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Is it possible to understand the intentions of other people by simply observing their movements? Many neuroscientists believe that this ability depends on the brain's mirror-neuron system, which provides a direct link between action and observation. Precisely how intentions can be inferred through movement-observation, however, has provoked much debate. One problem in inferring the cause of an observed action, is that the problem is ill-posed because identical movements can be made when performing different actions with different goals. Here we suggest that this problem is solved by the mirror-neuron system using predictive coding on the basis of a statistical approach known as empirical Bayesian inference. This means that the most likely cause of an observed movement can be inferred by minimizing the prediction error at all cortical levels that are engaged during movement observation. This account identifies a precise role for the mirror-neuron system in our ability to infer intentions from observed movement and outlines possible computational mechanisms.