Modals in Natural Language Optimize the Simplicity/Informativeness Trade-Off

Abstract

The meanings expressed by the world’s languages have been argued to support efficient communication. Evidence for this hypothesis has drawn on cross-linguistic analyses of vocabulary in semantic domains of both content words (e.g. kinship terms as shown in Kemp and Regier [2012]; color terms as in Zaslavsky et al. [2018]) and function words (e.g. quantifiers as in Steinert-Threlkeld [2021]; indefinite pronouns in Denic et al. [2020]) approaching the hypothesis concretely in terms of a trade-off between simplicity and informativeness. I apply the analysis to modals (e.g. can, ought, might). Two proposed universals in this domain from Nauze [2008] and Vander Klok [2013] are used for generating many artificial languages with varying degrees of quasi-naturalness as a proxy for natural data. A computational experiment shows that the Vander Klok universal picks out the optimal solutions to the simplicity/informativeness trade-off, suggesting that efficient communication is a leading explanation for constraints on modal semantic variation.