ArticleNeurobiology of language (Cambridge, Mass.)2024
Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training.
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 24 papers.
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24 citing papers in PubMed.
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- Article
- Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds.Nature communications · 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
- A Novel Approach to Map the Causal Impact of Brain Stimulation on Semantic Processing With Language Models.Neurobiology of language (Cambridge, Mass.) · 2026Article
- Do Language Models Know Who Did What to Whom?Open mind : discoveries in cognitive science · 2026Article
- Left-Right Asymmetry in Predicting Brain Activity From LLMs' Representations Emerges With Their Formal Linguistic Competence.Neurobiology of language (Cambridge, Mass.) · 2026Article
- Large Language Models as Distributional Baselines for Language Tasks.Open mind : discoveries in cognitive science · 2026Article
- Cognitive dynamics of verb-particle constructions: an eye-tracking study on phrasal verbs and verb-preposition combinations.Frontiers in psychology · 2026Article
- Temporal structure of natural language processing in the human brain corresponds to layered hierarchy of large language models.Nature communications · 2025Article
- Multilingual Computational Models Capture a Shared Meaning Component in Brain Responses across 21 Languages.bioRxiv : the preprint server for biology · 2025Article
- Parallel hierarchical encoding of linguistic representations in the human auditory cortex and recurrent automatic speech recognition systems.bioRxiv : the preprint server for biology · 2025Article
- Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation.Neuroinformatics · 2025Article
- Brain-model neural similarity reveals abstractive summarization performance.Scientific reports · 2025Article
- Can Large Language Models Generate Useful Linguistic Corpora?: A Case Study of the Word Frequency Effect in Young German Readers.Open mind : discoveries in cognitive science · 2025Article
- Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies.Findings of ACL. ACL · 2024Article
- Dissociating language and thought in large language models.Trends in cognitive sciences · 2024Review
- Large Language Models and the Wisdom of Small Crowds.Open mind : discoveries in cognitive science · 2024Article
- Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an Agent Preference Principle.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Family lexicon: Using language models to encode memories of personally familiar and famous people and places in the brain.PloS one · 2024Article
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Abstract
Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models' ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity-a measure of next-word prediction performance-is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although
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