ArticleNeurobiology of language (Cambridge, Mass.)2024
Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects.
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 29 papers.
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29 citing papers in PubMed.
- An event-related potentials account of brain predictive coding.Cognitive neurodynamics · 2026Review
- Linguistics and human brain: a perspective of computational neuroscience.Cognitive neurodynamics · 2026Review
- Not that cloze: Sentence constraint is influenced by language and language dominance.Behavior research methods · 2026Article
- Review
- Altered semantic prediction error processing with increasing schizotypal and autistic traits.Translational psychiatry · 2026Article
- Ultraslow brain dynamics as neurophysiological markers of information sampling during learning.iScience · 2026Article
- Neural tracking of surprisal and semantic distance in naturalistic movie viewing.bioRxiv : the preprint server for biology · 2026Article
- Surprisal from Large Language Models as an individual difference: Capturing the individual regardless of the text answer.Behavior research methods · 2026Article
- Cognitive Processes of Probabilistic Prediction in Reading: Language Model Surprisal Across Model Sizes, Token Granularity, and Reading Paradigms.Journal of Intelligence · 2026Article
- Quantifying the Influence of Lexical Surprisal on Acoustic Speech Encoding While Controlling for Within-Speaker Variability.The European journal of neuroscience · 2026Article
- Modulation of Predictive Coding in Auditory Paradigms of Varying Complexity in Children With Developmental Language Disorder.The European journal of neuroscience · 2026Article
- Large Language Models as Distributional Baselines for Language Tasks.Open mind : discoveries in cognitive science · 2026Article
- Cognitive processing of semantic mismatch in Chinese reading: the moderating effect of contextual constraint.Frontiers in psychology · 2026Article
- Euphemisms modulate the neural processing of norm violations.Frontiers in human neuroscience · 2026Article
- Broadening the Agent Preference Hypothesis Through Experiencers: Eye-Tracking and EEG Evidence of Proto-Agents and Proto-Patients.Open mind : discoveries in cognitive science · 2026Article
- Evaluating the compatibility of predictive coding as a model of linguistic predictability effects in reading.Frontiers in cognition · 2026Review
- The neurobiology of human sequential signal prediction: Insights from language, music, and mathematics.Neuroscience research · 2026Review
- Sentence processing by humans and machines: Large language models as a tool to better understand human reading.Psychonomic bulletin & review · 2025Review
- SIGNAL: Dataset for Semantic and Inferred Grammar Neurological Analysis of Language.Scientific data · 2025Article
- Turing Jest: Distributional Semantics and One-Line Jokes.Cognitive science · 2025Article
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5 authors.
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Abstract
Theoretical accounts of the N400 are divided as to whether the amplitude of the N400 response to a stimulus reflects the extent to which the stimulus was predicted, the extent to which the stimulus is semantically similar to its preceding context, or both. We use state-of-the-art machine learning tools to investigate which of these three accounts is best supported by the evidence. GPT-3, a neural language model trained to compute the conditional probability of any word based on the words that precede it, was used to operationalize contextual predictability. In particular, we used an information-theoretic construct known as surprisal (the negative logarithm of the conditional probability). Contextual semantic similarity was operationalized by using two high-quality co-occurrence-derived vector-based meaning representations for words: GloVe and fastText. The cosine between the vector representation of the sentence frame and final word was used to derive contextual cosine similarity estimates. A series of regression models were constructed, where these variables, along with cloze probability and plausibility ratings, were used to predict single trial N400 amplitudes recorded from healthy adults as they read sentences whose final word varied in its predictability, plausibility, and semantic relationship to the likeliest sentence completion. Statistical model comparison indicated GPT-3 surprisal provided the best account of N400 amplitude and suggested that apparently disparate N400 effects of expectancy, plausibility, and contextual semantic similarity can be reduced to variation in the predictability of words. The results are argued to support predictive coding in the human language network.
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