Language models as tools for investigating the distinction between possible and impossible natural languages
Abstract
We argue that language models (LMs) have strong potential as investigative tools for probing the distinction between possible and impossible natural languages and thus uncovering the inductive biases that support human language learning. We outline a phased research program in which LM architectures are iteratively refined to better discriminate between possible and impossible languages, supporting linking hypotheses to human cognition.
Fields of Study & Subjects
Text Readability and Simplification
Language and cultural evolution
Embodied and Extended Cognition
Publication details
- Journal / source
- Behavioral and Brain Sciences
- Publisher
- Cambridge University Press (CUP)
- Volume
- 49
- Pages
- e211–e211
- Publication date
- 1 January 2026
- Language
- EN