Composition and decomposition learning of reaching movements under altered environments: An examination of the multiplicity of internal models

We have studied the learning processes of reaching movements under novel environments whose kinematic and dynamic properties are altered. In the experiments, we have used, as the kinematic transformation, a rotational transformation which is displayed by rotating a cursor indicating hand position in the orthogonal coordinate system on a CRT; a viscous transformation using viscous field as the dynamic transformation; and a combined transformation of these two transformations. It is observed that the hand trajectory approaches a straight line along with learning and accurately reaches the target. When the combined transformation is learned after the rotational transformation and viscous transformation are learned first, respectively, the final error becomes smaller and the path length also becomes shorter than the case when the combined transformation is learned first. Moreover, the final error and path length of the movement under rotation al transformation and viscous transformation when the combined transformation is learned first also become smaller than the case when the rotational and viscous transformations are learned first. These results suggest that the central nervous system has learned separately the multiple internal models which compensate the respective transformations, and has composed or decomposed the respective internal models in accordance with the environmental changes. It may be considered that such multiplicity of internal models makes, it possible for the living body to flexibly cope with the environments or tools having various dynamic and kinematic properties.