Abstract This paper introduces the special issue on implications of learning models, such as deep neural networks and instance-based models, for linguistic theory. This special issue aims to address the core question of linguistic theory – what makes human languages the way they are – by comparing models of language learning (i.e., different theories of the language acquisition device). We bring the papers together by discussing a number of recurrent themes: (i) the importance of rich memory, which enables extensive memorization without impairing generalization; (ii) our increasing understanding of the learning mechanisms that enable the emergence of structure from experience (Hebbian and error-driven); and (iii) the tasks that need to be solved for successful language learning. Limitations and future directions are highlighted.
Kapatsinski et al. (Mon,) studied this question.