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Researchers develop motor learning model capable of simulating results of human experiments

Researchers at the University of Tsukuba have developed a mathematical model of motor learning that mirrors the process of motor learning in the human brain. Their results suggest that motor exploration – that is, increased variability of movements – is important when learning a new task. These findings may lead to better motor rehabilitation in patients after injury or illness.

Even seemingly simple movements are very complex to perform, and how we learn to perform new movements remains unclear. Japanese researchers recently proposed a new motor learning model that combines a number of different theories. A study published this month in Neural networks revealed that their model can simulate motor learning in humans surprisingly well, paving the way to a better understanding of how our brains work.

Even for a relatively simple task, such as reaching out and picking up an object, there are a large number of potential combinations of angles between your body and the various joints involved. The same goes for each of your muscles – there is an almost endless combination of muscles and forces that can be used together to perform an action. With all these possible combinations of joints and muscles – not to mention the underlying neural activity – how do we learn to make any movement? Researchers from the University of Tsukuba sought to answer this question.

The research team first created a mathematical model to mimic the learning process that occurs for new motor tasks. They designed the model to reflect many processes thought to occur in the brain when a new skill is learned. The researchers then tested their model by attempting to simulate the results of three recent studies in humans, in which individuals were asked to perform completely new motor tasks.

“We were surprised at how well our simulations replicated many of the results of previous studies in humans,” said Professor Jun Izawa, lead author of the study. “With our model, we were able to bridge the gap between a number of proposed motor learning mechanisms, such as motor exploration, redundancy solving, and error-based learning.”

In their model, greater amounts of motor exploration – i.e. movement variability – were found to be helpful in learning sensitivity derivatives, which measure how commands from the brain affect motor error. . In this way, errors were transformed into motor corrections.

“Our success in simulating actual results from human studies was encouraging,” says first author Lucas Rebelo Dal’Bello. “This suggests that our proposed learning mechanism could accurately reflect what happens in the brain during motor learning.”

The results of this study, which indicate the importance of motor exploration in motor learning, provide insight into how motor learning might occur in the human brain. They also suggest that motor exploration should be encouraged when learning a new motor task; this can be useful for motor rehabilitation after injury or illness.

This work was supported by KAKENHI (Scientific Research on Innovative Areas 19H04977 and 19H05729). LD was supported by a Japanese government scholarship (Monbukagakusho: MEXT).

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Material provided by University of Tsukuba. Note: Content may be edited for style and length.

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