Abstract

When static observers are exposed to a visual simulation of forward self-motion, they generally overestimate travelled distance relative to a previously seen distant target (Redlick, Harris, & Jenkin, 2001). Using the same experimental paradigm, we reproduced this result, which can be accounted for by a leaky path integration model (Lappe, Jenkin, & Harris, 2007). However, we also showed that a translational optical flow with additional oscillatory components, simulating head motion during walking, allows for better perception of travelled distance, as compared to a purely translational flow. This result can be discussed as an improvement of path integration, using an ecological optic flow pattern (close to natural walking feedback). In a subsequent experiment, we tested if it is the biological or the rhythmical nature of the simulation which provides better estimates of travelled distance. Participants were standing and passively confronted to three conditions of virtual simulation of self-motion in a 4-sided CAVE: (1) a translational optical flow (2) a translational flow, with an added component, via triangular oscillations in the vertical axis and (3) a flow, reproducing motion of the head during walking, as previously measured and modelled using a motion capture system. The results confirm that the addition of rhythmical components in the optic flow pattern improve the accuracy of travelled distance. However, they failed to reveal a significant difference between and rhythmic oscillations. Further experiments on the effect of spatio-temporal oscillatory components in the optical flow are required. However, these results can be related to recent studies comparing the effect of smooth and jittering optic flows on vection onset and strength (Palmisano et al., 2014). One suggestion is that oscillatory components produce a non-monotonous pattern of retinal motion, maintaining optimal sensitivity to optic flow, and consequently improving the perception of travelled distance. Meeting abstract presented at VSS 2016

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