Experimental Brain Research

, Volume 174, Issue 3, pp 528–543 | Cite as

Perception of angular displacement without landmarks: evidence for Bayesian fusion of vestibular, optokinetic, podokinesthetic, and cognitive information

  • Reinhart Jürgens
  • Wolfgang BeckerEmail author
Research Article


The perception of angular displacement during self turning is generally based on a combination of redundant signals from different sources. For example, during active turning in a visually structured environment devoid of landmarks, podokinesthetic, vestibular, and optokinetic velocity signals are fused and integrated over time to yield a unitary percept of the ongoing change in angular position (‘podokinesthetic’ refers to proprioceptive and corollary signals related to leg and foot movement). Previously we have shown that the fusion of two of these afferents improves perceptual accuracy and reliability in comparison to when only one is available. For example, with only a single modality available, slow rotations are perceived to be significantly larger than fast ones, whereas the combination of two modalities greatly reduces this difference. These observations spurred the hypothesis that displacement perception results from a weighted average of bottom-up (sensory) signals and top-down signals (a priori knowledge or expectation), with the weight of the latter decreasing the more sensory information is available. We now ask (1) whether the accuracy of angular displacement estimation can be further improved if it can draw on all three sensory modalities instead of only two, and (2) whether bottom-up sensory and top-down a priori information is combined for displacement estimation in a statistically optimal way. To this end 12 healthy subjects (Ss) standing on a turning platform surrounded by a rotatable optokinetic pattern were exposed to 6 different sensory conditions: pure podokinesthetic (P), vestibular (V), or optokinetic (O) stimulation, and combined podokinesthetic-vestibular (PV), vestibular-optokinetic (VO), or podokinesthetic-vestibular-optokinetic (PVO) stimulation. Stimuli had constant angular velocities of either 15, 30, or 60°/s. Subjects were to press a signal button when they felt that angular displacement had reached a previously instructed magnitude (150–900°). In agreement with earlier observations, the combination of two sensory signals improved the accuracy of displacement perception by reducing both the variance of subjects’ displacement estimates and their dependence on turning velocity. Adding a third sensory signal (condition PVO) led to a further reduction of variance and almost eliminated the effect of velocity. We show that these experimental results are compatible with a probabilistic fusion mechanism based on Bayes’ law. This mechanism would operate on logarithmic representations of turning velocity and proceed in two stages. A first stage fuses all available bottom-up information to create a unitary representation of the velocity signalled by the different sensory modalities. A second stage then fuses this sensory information with top-down a priori information; the latter creates a bias in favour of a ‘default velocity’ that grows as the uncertainty of the sensory information increases. Our experimental data agree with the relation between (1) the variance of displacement estimates and (2) their modulation by velocity predicted by this scheme.


Perception of angular displacement Multisensory convergence Sensory fusion Bayesian inference Vestibular stimulation Optokinetic stimulation Podokinesthetic stimulation Bottom-up information Top-down information Cognitive bias 



We are indebted to Ralph Kühne for his support with electronic and data processing equipment and to Bruno Glinkemann for taking care for the mechanical equipment. This work was supported by Deutsche Forschungsgemeinschaft, grant Be 783/3.


  1. Anastasio TJ, Patton PE, Belkacem-Boussaid K (2000) Using Bayes’ rule to model multisensory enhancement in the superior colliculus. Neural Comput 12:1165–1187PubMedCrossRefGoogle Scholar
  2. Bakker NH, Werkhoven PJ, Passenier PO (1999) The effects of proprioceptive and visual feedback on geographical orientation in virtual environments. Presence 8:36–53CrossRefGoogle Scholar
  3. Battaglia PW, Jacobs RA, Aslin RN (2003) Bayesian integration of visual and auditory signals for spatial localization. J Opt Soc Am [A] 20:1391–1397CrossRefGoogle Scholar
  4. Becker W, Nasios G, Raab S, Jürgens R (2002) Fusion of vestibular and podokinesthetic information during self-turning towards instructed targets. Exp Brain Res 144:458–474PubMedCrossRefGoogle Scholar
  5. van Beers R, Sittig AC, Denier van der Gon JJ (1999) Integration of proprioceptive and visual position-information: an experimentally supported model. J Neurophysiol 81:1355–1364PubMedGoogle Scholar
  6. Brandt T, Glasauer S, Stephan T, Bense S, Yousry TA, Deutschländer A, Dieterich M (2002) Visual-vestibular and visuovisual cortical interactions. Ann NY Acad Sci 956:230–241PubMedCrossRefGoogle Scholar
  7. Deneve S, Pouget A (2004) Bayesian multisensory integration and cross-modal spatial links. J Physiol Paris 98:249–258PubMedCrossRefGoogle Scholar
  8. Ernst MO, Banks MS (2002) Humans integrate visual and haptic information in a statistically optimal fashion. Nature (London) 415:429–433CrossRefGoogle Scholar
  9. Guo K, Nevado A, Robertson RG, Pulgarin M, Thiele A, Young MP (2004) Effects on orientation perception of manipulating the spatio-temporal prior probability of stimuli. Vision Res 44:2349–2358PubMedCrossRefGoogle Scholar
  10. Hürlimann F, Kiper DC, Carandini M (2002) Testing the Bayesian model of perceived speed. Vision Res 42:2253–2257PubMedCrossRefGoogle Scholar
  11. Howard IP (1997) Interactions within and between the spatial senses. J Vestib Res 7:311–345PubMedCrossRefGoogle Scholar
  12. Jürgens R, Nasios G, Becker W (2003) Vestibular, optokinetic, and cognitive contribution to the guidance of passive self-rotation toward instructed targets. Exp Brain Res 151:90–107PubMedCrossRefGoogle Scholar
  13. Jacobs RA (1999) Optimal integration of texture and motion cues to depth. Vision Res 39:3621–3629PubMedCrossRefGoogle Scholar
  14. Knill DC, Pouget A (2004) The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci 27:712–719PubMedCrossRefGoogle Scholar
  15. Knill DC, Saunders JA (2003) Do humans optimally integrate stereo and texture information for judgements of surface slant? Vision Res 43:2539–2558PubMedCrossRefGoogle Scholar
  16. Körding KP, Wolpert DM (2004) Bayesian integration in sensorimotor learning. Nature 427:244–247PubMedCrossRefGoogle Scholar
  17. Marlinsky VV (1999) Vestibular and vestibulo-proprioceptive perception of motion in the horizontal plane in blindfolded man - II. Estimations of rotations about the earth-vertical axis. Neuroscience 90:395–401PubMedCrossRefGoogle Scholar
  18. Maunsell JH, Van Essen DC (1983) Functional properties of neurons in middle temporal visual area of the macaque monkey. I. Selectivity for stimulus direction, speed, and orientation 49:1127–1147Google Scholar
  19. Mergner T, Siebold C, Schweigart G, Becker W (1991) Human perception of horizontal trunk and head rotation in space during vestibular and neck stimulation. Exp Brain Res 85:389–404PubMedCrossRefGoogle Scholar
  20. Mergner T, Schweigart G, Müller M, Hlavacka F, Becker W (2000) Visual contributions to human self-motion perception during horizontal body rotation. Arch Ital Biol 138:139–166PubMedGoogle Scholar
  21. Nover H, Anderson CH, DeAngelis GC (2005) A logarithmic, scale-invariant representation of speed in macaque middle temporal area accounts for speed discrimination performance. J Neurosci 25:10049–10060PubMedCrossRefGoogle Scholar
  22. Papoulis A (1965) Probability, random variables and stochastic processes. McGraw-Hill, NewYorkGoogle Scholar
  23. Poulton EC (1977) Quantitative subjective assessments are almost always biased, sometimes completely misleading. Br J Psychol 68:409–425Google Scholar
  24. Stocker AA, Simoncelli EP (2005) Constraining a Bayesian model of human visual speed perception. In: Saul K, Weiss Y, Bottou L (eds) Advances in neural information proceeding systems NIPS 17:1361–1368Google Scholar
  25. Taube JS (1995) Head-direction cells recorded in the anterior thalamic nuclei of freely moving rats. J Neurosci 15:70–86PubMedGoogle Scholar
  26. Weiss Y, Simoncelli EP, Adelson EH (2002) Motion illusions as optimal percepts. Nat Neurosci 5:598–604PubMedCrossRefGoogle Scholar
  27. Young LR (1981) Perception of the body in space: mechanisms. In: Geiger SR (ed) Handbook of physiology, Section 1: The nervous system, Vol III. American Physiological Society, Bethesda, pp 1023–1066Google Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Sektion NeurophysiologieUniversität UlmUlmGermany

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