  Neural language models, particularly large-scale ones, have been consistently
proven to be most effective in predicting brain neural activity across a range
of studies. However, previous research overlooked the comparison of these
models with psychologically plausible ones. Moreover, evaluations were reliant
on limited, single-modality, and English cognitive datasets. To address these
questions, we conducted an analysis comparing encoding performance of various
neural language models and psychologically plausible models. Our study utilized
extensive multi-modal cognitive datasets, examining bilingual word and
discourse levels. Surprisingly, our findings revealed that psychologically
plausible models outperformed neural language models across diverse contexts,
encompassing different modalities such as fMRI and eye-tracking, and spanning
languages from English to Chinese. Among psychologically plausible models, the
one incorporating embodied information emerged as particularly exceptional.
This model demonstrated superior performance at both word and discourse levels,
exhibiting robust prediction of brain activation across numerous regions in
both English and Chinese.
