Drift as a driver of language change: An artificial language experiment

Abstract

Over half a century ago, George Zipf observed that less frequent words tend to have entered the language more recently. Since then, corpus studies have accumulated evidence that rare words have higher rates of replacement and regularisation. Two main hypotheses have been proposed to explain this pattern: (a) less frequent words change more because deterministic selection against innovation is weaker for such words, and (b) less frequent words change more because stochastic drift is stronger in smaller populations. Here, we report an experimental test of these hypotheses. Participants were tasked with learning a miniature language consisting of two nouns and two plural markers. Nouns occurred at different frequencies and were subjected to treatments that varied drift and selection. Using a model that accounts for participant heterogeneity, we measured the rate of noun regularisation and the strength of selection and drift in participant responses. Our results indicate that the elevated rate of regularisation we observed in the low-frequency nouns was attributable to drift alone. These results add to a growing body of evidence that drift may be a major driver of language change.