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减轻选择过载:由层次漂移扩散建模和电脑图学揭示的集大小和总体偏好的互动效应
1Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), Shanghai International Studies University, Shanghai, China; School of Business and Management, Shanghai International Studies University, Shanghai, China.
主观偏好强度显著影响选择过载. 高偏好有助于决策,而低偏好会放大过度负荷,特别是在许多选择的情况下.
科学领域:
- 认知神经科学 认知神经科学
- 决策科学 决策科学 决策科学
- 神经经济学 神经经济学
背景情况:
- 过多的选择可能导致选择过载,压倒性的认知资源.
- 选择过载的神经生理基础,特别是偏好水平的影响,尚不清楚.
研究的目的:
- 调查选择集大小和整体偏好水平如何相互作用以影响决策过程.
- 使用多式联络方法探索选择过载的神经生理基础.
主要方法:
- 一个两阶段的实验范式,操纵选择集大小 (大与小) 和整体偏好水平 (高与低).
- 与事件相关的潜力 (ERP),多变量模式分析 (MVPA) 和层次漂移扩散建模 (HDDM) 的整合.
主要成果:
- 高偏好提升了选项识别和选择速度,特别是在大型选择集中.
- 低偏好放大了选择过载效应与较大的集合.
- ERP分析显示了与选择集大小和偏好相关的P2,N2和P3幅度的调制,表明注意力分配和认知控制发生了改变.
- MVPA证实,偏好水平调节了选择过载的神经编码.
结论:
- 主观偏好强度是减轻选择过载的关键因素.
- 内部偏好与外部复杂性相互作用,在决策过程中塑造认知控制.
- 这项研究促进了对基于价值的决策和认知控制架构的理解.
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