ArticleScientific reports2022
Deep language algorithms predict semantic comprehension from brain activity.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 88 citations in OpenAlex.
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- Towards decoding individual words from non-invasive brain recordings.Nature communications · 2025Article
- Hybrid spatial organization and magnitude-independent coding of linguistic information during sentence production.bioRxiv : the preprint server for biology · 2025Article
- Task-relevant representational spaces in human memory traces.PLoS computational biology · 2025Article
- Reading comprehension in L1 and L2 readers: neurocomputational mechanisms revealed through large language models.NPJ science of learning · 2025Article
- Incremental accumulation of linguistic context in artificial and biological neural networks.Nature communications · 2025Article
- Brain-model neural similarity reveals abstractive summarization performance.Scientific reports · 2025Article
- Making sense of transformer success.Frontiers in artificial intelligence · 2025Article
- Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.PLoS computational biology · 2024Article
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- Shared functional specialization in transformer-based language models and the human brain.Nature communications · 2024Article
- Brain-wide representation of social knowledge.Social cognitive and affective neuroscience · 2024Article
- Predicting the next sentence (not word) in large language models: What model-brain alignment tells us about discourse comprehension.Science advances · 2024Article
- The language network as a natural kind within the broader landscape of the human brain.Nature reviews. Neuroscience · 2024Review
- Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep language algorithms, like GPT-2, have demonstrated remarkable abilities to process text, and now constitute the backbone of automatic translation, summarization and dialogue. However, whether these models encode information that relates to human comprehension still remains controversial. Here, we show that the representations of GPT-2 not only map onto the brain responses to spoken stories, but they also predict the extent to which subjects understand the corresponding narratives. To this end, we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories. We then fit a linear mapping model to predict brain activity from GPT-2's activations. Finally, we show that this mapping reliably correlates ([Formula: see text]) with subjects' comprehension scores as assessed for each story. This effect peaks in the angular, medial temporal and supra-marginal gyri, and is best accounted for by the long-distance dependencies generated in the deep layers of GPT-2. Overall, this study shows how deep language models help clarify the brain computations underlying language comprehension.
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