Brain and Mind Institute.
Robarts Research Institute.
J Neurosci. 2018 Dec 5;38(49):10505-10514. doi: 10.1523/JNEUROSCI.1709-18.2018. Epub 2018 Oct 24.
Recent work has shown that, when countering external forces, the nervous system adjusts not only predictive (i.e., feedforward) control of reaching but also reflex (i.e., feedback) responses to mechanical perturbations. Here we show that altering the physical properties of the arm (i.e., intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning transfers to feedback responses even though the latter were never directly trained. Forty-five human participants (30 females) performed a single-joint elbow reaching task and countered mechanical perturbations that created pure elbow motion. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements when the shoulder joint was either free to rotate or locked by the robotic manipulandum. With the shoulder unlocked, we found robust activation of shoulder flexor muscles for pure elbow flexion trials, as required to counter the interaction torques that arise at the shoulder because of forearm rotation. After locking the shoulder joint, which cancels these interaction torques, we found a substantial reduction in shoulder muscle activity over many trials. In our second experiment, we tested whether such learning transfers to feedback control. Mechanical perturbations applied to the arm with the shoulder unlocked revealed that feedback responses also account for intersegmental dynamics. After locking the shoulder joint, we found a substantial reduction in shoulder feedback responses, as appropriate for the altered intersegmental dynamics. Our work suggests that feedforward and feedback control share an internal model of the arm's dynamics. Here we show that altering the physical properties of the arm causes people to learn new motor commands and that this learning transfers to their reflex responses to unexpected mechanical perturbations, even though the reflex responses were never directly trained. Our results suggest that feedforward motor commands and reflex responses share an internal model of the arm's dynamics.
最近的研究表明,在对抗外部力量时,神经系统不仅会调整用于预测(即前馈)的手臂控制,还会调整对机械扰动的反射(即反馈)反应。在这里,我们展示了改变手臂的物理特性(即体节间动力学)会导致神经系统调整前馈控制,并且这种学习会转移到反馈反应,即使后者从未经过直接训练。四十五名参与者(三十名女性)进行了单关节肘部伸展任务,并对抗了产生纯肘部运动的机械扰动。在我们的第一个实验中,我们通过要求参与者在肩关节自由旋转或被机器人操纵器锁定时产生纯肘部运动来改变体节间动力学。当肩关节解锁时,我们发现对于纯肘部伸展试验,会强烈激活肩部屈肌肌肉,这是为了抵消由于前臂旋转而在肩部产生的交互扭矩。锁定肩关节后,由于取消了这些交互扭矩,我们发现许多试验中肩部肌肉活动大大减少。在我们的第二个实验中,我们测试了这种学习是否会转移到反馈控制中。对解锁肩部的手臂施加机械扰动表明,反馈反应也会考虑体节间动力学。锁定肩关节后,我们发现肩部反馈反应大幅减少,这与改变的体节间动力学相符。我们的工作表明,前馈和反馈控制共享手臂动力学的内部模型。在这里,我们展示了改变手臂的物理特性会导致人们学习新的运动命令,并且这种学习会转移到他们对意外机械扰动的反射反应中,即使反射反应从未经过直接训练。我们的结果表明,前馈运动命令和反射反应共享手臂动力学的内部模型。