Learning and Decay of Prediction in Object Manipulation

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Abstract
Anticipating the consequences of our own actions is a fundamental component of normal sensorimotor control and is seen, for example, during the manipulation of objects. When one hand pulls on an object held in the other hand, there is an anticipatory increase in grip force in the restraining hand that prevents the object from slipping. This anticipation is thought to rely on a forward internal model of the manipulated object and motor system, enabling the prediction of the consequences of our motor commands. Here we investigate the development of such a predictive response. Each hand held an object that was attached to its own torque motor. On each trial the subject was required to pull on the object held in the left hand and to maintain the position of the object held in the right hand. The torque motors were computer controlled so that the objects could be either “linked” so that the forces on the objects were equal and opposite, acting as though they were a single object, or “unlinked,” so that they acted as two independent objects. A predictive response in the restraining hand is only necessary when the objects are linked and is unnecessary in the unlinked condition where there is no risk of the object slipping. To examine the learning and decay of predictive responses, we measured the grip force responses during unlinked trials that followed a linked trial. After a single linked trial, anticipatory grip force was quick to develop, but decayed slowly over the following unlinked trials. Varying the time between trials showed that the rate of decay depended on the number of trials since the last linked trial rather than time. Increasing the frequency of linked trials showed an increased level of subsequent grip force modulation, but did not alter the decay rate. When the torque motors simulated a linked object that did not have normal physical properties, prediction was reduced. These results show that the use of predictive responses has a different time course for learning and decay, and the response depends on experience and the physical properties of the objects.