Summary: When learning how to coordinate a new movement, the nervous system first explores and evaluates many different coordinated patterns. With experience, the nervous system then adapts specific aspects of movements and simultaneously decreases pattern variability.
Source: Simon Fraser University
Research that examines how the body adapts to new movements is shedding new light on how the nervous system learns, and could help to inform a wide range of applications, from customized rehabilitation and athletic training to wearable systems for healthcare.
The research is published this week in the journal Current Biology.
“How does our brain figure out how to best move our body? It turns out that this can be a challenging problem for the nervous system, considering we have hundreds of muscles that can be coordinated hundreds of times per second—with more possible coordination patterns to choose from than moves on a chessboard,” says study senior author and SFU professor Max Donelan, director of SFU’s Locomotion Lab.
“We often experience changes to our body and our environment. Perhaps you enjoy a long run on a Saturday morning—your muscles may fatigue as the length of the run increases. Perhaps you choose to run on the beach on vacation—the sand may be uneven and loose in comparison to the pavement on the sidewalk. While we might register that these changes have occurred, we might not appreciate how our body adapts to these changes.”
Donelan’s team of neuroscientists that study motor learning collaborated with a Stanford University team of mechanical engineers that design human-robot systems. Together, they tracked the walking characteristics of study participants wearing exoskeletons.
Researchers found that the nervous system solves the problem of learning a new movement coordination pattern by first exploring and evaluating many different coordination patterns. This exploration was measured as a general increase in variability spanning the levels of the whole movement, joint, and muscle.
With experience, the nervous system adapts specific aspects of movement and simultaneously decreases variability along these aspects. The researchers also found that these adaptive changes improved movement overall, reducing the energy cost of walking by about 25 percent.
“We created new contexts using exoskeletons that act to assist walking, and then studied how people explore new movements and learn more optimal ones,” says Sabrina Abram, the study lead author and former graduate student in the Locomotion Lab.
Participants experienced walking in this context over six days, resulting in about 30 hours of lab time for each and an extraordinary amount of data collected by co-author Katherine Poggensee.
While the nervous system appears to benefit from first searching among many different coordination patterns, it also benefits from reducing this search space over time, Abram adds. “This is because continuing to search among coordination patterns that already reduce energy can in turn increase energy, as well as add to the already challenging problem of figuring out the best way to move.”
Understanding how the brain searches for and figures out how to best move the body is important for a runner navigating new terrain, as well as a patient recovering from spinal injury or stroke.
For example, knowing when the body has adapted to a new training regimen can help coaches identify at which point an athlete should transition to learning new skills. This can also be useful for designing wearable systems—such as exoskeletons and prosthetics—by facilitating learning, and then evaluating people’s optimal responses to a range of designs.
Notes Donelan: “We would all like to move in the best way possible. For healthy people, it seems that, with the right circumstances, the brain can take care of this. For those recovering from an injury, we might learn about how to best rehabilitate this injury from a better understanding of how the nervous system learns to adapt.”
About this movement and neuroscience research news
Author: Marianne Meadahl
Source: Simon Fraser University
Contact: Marianne Meadahl – Simon Fraser University
Image: The image is in the public domain
Original Research: Closed access.
“General variability leads to specific adaptation toward optimal movement policies” by Max Donelan et al. Current Biology
General variability leads to specific adaptation toward optimal movement policies
- In new contexts, the nervous system explores through increases in gait variability
- With experience, the nervous system selectively adapts specific aspects of gait
- Simultaneously, the nervous system reduces exploration along these aspects
- The nervous system’s adaptive changes reduce the energetic cost of walking
Our nervous systems can learn optimal control policies in response to changes to our bodies, tasks, and movement contexts. For example, humans can learn to adapt their control policy in walking contexts where the energy-optimal policy is shifted along variables such as step frequency or step width. However, it is unclear how the nervous system determines which ways to adapt its control policy.
Here, we asked how human participants explore through variations in their control policy to identify more optimal policies in new contexts.
We created new contexts using exoskeletons that apply assistive torques to each ankle at each walking step. We analyzed four variables that spanned the levels of the whole movement, the joint, and the muscle: step frequency, ankle angle range, total soleus activity, and total medial gastrocnemius activity.
We found that, across all of these analyzed variables, variability increased upon initial exposure to new contexts and then decreased with experience. This led to adaptive changes in the magnitude of specific variables, and these changes were correlated with reduced energetic cost.
The timescales by which adaptive changes progressed and variability decreased were faster for some variables than others, suggesting a reduced search space within which the nervous system continues to optimize its policy.
These collective findings support the principle that exploration through general variability leads to specific adaptation toward optimal movement policies.