We advocate for a novel communication paradigm of docition which facilitates distributed and autonomous networking at minimal control overhead and maximal performance. We consider that the nodes in foreseen networks are intelligent radios able to learn and thus self-adapt to prior set performance targets within a given surrounding environment. We briefly review the state-of-the-art of purely distributed learning algorithms, and we identify the most appropriate approaches allowing for self-adaptation to particular system dynamics. In such distributed settings, however, the learning is typically complex, imprecise and slow due to mutually-impacting decisions resulting in non-stationarities. The docitive paradigm proposes a timely solution which encourages more knowledgeable nodes to teach surrounding nodes to speed up the development of their cognitive state. We advocate for different degrees of docition, such as teaching at start-up or run-time, and demonstrate that this improves the convergence speed and precision of known cognitive algorithms. We evaluate the docitive paradigm in the context of a femtocell network modeled as a multi-agent system, where the agents are the femto access points, implementing a realtime multi-agent reinforcement learning technique known as decentralized Q-learning. We propose different docitive algorithms and we show their superiority to the well know paradigm of independent learning.