School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Samsung SDS, Seoul, Republic of Korea.
Sci Rep. 2019 Jul 10;9(1):9975. doi: 10.1038/s41598-019-46098-8.
As intelligent machines have become widespread in various applications, it has become increasingly important to operate them efficiently. Monitoring human operators' trust is required for productive interactions between humans and machines. However, neurocognitive understanding of human trust in machines is limited. In this study, we analysed human behaviours and electroencephalograms (EEGs) obtained during non-reciprocal human-machine interactions. Human subjects supervised their partner agents by monitoring and intervening in the agents' actions in this non-reciprocal interaction, which reflected practical uses of autonomous or smart systems. Furthermore, we diversified the agents with external and internal human-like factors to understand the influence of anthropomorphism of machine agents. Agents' internal human-likenesses were manifested in the way they conducted a task and affected subjects' trust levels. From EEG analysis, we could define brain responses correlated with increase and decrease of trust. The effects of trust variations on brain responses were more pronounced with agents who were externally closer to humans and who elicited greater trust from the subjects. This research provides a theoretical basis for modelling human neural activities indicate trust in partner machines and can thereby contribute to the design of machines to promote efficient interactions with humans.
随着智能机器在各种应用中广泛普及，高效操作它们变得越来越重要。为了实现人机之间富有成效的交互，需要监测人类操作者对机器的信任。然而，人类对机器信任的神经认知理解是有限的。在这项研究中，我们分析了非互惠人机交互过程中人类的行为和脑电图 (EEG)。在这种非互惠交互中，人类主体通过监控和干预代理的行为来监督他们的伙伴代理，这反映了自主或智能系统的实际用途。此外，我们通过外部和内部的类人因素使代理多样化，以了解机器代理的拟人化程度的影响。代理的内部类人特征表现在他们执行任务的方式上，这影响了主体的信任水平。通过 EEG 分析，我们可以定义与信任增减相关的大脑反应。信任变化对大脑反应的影响在与外部更接近人类且能引起主体更大信任的代理时更为明显。这项研究为建模人类神经活动以指示对伙伴机器的信任提供了理论基础，从而有助于设计机器以促进与人类的高效交互。
