The process of meaning composition, wherein smaller units like morphemes or words combine to form the meaning of phrases and sentences, is essential for human sentence comprehension. Despite extensive neurolinguistic research into the brain regions involved in meaning composition, a computational metric to quantify the extent of composition is still lacking. Drawing on the key-value memory interpretation of transformer feed-forward network blocks, we introduce the Composition Score, a novel model-based metric designed to quantify the degree of meaning composition during sentence comprehension. Experimental findings show that this metric correlates with brain clusters associated with word frequency, structural processing, and general sensitivity to words, suggesting the multifaceted nature of meaning composition during human sentence comprehension.

翻译：意义组合过程（即语素或单词等较小单元组合形成短语和句子意义的过程）对人类句子理解至关重要。尽管神经语言学已对参与意义组合的脑区进行了广泛研究，但仍缺乏量化组合程度的计算指标。基于Transformer前馈网络模块的键值记忆解释，我们提出了组合分数这一新型模型指标，旨在量化句子理解过程中的意义组合程度。实验结果表明，该指标与涉及词频处理、结构加工及词汇普遍敏感性的脑区集群存在相关性，揭示了人类句子理解过程中意义组合的多维特性。