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.

通过大量的神经语言学研究，我们引入了一个新的基于模型的度量标准，即组合度分数，用于量化句子理解过程中的意义组合程度。实验结果表明，这个度量标准与与词频、结构处理和对词语的一般敏感性相关联，提示了人类句子理解过程中意义组合的多方面性。