Mammals, and humans in particular, are endowed with an exceptional capacity for cumulative learning. This capacity crucially de- pends on the presence of intrinsic motivations, i.e. motivations that are not directly related to an organism's survival and reproduction but rather to its ability to learn. Recently, there have been a number of attempts to model and reproduce intrinsic motivations in artificial systems. Different kinds of intrinsic motivations have been proposed both in psychology and in machine learning and robotics: some are based on the knowl- edge of the learning system, while others are based on its competence. In this contribution we discuss the distinction between knowledge-based and competence-based intrinsic motivations with respect to both the functional roles that motivations play in learning and the mechanisms by which those functions are implemented. In particular, after arguing that the principal function of intrinsic motivations consists in allowing the development of a repertoire of skills (rather than of knowledge), we suggest that at least two different sub-functions can be identified: (a) discovering which skills might be acquired and (b) deciding which skill to train when. We propose that in biological organisms knowledge-based intrinsic motivation mechanisms might implement the former function, whereas competence-based mechanisms might underly the latter one.
