Learning and Decay of Prediction in Object Manipulation (original) (raw)
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12 other HighWire hosted articles, the first 5 are: This article has been cited by [PDF] [Full Text] [Abstract] , April 1, 2005; 93 (4): 2021-2027. J Neurophysiol Impaired Grip Force Modulation in the Ipsilesional Hand after Unilateral Middle [PDF] [Full Text] [Abstract] , April 26, 2006; 26 (17): 4519-4525. J. Neurosci. Anticipatory movement timing using prediction and external cues. [PDF] [Full Text] [Abstract] , August 1, 2007; 98 (2): 851-860. J Neurophysiol Learning and decay of prediction in object manipulation. J Neuro physiol 84: 334 -343, 2000. 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.
Predictive Motor Learning of Temporal Delays
Journal of Neurophysiology, 1999
Anticipatory responses can minimize the disturbances that result from the action of one part of the body on another. Such a predictive response is evident in the anticipatory increase in grip force seen when one hand pulls on an object held in the other hand, thereby preventing the object from slipping. It is postulated that such a response depends on predicting the consequences of the descending motor command, as signaled by efference copy, using an internal model of both one's own body and the object. Here we investigate how the internal model learns the temporal consequences of the motor command. We employed two robots to simulate a virtual object held in one hand and acted on by the other. Delays were introduced between the action of one hand on the object and the effects of this action on the other hand. An initial reactive grip force response to the delayed load decayed with the development of appropriate anticipatory grip force modulation. However, no predictive modulatio...
Response force is sensitive to the temporal uncertainty of response stimuli
Perception & Psychophysics, 1997
Three experiments examined whether temporal uncertainty about the delivery of a response stimulus affects response force in a simple reaction time CRT) situation. All experiments manipulated the foreperiod; that is, the interval between a warning signal and the response stimulus. In the constant condition, foreperiod length was kept constant over a block of trials but changed from block to block In the variable condition, foreperiod length varied randomly from trial to trial. A visual warning and response stimulus were used in Experiment 1; response force decreased with foreperiod length in the variable condition, but increased in the constant condition. This result is consistent with the hypothesis that responses are less forceful when the temporal occurrence of the response stimulus is predictable. In a second experiment with an auditory warning signal and a response stimulus, response force was less sensitive to foreperiod manipulations. The third experiment manipulated both the modality and the intensity of the response signal and employed a tactile warning signal. This experiment indicated that neither the modality nor the intensity of the response signal affects the relation between response force and foreperiod length. An extension of Naatanen's (1971)motor-readiness model accounts for the main results.
Spatial Representation of Predictive Motor Learning
Journal of Neurophysiology, 2003
A key feature of skilled motor behavior is the ability of the CNS to predict the consequences of its actions. Such prediction occurs when one hand pulls on an object held in the other hand; the restraining hand generates an anticipatory increase in grip force, thereby preventing the object from slipping. When manipulating a novel object, the CNS adapts its predictive response to ensure that predictions are accurately tuned to the dynamics of the object. Here we examine whether learning to predict the consequences of an action on a novel object is restricted to the actions performed during manipulation or generalizes to novel actions. A bimanual task in which subjects held an object in each hand and the relationship between actions on one object and the motion of the other could be computer controlled from trial-to-trial was used. In four conditions we varied the spatial relationship between the direction of force subjects applied to the left-hand object and the consequent direction ...
7 other HighWire hosted articles, the first 5 are: This article has been cited by [PDF] [Full Text] [Abstract] , January 15, 2002; 22 (2): 600-610. [PDF] [Full Text] [Abstract] , September 1, 2002; 22 (17): 7721-7729. [PDF] [Full Text] [Abstract] , April 1, 2003; 89 (4): 1837-1843. J Neurophysiol A. G. Witney and D. M. Wolpert Spatial Representation of Predictive Motor Learning [PDF] [Full Text] [Abstract] , August 1, 2005; 94 (2): 1346-1357. J Neurophysiol M. Zago and F. Lacquaniti Visual Targets on Earth Internal Model of Gravity for Hand Interception: Parametric Adaptation to Zero-Gravity [PDF] [Full Text] [Abstract] , June 28, 2006; 26 (26): 7121-7126. J. Neurosci. P. M. Bays and D. M. Wolpert systems.
Experimental Brain Research, 2008
We examined whether or not conscious knowledge about the availability of visual feedback on an upcoming trial would influence the programming of a precision grip. Twenty healthy volunteers were asked to reach out and grasp objects under two viewing conditions: full visual feedback (closed loop) or no visual feedback (open loop). The two viewing conditions were presented in blocked, randomized, and alternating trial orders. Before each block of trials, participants were explicitly informed of the nature of the upcoming order of viewing conditions. Even though participants continued to scale their grip to the size of the goal objects which varied in size and distance, they opened their hand significantly wider when visual feedback was not available during movement execution. This difference was evident before peak grip aperture was reached, continued into the grip aperture closing phase, and presumably reflects the visuomotor system's ability to build in a margin of error to compensate for the absence of visual feedback. The difference in grip aperture between closed-and open-loop trials increased as a function of distance, which suggests that the visuomotor system can make use of visual feedback given enough time, even when that feedback is not anticipated. The difference in grip aperture between closed-and open-loop trials was larger when the two visual feedback conditions were blocked than when they were either randomized or alternated. Importantly, performance did not differ between the randomized and the alternating trial blocks. In other words, despite knowledge of the availability of visual feedback on an upcoming trial in the predictable alternating block, participants behaved no differently than they did on randomized trials. Taken together, these results suggest that motor planning tends to optimize performance largely on the basis of what has happened regularly in the past and cannot take full advantage of conscious knowledge of what will happen on a future occasion.
Anticipatory Engineering: Anticipation in Sensory-Motor Systems of Human
Cognitive Systems Monographs, 2015
In visual tracking experiments, distributions of the relative phase between target and tracer showed positive relative phase indicating that the tracer precedes the target position. We found a mode transition from the reactive to anticipatory mode. The proposed integrated model provides a framework to understand the anticipatory behaviour of human, focusing on the integration of visual and somatosensory information. The time delays in visual processing and somatosensory feedback are explicitly treated in the simultaneous differential equations. The anticipatory behaviour observed in the visual tracking experiments can be explained by the feedforward term of target velocity, internal dynamics, and time delay in somatosensory feedback.