The advent and fall of a vocabulary learning bias from communicative efficiency

It is well-known that, when sufficiently young children encounter a new word, they tend to attach it to a meaning that does not have a word yet in their lexicon. In previous research, the strategy was shown to be optimal from an information theoretic standpoint. However, the information theoretic model employed neither explains the weakening of that vocabulary learning bias in older children or polylinguals nor reproduces Zipf's meaning-frequency law, namely the non-linear relationship between the number of meanings of a word and its frequency. Here we consider a generalization of the model that is channeled to reproduce that law. The analysis of the new model reveals regions of the phase space where the bias disappears consistently with the weakening or loss of the bias in older children or polylinguals. In the deep learning era, the model is a transparent low-dimensional tool for future experimental research and illustrates the predictive power of a theoretical framework originally designed to shed light on the origins of Zipf's rank-frequency law.