Statistical learning (SL) is typically assumed to be a core mechanism by which organisms learn covarying structures and recurrent patterns in the environment, with the main purpose of facilitating processing of expected events. Within this theoretical framework, the environment is viewed as relatively stable, and SL "captures" the regularities therein through implicit unsupervised learning by mere exposure. Focusing primarily on language-the domain in which SL theory has been most influential-we review evidence that the environment is far from fixed: It is dynamic, in continual flux, and learners are far from passive absorbers of regularities
they interact with their environments, thereby selecting and even altering the patterns they learn from. We therefore argue for an alternative cognitive architecture, where SL serves as a subcomponent of an