The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other.Keywords: statistical learning, language development, implicit learning, word learning Humans live in a world filled with statistical regularities. Balls thrown into the air typically fall back to earth; nouns such as dog or boy are typically preceded by articles such as a or the. There is no doubt that learners are sensitive to these statistical regularities. One term to describe the ability to detect and use statistical structure is statistical learning. Saffran, Aslin, and Newport (1996) proposed this term to describe infants' ability to identify word boundaries solely from the statistical relation between sounds in the input. It is now widely acknowledged that infants and adults encode the statistical structure of their environment in a variety of tasks, including sequence learning (e.g., Haith, Wentworth, & Canfield, 1993;Stadler, 1992), category boundary detection (e.g., Maye, Werker, & Gerken, 2002), word-object association (Smith & Yu, 2008), cue-category association (Thiessen & Saffran, 2007), and causal learning (Sobel & Kirkham, 2007). Statistical learning likely plays a role in many different aspects of development, but it is thought to play an especially crucial role in language development. The discovery that infants are capable of benefiting from statistical structure in the input led to a reevaluation of the role of learning in language acquisition, ...