  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.
