{"title":"利用多层多模态潜在德里希勒分配整合视觉、生理和文字信息的情绪概念形成研究","authors":"Kazuki Tsurumaki, Chie Hieida, Kazuki Miyazawa","doi":"arxiv-2404.08295","DOIUrl":null,"url":null,"abstract":"How are emotions formed? Through extensive debate and the promulgation of\ndiverse theories , the theory of constructed emotion has become prevalent in\nrecent research on emotions. According to this theory, an emotion concept\nrefers to a category formed by interoceptive and exteroceptive information\nassociated with a specific emotion. An emotion concept stores past experiences\nas knowledge and can predict unobserved information from acquired information.\nTherefore, in this study, we attempted to model the formation of emotion\nconcepts using a constructionist approach from the perspective of the\nconstructed emotion theory. Particularly, we constructed a model using\nmultilayered multimodal latent Dirichlet allocation , which is a probabilistic\ngenerative model. We then trained the model for each subject using vision,\nphysiology, and word information obtained from multiple people who experienced\ndifferent visual emotion-evoking stimuli. To evaluate the model, we verified\nwhether the formed categories matched human subjectivity and determined whether\nunobserved information could be predicted via categories. The verification\nresults exceeded chance level, suggesting that emotion concept formation can be\nexplained by the proposed model.","PeriodicalId":501033,"journal":{"name":"arXiv - CS - Symbolic Computation","volume":"46 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study of Emotion Concept Formation by Integrating Vision, Physiology, and Word Information using Multilayered Multimodal Latent Dirichlet Allocation\",\"authors\":\"Kazuki Tsurumaki, Chie Hieida, Kazuki Miyazawa\",\"doi\":\"arxiv-2404.08295\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"How are emotions formed? Through extensive debate and the promulgation of\\ndiverse theories , the theory of constructed emotion has become prevalent in\\nrecent research on emotions. According to this theory, an emotion concept\\nrefers to a category formed by interoceptive and exteroceptive information\\nassociated with a specific emotion. An emotion concept stores past experiences\\nas knowledge and can predict unobserved information from acquired information.\\nTherefore, in this study, we attempted to model the formation of emotion\\nconcepts using a constructionist approach from the perspective of the\\nconstructed emotion theory. Particularly, we constructed a model using\\nmultilayered multimodal latent Dirichlet allocation , which is a probabilistic\\ngenerative model. We then trained the model for each subject using vision,\\nphysiology, and word information obtained from multiple people who experienced\\ndifferent visual emotion-evoking stimuli. To evaluate the model, we verified\\nwhether the formed categories matched human subjectivity and determined whether\\nunobserved information could be predicted via categories. The verification\\nresults exceeded chance level, suggesting that emotion concept formation can be\\nexplained by the proposed model.\",\"PeriodicalId\":501033,\"journal\":{\"name\":\"arXiv - CS - Symbolic Computation\",\"volume\":\"46 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Symbolic Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2404.08295\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Symbolic Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2404.08295","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
情绪是如何形成的?经过广泛的争论和各种理论的传播,建构情绪理论(the theory of constructed emotion)已成为近期情绪研究的主流。根据这一理论,情绪概念指的是由与特定情绪相关的内感知信息和外感知信息形成的一个类别。因此,在本研究中,我们试图从情绪建构理论的角度,用建构主义方法来模拟情绪概念的形成。特别是,我们使用多层多模态潜狄利克特分配(latent Dirichlet allocation)构建了一个模型,这是一个概率生成模型。然后,我们利用从多个经历过不同视觉情绪诱发刺激的人那里获得的视觉、生理和文字信息,为每个受试者训练模型。为了对模型进行评估,我们验证了所形成的类别是否与人类的主观性相匹配,并确定了未观察到的信息是否可以通过类别进行预测。验证结果超过了偶然水平,表明情绪概念的形成可以用提出的模型来解释。
Study of Emotion Concept Formation by Integrating Vision, Physiology, and Word Information using Multilayered Multimodal Latent Dirichlet Allocation
How are emotions formed? Through extensive debate and the promulgation of
diverse theories , the theory of constructed emotion has become prevalent in
recent research on emotions. According to this theory, an emotion concept
refers to a category formed by interoceptive and exteroceptive information
associated with a specific emotion. An emotion concept stores past experiences
as knowledge and can predict unobserved information from acquired information.
Therefore, in this study, we attempted to model the formation of emotion
concepts using a constructionist approach from the perspective of the
constructed emotion theory. Particularly, we constructed a model using
multilayered multimodal latent Dirichlet allocation , which is a probabilistic
generative model. We then trained the model for each subject using vision,
physiology, and word information obtained from multiple people who experienced
different visual emotion-evoking stimuli. To evaluate the model, we verified
whether the formed categories matched human subjectivity and determined whether
unobserved information could be predicted via categories. The verification
results exceeded chance level, suggesting that emotion concept formation can be
explained by the proposed model.