ArticleNeurobiology of language (Cambridge, Mass.)2024
Predictive Coding or Just Feature Discovery? An Alternative Account of Why Language Models Fit Brain Data.
Article in Neurobiology of language (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
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- The human language processing system straightens natural speech.bioRxiv : the preprint server for biology · 2026Article
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- Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds.Nature communications · 2026Article
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- Multilingual Computational Models Capture a Shared Meaning Component in Brain Responses across 21 Languages.bioRxiv : the preprint server for biology · 2025Article
- SIGNAL: Dataset for Semantic and Inferred Grammar Neurological Analysis of Language.Scientific data · 2025Article
- Progress, challenges and future of linguistic neural decoding with deep learning.Communications biology · 2025Review
- Evidence for compositionality in fMRI visual representations via Brain Algebra.Communications biology · 2025Article
- Evaluating scientific theories as predictive models in language neuroscience.bioRxiv : the preprint server for biology · 2025Article
- Generative language reconstruction from brain recordings.Communications biology · 2025Article
- Incremental accumulation of linguistic context in artificial and biological neural networks.Nature communications · 2025Article
- Beyond Letters: Optimal Transport as a Model for Sub-Letter Orthographic Processing.Neurobiology of language (Cambridge, Mass.) · 2025Article
- A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations.Neuron · 2024Article
- Language models outperform cloze predictability in a cognitive model of reading.PLoS computational biology · 2024Article
- Shared functional specialization in transformer-based language models and the human brain.Nature communications · 2024Article
- Lexical-Semantic Content, Not Syntactic Structure, Is the Main Contributor to ANN-Brain Similarity of fMRI Responses in the Language Network.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Computational Language Modeling and the Promise of In Silico Experimentation.Neurobiology of language (Cambridge, Mass.) · 2024Article
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Abstract
Many recent studies have shown that representations drawn from neural network language models are extremely effective at predicting brain responses to natural language. But why do these models work so well? One proposed explanation is that language models and brains are similar because they have the same objective: to predict upcoming words before they are perceived. This explanation is attractive because it lends support to the popular theory of predictive coding. We provide several analyses that cast doubt on this claim. First, we show that the ability to predict future words does not uniquely (or even best) explain why some representations are a better match to the brain than others. Second, we show that within a language model, representations that are best at predicting future words are strictly worse brain models than other representations. Finally, we argue in favor of an alternative explanation for the success of language models in neuroscience: These models are effective at predicting brain responses because they generally capture a wide variety of linguistic phenomena.
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