The decay of motor adaptation to novel movement dynamics reveals an asymmetry in the stability of motion state-dependent learning
Fig 5
Gain-space representation of adaptation and decay for training in combination force-fields.
(A and B) Gain-space trajectories during training in the unbiased combination (ucFF, green trace) and position biased force-field (pcFF, purple trace) are shown. The respective gain-space trajectories during the decay periods are shown in gray. The learning goal of the adaptation in gain space is shown by the filled green and purple squares. The directions of position-aligned and velocity-aligned components are shown by blue and red arrows, respectively. (C and D) Evolution of the applied position- and velocity-dependent gains during adaptation and decay of the combination force-field training. Position-dependent gains are shown in blue and velocity-dependent gains are shown in red. Each point during the adaptation period is the average across subjects for windows of 10 or 15 trials. The bar graphs show the amplitude of each component for the shaded regions numbered in panels A and B. (E and F) The normalized gain-space trajectories during the decay periods were computed by rescaling the gains during decay by their respective values at the start of the decay period. Thus, the first point is rescaled to [1.0, 1.0] in gain space. The gray lines represent the normalized decay in gain space, and the ellipses show standard error at each point. (G and H) Normalized decay of position- and velocity-dependent gains. The bar graph depicts the comparison between the normalized gains in the early (E) and late (L) epochs. Error bars show standard error of gains for each epoch. Insets in panels A, B, E and F show the predictions of the asymmetric primitive model fit simultaneously to the ucFF and pcFF behavioral data (αK = 0.914, αB = 0.958, σK = 0.546, σB = 0.565, η = 1.5 x 10−4, ρ = 0.48).