Learning New Motion Primitives in the Mirror Neuron System: A Self-organising Computational Model

Computational models of the mirror (neuron) system are attractive in robotics as they may inspire novel approaches to implement e.g. action understanding. Here, we present a simple self-organising map which forms the first part of larger ongoing work in building such a model. We show that minor modifications to the standard implementation of such a map allows it to continuously learn new motor concepts. We find that this learning is facilitated by an initial motor babbling phase, which is in line with an embodied view of cognition. Interestingly, we also find that the map is capable of reproducing neurophysiological data on goal-encoding mirror neurons. Overall, our model thus fulfils the crucial requirement of being able to learn new information throughout its lifetime. Further, although conceptually simple, its behaviour has interesting parallels to both cognitive and neuroscientific evidence.