Binomial expressions are more regularized--i.e. their ordering preferences (e.g. “bread and butter” vs. “butter and bread”) are more extreme—-the higher their frequency. Although standard iterated-learning models of language evolution can encode overall regularization biases, the stationary distributions in these standard models do not exhibit a relationship between expression frequency and regularization. We show that introducing a frequency-INdependent regularization bias into the data-generation stage of a 2-Alternative Iterated Learning Model yields frequency-dependent regularization in the stationary distribution. We also show that this model accounts for the distribution of binomial ordering preferences in corpus data
