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基于复发量化分析和电脑电图信号的PCB接工人工作效率奖励和惩罚的激励机制研究

Zhang Qian¹, Mingyue Guo², Fuwang Wang³

¹School of Economics and Management, Beihua University, Jilin, 132013, China. zhangqian2119909@163.com.

Scientific reports

|April 5, 2025

概括

本研究使用脑电图 (EEG) 和复发量化分析 (RQA) 客观地衡量薪资激励如何影响员工生产力,为商业战略提供了新的神经科学基础.

科学领域:

神经科学是一个神经科学.
认知科学认知科学
组织心理学组织心理学

背景情况:

传统方法缺乏评估工资激励对员工生产力影响的客观措施.
企业激励策略需要强大的科学基础才能有效实施.
现有的认知负载监测方法缺乏足够的灵敏度来进行动态激励评估.

研究的目的:

提出一种新的动态激励评估模型,使用复发量化分析 (RQA) 和电脑电图 (EEG) 信号.
客观地量化奖励和惩罚激励对员工工作效率的影响.
为优化企业激励机制建立神经科学基础.

主要方法:

综合复发量化分析 (RQA) 与电脑电图 (EEG) 信号分析.
与没有奖励/惩罚激励的工人之间的EEG信号进行比较 (对照组与实验组).
从RQA图表中提取了确定性 (DET) 和平均对角线长 (DLL) 等关键特征,并使用双支向量机 (TWSVM) 来进行分类.

主要成果:

激励措施下的EEG信号显示出明显增强的混乱特征.
确定性 (DET) 和平均对角线长 (DLL) 的值分别下降了13.3%和10.4%.
双支持向量机 (TWSVM) 实现了98.71%的准确性,优于现有的基于EEG的方法.

结论:

EEG与RQA相结合,为评估激励影响提供了一种可行且敏感的方法.
拟议的模型为优化企业激励策略提供了可量化的神经科学基础.
这种方法提高了激励机制设计和评估的科学严谨性.

关键词:

准确度准确度准确度准确度准确度电脑电流信号电脑电流信号员工的生产力员工的生产力在RQAQA RQA.奖励和惩罚是激励的激励因素.在TWSVM中使用TWSVM.

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