Local learning of inverse kinematics in human reaching movement

Abstract

We have investigated how the paths of reaching movements improve with motor learning, and whether these improvements transfer to movements other than those in which subjects were trained. Planar reaching movements were recorded in three groups moving in diagonal and lateral directions using a digitising table. All subjects made a number of reaching movements in a pre-test session. In the subsequent training phase of the experiment, one group of subjects was instructed to make lateral movements with as straight a path as possible; a second group made similar lateral movements following a straight line marked on the table; while a third group made diagonal movements, also following a marked line. All three groups were then tested making lateral and diagonal movements, without the benefit of any marked lines. The straightness and variability of movement paths were analysed to investigate improvements in neural control following training. A significant group by direction interaction indicated that movement straightness improved locally for the directions which were trained. Movement variability, in contrast, improved equally for all directions of movement. The results are consistent with local learning of a neural inverse kinematics model used in movement planning and global learning of a neural forward kinematics model used in movement execution.