Abstract
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.
Highlights
When we are challenged with a new sport or rehabilitation, the desired performance cannot be achieved at first
To validate that the POrtable Motor learning LABoratory (PoMLab) application could be an alternative to conventional motor learning experiments, we determined whether using PoMLab to conduct experiments in the laboratory could reproduce experimental results obtained for reaching movements and perturbation learning
We developed a novel application, the POrtable Motor learning LABoratory (PoMLab), that can be used to conduct motor learning experiments using smart devices, enabling the experiments to be conducted at any time or place, for free
Summary
When we are challenged with a new sport or rehabilitation, the desired performance cannot be achieved at first. Paradigms that involve reaching movements and perturbation learning, such as curl force field [6] or visuomotor rotation [7,8], are a popular way of investigating motor learning In such experiments, subjects are seated in a chair in front of a monitor while holding a manipulandum. The subjects are instructed to move the cursor towards a displayed target in as direct a path as possible within an appropriate length of time, e.g., within 500±50 msec [9] By employing these fast reaching movements, conventional experiments have investigated motor learning by measuring how subjects updated motor commands to decrease the prediction error, a discrepancy between the predicted and actual cursor movement. The adaptation process includes at least two processes, minimization of the prediction error and forgetting of motor memory [10]; here, we refer to these processes as the motor learning process
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