Predictive coding: an account of the mirror neuron system

Abstract

Is it possible to understand the intentions of other people by simply observing their actions? Many 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 action observation has provoked much debate. Here we suggest that the function of the mirror system can be understood within a predictive coding framework that appeals to the statistical approach known as empirical Bayes. 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 system in our ability to infer intentions from actions and provides the outline of the underlying computational mechanisms.

Fig. 1

Schemas of the mirror-neuron system

Fig. 2

Examples of the predictive coding account of the MNS. Here we consider four levels of attribution in an example hierarchy of the MNS; kinematics, goal, intention and context. In a action-observation is considered in the absence of a context, in b the identical action is observed in but now in the context of an operating theatre. The bars depict the level degree of prediction error. In a both intentions predict identical goals and kinematics and therefore the prediction error is identical in both schemes. In this case the model can not differentiate between the intentions causing the action. In b the context causes a large prediction error for the goal ‘to hurt’ and a small prediction error for the goal ‘to cure’. In this case the model can differentiate between the two intentions