Learning in intelligent systems is a result of direct or indirect interaction with the environment. Humans can learn by way of different states of (inter-)action such as the execution or the imagery of an action, but their unique potential to induce brain-related as well as mind-related changes in the motor action system is still being debated. The systematic repetition of different states of action (e.g., execution and imagery in terms of physical and mental practice) and their contribution to the learning (...) of complex motor actions has traditionally been approached by way of performance improvements. More recently, approaches highlighting the role of action representation in the learning of complex motor actions have evolved. In the present perspective paper, we build on brain-related findings and sketch recent research on learning by way of imagery and execution from a hierarchical, perceptual-cognitive approach to the motor action system. These findings provide valuable insights into the learning of intelligent systems from a perceptual-cognitive, representation-based perspective and as such add to our current understanding of action representation in memory and its changes with practice. Future research should build bridges between approaches in order to more thoroughly understand functional changes throughout the learning process, and to jointly contribute to developing intelligent cognitive systems and their application in robotics, rehabilitation, or sports. (shrink)