离散情绪类别对行为和神经事件在时间尺度上的细分的差异性贡献
Siyuan Zhou1, Yulin Chen2, Yunhong Wang1
1Faculty of Psychology, Southwest University (SWU), Chongqing, China; Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.
Neuropsychologia
|February 28, 2026
概括
离散的情感类别,比如悲伤,有助于将连续的经历分成不同的事件. 这项研究揭示了特定的情绪,而不仅仅是价值,如何塑造我们对叙事结构的感知和记忆.
科学领域:
- 认知神经科学 认知神经科学
- 情感科学是一种情感科学.
- 计算神经科学是一种神经科学.
背景情况:
- 事件细分对于感知和记忆至关重要.
- 特定情感类别在事件细分中的作用尚未得到充分理解.
研究的目的:
- 调查细粒度的情感类别如何预测行为和神经事件细分.
- 确定情感语义是否对叙事结构和经验组织有独特的贡献.
主要方法:
- 在电影观看期间,利用了26种情感类别,价值和情感波动的级注释.
- 雇佣了层次行为任务和功能磁共振成像 (fMRI).
- 应用隐藏的马尔科夫模型 (HMM) 用于神经边界检测和分析的核聚集体 (NAcc) 活动.
主要成果:
- 情绪类型模型,特别是悲伤和失望,比基于价值的模型更好地预测行为事件边界.
- 特定的情感类别预测了皮质层次结构中的神经边界,包括感官和默认模式区域.
- 短暂的情绪波动预测了NAcc中的粗略尺度边界;社会线索提供了补充的预测能力.
结论:
- 离散的情感语义及其动态为事件细分提供跨时间尺度的信号.
- 情绪对在行为和神经层面上组织连续体验做出了重大贡献.
- 这突显了情绪在构建知觉和记忆方面的语义丰富性.
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