A novel way to model autonomous learning in artificial agents and robots is introduced, called an Enactive Markov Decision Process (EMDP). An EMDP keeps perception and action embedded within sensorimotor schemes rather than dissociated. On each decision cycle, the agent tries to enact a sensorimotor scheme, and the environment informs the agent whether it was indeed enacted or whether another sensorimotor scheme was enacted instead. This new modeling approach leads to implementing a new form of self-motivation called interactional motivation. An EMDP learning algorithm is presented. Results show that this algorithm allows the agent to develop active perception as it learns to master the sensorimotor contingences afforded by its coupling with the environment.