A core question in linguistics research concerns the types of mechanisms that readers/listeners rely on during online language processing. One such mechanism, prediction (the ability to use linguistic cues to anticipate what is likely to come up), plays a central role in many models of language processing. In line with the idea that the human brain is a predictive machine, there is evidence that native speakers actively generate predictions about what is likely to be uttered, which allows language comprehension to be fast and efficient. In contrast, the question of whether second language (L2) speakers can also generate predictions online remains open. This project uses EEG (a brain-imaging method with high temporal precision) to examine predictive processing in L2 speakers. To date, very few studies have addressed this question. Thus, the project carries the potential to further our understanding of the qualitatively nature of L2 processing and to identify areas of divergence between L1 and L2 speakers. The project examines prediction across three domains of grammar (semantics, syntax, discourse), some of which remain understudied (syntax, discourse). Moreover, it examines the extent to which L2 predictive processing is impacted by (a) individual differences in cognitive (e.g. working memory) and linguistic skills (e.g. aptitude for L2 learning) and (b) L1-L2 similarity, two factors that have been found to impact prediction but have not been systematically examined.