PUB - Publikationen an der Universität Bielefeld

The plasticity of the human nervous system allows us to acquire an open-ended repository of sensorimotor skills in adulthood, such as the mastery of tools, musical instruments or sports. How novel sensorimotor skills are learned from scratch is yet largely unknown. In particular, the so-called inverse mapping from goal states to motor states is underdetermined because a goal can often be achieved by many different movements (motor redundancy). How humans learn to resolve motor redundancy and by which principles they explore high-dimensional motor spaces has hardly been investigated. To study this question, we trained human participants in an unfamiliar and redundant visually-guided manual control task. We qualitatively compare the experimental results with simulation results from a population of artificial agents that learned the same task by Goal Babbling, which is an inverse-model learning approach for robotics. In Goal Babbling, goal-related feedback guides motor exploration and thereby enables robots to learn an inverse model directly from scratch, without having to learn a forward model first. In the human experiment, we tested whether different initial conditions (starting positions of the hand) influence the acquisition of motor synergies, which we identified by Principal Component Analysis in the motor space. The results show that the human participants' solutions are spatially biased towards the different starting positions in motor space and are marked by a gradual co-learning of synergies and task success, similar to the dynamics of motor learning by Goal Babbling. However, there are also differences between human learning and the Goal Babbling simulations, as humans tend to predominantly use Degrees of Freedom that do not have a large effect on the hand position, whereas in Goal Babbling, Degrees of Freedom with a large effect on hand position are used predominantly. We conclude that humans use goal-related feedback to constrain motor exploration and resolve motor redundancy when learning a new sensorimotor mapping, but in a manner that differs from the current implementation of Goal Babbling due to different constraints on motor exploration. Author summary Even in adulthood, humans can learn to master new motor skills with unfamiliar mappings between desired goals or sensations and corresponding movements, such as playing tennis or musical instruments. To master a new skill involves the resolution of motor redundancy; that is a selection from many possible movements that all achieve the same goal. Here, we trained participants in a redundant and unfamiliar task that mapped their hand movements to shapes, in order to investigate which of the many possible redundant solution participants learn. The results show that local task feedback, which depends on the starting posture of the hand, influences participants' motor learning. We qualitatively compared the experimental results to computer simulations of artificial agents that learned the same task by Goal Babbling, i.e., a motor learning approach used in robotics, to assess if humans might learn the task following similar principles. Both the simulated agents and the participants show sensitivity to goal-directed feedback during learning, but they use different strategies to explore the movement space. We conclude that human motor learning and redundancy resolution is guided by local goal feedback, like in Goal Babbling, but that differences in motor exploration lead to different learning outcomes.