  Language models have been shown to be very effective in predicting brain
recordings of subjects experiencing complex language stimuli. For a deeper
understanding of this alignment, it is important to understand the
correspondence between the detailed processing of linguistic information by the
human brain versus language models. We investigate this correspondence via a
direct approach, in which we eliminate information related to specific
linguistic properties in the language model representations and observe how
this intervention affects the alignment with fMRI brain recordings obtained
while participants listened to a story. We investigate a range of linguistic
properties (surface, syntactic, and semantic) and find that the elimination of
each one results in a significant decrease in brain alignment. Specifically, we
find that syntactic properties (i.e. Top Constituents and Tree Depth) have the
largest effect on the trend of brain alignment across model layers. These
findings provide clear evidence for the role of specific linguistic information
in the alignment between brain and language models, and open new avenues for
mapping the joint information processing in both systems. We make the code
publicly available
[https://github.com/subbareddy248/linguistic-properties-brain-alignment].
