Franklin Sae, Wolpert Daniel M, Franklin David W
Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Institute for Cognitive Systems, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; and.
J Neurophysiol. 2017 Nov 1;118(5):2711-2726. doi: 10.1152/jn.00748.2016. Epub 2017 Aug 23.
Adaptation to novel dynamics requires learning a motor memory, or a new pattern of predictive feedforward motor commands. Recently, we demonstrated the upregulation of rapid visuomotor feedback gains early in curl force field learning, which decrease once a predictive motor memory is learned. However, even after learning is complete, these feedback gains are higher than those observed in the null field trials. Interestingly, these upregulated feedback gains in the curl field were not observed in a constant force field. Therefore, we suggest that adaptation also involves selectively tuning the feedback sensitivity of the sensorimotor control system to the environment. Here, we test this hypothesis by measuring the rapid visuomotor feedback gains after subjects adapt to a variety of novel dynamics generated by a robotic manipulandum in three experiments. To probe the feedback gains, we measured the magnitude of the motor response to rapid shifts in the visual location of the hand during reaching. While the feedback gain magnitude remained similar over a larger than a fourfold increase in constant background load, the feedback gains scaled with increasing lateral resistance and increasing instability. The third experiment demonstrated that the feedback gains could also be independently tuned to perturbations to the left and right, depending on the lateral resistance, demonstrating the fractionation of feedback gains to environmental dynamics. Our results show that the sensorimotor control system regulates the gain of the feedback system as part of the adaptation process to novel dynamics, appropriately tuning them to the environment. Here, we test whether rapid visuomotor feedback responses are selectively tuned to the task dynamics. The responses do not exhibit gain scaling, but they do vary with the level and stability of task dynamics. Moreover, these feedback gains are independently tuned to perturbations to the left and right, depending on these dynamics. Our results demonstrate that the sensorimotor control system regulates the feedback gain as part of the adaptation process, tuning them appropriately to the environment.
适应新的动力学需要学习一种运动记忆,即一种新的预测性前馈运动命令模式。最近,我们证明了在卷曲力场学习早期快速视觉运动反馈增益会上调,而一旦学习到预测性运动记忆,这些增益就会下降。然而,即使学习完成后,这些反馈增益仍高于在零场试验中观察到的增益。有趣的是,在恒力场中未观察到卷曲场中这些上调的反馈增益。因此,我们认为适应还涉及选择性地调整感觉运动控制系统对环境的反馈敏感性。在这里,我们通过在三个实验中测量受试者适应由机器人操作器产生的各种新动力学后快速视觉运动反馈增益来检验这一假设。为了探究反馈增益,我们测量了在伸手过程中手部视觉位置快速变化时运动反应的大小。虽然在恒定背景负载增加四倍以上时反馈增益大小保持相似,但反馈增益随侧向阻力增加和不稳定性增加而缩放。第三个实验表明,根据侧向阻力,反馈增益也可以独立地调整到向左和向右的扰动,这表明反馈增益对环境动力学的分化。我们的结果表明,感觉运动控制系统在适应新动力学的过程中调节反馈系统的增益,并适当地根据环境进行调整。在这里,我们测试快速视觉运动反馈反应是否被选择性地调整到任务动力学。这些反应没有表现出增益缩放,但它们确实随任务动力学的水平和稳定性而变化。此外,根据这些动力学,这些反馈增益被独立地调整到向左和向右的扰动上。我们的结果表明,感觉运动控制系统在适应过程中调节反馈增益,并适当地根据环境进行调整。