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交互式艺术治疗中情绪调节的多模式建模:基于EEG和PANAS的动态模拟

Xiaowei Chen¹, Miaomiao Zeng²

¹College of Arts, Zhejiang Shuren University, Hangzhou, China.

Frontiers in psychology

|December 17, 2025

概括

主观评估,而不仅仅是大脑活动,最好预测沉浸式艺术治疗中的情感转变. 这项研究为设计适应性治疗环境提供了洞察力.

科学领域:

神经科学是一个神经科学.
心理学心理学心理学
计算建模计算建模

背景情况:

情绪调节涉及认知控制,生理兴奋和情感评估.
神经生理学标记与主观情感动态之间的联系需要进一步研究.

研究的目的:

调查EEG特征和自我报告的影响如何共同预测情绪变化.
提出和测试一个双模型框架,整合线性回归和非线性模拟.

主要方法:

收集了来自50名参与者接触互动装置的EEG和自我报告的情感数据.
衍生预测因素:Theta_Change (认知负载),Gamma_Change (生理兴奋),以及Affective_Shift (主观价值变化).
使用引导回归分析和动态模拟.

主要成果:

Affective_Shift是积极和消极影响变化的最强有力的预测指标.
Gamma_Change显著预测了积极的情绪结果.
Theta_Change显示了基于初始情绪状态的非线性效应,模拟说明了适应性调节.

结论:

这些发现支持了综合主观和神经生理学数据的多维情绪调节模型.
计算方法为影响意识的环境设计提供了机械的理解.
结果与已建立的情绪调节理论一致.

关键词:

这是一个EEGEEGEEGEEGEEGEEGEEG.帕纳斯 (PANAS) 是一个国家.情感转移是影响性的转移.动态模拟的动态模拟情绪调节情绪调节反的适应反的适应交互式艺术疗法互动式艺术疗法非线性模拟的非线性模拟

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