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. Key words: internal model; forward models; prediction; grip force; virtual reality; bimanual coordinationThe ability to predict the consequences of our own actions using an internal model of both the motor system and the external world has emerged as an important theoretical concept in motor control (Kawato et al

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Quantifying feedforward control: a linear scaling model for fingertip forces and object weight

“…The ability to predict the optimal force on subsequent attempts is called feedforward control and is thought to occur due to the formation of an internal representation of the object's properties, in this case the objects' weight, in the central nervous system (Davidson and Wolpert 2004;Gordon et al 1993;Johansson and Westling 1988a). Feedforward control implies that two critical processes are occurring: 1) the error between the estimated force and the needed force is accurately detected, and 2) the error information is used to update or adapt the motor response on a subsequent attempt to execute the task optimally (Blakemore et al 1998;Drake and Palmer 2000;Kawato and Wolpert 1998;Shadmehr et al 2010;Wolpert et al 1995;Wolpert and Miall 1996). The development of feedforward control is important because it leads to rapid reduction in error and faster relearning on subsequent attempts (Huang et al 2011;Krakauer and Mazzoni 2011) and suggests that the motor system is capable of learning.…”

Studenti učitelství mezi tacitními a explicitními znalostmi

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