Self versus Environment Motion in Postural Control

To stabilize our position in space we use visual information as well as non-visual physical motion cues. However, visual cues can be ambiguous: visually perceived motion may be caused by self-movement, movement of the environment, or both. The nervous system must combine the ambiguous visual cues with noisy physical motion cues to resolve this ambiguity and control our body posture. Here we have developed a Bayesian model that formalizes how the nervous system could solve this problem. In this model, the nervous system combines the sensory cues to estimate the movement of the body. We analytically demonstrate that, as long as visual stimulation is fast in comparison to the uncertainty in our perception of body movement, the optimal strategy is to weight visually perceived movement velocities proportional to a power law. We find that this model accounts for the nonlinear influence of experimentally induced visual motion on human postural behavior both in our data and in previously published results

Ageing vision and falls: a review

Background: Falls are the leading cause of accidental injury and death among older adults. One of three adults over the age of 65 years falls annually. As the size of elderly population increases, falls become a major concern for public health and there is a pressing need to understand the causes of falls thoroughly. Main body of the abstract: While it is well documented that visual functions such as visual acuity, contrast sensitivity, and stereo acuity are correlated with fall risks, little attention has been paid to the relationship between falls and the ability of the visual system to perceive motion in the environment. The omission of visual motion perception in the literature is a critical gap because it is an essential function in maintaining balance. In the present article, we first review existing studies regarding visual risk factors for falls and the effect of ageing vision on falls. We then present a group of phenomena such as vection and sensory reweighting that provide information on how visual motion signals are used to maintain balance. Conclusion: We suggest that the current list of visual risk factors for falls should be elaborated by taking into account the relationship between visual motion perception and balance control

The influence of non-visual signals of walking on the perceived speed of optic flow

We considered how non-visual signals that accompany walking might influence the visual processing of optic flow. During natural locomotion optic-flow speed is determined by walking speed in a closed-loop manner. In the experiments subjects were required to adjust the speed of an optic-flow pattern to match that of a reference flow pattern in an open-loop manner. The visual speed was matched while walking by turning a hand-held knob that controlled the presented optic-flow speed. Subjects were also required to change their pace according to a written instruction at the beginning of each trial to either 'very slow', 'slow', 'normal', 'fast', or 'very fast'. A non-motorised, self-driven treadmill simulated natural walking by allowing subjects to walk at their chosen pace. The optic-flow pattern consisted of bright rectangles expanding against a dark background displayed on a large rear-projected screen. An experimental block consisted of a 15 s presentation of a reference pattern followed by 5 test trials, one at each pace randomly ordered and matched. Results showed a consistent increase of optic-flow speed with increasing walking speeds. However, speed settings were most accurate when subjects were walking at their chosen 'normal' walking pace. We conclude that the perceived speed of optic-flow information is influenced by non-visual signals (eg proprioception) such that optic-flow speed is overestimated at lower walking speeds while underestimated at higher speeds

The perception of absolute speed during self-motion

Perceived velocity and smooth-pursuit eye movements for drifting gratings were shown to involve different combinations of signals (Gegenfurtner and Hawken, 1995, ARVO). We considered whether the estimation of absolute speeds of optic flow might also be different for a speed-matching task done by walking compared with a perceptual rating task. In the motor task, subjects were required to walk on a non-motorised, manually driven treadmill at their chosen pace while being presented with optic flow in the form of bright rectangles expanding against a dark background. In each trial an optic flow pattern was displayed for 10 s on a large rear-projected screen, and subjects were asked to adjust their walking speed to match the optic flow. Thirteen levels of optic flow ranging from 1 to 7 km h-1 were tested. In the rating task, standing subjects were required to make verbal judgments of speed (in km h-1) for the same optic flow presentations. We ran a third condition in which subjects rated as well as matched the flow by walking in each trial. The results for all subjects and conditions showed walking and rating speeds linearly related to the speed of the optic flow ( R2 ࣙ 0.91). However, the slopes of the regression lines were consistently lower for matched walking compared with the rating condition. Results from the third condition showed no direct effect of walking on the rating task. We conclude that visual velocity while walking relies on different mechanisms than those used for speed judgments based on visual signals alone