Accelerating motor adaptation by influencing neural computations

When people learn to reach or step in a novel dynamic environment, they initially exhibit a large trajectory error, which they gradually reduce with practice. The error evolution is well modeled by a process in which the motor command on the next movement is adjusted in proportion to the previous movement's trajectory error. We hypothesized that we could accelerate motor adaptation by transiently increasing trajectory error. We tested this hypothesis by quantifying adaptation to a viscous force field applied during the swing phase of stepping in two conditions. In the first condition, we applied then removed the field for 75 steps each, for four iterations. Subjects adapted to each field exposure with a mean time constant of 3.4 steps. In the second condition, we repeated this experiment, but increased the strength of the field for only the first step in each field exposure. We predicted the field strength increase needed by solving a finite difference equation that described the error evolution. Adaptation was significantly faster when the field was transiently amplified (mean time constant = 2 trials). These results demonstrate that it is possible to increase the rate of adaptation to a novel dynamic environment based on knowledge of the computational mechanisms that underlie adaptation.