Data from: Adaptation to random and systematic errors: Comparison of amputee and non-amputee control interfaces with varying levels of process noise

The objective of this study was to understand how people adapt to errors
when using a myoelectric control interface. We compared adaptation across
1) non-amputee subjects using joint angle, joint torque, and myoelectric
control interfaces, and 2) amputee subjects using myoelectric control
interfaces with residual and intact limbs (five total control interface
conditions). We measured trial-by-trial adaptation to self-generated
errors and random perturbations during a virtual, single degree-of-freedom
task with two levels of feedback uncertainty, and evaluated adaptation by
fitting a hierarchical Kalman filter model. We have two main results.
First, adaptation to random perturbations was similar across all control
interfaces, whereas adaptation to self-generated errors differed. These
patterns matched predictions of our model, which was fit to each control
interface by changing the process noise parameter that represented system
variability. Second, in amputee subjects, we found similar adaptation
rates and error levels between residual and intact limbs. These results
link prosthesis control to broader areas of motor learning and adaptation
and provide a useful model of adaptation with myoelectric control. The
model of adaptation will help us understand and solve prosthesis control
challenges, such as providing additional sensory feedback.