IEEE Transactions on Evolutionary Computation
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Greater learnability has been offered as an explanation as to why certain properties appear in human languages more frequently than others. Languages with greater learnability are more likely to be accurately transmitted from one generation of learners to the next. We explore whether such a learnability bias is sufficient to result in a property becoming prevalent across languages by formalizing language transmission using a linear model. We then examine the outcome of repeated transmission of languages using a mathematical analysis, a computer simulation, and an experiment with human participants, and show several ways in which greater learnability may not result in a property becoming prevalent. Both the ways in which transmission failures occur and the relative number of languages with and without a property can affect whether the relationship between learnability and prevalence holds. Our results show that simply finding a learnability bias is not sufficient to explain why a particular property is a linguistic universal, or even frequent among human languages.