Back to reality: differences in learning strategy in a simplified virtual and a real throwing task.
Back to reality: differences in learning strategy in a simplified virtual and a real throwing task.
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回到现实：简化的虚拟和真实投掷任务中学习策略的差异。
DOI：
10.1152/jn.00197.2020
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发表时间：
2021
影响因子：
2.5
通讯作者：
Sternad,Dagmar
中科院分区：
文献类型：
--
作者：
Zhang,Zhaoran;Sternad,Dagmar
虚拟环境已被广泛应用于运动神经科学和康复，因为它们提供了严格的控制的感觉运动条件，并容易提供视觉和触觉操纵。然而，通常情况下，研究只检查了虚拟测试台中的性能，而没有询问虚拟环境中的简化和受控运动与真实的世界中的行为相比如何。为了测试在虚拟环境中的表现是否是真实的世界中相应行为的有效表示，本研究将虚拟设置中的投掷与现实投掷进行了比较，其中任务参数精确匹配。尽管虚拟任务只需要水平单关节手臂运动，类似于运动神经科学中的许多简化运动试验，但投掷准确性和精确度明显低于涉及手臂所有自由度的真实的任务;只有经过3天的练习，成功率和错误率才达到相似的水平。为了更深入地了解学习过程的结构，将运动可变性分解为确定性和随机性贡献。使用的公差噪声协变分解方法，不同的学习阶段被揭示：虽然公差优化首先在两个环境中，它是更高的虚拟环境中，这表明更多的熟悉和探索，需要在虚拟任务。协变和噪声在真实的任务中表现出更大的贡献，表明被试仅在真实的任务中才达到变异性微调的阶段。这些结果表明，虽然任务是精确匹配的，但虚拟环境中的简化动作需要更多的时间才能成功。这些研究结果与报道的问题，在转移的治疗效益从虚拟到真实的环境和警报，使用虚拟环境中的研究和康复需要更多的caution.NEW & NOTEWORTHThis研究比较人类的性能相同的投掷任务在一个真实的和匹配的虚拟环境。经过3天的练习，受试者在真实的任务中的进步明显更快，尽管手臂和手的动作更复杂。分解变异性显示，在虚拟环境中的性能，尽管简化了手部动作，需要更多的探索。此外，由于真实的任务中的约束较少，受试者可以通过改变释放位置来修改解流形的几何形状，从而简化任务。
Virtual environments have been widely used in motor neuroscience and rehabilitation, as they afford tight control of sensorimotor conditions and readily afford visual and haptic manipulations. However, typically, studies have only examined performance in the virtual testbeds, without asking how the simplified and controlled movement in the virtual environment compares to behavior in the real world. To test whether performance in the virtual environment was a valid representation of corresponding behavior in the real world, this study compared throwing in a virtual set-up with realistic throwing, where the task parameters were precisely matched. Even though the virtual task only required a horizontal single-joint arm movement, similar to many simplified movement assays in motor neuroscience, throwing accuracy and precision were significantly worse than in the real task that involved all degrees of freedom of the arm; only after 3 practice days did success rate and error reach similar levels. To gain more insight into the structure of the learning process, movement variability was decomposed into deterministic and stochastic contributions. Using the tolerance-noise-covariation decomposition method, distinct stages of learning were revealed: While tolerance was optimized first in both environments, it was higher in the virtual environment, suggesting that more familiarization and exploration was needed in the virtual task. Covariation and noise showed more contributions in the real task, indicating that subjects reached the stage of fine-tuning of variability only in the real task. These results showed that while the tasks were precisely matched, the simplified movements in the virtual environment required more time to become successful. These findings resonate with the reported problems in transfer of therapeutic benefits from virtual to real environments and alert that the use of virtual environments in research and rehabilitation needs more caution.NEW & NOTEWORTHYThis study compared human performance of the same throwing task in a real and a matched virtual environment. With 3 days’ practice, subjects improved significantly faster in the real task, even though the arm and hand movements were more complex. Decomposing variability revealed that performance in the virtual environment, despite its simplified hand movements, required more exploration. Additionally, due to fewer constraints in the real task, subjects could modify the geometry of the solution manifold, by shifting the release position, and thereby simplify the task.