We referred to it as вЂњplanning variabilityвЂќ, and it directly affects the reliability of the forward model prediction 22, 23, 24, 25, 26. Variability is also present in the forward model in the form of variability in movement planning 11, 12, 17, 18, 19, 20, 21 which might result from the stochastic nature of neuronal processes in sensorimotor transformations. During motor adaptation, this forward model is updated to reduce a sensory prediction error arising from a mismatch between its prediction and the sensory feedback.

visual and proprioceptive) information with a forward prediction of the bodyвЂ™s state to control movements and to correct errors 13, 14, 15, 16. According to optimal feedback control theory (OFC), the nervous system optimally (with respect to a specified goal) combines sensory feedback (e.g. However, not only measurement variability matters. This term does not only include the variability that is accessible to the central nervous representations only via sensory feedback but also includes the variability that is added to the movement during execution due to motor noise at the вЂњperipheryвЂќ 12 (e.g., muscles). We will refer to the variability attributed to visual or proprioceptive feedback and affecting the estimate of the effector (hand) position as вЂњmeasurement variabilityвЂќ 11. The authors proposed that their results are a consequence of the higher weight given to proprioception when adjusting movements along the vertical axis compared to sagittal or horizontal axis, considering/assuming that motor learning based on proprioceptive feedback entails a lower learning rate than based on visual feedback. Studies in 3D virtual reality (VR) found that adaptation along a vertical axis entails a reduced learning rate relative to the other two axes when a simultaneous triaxial perturbation was applied 10. Perceptual as well as other factors might contribute to adaptation anisotropies. Here we directly compared adaptation in different dimensions in the context of naturalistic movements in 3D. It is unknown, if and how differences in the way subjects plan movements and in the way they perceive the environment and their own movement along different dimensions translate into motor adaptation anisotropy. Unless movements are constrained in a horizontal plane by a table or supported by an exoskeleton, gravity induces a force anisotropy unless movements are conducted in a frontoparallel plane, visual depth induces a perceptual anisotropy. It is unclear, how well these findings generalize to movements in 3D, since 3D settings apply much less physical constraints on body pose and movements 8, 9. Studies of motor control and motor adaptation previously have been performed mostly in one or two-dimensional settings 1, 2, 3, 4, 5, 6, 7. This indicates that optimal integration theory for error correction holds for 3D movements and explains adaptation rate variation between movements in different planes. Our results show that differences in adaptation rate occur between the coronal, sagittal and horizontal planes and can be explained by the Kalman gain, i.e., a statistically optimal solution integrating planning and sensory information weighted by the inverse of their variability. To extract variability and relate it to adaptation rate, we employed a novel hierarchical two-state space model using Bayesian modeling via Hamiltonian Monte Carlo procedures. We ask how a subjectвЂ™s variability in movement planning and sensory perception influences motor adaptation along three different body axes.

Here we test how well existing concepts of motor learning generalize to movements in 3D. Since everyday movements are conducted in three-dimensional space, it is important to further our understanding about the effect that gravitational forces or perceptual anisotropy might or might not have on motor learning along all different dimensions relative to the body.

Yet our understanding of motor learning is based mostly on results from one or two-dimensional experimental paradigms with highly confined movements.

Neurorehabilitation in patients suffering from motor deficits relies on relearning or re-adapting motor skills.

The plane effect