Commonalities and variations in emotion representation across modalities and brain regions

Humans express emotions through various modalities such as facial expressions and natural language. However, the relationships between emotions expressed through different modalities and their correlations with neural activities remain uncertain. Here, we aimed to unveil some of these uncertainties by investigating the similarity of emotion representations across modalities and brain regions. First, we represented various emotion categories as multi-dimensional vectors derived from visual (face), linguistic, and visio-linguistic data, and used representational similarity analysis to compare these modalities. Second, we examined the linear transferability of emotion representation from other modalities to the visual modality. Third, we compared the representational structure derived in the first step with those from brain activities across 360 regions. Our findings revealed that emotion representations share commonalities across modalities with modality-type dependent variations, and they can be linearly mapped from other modalities to the visual modality. Additionally, emotion representations in uni-modalities showed relatively higher similarity with specific brain regions, while multi-modal emotion representation was most similar to representations across the entire brain region. These findings suggest that emotional experiences are represented differently across various brain regions with varying degrees of similarity to different modality types, and that they may be multi-modally conveyable in visual and linguistic domains.

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Publication Details

Journal
Scientific Reports
Published
2024-09-09
DOI
https://doi.org/10.1038/s41598-024-71690-y
Citations
6
Primary Topic
Face Recognition and Perception
Type
article
Field-Weighted Citation Impact
2.06

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article

Commonalities and variations in emotion representation across modalities and brain regions

Ryusuke Hayashi, Hiroaki Kiyokawa
6 citations
Scientific Reports
Face Recognition and Perception
2.06
article

Commonalities and variations in emotion representation across modalities and brain regions

Ryusuke Hayashi, Hiroaki Kiyokawa
article en
6 citations

Abstract

Humans express emotions through various modalities such as facial expressions and natural language. However, the relationships between emotions expressed through different modalities and their correlations with neural activities remain uncertain. Here, we aimed to unveil some of these uncertainties by investigating the similarity of emotion representations across modalities and brain regions. First, we represented various emotion categories as multi-dimensional vectors derived from visual (face), linguistic, and visio-linguistic data, and used representational similarity analysis to compare these modalities. Second, we examined the linear transferability of emotion representation from other modalities to the visual modality. Third, we compared the representational structure derived in the first step with those from brain activities across 360 regions. Our findings revealed that emotion representations share commonalities across modalities with modality-type dependent variations, and they can be linearly mapped from other modalities to the visual modality. Additionally, emotion representations in uni-modalities showed relatively higher similarity with specific brain regions, while multi-modal emotion representation was most similar to representations across the entire brain region. These findings suggest that emotional experiences are represented differently across various brain regions with varying degrees of similarity to different modality types, and that they may be multi-modally conveyable in visual and linguistic domains.

Scientific ReportsVol. 14(1)
Saitama University (JP), National Institute of Advanced Industrial Science and Technology (JP)
Iran Telecommunication Research Center, Japan Society for the Promotion of Science, Japan Science and Technology Agency, National Institute of Information and Communications Technology, Moonshot Research and Development Program
Openalex Percentile: Top 13%
Face Recognition and Perception
2.06
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