ArticleNeurobiology of language (Cambridge, Mass.)2024
Computational Language Modeling and the Promise of In Silico Experimentation.
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 23 papers.
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Who cites it
23 citing papers in PubMed.
- Review
- Hierarchical learning creates invariant schema within plastic neural networks.Journal of computational neuroscience · 2026Article
- Rapid cortical mapping with cross-participant encoding models.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Temporal integration in human auditory cortex is predominantly yoked to absolute time.Nature neuroscience · 2025Article
- Hierarchical dynamic coding coordinates speech comprehension in the human brain.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- In silico discovery of representational relationships across visual cortex.Nature human behaviour · 2025Article
- The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension.Scientific data · 2025Article
- Low-Rank Tensor Encoding Models Decompose Natural Speech Comprehension Processes.bioRxiv : the preprint server for biology · 2025Article
- A two-dimensional space of linguistic representations shared across individuals.bioRxiv : the preprint server for biology · 2025Article
- Hierarchical dynamic coding coordinates speech comprehension in the human brain.bioRxiv : the preprint server for biology · 2025Article
- Challenges and Methods in Annotating Natural Speech for Neurolinguistic Research.Neurobiology of language (Cambridge, Mass.) · 2025Article
- Can large language models help augment English psycholinguistic datasets?Behavior research methods · 2024Article
- Studying and improving reasoning in humans and machines.Communications psychology · 2024Article
- Dissociating language and thought in large language models.Trends in cognitive sciences · 2024Review
- Predicting the next sentence (not word) in large language models: What model-brain alignment tells us about discourse comprehension.Science advances · 2024Article
- Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain.Social cognitive and affective neuroscience · 2024Review
- Large Language Models and the Wisdom of Small Crowds.Open mind : discoveries in cognitive science · 2024Article
- Word Frequency and Predictability Dissociate in Naturalistic Reading.Open mind : discoveries in cognitive science · 2024Article
- Crafting Interpretable Embeddings for Language Neuroscience by Asking LLMs Questions.Advances in neural information processing systems · 2024Article
- Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows.Advances in neural information processing systems · 2023Article
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Abstract
Language neuroscience currently relies on two major experimental paradigms: controlled experiments using carefully hand-designed stimuli, and natural stimulus experiments. These approaches have complementary advantages which allow them to address distinct aspects of the neurobiology of language, but each approach also comes with drawbacks. Here we discuss a third paradigm-in silico experimentation using deep learning-based encoding models-that has been enabled by recent advances in cognitive computational neuroscience. This paradigm promises to combine the interpretability of controlled experiments with the generalizability and broad scope of natural stimulus experiments. We show four examples of simulating language neuroscience experiments in silico and then discuss both the advantages and caveats of this approach.
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