10.5061/DRYAD.HV02N
Hosseini, Eghbal A.
George Mason University
Massachusetts Institute of Technology
Nguyen, Katrina P.
George Mason University
Carnegie Mellon University
Joiner, Wilsaan M.
George Mason University
Institute for Advanced Study
Data from: The decay of motor adaptation to novel movement dynamics
reveals an asymmetry in the stability of motion state-dependent learning
Dryad
dataset
2017
Musculoskeletal system
Motion
Evolutionary adaptation
Perturbation (geology)
Simulation and modeling
Velocity
2017-07-24T16:00:44Z
2017-07-24T16:00:44Z
en
https://doi.org/10.1371/journal.pcbi.1005492
97971415 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Motor adaptation paradigms provide a quantitative method to study
short-term modification of motor commands. Despite the growing
understanding of the role motion states (e.g., velocity) play in this form
of motor learning, there is little information on the relative stability
of memories based on these movement characteristics, especially in
comparison to the initial adaptation. Here, we trained subjects to make
reaching movements perturbed by force patterns dependent upon either limb
position or velocity. Following training, subjects were exposed to a
series of error-clamp trials to measure the temporal characteristics of
the feedforward motor output during the decay of learning. The
compensatory force patterns were largely based on the perturbation
kinematic (e.g., velocity), but also showed a small contribution from the
other motion kinematic (e.g., position). However, the velocity
contribution in response to the position-based perturbation decayed at a
slower rate than the position contribution to velocity-based training,
suggesting a difference in stability. Next, we modified a previous model
of motor adaptation to reflect this difference and simulated the behavior
for different learning goals. We were interested in the stability of
learning when the perturbations were based on different combinations of
limb position or velocity that subsequently resulted in biased amounts of
motion-based learning. We trained additional subjects on these combined
motion-state perturbations and confirmed the predictions of the model.
Specifically, we show that (1) there is a significant separation between
the observed gain-space trajectories for the learning and decay of
adaptation and (2) for combined motion-state perturbations, the gain
associated to changes in limb position decayed at a faster rate than the
velocity-dependent gain, even when the position-dependent gain at the end
of training was significantly greater. Collectively, these results suggest
that the state-dependent adaptation associated with movement velocity is
relatively more stable than that based on position.
Exp_Data