In this project, we will investigate how far the transfer of learning is responsible for the development of a “self”. We will, therefore, present a computational ideomotor approach, and hypothesize that the transfer is possible due to a hierarchical structure of action-effect associations, such that training a specific narrow low-level task indirectly trains higher cognitive skills that are involved in other low-level tasks. For example, manipulating objects and balancing are two low-level tasks that both involve the cognitive skill of mental rotation. Consequently, according to our hypothesis, the mental rotation skill will benefit from balance training, which in turn also triggers an improvement of the learning of the grasping task. We will address our hypothesis by implementing a computational and neurocognitively plausible neural network architecture evaluated on a physical humanoid robot. Our expected contribution is a functional neurocognitively plausible deep reinforcement neural network model for ideomotor transfer learning that is verifiable on a reproducible robotic platform.