Takiyama Ken, Shinya Masahiro
Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.
PLoS One. 2016 Jun 27;11(6):e0157588. doi: 10.1371/journal.pone.0157588. eCollection 2016.
Most motor learning experiments have been conducted in a laboratory setting. In this type of setting, a huge and expensive manipulandum is frequently used, requiring a large budget and wide open space. Subjects also need to travel to the laboratory, which is a burden for them. This burden is particularly severe for patients with neurological disorders. Here, we describe the development of a novel application based on Unity3D and smart devices, e.g., smartphones or tablet devices, that can be used to conduct motor learning experiments at any time and in any place, without requiring a large budget and wide open space and without the burden of travel on subjects. We refer to our application as POrtable Motor learning LABoratory, or PoMLab. PoMLab is a multiplatform application that is available and sharable for free. We investigated whether PoMLab could be an alternative to the laboratory setting using a visuomotor rotation paradigm that causes sensory prediction error, enabling the investigation of how subjects minimize the error. In the first experiment, subjects could adapt to a constant visuomotor rotation that was abruptly applied at a specific trial. The learning curve for the first experiment could be modeled well using a state space model, a mathematical model that describes the motor leaning process. In the second experiment, subjects could adapt to a visuomotor rotation that gradually increased each trial. The subjects adapted to the gradually increasing visuomotor rotation without being aware of the visuomotor rotation. These experimental results have been reported for conventional experiments conducted in a laboratory setting, and our PoMLab application could reproduce these results. PoMLab can thus be considered an alternative to the laboratory setting. We also conducted follow-up experiments in university physical education classes. A state space model that was fit to the data obtained in the laboratory experiments could predict the learning curves obtained in the follow-up experiments. Further, we investigated the influence of vibration function, weight, and screen size on learning curves. Finally, we compared the learning curves obtained in the PoMLab experiments to those obtained in the conventional reaching experiments. The results of the in-class experiments show that PoMLab can be used to conduct motor learning experiments at any time and place.
大多数运动学习实验都是在实验室环境中进行的。在这种环境下，经常会使用巨大且昂贵的操作设备，这需要大量预算和宽敞的空间。受试者还需要前往实验室，这对他们来说是一种负担。对于患有神经障碍的患者而言，这种负担尤为沉重。在此，我们描述了一种基于Unity3D和智能设备（如智能手机或平板电脑）开发的新型应用程序，它可用于随时随地进行运动学习实验，无需大量预算和宽敞空间，也不会给受试者带来出行负担。我们将我们的应用程序称为便携式运动学习实验室（Portable Motor learning LABoratory），简称PoMLab。PoMLab是一个多平台应用程序，可免费获取和共享。我们使用一种会导致感觉预测误差的视觉运动旋转范式，研究了PoMLab是否可以替代实验室环境，从而能够研究受试者如何将误差最小化。在第一个实验中，受试者能够适应在特定试验中突然施加的恒定视觉运动旋转。第一个实验的学习曲线可以很好地用状态空间模型进行建模，状态空间模型是一种描述运动学习过程的数学模型。在第二个实验中，受试者能够适应每次试验中逐渐增加的视觉运动旋转。受试者在未意识到视觉运动旋转的情况下适应了逐渐增加的视觉运动旋转。这些实验结果在实验室环境中进行的传统实验中已有报道，并且我们的PoMLab应用程序能够重现这些结果。因此，PoMLab可以被视为实验室环境的一种替代方案。我们还在大学体育课上进行了后续实验。一个与实验室实验中获得的数据相拟合的状态空间模型能够预测后续实验中获得的学习曲线。此外，我们研究了振动功能、重量和屏幕尺寸对学习曲线的影响。最后，我们将PoMLab实验中获得的学习曲线与传统伸手实验中获得的学习曲线进行了比较。课堂实验结果表明，PoMLab可用于在任何时间和地点进行运动学习实验。
