Language-users choose short words in predictive contexts in an artificial language task

Zipf (1935) observed that word length is inversely proportionalto word frequency in the lexicon. He hypothesised that thiscross-linguistically universal feature was due to the Principleof Least Effort: language-users align form-meaning mappingsin such a way that the lexicon is optimally coded for efficientinformation transfer. However, word frequency is not the onlyreliable predictor of word length: Piantadosi, Tily, and Gib-son (2011) show that a word’s predictability in context is infact more strongly correlated with word length than word fre-quency. Here, we present an artificial language learning studyaimed at investigating the mechanisms that could give rise tosuch a distribution at the level of the lexicon. We find thatparticipants are more likely to use an ambiguous short form inpredictive contexts, and distinct long forms in surprising con-texts, only when they are subject to the competing pressures tocommunicate accurately and efficiently. These results supportthe hypothesis that language-users are driven by a least-effortprinciple to restructure their input in order to align word lengthwith information content, and this mechanism could thereforeexplain the global pattern observed at the level of the lexicon.