  In computational psycholinguistics, various language models have been
evaluated against human reading behavior (e.g., eye movement) to build
human-like computational models. However, most previous efforts have focused
almost exclusively on English, despite the recent trend towards linguistic
universal within the general community. In order to fill the gap, this paper
investigates whether the established results in computational psycholinguistics
can be generalized across languages. Specifically, we re-examine an established
generalization -- the lower perplexity a language model has, the more
human-like the language model is -- in Japanese with typologically different
structures from English. Our experiments demonstrate that this established
generalization exhibits a surprising lack of universality; namely, lower
perplexity is not always human-like. Moreover, this discrepancy between English
and Japanese is further explored from the perspective of (non-)uniform
information density. Overall, our results suggest that a cross-lingual
evaluation will be necessary to construct human-like computational models.
