ArticleProceedings of the National Academy of Sciences of the United States of America2022
One model for the learning of language.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Selectivity for high-level language processing is highly localized in individual brains.bioRxiv : the preprint server for biology · 2026Article
- Children use algorithm induction to discover patterns in data.Nature communications · 2026Article
- Do Humans Use Push-Down Stacks When Learning or Producing Center-Embedded Sequences?Cognitive science · 2025Article
- Origins of numbers: a shared language-of-thought for arithmetic and geometry?Trends in cognitive sciences · 2025Review
- Modeling rapid language learning by distilling Bayesian priors into artificial neural networks.Nature communications · 2025Article
- Successive-cyclic movement in humans and neural language models: testing wh-filler-gap dependencies.Frontiers in psychology · 2025Article
- Building machines that learn and think with people.Nature human behaviour · 2024Review
- Symbolic metaprogram search improves learning efficiency and explains rule learning in humans.Nature communications · 2024Article
- Languages with more speakers tend to be harder to (machine-)learn.Scientific reports · 2023Article
- Multiplicative processing in the modeling of cognitive activities in large neural networks.Biophysical reviews · 2023Review
- Symbols and grounding in large language models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Cognitive Mechanisms Underlying Recursive Pattern Processing in Human Adults.Cognitive science · 2023Article
- Origins of Hierarchical Logical Reasoning.Cognitive science · 2023Article
- Learning for early childhood using the IcanDO platform: Breakthroughs for golden age education in Arabic learning.Education and information technologies · 2023Article
- Reply to Kodner et al.: Fundamental misunderstanding of both model and methods.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Another model not for the learning of language.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Reply to Murphy and Leivada: Program induction can learn language.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- A model for learning strings is not a model of language.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
Corrections and comments
- Commented on by
- Commented on by
- Commented on by
Authors and funding
2 authors.
Funding
Abstract
A major goal of linguistics and cognitive science is to understand what class of learning systems can acquire natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire many of the key structures present in natural language from positive evidence alone. We demonstrate this by providing the same learning model with data from 74 distinct formal languages which have been argued to capture key features of language, have been studied in experimental work, or come from an interesting complexity class. The model is able to successfully induce the latent system generating the observed strings from small amounts of evidence in almost all cases, including for regular (e.g.,
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.