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首页> 外文期刊>The Journal of Neuroscience: The Official Journal of the Society for Neuroscience >A real-time state predictor in motor control: study of saccadic eye movements during unseen reaching movements.
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A real-time state predictor in motor control: study of saccadic eye movements during unseen reaching movements.

机译:运动控制中的实时状态预测器:研究看不见的伸手动作期间眼跳的运动。

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Theoretical motor control predicts that because of delays in sensorimotor pathways, a neural system should exist in the brain that uses efferent copy of commands to the arm, sensory feedback, and an internal model of the dynamics of the arm to predict the future state of the hand (i.e., a forward model). We tested this theory under the hypothesis that saccadic eye movements, tracking an unseen reaching movement, would reflect the output of this state predictor. We found that in unperturbed reaching movements, saccade occurrence at any time t consistently provided an unbiased estimate of hand position at t + 196 msec. To investigate the behavior of this predictor during feedback error control, we applied 50 msec random-force perturbations to the moving hand. Saccades showed a sharp inhibition at 100 msec after perturbation. At approximately 170 msec, there was a sharp increase in saccade probabilities. These postperturbation saccades were an unbiased estimator of hand position at saccade time t + 150 msec. The ability of the brain to guide saccades to the future position of the hand failed when a force field unexpectedly changed the dynamics of the hand immediately after perturbation. The behavior of the eyes suggested that during reaching movements, the brain computes an estimate of future hand position based on an internal model that relies on real-time proprioceptive feedback. When an error occurs in reaching movements, the estimate of future hand position is recomputed. The saccade inhibition period that follows the hand perturbation may indicate the length of time it takes for this computation to take place.
机译:理论运动控制预测,由于感觉运动通路的延迟,大脑中应该存在一个神经系统,该神经系统使用对手臂的传出命令副本,感觉反馈以及手臂动力学的内部模型来预测手臂的未来状态。手(即前向模型)。我们在以下假设下对该理论进行了检验,即,眼跳的眼动,跟踪看不见的运动,将反映该状态预测器的输出。我们发现,在平稳的伸手动作中,在任何时间t发生扫视,始终可以在t + 196毫秒时对手的位置进行无偏估计。为了研究反馈误差控制期间此预测变量的行为,我们对移动的手施加了50毫秒的随机力扰动。扰动后,扫视在100毫秒处表现出强烈的抑制作用。大约170毫秒,扫视概率急剧增加。这些扰动后扫视是扫视时间t + 150毫秒时手位置的无偏估计。当力场在扰动后立即意外改变了手的动力学时,大脑将扫视运动引导到手的未来位置的能力就失败了。眼睛的行为表明,在达到运动状态时,大脑会基于依赖于实时本体感受反馈的内部模型来计算对未来手部位置的估计。当到达动作时发生错误时,将重新计算未来手部位置的估计值。跟随手部扰动的扫视抑制周期可以指示进行此计算所需的时间长度。

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