The Brain Basis of Emotion: A Category Construction Problem

情绪在人类生活中扮演着核心角色，但人们对其神经基础仍知之甚少。在情绪科学中，目前人们争论的是，特定的情绪类别，如愤怒、悲伤或恐惧，是否存在共同的大脑模式，或者每个类别内的模式是否存在有意义的差异，以及类别之间是否存在相似性。一个跨学科的研究团队将开发创新的建模算法，以学习而不是规定根据每个参与者的大脑数据证明合理的类别数量。这种创新的方法将对大脑中情绪的本质产生基本的见解。更广泛地说，建模技术将为人类神经科学界提供一种灵活调查心理学类别的手段，而不会对他们的数据强加关于类别性质的充满理论的假设。这项工作还将有助于为将神经活动与心理范畴联系起来的更上下文敏感的、个性化的方法奠定基础。神经科学数据分析的标准分类方法假设固定的、实验者分配的标签代表基本事实。本研究将对这些假设提出质疑，并探索一种新的假设，即理解情绪的大脑基础是一个范畴构建问题，而不是一个分类问题。分类问题假设大脑已经代表了所讨论的类别，而类别结构问题则测试了这一假设。该项目将在参与者观看情感视频时进行fMRI扫描，并随后对他们的主观体验进行评级。这项研究将广泛抽样同一情绪类别在个体中的多个实例。研究人员将通过使用无监督机器学习算法与经验模型阶数选择分析相结合的无监督机器学习算法，灵活地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数据，为了解这些数据如何反映情绪科学中的大脑组织和精神经验开辟了新的智力模式。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.