2026-04-21 ニューヨーク大学（NYU）
In predicting others’ words, we take into account a larger linguistic structure, focusing on a word’s surroundings within groups of words rather than only which word comes next. This is similar to how we look at surrounding pieces of a puzzle in deciding where to place the next piece. Photo credit: Virojt Changyencham/Getty Images
＜関連情報＞
- https://www.nyu.edu/about/news-publications/news/2026/april/does-the-brain-work-like-an-llm-in-predicting-words–new-study-s.html
- https://www.nature.com/articles/s41593-026-02272-6
言語理解における構成要素制約付き単語予測 Constituent-constrained word prediction during language comprehension
Jiajie Zou (邹家杰),David Poeppel & Nai Ding (丁鼐)
Nature Neuroscience Published:21 April 2026
DOI:https://doi.org/10.1038/s41593-026-02272-6
Abstract
Next-word prediction has been hypothesized as the central computational objective of the human language system, akin to that of current large language models. Here we put this conjecture to the test, investigating whether the brain predicts each upcoming word as precisely as possible when listening to connected speech. In three magnetoencephalography experiments with Mandarin Chinese speakers, we demonstrate that the response related to word unpredictability, that is, word surprisal calculated using large language models, is significantly stronger for words within an ongoing constituent than words across a major constituent boundary, and this effect is further modulated by the certainty of a constituent boundary. This constituent-boundary effect is also observed behaviorally unless speech is very slowly presented, and it is confirmed by analyzing a dataset of electrocorticography responses to natural English narratives. The constituent-boundary effect demonstrates that the language system does not solely optimize word-prediction precision; rather, it balances word-prediction contributions by constituent-constrained management of linguistic contextual representations.
