  The world's languages exhibit certain so-called typological or implicational
universals; for example, Subject-Object-Verb (SOV) languages typically use
postpositions. Explaining the source of such biases is a key goal of
linguistics. We study word-order universals through a computational simulation
with language models (LMs). Our experiments show that typologically-typical
word orders tend to have lower perplexity estimated by LMs with cognitively
plausible biases: syntactic biases, specific parsing strategies, and memory
limitations. This suggests that the interplay of cognitive biases and
predictability (perplexity) can explain many aspects of word-order universals.
It also showcases the advantage of cognitively-motivated LMs, typically
employed in cognitive modeling, in the simulation of language universals.
