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Related Concept Videos

Somatosensation01:33

Somatosensation

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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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Related Experiment Video

Updated: Mar 13, 2026

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback
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Bayesian Alternation during Tactile Augmentation.

Caspar M Goeke1, Serena Planera1, Holger Finger1

  • 1Institute of Cognitive Science, University of Osnabrück Osnabrück, Germany.

Frontiers in Behavioral Neuroscience
|October 25, 2016
PubMed
Summary
This summary is machine-generated.

Adults integrate augmented sensory information with native senses using a subjective Bayesian alternation process. This top-down weighting model, not objective reliability, best explains combined sensory perception.

Keywords:
Bayesian alternationmultimodal integrationsensory augmentationsubjective uncertaintytactile stimulationvestibular system

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Area of Science:

  • Neuroscience
  • Human Perception
  • Sensory Augmentation

Background:

  • Bayesian principles accurately describe multisensory integration in humans.
  • Limited research exists on cue combination between native and augmented senses.

Purpose of the Study:

  • To investigate if untrained adults can integrate augmented sensory cues with native sensory information.
  • To compare different models of cue combination from native and augmented senses.

Main Methods:

  • Developed a tactile augmentation device translating whole-body yaw rotation to tactile stimulation.
  • Used a two-interval forced choice task with blindfolded participants on a rotating platform.
  • Compared objective Bayesian alternation, Bayesian integration, and winner-takes-all (WTA) models, and a subjective Bayesian alternation model.

Main Results:

  • The objective Bayesian alternation model predicted bimodal performance better than the Bayesian integration model.
  • A non-Bayesian WTA model showed slightly higher accuracy than the objective Bayesian alternation model.
  • The subjective Bayesian alternation model, using weights from a questionnaire, provided the most accurate prediction of bimodal performance.

Conclusions:

  • Untrained adults integrate information from augmented and native sensory modalities through a subjective Bayesian alternation process.
  • Top-down subjective weighting, rather than bottom-up objective cue reliability, better explains performance in bimodal sensory integration.