title: Self-motion perception in the elderly.
abstract: Self-motion through space generates a visual pattern called optic flow. It can be used to determine one's direction of self-motion (heading). Previous studies have already shown that this perceptual ability, which is of critical importance during everyday life, changes with age. In most of these studies subjects were asked to judge whether they appeared to be heading to the left or right of a target. Thresholds were found to increase continuously with age. In our current study, we were interested in absolute rather than relative heading judgments and in the question about a potential neural correlate of an age-related deterioration of heading perception. Two groups, older test subjects and younger controls, were shown optic flow stimuli in a virtual-reality setup. Visual stimuli simulated self-motion through a 3-D cloud of dots and subjects had to indicate their perceived heading direction after each trial. In different subsets of experiments we varied individually relevant stimulus parameters: presentation time, number of dots in the display, stereoscopic vs. non-stereoscopic stimulation, and motion coherence. We found decrements in heading performance with age for each stimulus parameter. In a final step we aimed to determine a putative neural basis of this behavioral decline. To this end we modified a neural network model which previously has proven to be capable of reproduce and predict certain aspects of heading perception. We show that the observed data can be modeled by implementing an age related neuronal cell loss in this neural network. We conclude that a continuous decline of certain aspects of motion perception, among them heading, might be based on an age-related progressive loss of groups of neurons being activated by visual motion.
authors.affiliation: Department Neurophysics, Philipps-Universität Marburg Marburg, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Lich
authors.affiliation: Department Neurophysics, Philipps-Universität Marburg Marburg, Germany.
authors.forename: Frank
authors.initials: F
authors.lastname: Bremmer
keywords: aging; area MST; heading; neural network model; virtual reality