The Brain Basis of Emotion: A Category Construction Problem

情绪在人类生活中起着核心作用，但其神经基础仍然知之甚少。在情绪科学中，目前正在争论是否存在特定情绪类别的共同大脑模式，例如愤怒，悲伤或恐惧，或者每个类别中的模式是否存在有意义的变化以及类别之间的相似性。一个跨学科的研究团队将开发创新的建模算法，以学习而不是规定每个参与者的大脑数据所证明的类别数量。这种创新的方法将产生对大脑中情绪本质的基本见解。更广泛地说，建模技术将为人类神经科学界提供一种灵活地研究心理类别的方法，而无需对他们的数据进行关于类别性质的理论假设。这项工作也将有助于为一种更情境敏感的、个性化的方法奠定基础，这种方法将神经活动与心理类别联系起来。分析神经科学数据的标准分类方法假设，固定的、实验者分配的标签代表了基础事实。本研究将质疑这些假设，并调查一个新的假设，即理解情绪的大脑基础是一个类别构建问题，而不是一个分类问题。分类问题假设大脑已经表示了所讨论的类别，而类别构建问题则测试了这一假设。该项目将涉及功能磁共振成像扫描，因为参与者观看情感视频，随后评价他们的主观体验。 这项研究将广泛地对一个人体内同一情感类别的多个实例进行采样。研究人员将通过使用无监督机器学习算法结合经验模型顺序选择分析灵活测试和发现潜在结构，将数据建模为类别构建问题。这项研究将使未来的研究人员能够更灵活和可靠地分析功能磁共振成像数据，为理解这些数据如何反映情感科学及其他领域的大脑组织和心理体验开辟新的知识图式。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Emotions play a central role in human life, yet their neural basis remains poorly understood. Within the science of emotion, it is currently debated whether there is a common brain pattern for a specific emotion category, such as anger, sadness, or fear, or whether there is meaningful variation in the patterns within each category and similarity across categories. An interdisciplinary research team will develop innovative modeling algorithms to learn, rather than stipulate, the number of categories justified by the brain data of each participant. This innovative approach will yield fundamental insights into the nature of emotion in the brain. More broadly, the modeling techniques will provide a means for the human neuroscience community to flexibly investigate psychological categories, without imposing theory-laden assumptions on their data as to the nature of the categories. This work will also help lay the groundwork for a more context-sensitive, personalized approach for relating neural activity with psychological categories.Standard classification approaches to analysis of neuroscience data assume that fixed, experimenter-assigned labels are representative of the ground truth. The present research will question these assumptions and investigate a novel hypothesis that understanding the brain basis of emotion is a category construction problem, not a classification problem. A classification problem assumes that the brain already represents the categories in question, whereas a category construction problem tests this assumption. This project will involve fMRI scanning as participants view emotional videos and subsequently rate their subjective experiences. The research will extensively sample multiple instances of the same emotion category within an individual. The investigators will model the data as a category construction problem by flexibly testing and discovering latent constructs using unsupervised machine learning algorithms combined with empirical model order selection analyses. This research will allow for future researchers to more flexibly and reliably analyze fMRI data, opening up new intellectual schema for understanding how such data reflects brain organization and mental experience in the science of emotion and beyond.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.