Figure 4.

State-space model of visuomotor adaptation. A, Visuomotor adaptation. Average movement angle of the 69 subjects with standard deviations are shown in brown tone colors. The black line indicates the average perturbation signal, and the green line, the average posterior estimate of the aiming angle. B, Planning noise examples. The gray line shows a subject with low planning noise (${\sigma }_{\eta }=0.15°\hspace{0.17em}{\sigma }_{ϵ}=4.6°$), and the red line, a subject with high planning noise (${\sigma }_{\eta }=0.65°\hspace{0.17em}{\sigma }_{ϵ}=4.6°$). C, Execution noise examples. The gray line shows a subject with low execution noise (${\sigma }_{\eta }=0.36°\hspace{0.17em}{\sigma }_{ϵ}=2.3°$), and the green line, a subject with high execution noise (${\sigma }_{\eta }=0.29°\hspace{0.17em}{\sigma }_{ϵ}=5.0°$). D, Relation between the parameter estimate ${\sigma }_{\eta }$ and baseline measure ${\sigma }_{y,baseline}$. The black line is a linear regression of ${\sigma }_{y,baseline}\left[s\right]$ onto ${\sigma }_{\eta }\left[s\right]$ and ${\sigma }_{ϵ}\left[s\right]$ for average ${\sigma }_{ϵ}\left[s\right]$. E, Relation between the parameter estimate ${\sigma }_{\eta }$ and baseline measure $R{\left(1\right)}_{baseline}$. The black line is a linear regression of $R{\left(1\right)}_{baseline}\left[s\right]$ onto ${\sigma }_{\eta }\left[s\right]$ and ${\sigma }_{ϵ}\left[s\right]$ for average ${\sigma }_{ϵ}\left[s\right]$. F, Relation between the parameter estimate ${\sigma }_{ϵ}$ and baseline measure ${\sigma }_{y,baseline}$. The black line is a linear regression of ${\sigma }_{y,baseline}\left[s\right]$ onto ${\sigma }_{\eta }\left[s\right]$ and ${\sigma }_{ϵ}\left[s\right]$ for average ${\sigma }_{\eta }\left[s\right]$. G, Relation between the parameter estimate ${\sigma }_{ϵ}$ and baseline measure $R{\left(1\right)}_{baseline}$. The black line is a linear regression of $R{\left(1\right)}_{baseline}\left[s\right]$ onto ${\sigma }_{\eta }\left[s\right]$ and ${\sigma }_{ϵ}\left[s\right]$ for average ${\sigma }_{\eta }\left[s\right]$. H, Adaptation rate examples. The thick lines show a slow (gray, $B=0.055$) and fast (blue, $B=0.14$) subject smoothed with a 6th-order Butterworth filter. The black shows the perturbation signal for the fast subject. I, Relation between the parameter estimate $B\left[s\right]$ and perturbation block estimate ${\sigma }_{py}\left[s\right]$. Parameter estimates and 68% HDIs are shown for every subject as a dot with error bars.