In our daily lives, we encounter various sensorimotor events, in which sensory perception and actions are intertwined. The sense of agency for a given action refers to the sense of authorship regarding that action. Computational models of motor control suggest that prediction of sensory information by internal models is matched against subsequent sensory information. If predicted and sensed information match, then the sensory events are self-generated, and the subject experiences a sense of agency for those events. If there is mismatch, then subjects attribute the outcome to an external cause/agent. Central feedback mechanisms play significant roles in the motor control and self-attribution of agency. Previously, we found that external feedback about the action can alter the attribution of agency before assimilating the actual sensory consequences, by updating motor predictions in real-time.