This chapter builds the theoretical foundation of differential learning (DL) by integrating principles from systems theory, cybernetics, and neurophysiology. It outlines the conceptual shift from linear, prescriptive models of movement learning toward a dynamic understanding of movement as a self-organized behavior shaped by variability and context. Traditional motor learning models based on repetition, error correction, and ideal movement patterns are reevaluated through the lens of first- and second-order cybernetics, which emphasize observer dependence, feedback loops, and emergence. Systems theory provides the language to describe movement and movement learning as processes evolving through the interaction between an organism and its environment, while topological transformations complement static metrics to better represent motor learning. Neuroscientific insights—from the role of sensorimotor systems to the reafference principle and plasticity—further substantiate the DL approach. These insights reinforce the DL paradigm, suggesting that effective learning is not achieved through the correction of errors but through purposeful variability tailored to the learner’s context, goals, and sensorimotor experience.