The neural representation of vocal emotion: representational similarity analysis and information-theoretic approaches

声音情感的神经表征：表征相似性分析和信息论方法

• 批准号: BB/M009742/1
• 负责人: Joachim Gross
• 金额: $ 94.69万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM009742%2F1
• 关键词: neural; representation; vocal; emotion; representational

Recognizing and interpreting emotions in other persons is crucial for social interactions. In particular, people of all cultures are able to recognize emotions in vocalizations without speech such as laughs, cries or screams of fear. But how our brain analyses emotion in voices remains poorly understood, compared to how we perceive emotion in faces, for example.In this project we bring together several expert co-investigators and external collaborators to address two new streams of unanswered questions on the way our brain processes emotional information in voice. We will use several complementary neuroimaging techniques to measure cerebral activity with the best precision currently possible, and advanced analysis techniques to ask two main series of questions:(1) Does the brain processes emotions as different categories (e.g., fear, anger, pleasure) or rather as a continuum emotions each characterized by different amount of activation (arousal) or how negative/positive the expression is (valence)?(2) How do different nodes of the oscillatory network of brain regions responsible for analysing emotion in voice interact with one another? Do they share information between nodes in a synergistic way, or is there redundancy between the different nodes? And if yes, how, where and when is emotional information combined to arrive at the recognition of a specific emotion?The results from this project will bring important new knowledge on how the normal brain works, with longer-term impact on the treatment and diagnosis of communication disorders and the development of better neural prostheses such as cochlear implants. The project will also contribute to consolidating and preserving UK's leading position in advanced neuroscience research.

识别和解释他人的情绪对于社交互动至关重要。特别是，各种文化背景的人们都能够在不言语的情况下识别发声中的情绪，例如笑、哭或恐惧的尖叫。但是，与我们如何感知面部情感相比，我们的大脑如何分析声音中的情感仍然知之甚少。在这个项目中，我们汇集了几位专家联合研究人员和外部合作者，以解决关于我们的大脑处理声音中的情感信息的方式的两个新的未解答的问题。我们将使用几种互补的神经成像技术以目前可能的最高精度测量大脑活动，并使用先进的分析技术来提出两个主要系列问题：（1）大脑是否将情绪处理为不同的类别（例如恐惧，愤怒，快乐），或者更确切地说作为连续情绪，每个情绪的特征是不同的激活量（唤醒）或表达的消极/积极程度（效价）？（2）大脑的不同节点如何处理情绪？ 负责分析声音中情绪的大脑区域的振荡网络是否相互影响？它们是否以协同方式在节点之间共享信息，或者不同节点之间是否存在冗余？如果是的话，情绪信息如何、在何处、何时结合起来，以达到对特定情绪的识别？该项目的结果将带来关于正常大脑如何工作的重要新知识，对沟通障碍的治疗和诊断以及更好的神经假体（如人工耳蜗）的开发产生长期影响。该项目还将有助于巩固和保持英国在先进神经科学研究领域的领先地位。

项目成果

"Hearing faces and seeing voices": Amodal coding of person identity in the human brain.

• DOI: 10.1038/srep37494
• 发表时间: 2016-11-24
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Awwad Shiekh Hasan B;Valdes-Sosa M;Gross J;Belin P
• 通讯作者: Belin P

Vocal laziness by sex: Supplementary figures from Poor neuro-motor tuning of the human larynx: a comparison of sung and whistled pitch imitation

不同性别的声音懒惰：来自人类喉部神经运动调节不良的补充数据：歌唱和口哨音调模仿的比较

• DOI: 10.6084/m9.figshare.6056933
• 发表时间: 2018
• 作者: Belyk M

Graphical depiction of bivariate functional principal components analysis from Whistling shares a common tongue with speech: bioacoustics from real-time MRI of the human vocal tract

口哨的双变量功能主成分分析的图形描述与语音有共同的语言：来自人类声道实时 MRI 的生物声学

• DOI: 10.6084/m9.figshare.9830879
• 发表时间: 2019
• 作者: Belyk M

Voice selectivity in the temporal voice area despite matched low-level acoustic cues.

• DOI: 10.1038/s41598-017-11684-1
• 发表时间: 2017-09-14
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Agus TR;Paquette S;Suied C;Pressnitzer D;Belin P
• 通讯作者: Belin P

Demonstration and validation of Kernel Density Estimation for spatial meta-analyses in cognitive neuroscience using simulated data.

• DOI: 10.1016/j.dib.2017.06.003
• 发表时间: 2017-08
• 期刊: Data in brief
• 影响因子: 1.2
• 作者: Belyk M;Brown S;Kotz SA
• 通讯作者: Kotz SA