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用N2pc组件测量对人工智能的信任

Eva Wiese¹, Tobias Feldmann-Wüstefeld²

¹Institute of Psychology and Ergonomics, Berlin Institute of Technology, Berlin, Germany; Human Factors and Applied Cognition, George Mason University, Fairfax, USA.

|January 15, 2026

概括

人类和AI的合作需要有效的关注. 一种新的EEG方法跟踪注意力共享,显示像N2pc这样的神经标记在视觉搜索任务中反映了对AI能力的信任.

科学领域:

认知科学认知科学
神经科学是一个神经科学.
人与计算机的交互

背景情况:

有效的注意力分配对于人类-人工智能协作至关重要,用户必须监控人工智能性能以防止错误.
过度依赖人工智能或过度监控人工智能可能导致性能下降和关键故障.
对人工智能的信任是影响注意力的关键因素,但很难直接衡量.

研究的目的:

引入和验证基于脑电图 (EEG) 的方法,用于直接跟踪人类和人工智能之间的注意力资源共享.
调查人工智能能力如何影响人类注意力部署和合作任务期间的信任校准.
建立神经生理学标记作为对人工智能系统信任的隐性衡量标准.

主要方法:

参与者从事视觉搜索任务,与不同能力水平的AI合作.
使用脑电图 (EEG) 来记录大脑活动.
测量了选择性视觉注意力的神经标记物N2pc成分,以量化注意力部署.

主要成果:

N2pc的振幅被人工智能的能力显著调节.
较小的N2pc振幅与与高能力AI相比较的低能力AI进行交互时增加的注意力卸载和信任相关.
这些发现表明,神经标记可以隐含地反映人类-人工智能合作中的信任校准.

结论:

该N2pc组件作为一个有效和非破坏性的神经生理学标记,用于量化人类-AI协作搜索任务中的注意分配.
这种基于EEG的方法为隐式测量对人工智能的信任提供了一个有前途的方法,进步了我们对信任校准的理解.
该研究将N2pc的应用从视觉注意力研究扩展到对自动化的信任这一关键领域.

关键词:

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