Human languages order information efficiently
This research addresses how cognitive biases influence language evolution, providing insights for linguistics and cognitive science, though it is incremental in building on existing theories of language efficiency.
The study tested whether human languages, despite differing word orders, are efficient for processing by analyzing dependency length and local lexical probability across five languages using Monte Carlo simulations, finding that their word orders are optimized for cognitive processing.
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.