Clear vision despite a heavy head
In contrast to most previous models, the researchers considered the movement of head and eye to the target as well as the counter-movement of the eye after the gaze has reached the target, but the head is still moving. “The longer the movement, the more perturbations add up,” says Glasauer. “However, the faster the movement, the more errors arise from acceleration and large muscle forces.” On the basis of this information, the Munich researchers calculated eye and head movements and determined the movement combination that caused the fewest disturbances. This movement matched that chosen by healthy volunteers not only in natural conditions but also in an experiment where subjects’ head movements were altered by an experimental increase in the head’s rotational inertia.
These findings could help teach robots humanoid movements and thus facilitate interaction with service robots. It may also be helpful in the construction of “smart” prostheses. These devices could offer the carrier a choice of movements that come closest to the natural human ones. For the next step, Glasauer and colleagues want to examine three-dimensional eye-head movements and aim to better understand simple movement learning.
The Bernstein Center Munich is part of the National Bernstein Network Computational Neuroscience (NNCN) in Germany. The NNCN was established by the German Federal Ministry of Education and Research with the aim of structurally interconnecting and developing German capacities in the new scientific discipline of computational neuroscience. The network is named after the German physiologist Julius Bernstein (1835–1917).
Saglam M., Lehnen N., Glasauer S. (2011): Optimal control of natural eye-head movements minimizes the impact of noise.
J Neurosci. 31(45):16185–16193
Dr. Stefan Glasauer
Bernstein Center Munich and LMU Munich
Institute of Clinical Neurosciences
Phone: +49 89 7095 4839