Hosseini Eghbal A, Nguyen Katrina P, Joiner Wilsaan M
Department of Bioengineering, Sensorimotor Integration Laboratory, George Mason University, Fairfax, VA United States.
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA United States.
PLoS Comput Biol. 2017 May 8;13(5):e1005492. doi: 10.1371/journal.pcbi.1005492. eCollection 2017 May.
Motor adaptation paradigms provide a quantitative method to study short-term modification of motor commands. Despite the growing understanding of the role motion states (e.g., velocity) play in this form of motor learning, there is little information on the relative stability of memories based on these movement characteristics, especially in comparison to the initial adaptation. Here, we trained subjects to make reaching movements perturbed by force patterns dependent upon either limb position or velocity. Following training, subjects were exposed to a series of error-clamp trials to measure the temporal characteristics of the feedforward motor output during the decay of learning. The compensatory force patterns were largely based on the perturbation kinematic (e.g., velocity), but also showed a small contribution from the other motion kinematic (e.g., position). However, the velocity contribution in response to the position-based perturbation decayed at a slower rate than the position contribution to velocity-based training, suggesting a difference in stability. Next, we modified a previous model of motor adaptation to reflect this difference and simulated the behavior for different learning goals. We were interested in the stability of learning when the perturbations were based on different combinations of limb position or velocity that subsequently resulted in biased amounts of motion-based learning. We trained additional subjects on these combined motion-state perturbations and confirmed the predictions of the model. Specifically, we show that (1) there is a significant separation between the observed gain-space trajectories for the learning and decay of adaptation and (2) for combined motion-state perturbations, the gain associated to changes in limb position decayed at a faster rate than the velocity-dependent gain, even when the position-dependent gain at the end of training was significantly greater. Collectively, these results suggest that the state-dependent adaptation associated with movement velocity is relatively more stable than that based on position.
运动适应范式提供了一种定量方法来研究运动指令的短期修改。尽管人们对运动状态（如速度）在这种运动学习形式中所起的作用有了越来越多的了解，但基于这些运动特征的记忆的相对稳定性方面的信息却很少，特别是与初始适应相比。在这里，我们训练受试者进行受基于肢体位置或速度的力模式干扰的伸手动作。训练后，受试者接受一系列误差钳制试验，以测量学习消退期间前馈运动输出的时间特征。补偿力模式在很大程度上基于扰动运动学（如速度），但也显示出另一种运动运动学（如位置）的小贡献。然而，响应基于位置的扰动时的速度贡献比基于速度的训练中位置贡献的衰减速度要慢，这表明稳定性存在差异。接下来，我们修改了先前的运动适应模型以反映这种差异，并针对不同的学习目标模拟了行为。我们感兴趣的是当扰动基于肢体位置或速度的不同组合时学习的稳定性，这些组合随后导致基于运动的学习量存在偏差。我们对另外的受试者进行了这些组合运动状态扰动的训练，并证实了模型的预测。具体而言，我们表明：（1）在适应的学习和消退的观察到的增益空间轨迹之间存在显著分离；（2）对于组合运动状态扰动，与肢体位置变化相关的增益比与速度相关的增益衰减得更快，即使训练结束时基于位置的增益明显更大。总体而言，这些结果表明与运动速度相关的状态依赖性适应比基于位置的适应相对更稳定。
