Over the past 10 years, Cognitive Linguistics has taken a quantitative turn. While this move has been hailed as a game-changer, an important step towards the “scientization” of linguistics, concerns have also been raised. Some feel that this preoccupation with quantification and modelling may not bring us any closer to understanding how language works.

In our paper we show that this objection does not need to be true, especially if we focus on modelling techniques based on biologically and psychologically plausible learning algorithms.

These algorithms make it possible to take a quantitative approach, while generating and testing specific hypotheses that will advance our understanding of how knowledge of language emerges from exposure to usage. Such models allow empirical evidence to accrue and alter the way in which we think about language, during development and in representation.