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The Influence of Cognition on Affect01:29

The Influence of Cognition on Affect

327
Cognition plays a pivotal role in shaping emotional experiences, as demonstrated by Schachter and Singer’s two-factor theory of emotion. According to this model, emotion arises from a combination of physiological arousal and cognitive interpretation. The body’s physiological response to stimuli is ambiguous and only gains emotional significance through cognitive labeling. For instance, an increased heart rate and adrenaline surge while standing near an attractive person may be...
327

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使用机器学习研究受感性和影响力:生态瞬间评估和可穿戴感应研究

Zachary D King1, Han Yu1, Thomas Vaessen2,3

  • 1Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States.

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概括
此摘要是机器生成的。

机器学习驱动的生态瞬间评估 (EMA) 交付可以影响参与者的情绪状态. 这项研究发现,优化基于受感性的EMA时间减少了负面影响,但可能会引入偏见,在积极的情绪状态期间触发.

关键词:
欧洲药品监督管理局 (EMA) 是一个.吉大利语 (JITAI)影响推论影响推论.生态瞬间评估 环境瞬间评估恰到好处的适应性干预措施.移动健康 移动健康 移动健康 移动健康移动健康的移动健康手机电话 手机电话手机电话接收能力 接收能力研究设计研究设计

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科学领域:

  • 移动健康 (mHealth) 服务提供商
  • 可穿戴式传感器技术的技术.
  • 机器学习 (ML) 是指机器学习.
  • 生态瞬间评估 (EMA) 是一种

背景情况:

  • 参与者的接受度对于mHealth研究的成功至关重要,特别是在收集主观健康数据时.
  • 某些群体的低合规率可能会影响数据质量.
  • 机器学习和传感器数据提供了创新的方法来优化调查交付时间.

研究的目的:

  • 调查基于ML的EMA交付系统对参与者报告的情绪状态的影响.
  • 在基于可穿戴传感器的研究中确定影响EMA受体性的因素.
  • 分析受感性和影响力的生理指标,以及它们的相互作用.

主要方法:

  • 收集了45名健康参与者的数据,使用可穿戴传感器 (皮肤活动,加速度计,心电图,皮肤温度).
  • 每天服用10个EMA来评估感知到的情绪.
  • 使用无监督 (k-means集群) 和监督 (随机森林,神经网络) 的ML来推断非响应期间的影响和受感性.

主要成果:

  • 通过感受性模型触发EMA,自我报告的负面影响减少了超过3个点 (0.29 SDs).
  • 在不响应期间预测的影响显示出双模分布,表明在积极情绪状态期间更频繁地启动了EMA.
  • 观察到影响力和受感性之间存在显著的关系.

结论:

  • 影响和受感之间的关系可以影响mHealth研究的有效性,特别是ML触发的EMA.
  • 未来的研究应该旨在寻找智能触发器,以提高EMA的接受度,而不影响情绪状态报告.