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Huo H, Liu X, Tang Z, Dong Y, Zhao D, Chen D, Tang M, Qiao X, Du X, Guo J, Wang J, Fan Y. Interhemispheric multisensory perception and Bayesian causal inference. iScience 2023; 26:106706. [PMID: 37250338 PMCID: PMC10214730 DOI: 10.1016/j.isci.2023.106706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/07/2023] [Accepted: 04/17/2023] [Indexed: 05/31/2023] Open
Abstract
In daily life, our brain needs to eliminate irrelevant signals and integrate relevant signals to facilitate natural interactions with the surrounding. Previous study focused on paradigms without effect of dominant laterality and found that human observers process multisensory signals consistent with Bayesian causal inference (BCI). However, most human activities are of bilateral interaction involved in processing of interhemispheric sensory signals. It remains unclear whether the BCI framework also fits to such activities. Here, we presented a bilateral hand-matching task to understand the causal structure of interhemispheric sensory signals. In this task, participants were asked to match ipsilateral visual or proprioceptive cues with the contralateral hand. Our results suggest that interhemispheric causal inference is most derived from the BCI framework. The interhemispheric perceptual bias may vary strategy models to estimate the contralateral multisensory signals. The findings help to understand how the brain processes the uncertainty information coming from interhemispheric sensory signals.
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Affiliation(s)
- Hongqiang Huo
- Key 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
| | - Xiaoyu Liu
- Key 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
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100083, China
| | - Zhili Tang
- Key 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
| | - Ying Dong
- Key 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
| | - Di Zhao
- Key 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
| | - Duo Chen
- Key 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
| | - Min Tang
- Key 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
| | - Xiaofeng Qiao
- Key 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
| | - Xin Du
- Key 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
| | - Jieyi Guo
- Key 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
| | - Jinghui Wang
- Key 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
| | - Yubo Fan
- Key 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
- School of Medical Science and Engineering Medicine, Beihang University, Beijing 100083, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100083, China
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Hertz U, Amedi A. Flexibility and Stability in Sensory Processing Revealed Using Visual-to-Auditory Sensory Substitution. Cereb Cortex 2014; 25:2049-64. [PMID: 24518756 PMCID: PMC4494022 DOI: 10.1093/cercor/bhu010] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The classical view of sensory processing involves independent processing in sensory cortices and multisensory integration in associative areas. This hierarchical structure has been challenged by evidence of multisensory responses in sensory areas, and dynamic weighting of sensory inputs in associative areas, thus far reported independently. Here, we used a visual-to-auditory sensory substitution algorithm (SSA) to manipulate the information conveyed by sensory inputs while keeping the stimuli intact. During scan sessions before and after SSA learning, subjects were presented with visual images and auditory soundscapes. The findings reveal 2 dynamic processes. First, crossmodal attenuation of sensory cortices changed direction after SSA learning from visual attenuations of the auditory cortex to auditory attenuations of the visual cortex. Secondly, associative areas changed their sensory response profile from strongest response for visual to that for auditory. The interaction between these phenomena may play an important role in multisensory processing. Consistent features were also found in the sensory dominance in sensory areas and audiovisual convergence in associative area Middle Temporal Gyrus. These 2 factors allow for both stability and a fast, dynamic tuning of the system when required.
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Affiliation(s)
- Uri Hertz
- Department of Medical Neurobiology, Institute for Medical Research Israel-Canada (IMRIC), Hadassah Medical School, Hebrew University of Jerusalem, Jerusalem 91220, Israel Interdisciplinary Center for Neural Computation, The Edmond & Lily Safra Center for Brain Sciences (ELSC), Hebrew University of Jerusalem, Jerusalem 91905, Israel
| | - Amir Amedi
- Department of Medical Neurobiology, Institute for Medical Research Israel-Canada (IMRIC), Hadassah Medical School, Hebrew University of Jerusalem, Jerusalem 91220, Israel Interdisciplinary Center for Neural Computation, The Edmond & Lily Safra Center for Brain Sciences (ELSC), Hebrew University of Jerusalem, Jerusalem 91905, Israel
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