Cerebral processing of affective nonverbal vocalizations: a combined fMRI and MEG study.

情感非语言发声的大脑处理：功能磁共振成像和脑磁图联合研究。

• 批准号: BB/J003654/1
• 负责人: Pascal Belin
• 金额: $ 33.52万
• 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: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FJ003654%2F1
• 关键词: Cerebral; processing; affective; nonverbal; vocalizations

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 combine a range of advanced techniques to precisely map the brain network involved in recognizing emotions from the voice and determine its exact time-course. We will first use recent morphing technology to manipulate a database of affective voices in order to generate new vocalizations with more or less intense, possibly ambiguous expressions (e.g., pleasure mixed with fear). We will then measure a number of parameters in the vocalizations thus generated. A large group of listeners will be asked to rate each vocalization on what emotion they think is expressed, on how intense and on how positive/negative they think it is. We will also precisely measure important physical properties of the sounds such as their intensity and pitch. In parallel we will use state-of-the art, complementary brain imaging techniques (functional magnetic resonance imaging and magneto-encephalography) to measure brain activity in a smaller number of participants while they listen to the affective vocalizations and perform simple tasks-a Male/Female gender categorisation task, and a Fear/Anger/Pleasure emotion categorization task. The combination of these two brain imaging techniques will allow measurements of cerebral activity with high time (millisecond) and space (millimetre) accuracy. Analysis of this high-resolution, high density dataset will use the most recent algorithms to address three important, unresolved questions. First we want to differentiate the part of the brain that reacts to the acoustics in the sounds - a vocalization of pleasure sounds different from an angry shout-from that part of the brain that reflects genuine affective value-these two vocalizations express different affective states in the speaker. This important distinction has generally not been adequately addressed in past studies. Second, we want to better understand, in those parts of the brain genuinely related to emotional processing, exactly to which parameter they react: To the emotional category, e.g., a response to fear but not to pleasure? To the negative/positive dimension, e.g., a response to all threatening sounds but not happy or joyful sounds? Or to the task being performed by the subject, e.g., a response during an emotional task but not a gender task? Third, we want to better understand the time-course of processing of affective information at different nodes of the network of brain areas involved in processing these vocalizations. If what has been observed with facial expressions of emotion also applies to voices, then we should observe a very fast, probably unconscious reaction to affective vocalizations, in a 'fast route' that bypasses detailed analysis for the sake of a fast reaction. Advanced algorithms will allow us to determine the precise time course of neuronal activity in different parts of the brain network and understand how different emotional parameters affect the speed of brain reaction. Overall, the results of this project will allow us to understand how the brain processes a socially central dimension of voices-the emotion they carry-and what are the key parameters. They will contribute to the advancement of knowledge, but also in the longer term to a better understanding of impairments of emotion processing in pathologies such as autism or schizophrenia. They also have high potential importance for the growing industry of automated voice processing, as engineers need to know not only how to best automatically recognize emotions in people but also how to best generate realistic emotions in artificial voices.

识别和解释他人的情绪对于社交互动至关重要。特别是，所有文化的人都能够在没有语言的情况下识别声音中的情感，如笑声，哭泣或恐惧的尖叫声。但是，我们的大脑如何分析声音中的情绪仍然知之甚少，例如，与我们如何感知面部表情相比。在这个项目中，我们联合收割机结合了一系列先进的技术，精确地绘制了从声音中识别情绪的大脑网络，并确定了它的确切时间过程。我们将首先使用最新的变形技术来操纵情感语音的数据库，以便生成具有或多或少的强烈，可能模糊的表达的新发声（例如，快乐与恐惧交织在一起）。然后，我们将测量由此产生的发声中的一些参数。一大群听众将被要求根据他们认为表达了什么样的情感、有多强烈以及他们认为有多积极/消极来对每一种发声进行评分。我们还将精确测量声音的重要物理特性，如强度和音高。同时，我们将使用最先进的，互补的脑成像技术（功能性磁共振成像和脑磁图），以测量大脑活动的参与者人数较少，而他们听的情感发声，并执行简单的任务，男性/女性性别分类任务，和恐惧/愤怒/愉悦情绪分类任务。这两种脑成像技术的结合将允许以高时间（毫秒）和空间（毫米）精度测量大脑活动。对这个高分辨率、高密度数据集的分析将使用最新的算法来解决三个重要的、尚未解决的问题。首先，我们要区分大脑中对声音声学特性作出反应的部分（快乐的声音不同于愤怒的叫喊）和反映真实情感价值的部分（这两种声音表达了说话者不同的情感状态）。这一重要区别在过去的研究中一般没有得到充分的处理。第二，我们想更好地了解，在那些真正与情绪处理有关的大脑部位，它们对哪些参数做出反应：对情绪类别，例如，对恐惧的反应而不是对快乐的反应到负/正维度，例如，对所有威胁性的声音都有反应，但对快乐或喜悦的声音没有反应？或者与受试者正在执行的任务相关联，例如，在情感任务中有反应，而在性别任务中没有？第三，我们希望更好地了解情感信息在大脑区域网络中不同节点的处理时间过程，这些节点涉及处理这些发声。如果我们观察到的面部表情也适用于声音，那么我们应该观察到一种非常快速的，可能是无意识的对情感发声的反应，在一个“快速路线”中，为了快速反应而绕过详细的分析。先进的算法将使我们能够确定大脑网络不同部分神经元活动的精确时间过程，并了解不同的情绪参数如何影响大脑反应的速度。总的来说，这个项目的结果将使我们了解大脑如何处理一个社会中心维度的情绪，他们携带的，以及什么是关键参数。它们将有助于知识的进步，但从长远来看，也有助于更好地理解自闭症或精神分裂症等病理学中的情感处理障碍。它们对于不断发展的自动语音处理行业也具有很高的潜在重要性，因为工程师不仅需要知道如何最好地自动识别人们的情绪，还需要知道如何最好地在人工语音中生成逼真的情绪。

项目成果

Single-subject analyses of magnetoencephalographic evoked responses to the acoustic properties of affective non-verbal vocalizations.

• DOI: 10.3389/fnins.2014.00422
• 发表时间: 2014
• 期刊: Frontiers in neuroscience
• 影响因子: 4.3
• 作者: Salvia E;Bestelmeyer PE;Kotz SA;Rousselet GA;Pernet CR;Gross J;Belin P
• 通讯作者: Belin P

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

The human voice areas: Spatial organization and inter-individual variability in temporal and extra-temporal cortices.

• DOI: 10.1016/j.neuroimage.2015.06.050
• 发表时间: 2015-10-01
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Pernet CR;McAleer P;Latinus M;Gorgolewski KJ;Charest I;Bestelmeyer PE;Watson RH;Fleming D;Crabbe F;Valdes-Sosa M;Belin P
• 通讯作者: Belin P