Integration of vestibular and proprioceptive signals for spatial updating

  • Ilja FrissenEmail author
  • Jennifer L. CamposEmail author
  • Jan L. Souman
  • Marc O. Ernst
Research Article


Spatial updating during self-motion typically involves the appropriate integration of both visual and non-visual cues, including vestibular and proprioceptive information. Here, we investigated how human observers combine these two non-visual cues during full-stride curvilinear walking. To obtain a continuous, real-time estimate of perceived position, observers were asked to continuously point toward a previously viewed target in the absence of vision. They did so while moving on a large circular treadmill under various movement conditions. Two conditions were designed to evaluate spatial updating when information was largely limited to either proprioceptive information (walking in place) or vestibular information (passive movement). A third condition evaluated updating when both sources of information were available (walking through space) and were either congruent or in conflict. During both the passive movement condition and while walking through space, the pattern of pointing behavior demonstrated evidence of accurate egocentric updating. In contrast, when walking in place, perceived self-motion was underestimated and participants always adjusted the pointer at a constant rate, irrespective of changes in the rate at which the participant moved relative to the target. The results are discussed in relation to the maximum likelihood estimation model of sensory integration. They show that when the two cues were congruent, estimates were combined, such that the variance of the adjustments was generally reduced. Results also suggest that when conflicts were introduced between the vestibular and proprioceptive cues, spatial updating was based on a weighted average of the two inputs.


Multisensory integration Locomotion Vestibular Proprioceptive Spatial updating Maximum likelihood estimation 



The first two authors contributed equally to this work. We thank Michael Weyel for technical assistance and Dr John Butler for valuable advice on the data analysis. We also thank Dr Jack Loomis for earlier valuable discussions and insights. We gratefully acknowledge the work done by the mechanical and electrical workshops of the Max Planck Institute for Biological Cybernetics in adapting the CTM and making it operational. This work was funded by the European 6th Framework Programme CyberWalk (FP6-511092). The writing of this paper by the first author was partially supported by a NSERC grant attributed to Catherine Guastavino (McGill University).


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Institut de Recherche en Communication et Cybernétique de Nantes (IRCCyN), UMR CNRS 6597Nantes Cedex 03France
  2. 2.Max Planck Institute for Biological Cybernetics, Multisensory Perception and Action GroupTübingenGermany
  3. 3.Max Planck Institute for Biological Cybernetics, Human Perception, Cognition, and Action GroupTübingenGermany
  4. 4.Department of PsychologyUniversity of TorontoTorontoCanada
  5. 5.Toronto Rehabilitation InstituteTorontoCanada
  6. 6.Department of Cognitive NeuroscienceUniversity of BielefeldBielefeldGermany

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