The standard reinforcement learning (RL) framework faces the problem of transfer learning and sparse rewards explorations. To address these problems, a large number of heterogeneous intrinsic motivation have been proposed, like reaching unpredictable states or unvisited states. Yet, it lacks a coherent view on these intrinsic motivations, making hard to understand their relations as well as their underlying assumptions. Here, we propose a new taxonomy of intrinsic motivations based on information theory: we computationally revisit the notions of surprise, novelty and skill learning and identify their main implementations through a short review of intrinsic motivations in RL. Our information theoretic analysis paves the way towards an unifying view over complex behaviors, thereby supporting the development of new objective functions.