脑网络服务于功能核心过程的情绪识别与组件建模

Gelareh Mohammadi, D. Van de Ville, P. Vuilleumier
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引用次数: 9

摘要

情绪对思想、身体和行为都有强大的影响。尽管心理学理论强调情绪的多成分特征,但人们对这些成分在大脑中的性质和神经结构知之甚少。我们使用多元数据驱动的方法将各种情绪分解为功能核心过程,并确定其神经组织。20名参与者观看了40个情感片段,并根据先前验证的成分模型定义的32个组成特征对119个情感时刻进行了评级。研究结果表明,不同的情绪是如何通过一组大脑网络的协调活动产生的,这些网络编码与评估、享乐体验、新颖性、目标相关性、接近/回避倾向和社会关注相关的组成过程。我们的研究超越了以往专注于分类或维度情感的研究,并强调了与组件建模相结合的新方法如何使情感神经科学向前发展并揭示人类情感体验的功能架构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain networks subserving functional core processes of emotions identified with componential modeling
Emotions have powerful effects on the mind, body, and behavior. Although psychology theories emphasized multi-componential characteristics of emotions, little is known about the nature and neural architecture of such components in the brain. We used a multivariate data-driven approach to decompose a wide range of emotions into functional core processes and identify their neural organization. Twenty participants watched 40 emotional clips and rated 119 emotional moments in terms of 32 component features defined by a previously validated componential model. Results show how different emotions emerge from coordinated activity across a set of brain networks coding for component processes associated with valuation appraisal, hedonic experience, novelty, goal-relevance, approach/avoidance tendencies, and social concerns. Our study goes beyond previous research that focused on either categorical or dimensional emotions and highlighting how novel methodology combined with componential modelling may allow emotion neuroscience to move forward and unveil the functional architecture of human affective experiences.
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