PREDICTING THE CONSEQUENCES OF OUR OWN ACTIONS - THE ROLE OF SENSORIMOTOR CONTEXT ESTIMATION

During self-generated movement it is postulated that an efference copy of the descending motor command, in conjunction with an internal mode l of both the motor system and environment, enables us to predict the consequences of our own actions (von Helmholtz, 1867; Sperry, 1950; vo n Hoist, 1954; Wolpert, 1997). Such a prediction is evident in the pre cise anticipatory modulation of grip force seen when one hand pushes o n an object gripped in the other hand (Johansson and Westling, 1984; F lanagan and Wing, 1993). Here we show that self-generation is not in i tself sufficient for such a prediction. We used two robots to simulate virtual objects held in one hand and acted on by the other. Precise p redictive grip force modulation of the restraining hand was highly dep endent on the sensory feedback to the hand producing the load. The res ults show that predictive modulation requires not only that the moveme nt is self-generated, but also that the efference copy and sensory fee dback are consistent with a specific context; in this case, the manipu lation of a single object. We propose a novel computational mechanism whereby the CNS uses multiple internal models, each corresponding to a different sensorimotor context, to estimate the probability that the motor system is acting within each context.