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：意义组合是人类句子理解的核心过程，其中语素或单词等较小单元通过组合形成短语和句子的意义。尽管神经语言学对参与意义组合的大脑区域进行了广泛研究，但目前仍缺乏一种计算指标来量化组合的程度。基于transformer前馈网络块的键值记忆解释，我们引入了组合分数（Composition Score），这是一种基于模型的新型指标，旨在量化句子理解过程中意义组合的程度。实验结果显示，该指标与词汇频率、结构处理以及单词一般敏感性相关的大脑簇存在相关性，这表明人类句子理解过程中的意义组合具有多面性。