Learning to shape virtual patient locomotor patterns: internal representations adapt to exploit interactive dynamics.

Area of Science:

• Neuroscience
• Robotics
• Biomechanics

Background:

• Humans develop internal models to predict and control limb movements in response to novel dynamics.
• The application of internal model formation to locomotor rehabilitation, a rhythmic and complex activity, remains unclear, with potential for model-free strategies.

Purpose of the Study:

• To investigate sensorimotor processes in learning to manipulate virtual locomotor dynamics.
• To determine if humans form internal models during rehabilitation tasks involving complex gait dynamics.

Main Methods:

• A novel interactive locomotor simulator modeled hemiparetic gait dynamics.
• Healthy subjects (n=16) practiced altering a virtual patient's gait using a robotic manipulandum.
• Internal model formation was assessed using force channels and null force fields; generalization was tested with altered gait patterns.

Main Results:

• Results supported the internal model hypothesis, showing aftereffects and generalization of manipulation skills.
• Internal models were refined, leveraging the natural pendular dynamics of human locomotion.
• Participants developed and utilized internal models, refined through experience, to shape locomotor patterns.

Conclusions:

• Humans form and refine internal models for manipulating complex locomotor dynamics, even in simulated rehabilitation settings.
• These findings suggest internal models are crucial for sensorimotor adaptation in gait rehabilitation.
• The study highlights the brain's ability to adapt and optimize motor control strategies based on experience and physical dynamics.