"How can the mirror neuron system infer intentions from observed actions"
Is it possible to understand the intentions of another person by simply observing their movements? Many neuroscientists believe that this ability is made possible by the brain's mirror-neuron system, through its direct link between action and observation. However, precisely how intentions can be inferred through movement-observation has provoked much debate. In particular, in order to infer intentions from movements we have to be able to solve an ill-posed inverse problem: the problem that the same movement can be caused by many different intentions. In this talk I will provide evidence from behavioural and EEG/MEG studies that the function of mirror-neuron system is best considered within a predictive coding framework that appeals to the statistical approach known as empirical Bayesian inference. Within this scheme the most likely cause of an observed action can be inferred by minimizing the prediction error at all levels of the cortical hierarchy that are engaged during action observation. This account identifies a precise role for the mirror-neuron system in our ability to infer intentions from actions and provides the outline of the underlying computational mechanisms.