We build on the Maximum Occupancy Principle (MOP) and show complex behavior emerging from intrinsic motivation to occupy action space. Relevantly, the drive to occupy action space as uniformly as possible in the long run leads to interesting behaviors, such as non-trivial interaction with external objects. We show that MOP agents in navigation tasks are inherently curious, as they are attracted by the possibility of playing with available objects or using them as tools to visit larger regions of space. This principle is then extended to a more complex continuous navigation task where the motor output of the agent is defined by two units of a recurrent neural network of fixed weights. We show that a MOP controller can drive the network's activity and lead the motor output units to occupy the whole available space. This example highlights the potential of MOP as a principle not only for behavior but also for neural activity. All together, these results indicate MOP as a possible principle underlying various aspects of natural behavior, reconciling multiple perspectives of intrinsic motivation, such as curiosity and exploration.