ArticleCommunications biology2022
Brains and algorithms partially converge in natural language processing.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 112 papers, 1 of them a synthesis that pooled it.
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Who cites it
112 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Challenges and Opportunities for Professional Medical Publications Writers to Contribute to Plain Language Summaries (PLS) in an AI/ML Environment - A Consumer Health Informatics Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Pooled it
- Linguistics and human brain: a perspective of computational neuroscience.Cognitive neurodynamics · 2026Review
- The neuroscience of transformers.Cognitive neurodynamics · 2026Article
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- Functional identification of language-responsive sensors in individual participants in MEG investigations.Journal of neurophysiology · 2026Article
- The human language processing system straightens natural speech.bioRxiv : the preprint server for biology · 2026Article
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- Shared representations in brains and models reveal a two-route cortical organization during scene perception.Communications biology · 2026Article
- Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model.Linguistics vanguard : multimodal online journal · 2026Article
- Representations in vision and language converge in a shared, multidimensional space of perceived similarities.Journal of vision · 2026Article
- How AI can advance psychological science.British journal of psychology (London, England : 1953) · 2026Article
- Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds.Nature communications · 2026Article
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- Decoding concept representations in aphasia after stroke.bioRxiv : the preprint server for biology · 2026Article
- Large Language Models Reveal the Neural Tracking of Linguistic Context in Attended and Unattended Multi-Talker Speech.bioRxiv : the preprint server for biology · 2026Article
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- Aligning brains into a shared space improves their alignment with large language models.Nature computational science · 2026Article
- Article
- Temporal Context Reinstatement Drives Episodic-Like Order Memory in Long-Context Language Models.Proceedings of machine learning research · 2026Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, we systematically compare a variety of deep language models to identify the computational principles that lead them to generate brain-like representations of sentences. Specifically, we analyze the brain responses to 400 isolated sentences in a large cohort of 102 subjects, each recorded for two hours with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). We then test where and when each of these algorithms maps onto the brain responses. Finally, we estimate how the architecture, training, and performance of these models independently account for the generation of brain-like representations. Our analyses reveal two main findings. First, the similarity between the algorithms and the brain primarily depends on their ability to predict words from context. Second, this similarity reveals the rise and maintenance of perceptual, lexical, and compositional representations within each cortical region. Overall, this study shows that modern language algorithms partially converge towards brain-like solutions, and thus delineates a promising path to unravel the foundations of natural language processing.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.