SOURCE-LINKED INTELLIGENCE
A Data-free Universal Prior over Syntactic Structures
Probability is fundamental to theories of language comprehension, production, acquisition, and evolution, as well as to large language models. Existing theories estimate the probability of syntactic structures from language-specific data. Whether part of this probability structure can arise independently of language-specific experience remains unknown. Here I show that a universal prior over syntactic structures emerges from a cognitively motivated model of incremental language production, in which words are progressively integrated into syntactic structure through network growth. The resultin
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T08:45:27.000Z
First collected: 2026-09-20T09:01:24.920Z. This is not the publication date.