ArticleNature human behaviour2023
Evidence of a predictive coding hierarchy in the human brain listening to speech.
Article in Nature human behaviour, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers.
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Who cites it
119 citing papers in PubMed.
- Linguistics and human brain: a perspective of computational neuroscience.Cognitive neurodynamics · 2026Review
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- Developmental changes in similarity between neural representations of mental arithmetic and artificial neural networks.iScience · 2026Article
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Deep learning reveals a neurocomputational mechanism predicting depression risk in adolescents.Science advances · 2026Article
- Behavioral engagement and stimulus regularities coordinate auditory-prefrontal network activity.PLoS biology · 2026Article
- Perceiving the uncertain: predictive coding and individual differences in the dynamics of multistable perception.Cognitive processing · 2026Review
- The human language processing system straightens natural speech.bioRxiv : the preprint server for biology · 2026Article
- The primate claustrum as a hub for precision weighting of prediction errors across the cortical hierarchy.PLoS biology · 2026Article
- Competing speech streams are simultaneously represented in the human cortex during attention switching.PLoS biology · 2026Article
- Brain states recur across diverse narrative contexts during longitudinal viewing.bioRxiv : the preprint server for biology · 2026Article
- Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?The European journal of neuroscience · 2026Review
- Constituent-constrained word prediction during language comprehension.Nature neuroscience · 2026Article
- The effects of spoiler types on audience immersion: an inter-subject correlation study of heart rate.Cognitive research: principles and implications · 2026Article
- Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds.Nature communications · 2026Article
- Article
- Sender-receiver subdivisions of the default mode network in perceptual and memory-guided cognition.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Munich Sentence (MuSe) Database: Completion norms and audio recordings for 619 German sentences.Behavior research methods · 2026Article
- Article
- Insights into effortful adaptation during speech perception: The role of dopamine, norepinephrine, and prediction error.Cognitive, affective & behavioral neuroscience · 2026Review
59 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Considerable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, translate and classify texts. Yet, these language models still fail to match the language abilities of humans. Predictive coding theory offers a tentative explanation to this discrepancy: while language models are optimized to predict nearby words, the human brain would continuously predict a hierarchy of representations that spans multiple timescales. To test this hypothesis, we analysed the functional magnetic resonance imaging brain signals of 304 participants listening to short stories. First, we confirmed that the activations of modern language models linearly map onto the brain responses to speech. Second, we showed that enhancing these algorithms with predictions that span multiple timescales improves this brain mapping. Finally, we showed that these predictions are organized hierarchically: frontoparietal cortices predict higher-level, longer-range and more contextual representations than temporal cortices. Overall, these results strengthen the role of hierarchical predictive coding in language processing and illustrate how the synergy between neuroscience and artificial intelligence can unravel the computational bases of human cognition.
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