communications biology · 2025Article
- Why large language models cannot possess consciousness: an integrated information theory perspective.Journal of Yeungnam medical science · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we show how the brain, unlike LLMs that process large text windows in parallel, integrates short-term and long-term contextual information through an incremental mechanism. Using fMRI data from 219 participants listening to spoken narratives, we first demonstrate that LLMs predict brain activity effectively only when using short contextual windows of up to a few dozen words. Next, we introduce an alternative LLM-based incremental-context model that combines incoming short-term context with an aggregated, dynamically updated summary of prior context. This model significantly enhances the prediction of neural activity in higher-order regions involved in long-timescale processing. Our findings reveal how the brain's hierarchical temporal processing mechanisms enable the flexible integration of information over time, providing valuable insights for both cognitive neuroscience and AI development.
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Registered trials
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