Realistic appearance and complexity in the visual field are known to affect the strength of vection (visually induced self-motion perception). Although surface properties of materials are, therefore, expected to be visual features that influence vection, to date, the results have been mixed. Here, we used computer graphics to simulate self-motion through rendered 3D tunnels constructed from nine different materials (bark, ceramic, fabric, fur, glass, leather, metal, stone, and wood). There are three ways in which the new stimuli are changed from those found in previous studies: (1) as they move, their appearances interactively change with the 3D structures of the simulated world, as do all the lighting effects and 3D geometric appearances, (2) they are colored, (3) and their components covered a large portion of the visual field. The entire inner surface of each tunnel was composed from one of the nine materials, and optic flow was evoked when an observer virtually moved through the tunnel. Bark, fabric, leather, stone, and wood effectively induced strong vection, whereas, ceramic, glass, fur, and metal did not. Regression analyses suggested that low-level image features such as the lighting and amplitude of spatial frequency were the main factors that modulated vection strength. Additionally, subjective impressions of the nine surface materials showed that the perceived depth, smoothness, and rigidity were related to the perceived vection strength. Overall, our results indicate that surface properties of materials do indeed modulate vection strength.
Vection Surface properties Material Simulation Computer graphics Spatial frequency
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This work was supported by MEXT KAKENHI (Grant numbers JP26700016, JP17K12869, and JP18H01100) to TS. Part of this work was carried out under the Cooperative Research Project Program of the Research Institute of Electrical Communication, Tohoku University. We are grateful to the two anonymous reviewers for constructive comments on the manuscript and to Dr. Motohide Seki for advice on statistical analysis. We thank Adam Phillips, PhD, from Edanz Group (http://www.edanzediting.com/ac) for English editing a draft of this manuscript.
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