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

Abstract

Zipf (1935) observed that word length is inversely proportional to word frequency in the lexicon. He hypothesised that this universal feature was due to the Principle of Least Effort: language-users align form-meaning mappings so that the lexicon is optimally coded for efficient information transfer. However, word frequency is not the only reliable predictor of word length: Piantadosi et al. (2011) show that a word’s predictability in context is more strongly correlated with word length than frequency. Here, we present an artificial language study investigating the mechanisms that could give rise to this distribution. We find that participants are more likely to use an ambiguous short form in predictive contexts, and distinct long forms in surprising contexts, only when they are subject to the competing pressures to communicate accurately and efficiently. These results support the hypothesis that language-users are driven by a least-effort principle to align word length with information content.


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