Skip to main content
Log in

Material surface properties modulate vection strength

  • Research Article
  • Published:
Experimental Brain Research Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. “Shader” is a technical computer-graphics term that refers to the whole program that calculates shade, shadows, and lighting. Here, we used various shaders for each material, as described in the main text.

References

  • Adelson EH, Bergen J (1985) Spatiotemporal energy models for the perception of motion. J Opt Soc Am A 2:284–299

    Article  CAS  PubMed  Google Scholar 

  • Allison RS, Howard IP, Zacher JE (1999) Effect of field size, head motion, and rotational velocity on roll vection and illusory self-tilt in a tumbling room. Perception 28(3):299–306. https://doi.org/10.1068/p2891

    Article  CAS  PubMed  Google Scholar 

  • Allison RS, Ash A, Palmisano S (2014) Binocular contributions to linear vertical vection. J Vis 14(12):5

    Article  PubMed  Google Scholar 

  • Berthoz A, Parvard B, Young LR (1975) Perceotion of linier horizontal self-motion induced by peripheral vision (linear vection). Basic characteristics and visual vestibular interactions. Exp Brain Res 25:936–945

    Google Scholar 

  • Brandt T, Dichgans J, Koenig E (1973) Differential effects of central versus peripheral vision on egocentric and exocentric motion perception. Exp Brain Res 16:476–491. https://doi.org/10.1007/BF00234474

    Article  CAS  PubMed  Google Scholar 

  • Brandt T, Wist ER, Dichgans J (1975) Foreground and background in dynamic spatial orientation. Percept Psychophys 17:497–503

    Article  Google Scholar 

  • Bubka A, Bonato F (2010) Natural visual-field features enhance vection. Perception 39(5):627–635

    Article  PubMed  Google Scholar 

  • Dichgans J, Brandt T (1978) Visual–vestibular interaction: effects on self-motion perception and postural control. In: Held R, Leibowitz H, Teuber HL (eds) Handbook of sensory physiology, vol 8. Springer, New York, pp 755–804

    Google Scholar 

  • Emerson RC, Bergen JR, Adelson EH (1992) Directionally selective complex cells and the computation of motion energy in cat visual cortex. Vis Res 32:203–218

    Article  CAS  PubMed  Google Scholar 

  • Gurnsey R, Fleet D, Potechin C (1998) Second-order motions contribute to vection. Vis Res 38:2801–2816

    Article  CAS  PubMed  Google Scholar 

  • Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Advances in neural information processing systems, pp 545–552

  • Held R, Dichigans J, Bauer J (1975) Characteristics of moving visual scenes influencing spatial orientation. Vis Res 15:357–365

    Article  CAS  PubMed  Google Scholar 

  • Hettinger LJ, Schmidt T, Jones DL, Keshavarz B (2014) Illusory self-motion in virtual environments. In: Hale KS, Stanney KM (eds) Handbook of virtual environments: design, implementation, and applications, 2nd edn. CRC Press, New York, pp 435–466

    Chapter  Google Scholar 

  • Hiramatsu C, Fujita K (2015) Visual categorization of surface qualities of materials by capuchin monkeys and humans. Vis Res 115:71–82. https://doi.org/10.1016/j.visres.2015.07.006

    Article  PubMed  Google Scholar 

  • Hiramatsu C, Goda N, Komatsu H (2011) Transformation from image-based to perceptual representation of materials along the human ventral visual pathway. Neuroimage 57:482–494. https://doi.org/10.1016/j.neuroimage.2011.04.056

    Article  PubMed  Google Scholar 

  • Howard IP (1982) Human visual orientation. Wiley, New York

    Google Scholar 

  • Howard IP, Heckman T (1989) Circular vection as a function of the relative sizes, distances, and positions of two competing visual displays. Perception 18:657–665

    Article  CAS  PubMed  Google Scholar 

  • Ito H, Fujimoto C (2003) Compound self-motion perception induced by two kinds of optical motion. Percept Psychophys 65:874–887

    Article  PubMed  Google Scholar 

  • Ito H, Shibata I (2005) Self-motion perception from expanding and contracting optical flows overlapped with binocular disparity. Vis Res 45:397–402

    Article  PubMed  Google Scholar 

  • Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259

    Article  Google Scholar 

  • Kim J, Khuu S, Palmisano S (2016) Vection depends on perceived surface properties. Atten Percept Psychophys 78(4):1163–1173

    Article  PubMed  Google Scholar 

  • Klient H (1937) Versuche über die Wahrnehmung: I. Über Bewegung. Zeitschrift Für Psychologie 141:9–44

    Google Scholar 

  • Lishman JR, Lee DN (1973) The autonomy of visual kinaesthesis. Perception 2:287–294. https://doi.org/10.1068/p020287

    Article  CAS  PubMed  Google Scholar 

  • Lu ZL, Sperling G (1995) The functional architecture of human visual motion perception. Vis Res 35(19):2697–2722

    Article  CAS  PubMed  Google Scholar 

  • Nakamura S (2008) Effects of stimulus eccentricity on vection reevaluated with a binocularly defined depth. Jpn Psychol Res 50:77–86

    Article  Google Scholar 

  • Nakamura S (2013) The influence of miniature effects applied to the motion image upon visually induced self-motion perception. TVRSJ 18(1):1–3 [in Japanese]

    Google Scholar 

  • Nakamura S, Shimojo S (2000) A slowly moving foreground can capture an observer’s self-motion a report of new motion illusion: inverted vection. Vis Res 40:2915–2923

    Article  CAS  PubMed  Google Scholar 

  • Nakamura S, Seno T, Ito H, Sunaga S (2010) Coherent modulation of stimulus colour can affect visually induced self-motion perception. Perception 39:1579–1590

    Article  PubMed  Google Scholar 

  • Ogawa M, Hiramatsu C, Seno T (2014) Surface qualities have little effect on vection strength. Front Psychol 5:610. https://doi.org/10.3389/fpsyg.2014.00610

    Article  PubMed  PubMed Central  Google Scholar 

  • Ohmi M, Howard IP (1988) Effect of stationary objects on illusory forward self-motion induced by looming display. Perception 17:5–12

    Article  CAS  PubMed  Google Scholar 

  • Ohmi M, Howard IP, Landolt JP (1987) Circular vection as a function of foreground and background relationship. Perception 16:17–22

    Article  CAS  PubMed  Google Scholar 

  • Palmisano S (2002) Consistent stereoscopic information increases the perceived speed of vection in depth. Perception 31(4):463–480

    Article  PubMed  Google Scholar 

  • Palmisano S, Gillam B (1998) Stimulus eccentricity and spatial frequency interact to determine circular vection. Perception 27:1067–1077

    Article  CAS  PubMed  Google Scholar 

  • Palmisano S, Allison RS, Kim J, Bonato F (2011) Simulated viewpoint jitter shakes sensory conflict accounts of self-motion perception. Seeing Perceiving 24:173–200

    Article  PubMed  Google Scholar 

  • Palmisano S, Allison RS, Schira MM, Barry RJ (2015) Future challenges for vection research: definitions, functional significance, measures and neural bases. Front Psychol 6:193. https://doi.org/10.3389/fpsyg.2015.00193

    Article  PubMed  PubMed Central  Google Scholar 

  • Palmisano S, Summersby S, Davies RG, Kim J (2016) Stereoscopic advantages for vection induced by radial, circular, and spiral optic flows. J Vis 16(14):7

    Article  PubMed  Google Scholar 

  • Perlin K (2002) Improving noise. ACM Trans Graph 21(3):681–682. https://doi.org/10.1145/566654.566636

    Article  Google Scholar 

  • Riecke BE, Schulte-Pelkum J, Avraamides MN, Heyde MVD, Bülthoff HH (2006) Cognitive factors can influence selfmotion perception (vection) in virtual reality. ACM Trans Appl Percept 3:194–216

    Article  Google Scholar 

  • Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, pp 234–241

  • Sauvan XM, Bonnet C (1993) Properties of curvilinear vection. Percept Psychophys 53:429–435

    Article  CAS  PubMed  Google Scholar 

  • Sauvan XM, Bonnet C (1995) Spatiotemporal boundaries of linear vection. Percept Psychophys 57:898–904

    Article  CAS  PubMed  Google Scholar 

  • Schmid AC, Doerschner K (2018) Shatter and splatter: the contribution of mechanical and optical properties to the perception of soft and hard breaking materials. J Vis 18(1):14

    Article  PubMed  Google Scholar 

  • Seno T, Nakamura S, Ito H, Sunaga S (2010) Static visual components without depth modulation alter the strength of vection. Vis Res 50(19):1972–1981

    Article  PubMed  Google Scholar 

  • Seno T, Abe K, Kiyokawa S (2013) Wearing heavy iron clogs can inhibit vection. Multisens Res 26(6):569–580

    Article  PubMed  Google Scholar 

  • Seno T, Palmisano S, Riecke BE, Nakamura S (2015) Walking without optic flow reduces subsequent vection. Exp Brain Res 233(1):275–281

    Article  PubMed  Google Scholar 

  • Seno T, Sawai KI, Kanaya H, Wakebe T, Ogawa M, Fujii Y, Palmisano S (2017) The oscillating potential model of visually induced vection. i-Perception 8(6):2041669517742176

    Article  PubMed  PubMed Central  Google Scholar 

  • Sharan L (2009) The perception of material qualities in real-world images (Unpublished doctoral dissertation). Massachusetts Institute of Technology, Boston

    Google Scholar 

  • Sharan L, Rosenholtz R, Adelson EH (2014) Accuracy and speed of material categorization in real-world images. J Vis 14(9):12

    Article  PubMed  PubMed Central  Google Scholar 

  • Telford L, Spratley J, Frost B (1992) Linear vection in the central visual field facilitated by kinetic depth cues. Perception 21:337–349

    Article  CAS  PubMed  Google Scholar 

  • Telford L, Howard I, Ohmi M (1995) Heading judgments during active and passive self-motion. Exp Brain Res. https://doi.org/10.1007/BF00231984

    Article  PubMed  Google Scholar 

  • Uesaki M, Ashida H (2015) Optic-flow selective cortical sensory regions associated with self-reported states of vection. Front Psychol 6:775

    Article  PubMed  PubMed Central  Google Scholar 

  • Wada A, Sakano Y, Ando H (2016) Differential responses to a visual self-motion signal in human medial cortical regions revealed by wide-view stimulation. Front Psychol 7:309

    Article  PubMed  PubMed Central  Google Scholar 

  • Witkin HA, Asch SE (1948) Studies in space orientation. IV. Further experiments on perception of the upright with displaced visual fields. J Exp Psychol 38(6):762–782. https://doi.org/10.1037/h0053671

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Takeharu Seno.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (MP4 47,091 kb)

Supplementary material 2 (DOCX 2204 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Morimoto, Y., Sato, H., Hiramatsu, C. et al. Material surface properties modulate vection strength. Exp Brain Res 237, 2675–2690 (2019). https://doi.org/10.1007/s00221-019-05620-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00221-019-05620-0

Keywords

Navigation