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Related Experiment Video

Updated: Jun 3, 2026

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
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Central tendency and serial dependence in vestibular path integration.

Sophie C M J Willemsen1, Leonie Oostwoud Wijdenes1, Robert J van Beers1,2

  • 1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.

Journal of Neurophysiology
|October 3, 2024
PubMed
Summary
This summary is machine-generated.

Vestibular path integration, updating position from self-motion, shows biases similar to visual navigation. Reproductions are affected by distance context, aligning with Bayesian models.

Keywords:
central tendencydistance reproductionpath integrationserial dependencevestibular system

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Area of Science:

  • Neuroscience
  • Human Perception
  • Spatial Navigation

Background:

  • Path integration relies on self-motion cues to update position.
  • Visual path integration studies reveal central tendency and serial dependence biases.
  • Vestibular system is crucial for self-motion perception.

Purpose of the Study:

  • Investigate biases in vestibular path integration.
  • Determine if central tendency and serial dependence effects are present in vestibular navigation.
  • Examine the influence of stimulus presentation context on vestibular distance reproduction.

Main Methods:

  • Participants performed a distance reproduction task in darkness on a linear motion platform.
  • Stimulus distances were varied and presented in randomized or blocked orders.
  • Vestibular signals were the primary source of self-motion information.

Main Results:

  • Reproduced distances exhibited positive central tendency and serial dependence effects.
  • Presentation context significantly influenced reproduction behavior.
  • Results align with Bayesian Kalman filter models for path integration.

Conclusions:

  • Vestibular path integration is subject to biases similar to visual path integration.
  • Context-dependent effects influence spatial reproductions based on vestibular input.
  • Bayesian models can predict biases in both visual and vestibular path integration.