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

识别和解释他人的情绪对于社会互动至关重要。特别是，所有文化的人都能够识别发声中的情绪，而不需要说话，比如笑、哭或恐惧的尖叫。但我们的大脑如何分析声音中的情感仍然知之甚少，例如，与我们如何感知面部情感相比。在这个项目中，我们结合了一系列先进的技术，以精确地绘制涉及从语音中识别情感的大脑网络，并确定其准确的时间进程。我们将首先使用最新的变形技术来处理情感声音的数据库，以便生成或多或少具有强烈、可能是模棱两可的表情的新发声(例如，喜悦与恐惧的混合)。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的是什么情绪，根据他们认为有多强烈以及他们认为有多积极/多消极来对每个发声进行评分。我们还将精确测量声音的重要物理属性，如强度和音调。同时，我们将使用最先进的、互补的大脑成像技术(功能磁共振成像和脑磁图)来测量少数参与者的大脑活动，同时他们听情感发声并执行简单的任务--男性/女性性别分类任务，以及恐惧/愤怒/快乐情绪分类任务。这两种脑成像技术的结合将能够以高时间(毫秒)和空间(毫米)的精度测量大脑活动。对这个高分辨率、高密度的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，我们想要区分大脑中对声音中的声学做出反应的部分--发出不同于愤怒喊叫的愉悦声音--和大脑中反映真正情感价值的部分--这两种发声在说话者中表达不同的情感状态。在过去的研究中，这一重要的区别通常没有得到充分的解决。其次，我们想要更好地了解，在大脑中那些真正与情绪处理相关的部分，它们到底对哪个参数做出反应：对情绪类别的反应，例如，对恐惧的反应，而不是对快乐的反应？对于消极/积极的维度，例如，对所有威胁声音的反应，而不是对快乐或快乐声音的反应？或受试者正在执行的任务，例如，情绪任务中的反应，而不是性别任务中的反应？第三，我们希望更好地了解在处理这些发声的大脑区域网络的不同节点上处理情感信息的时间进程。如果所观察到的面部表情也适用于声音，那么我们应该观察到对情感发声的非常快速的、可能是无意识的反应，以一种为了快速反应而绕过详细分析的快速路线。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准确时间进程，并了解不同的情绪参数如何影响大脑反应的速度。总体而言，这个项目的结果将让我们了解大脑是如何处理声音的社会中心维度--它们携带的情感--以及关键参数是什么。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疾病的情绪处理障碍。对于日益增长的自动语音处理行业，它们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的情感，还需要知道如何最好地在人工声音中产生逼真的情感。

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.