  Despite differing from the human language processing mechanism in
implementation and algorithms, current language models demonstrate remarkable
human-like or surpassing language capabilities. Should computational language
models be employed in studying the brain, and if so, when and how? To delve
into this topic, this paper reviews efforts in using computational models for
brain research, highlighting emerging trends. To ensure a fair comparison, the
paper evaluates various computational models using consistent metrics on the
same dataset. Our analysis reveals that no single model outperforms others on
all datasets, underscoring the need for rich testing datasets and rigid
experimental control to draw robust conclusions in studies involving
computational models.
