Predicting the Consequences of Our Own Actions: The Role of Sensorimotor Context Estimation

Abstract

During self-generated movement it is postulated that an efference copy of the descending motor command, in conjunction with an internal model of both the motor system and environment, enables us to predict the consequences of our own actions (von Helmholtz, 1867; Sperry, 1950;von Holst, 1954; Wolpert, 1997). Such a prediction is evident in the precise anticipatory modulation of grip force seen when one hand pushes on an object gripped in the other hand (Johansson and Westling, 1984;Flanagan and Wing, 1993). Here we show that self-generation is not in itself sufficient for such a prediction. We used two robots to simulate virtual objects held in one hand and acted on by the other. Precise predictive grip force modulation of the restraining hand was highly dependent on the sensory feedback to the hand producing the load. The results show that predictive modulation requires not only that the movement is self-generated, but also that the efference copy and sensory feedback are consistent with a specific context; in this case, the manipulation 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.