ArticleNature communications2025
Modeling rapid language learning by distilling Bayesian priors into artificial neural networks.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations.Cognitive science · 2026Article
- Modeling rapid language learning by distilling Bayesian priors into artificial neural networks.Nature communications · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Humans can learn languages from remarkably little experience. Developing computational models that explain this ability has been a major challenge in cognitive science. Existing approaches have been successful at explaining how humans generalize rapidly in controlled settings but are usually too restrictive to tractably handle naturalistic data. We show that learning from limited naturalistic data is possible with an approach that bridges the divide between two popular modeling traditions: Bayesian models and neural networks. This approach distills a Bayesian model's inductive biases-the factors that guide generalization-into a neural network that has flexible representations. Like a Bayesian model, the resulting system can learn formal linguistic patterns from limited data. Like a neural network, it can also learn aspects of English syntax from naturally-occurring sentences. Thus, this model provides a single system that can learn rapidly and can handle naturalistic data.
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Registered trials
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