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Interhemispheric multisensory perception and Bayesian causal inference.

Hongqiang Huo1, Xiaoyu Liu1,2, Zhili Tang1

  • 1Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.

Iscience
|May 30, 2023
PubMed
Summary
This summary is machine-generated.

The brain integrates sensory signals for interaction, using Bayesian causal inference (BCI). This study shows BCI also explains how the brain processes information from both hands, crucial for bilateral interactions.

Keywords:
Biological sciencesComputing methodologySensory neuroscience

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Sensory Processing

Background:

  • The brain filters irrelevant sensory input and integrates relevant signals for effective interaction with the environment.
  • Previous research established Bayesian causal inference (BCI) as a model for processing multisensory signals in tasks without dominant laterality.
  • Human activities often involve bilateral interactions and interhemispheric sensory signal processing, a domain where BCI's applicability remains less understood.

Purpose of the Study:

  • To investigate the applicability of the Bayesian causal inference (BCI) framework to interhemispheric sensory signal processing.
  • To elucidate the causal structure underlying multisensory integration between the two hemispheres of the brain.
  • To understand how the brain manages uncertainty in interhemispheric sensory information during bilateral tasks.

Main Methods:

  • Development and implementation of a bilateral hand-matching task.
  • Participants were presented with ipsilateral visual or proprioceptive cues to match with their contralateral hand.
  • Analysis of perceptual biases and strategy models used for estimating contralateral multisensory signals.

Main Results:

  • The study provides evidence that interhemispheric causal inference aligns with the Bayesian causal inference (BCI) framework.
  • Perceptual biases in interhemispheric processing suggest varied strategy models for estimating contralateral multisensory signals.
  • The findings indicate that BCI is a suitable model for understanding how the brain integrates information across hemispheres.

Conclusions:

  • The Bayesian causal inference (BCI) framework effectively explains the processing of interhemispheric sensory signals.
  • The brain employs flexible strategy models to estimate multisensory signals from the contralateral side during bilateral interactions.
  • This research enhances our understanding of neural mechanisms for processing sensory uncertainty in bilateral human activities.