Jarosz, Gaja, Cerys Hughes, Andrew Lamont, Brandon Prickett, Maggie Baird, Seoyoung Kim & Max Nelson. 2024. Type and Token Frequency Jointly Drive Learning of Morphology. OSF. https://doi.org/10.31234/osf.io/mbp24.

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 that token frequency and productivity are inversely related, other results indicate that they are positively related, and yet others argue that token frequency plays no role in productivity. We take up both of these questions. Our learning framework makes it possible to examine the effects of these variables on generalization to novel forms as well as 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, with type frequency playing a dominant role. 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. Neither of these models is designed to encode a particular relationship between token frequency, type frequency, and productivity, but we show that both models correctly predict the general trends in generalization rates, learning curves, and the influence of token frequency observed across the experimental conditions.

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