Motor planning according to reliability of internal model

The authors propose a motor planning algorithm which reflects reliability of the internal model. The algorithm is to calculate the destination and its expected error for every motor command using the internal model, and to select the most effective command whose expected error would not exceed a given limit. Utilizing this algorithm, the system behaves moderately before adaptation, and its performance is improved as the internal model becomes more reliable through adaptation. It is also shown that the proposed algorithm simulates well the human behavior in a shape-tracing task.