Evaluating Methods for Improving Motor Skills

In sports and various activities, people often try to reduce mistakes in their movements. For instance, when shooting a basketball, the difference between hitting the target and missing is related to how well someone performs this action. As people learn new motor skills, they usually make fewer mistakes over time. However, most individuals see their improvement slow down or stop early in the learning process, and only a few reach a top level of skill, as shown by professional athletes. This indicates that we need better ways to help people improve their Performance beyond just practicing.

One example is piano players using special devices that help them improve how they play by feeling the keys. The aim is to create a system that doesn’t rely heavily on the ability of the person practicing or the coach but can be used by many people.

To develop such a system, using visual or touch Feedback from computers or robotic devices can be effective. This approach minimizes the need for any verbal or physical help from a coach or another person. Recently, some methods have been shown to boost learning by purposely increasing mistakes during practice.

For example, in a task where individuals reach for a target while encountering some resistance, the learning process sped up when visual feedback showed their hands making larger mistakes. In other studies, when people practiced with robotic devices that forced their legs to move incorrectly, they had better results. Different studies have also looked at how introducing random changes could help people learn different ways to complete Tasks like reaching or controlling their hand positioning.

#Mixed Results in Learning

Even though many studies suggest that increasing errors or variability can help with learning new movements, some have had the opposite findings. In one study focusing on rowing, using techniques to exaggerate mistakes didn’t improve accuracy in the stroke. Similarly, in other tasks that provided sound feedback based on success or failure and in a virtual game where players had to control the speed of a disk, adding random changes appeared to worsen learning. These findings show that it’s critical to test new methods under real-world conditions to know if they genuinely help in learning new motor skills.

Most of the earlier research had focused on specific robotic setups in labs. These settings are great for controlling conditions and measuring movement accurately but don’t fully connect with improving skills in everyday situations. The COVID-19 pandemic has increased the need for remote testing, leading to a rise in online experiments. Some online programs have been developed to help with learning motor tasks, allowing researchers to study motor skills outside traditional lab setups.

However, it remains unclear how Interventions that change errors and variability affect learning in these remote tasks. As a result, this study planned to examine whether visual methods that increase mistakes in online experiments could help with motor learning.

#Study Methodology

Participants were recruited for the study if they spoke Japanese and had laptops for the web-based tasks. A total of 48 people took part in the study, providing their consent before starting. They completed the tasks over several days, sending back the necessary data and receiving a reward for their time. Not all participants finished the entire study due to dropouts or misunderstandings about the guidelines.

#Motor Tasks

Participants used their laptops and trackpads to access a webpage designed for the experiments. The tasks were developed from an open-source software package. Each task lasted three days, with different interventions on each day. On the first day, there were no changes, but the second and third days featured methods to increase errors or variability. The first five trials each day were meant for familiarization, while the following 120 trials were the main practice sessions. The final trials on Day 3 served as a test without any intervention.

#Experiment 1: Reaching Task

In the first experiment, participants engaged in a reaching task where they aimed for a target on their screens, with specific adjustments to account for their movements. They were split into four groups, each receiving different levels of error feedback during reaching tasks. Some groups received adjustments that amplified their movement errors, while others received mixed feedback that included random elements.

#Experiment 2: Curling Task

The second experiment involved a task similar to curling, where participants threw a virtual ball. They again were divided into groups that received varying levels of noise feedback that could change their performance and the directions of their throws.

#Experiment 3: Ball-Throwing Task

In the third experiment, participants worked on a task similar to basketball shooting. They aimed to throw a virtual ball toward a target on the screen, with various adjustments made to their throwing angle and speed based on their performance.

#Findings from the Experiments

Throughout the experiments, the overall performance did not show significant improvements despite the interventions designed to amplify errors and variability. For instance, when analyzing the reaching task in Experiment 1, the groups that were supposed to receive benefits from the interventions displayed no considerable differences in their performance during the final tests.

In the second experiment involving putting, while some feedback methods seemed to increase errors initially, they did not lead to better results in the end. The same occurred in the ball-throwing task, showing that the expected benefits from the interventions were not realized.

The findings did not align with existing research that suggested amplifying errors would improve learning. Instead, the results suggested that the methods tested did not enhance learning as hoped.

#Reasons for Negative Outcomes

Several reasons could explain these unexpected outcomes. A common limitation across all experiments was how movements were recorded. Standard laptops are not as precise as specialized motion capture systems, which may have affected the accuracy of the feedback participants received about their movements. Furthermore, individual factors such as motivation and concentration likely varied among participants, possibly impacting their performance.

The type of feedback given to participants in the study differed from previous setups that reported better learning outcomes, highlighting the importance of how motor skills and feedback interact in practice.

#Conclusion

This study aimed to test whether interventions that increase errors and variability would facilitate motor learning in a remote setup. However, the results showed that none of the methods produced the desired effect on learning motor skills. This suggests that the relationships between the type of feedback given, the method of practicing, and the outcomes in real tasks need further exploration.

For future research, it’s critical to examine whether similar methods yield better results in controlled lab settings with more precise measurement tools. This could help clarify the relationship between different forms of feedback and learning outcomes, ultimately assisting in the development of effective training programs for various motor tasks.