Motor Experts Care about Consistency and Are Reluctant to Change Motor Outcome - Fig 2
A displays the variance (group mean and standard error of the mean, SEM) calculated in the different phases of the experiment, the baseline phase, the reinforcement phase (100 trials and first 50 trials), and the no-vision phase. The colors label the different groups and subgroups: experts with BE (dark blue), experts receiving VE+BE (light blue), novices with BE (dark red), and novices receiving VE+BE (yellow). B shows changes of the horizontal directional error between successful trials and the corresponding subsequent trials (framed in green), and the same for non-successful trials (framed in red), for subgroups receiving BE in the reinforcement phase (blue: experts; red: novices). The individual mean and the individual variance of the changes was calculated. The bars refer to the group mean values (for mean and for variance). Bars indicate SEM of the group mean values. C shows the directional error (group mean and SEM) for distinct parts in the no-vision phase. We divided the 100 trials of the no-vision phase in three parts (33 trials each), termed P1, P2, and P3, and calculated the individual mean values for each of the 33 trials. The blue bars in this graph refer to the experts and the red bars refer to the novices. D shows individual subject data of two variables, learning rate in the adaptation phase (summed changes in directional error of the first 20 trials) and variance in the baseline phase, plotted against each other. The blue dots refer to the experts and the red dots refer to the novices. The black line depicts the linear regression of all data. E shows the learning rate in the adaptation phase (group mean and SEM) for the tested groups and subgroups separately. F displays the directional error of the last BIN of the reinforcement phase and the directional error of the first BIN of the second adaptation phase for each subject. Blue dots refer to the experts and the red dots refer to the novices. The black line shows the linear regression of all data.