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Updated: May 4, 2026
08:04
Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation
Published on: August 23, 2017
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Savings in locomotor adaptation explained by changes in learning parameters following initial adaptation
Firas Mawase1, Lior Shmuelof, Simona Bar-Haim
1Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel;
Journal of Neurophysiology
|January 17, 2014
Summary
Faster relearning, known as savings, links motor learning and memory. This study shows savings occur in walking adaptation, suggesting shared mechanisms with reaching movements and highlighting the role of cortical plasticity.
Area of Science:
- Neuroscience
- Motor Control
- Cognitive Science
Background:
- Savings, or faster relearning after initial exposure, links motor learning and memory.
- Existing models explain savings in reaching but not rhythmic movements like walking.
- The computational and neural basis of savings in locomotor adaptation remains unstudied.
Purpose of the Study:
- Investigate savings effects in locomotor adaptation.
- Determine if existing motor learning models adequately explain savings in walking.
- Explore the relationship between initial adaptation and savings during relearning.
Main Methods:
- Two experiments involving split-belt treadmill walking with speed perturbations.
- Subjects adapted, experienced counter-perturbations or washout, and then readapted.
- Analysis focused on learning rates and model parameter changes.
Main Results:
- Clear evidence of savings (increased learning rates) during locomotor relearning.
- A basic multiple timescales model failed to explain locomotor savings.
- Locomotor adaptation alters learning parameters, leading to faster relearning.
- A correlation was found between slow initial learning and fast relearning components.
Conclusions:
- Locomotor adaptation exhibits savings, similar to reaching.
- Standard models need modification to account for savings in locomotion.
- Savings in locomotion and reaching may share underlying computational and neural mechanisms, potentially involving cortical plasticity.
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