This book examines the role of experience-based learning on children's acquisition of language and concepts. It reviews, compares, and contrasts accounts of how the opportunity to recognize and generalize patterns influences learning. The book offers the first systematic integration of three highly influential research traditions in the domains of language and concept acquisition: Statistical Learning, Structural Alignment, and the Bayesian learning perspective. Chapters examine the parameters that constrain learning, address conditions that optimize learning, and offer explanations for cases in which implicit exemplar-based learning fails to occur. By exploring both the benefits and challenges children face as they learn from multiple examples, the book offers insight on how to better able to understand children's early unsupervised learning about language and concepts. Topics featured in this book include: Competing models of statistical learning and how learning might be constrained by infants' developing cognitive abilities. How experience with multiple exemplars helps infants understand space and other relations. The emergence of category-based inductive reasoning during infancy and early childhood. How children learn individual verbs and the verb system over time. How statistical learning leads to aggregation and abstraction in word learning. Mechanisms for evaluating others' reliability as sources of knowledge when learning new words. The Search for Invariance (SI) hypothesis and its role in facilitating causal learning. Language and Concept Acquisition from Infancy Through Childhood is an essential resource for researchers, clinicians and related professionals, and graduate students in infancy and early child development, applied linguistics, language education, child, school, and developmental psychology and related mental health and education services.