  Most languages use the relative order between words to encode meaning
relations. Languages differ, however, in what orders they use and how these
orders are mapped onto different meanings. We test the hypothesis that, despite
these differences, human languages might constitute different `solutions' to
common pressures of language use. Using Monte Carlo simulations over data from
five languages, we find that their word orders are efficient for processing in
terms of both dependency length and local lexical probability. This suggests
that biases originating in how the brain understands language strongly
constrain how human languages change over generations.
