Recent research indicates that perceptual learning (PL)--experience-induced changes in the way perceivers extract information--plays a larger role in complex cognitive tasks, including abstract and symbolic domains, than has been understood in theory or implemented in instruction. Here, we describe the involvement of PL in complex cognitive tasks and why these connections, along with contemporary experimental and neuroscientific research in perception, challenge widely held accounts of the relationships among perception, cognition, and learning. We outline three revisions to common assumptions about these relations: 1) Perceptual mechanisms provide complex and "abstract" descriptions of reality; 2) Perceptual representations are often "amodal," not limited to modality-specific sensory features; and 3) Perception is "selective." These three properties enable relations between perception and cognition that are both synergistic and dynamic, and they make possible PL processes that adapt information extraction to optimize task performance. While PL is pervasive in natural learning and in expertise, it has largely been neglected in formal instruction. We describe an emerging PL technology that has already produced dramatic learning gains in a variety of academic and professional learning contexts, including mathematics, science, aviation, and medical learning.