Type and token frequency jointly drive learning of morphology

Abstract

We examine the joint roles of type frequency and token frequency in three artificial language learning experiments involving lexicalized plural allomorphy. The primary role of type frequency in productivity is well-established, but debates about the precise relationship between type frequency and productivity continue. The effect of token frequency on productivity is even more controversial: some lines of research suggest token frequency and productivity are inversely related, other results indicate they are positively related, and yet others argue token frequency plays no role in productivity. We address both of these questions. Our learning framework makes it possible to examine the effects of these variables on generalization to novel forms and to examine how sensitivity to these factors affects the time-course of learning. The first two experiments differentiate predictions for generalization of three distinct hypotheses about the role of type frequency, while the third experiment investigates the independent role of token frequency. We find that both type and token frequency independently and positively contribute to learning rates and generalization across the three experiments. We also apply two computational learning theories – implementing two prominent theoretical linguistic frameworks – to the learning of the lexically-conditioned allomorphy patterns in our experiments. Despite their differences, we show that the incremental learning dynamics of both models correctly predict the general trends in generalization rates, learning curves, and the influence of token frequency observed across the experimental conditions.