Crossmodal hierarchical predictive coding for audiovisual sequences in the human brain
Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledg...
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| Published in: | Communications biology Vol. 7; no. 1; pp. 965 - 15 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Nature Publishing Group UK
09.08.2024
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2399-3642, 2399-3642 |
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| Abstract | Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge.
A generalized framework for predictive coding across modalities and hierarchies reveals how the brain represents and learns crossmodal knowledge in sequences. |
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| AbstractList | Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge. Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge. A generalized framework for predictive coding across modalities and hierarchies reveals how the brain represents and learns crossmodal knowledge in sequences. Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge.Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge. Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge.A generalized framework for predictive coding across modalities and hierarchies reveals how the brain represents and learns crossmodal knowledge in sequences. Abstract Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within individual sensory modalities, evidence for predictive processing across sensory modalities is limited. Here, we examine how crossmodal knowledge is represented and learned in the brain, by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at both the stimulus and sequence levels. To dissect the complex predictive signals in our EEG data, we employed a model-fitting approach to untangle neural interactions across modalities and hierarchies. The model-fitting result demonstrates that audiovisual integration occurs at both the levels of individual stimulus interactions and multi-stimulus sequences. Furthermore, we identify the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and reveal that auditory and visual prediction errors are rapidly redirected to the central-parietal electrodes during learning through alpha-band interactions. Our study suggests a crossmodal predictive coding mechanism where unimodal predictions are processed by distributed brain networks to form crossmodal knowledge. |
| ArticleNumber | 965 |
| Author | Chao, Zenas C. Fu, Chin-Kun Koike, Shinsuke Huang, Yiyuan Teresa Wu, Chien-Te Fang, Yi-Xin Miranda |
| Author_xml | – sequence: 1 givenname: Yiyuan Teresa orcidid: 0000-0002-8795-9358 surname: Huang fullname: Huang, Yiyuan Teresa organization: International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Department of Multidisciplinary Sciences, Graduate School of Arts and Sciences, The University of Tokyo – sequence: 2 givenname: Chien-Te orcidid: 0000-0001-7349-0731 surname: Wu fullname: Wu, Chien-Te organization: International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, School of Occupational Therapy, College of Medicine, National Taiwan University – sequence: 3 givenname: Yi-Xin Miranda surname: Fang fullname: Fang, Yi-Xin Miranda organization: School of Occupational Therapy, College of Medicine, National Taiwan University – sequence: 4 givenname: Chin-Kun surname: Fu fullname: Fu, Chin-Kun organization: School of Occupational Therapy, College of Medicine, National Taiwan University – sequence: 5 givenname: Shinsuke surname: Koike fullname: Koike, Shinsuke organization: International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Department of Multidisciplinary Sciences, Graduate School of Arts and Sciences, The University of Tokyo, University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM) – sequence: 6 givenname: Zenas C. orcidid: 0000-0001-9402-0851 surname: Chao fullname: Chao, Zenas C. email: zenas.c.chao@gmail.com organization: International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39122960$$D View this record in MEDLINE/PubMed |
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| Snippet | Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched within... Abstract Predictive coding theory suggests the brain anticipates sensory information using prior knowledge. While this theory has been extensively researched... |
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| SubjectTerms | 631/378/2649 631/378/3917 Acoustic Stimulation Adult Auditory discrimination learning Auditory Perception - physiology Biomedical and Life Sciences Brain - physiology Coding theory Cross-modal EEG Electroencephalography Female Frequency Humans Information processing Life Sciences Male Neural coding Photic Stimulation Predictions Sensorimotor integration Sensory integration Temporal variations Visual discrimination learning Visual Perception - physiology Young Adult |
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| Title | Crossmodal hierarchical predictive coding for audiovisual sequences in the human brain |
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