Objective

Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other—effective connectivity (EC)—in the context of trust in automation.

Background

Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions.

Method

Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions.

Results

Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections.

Conclusion

Results indicated that trust and distrust can be two distinctive neural processes in human–automation interaction—distrust being a more complex network than trust, possibly due to the increased cognitive load.

Application

The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human–automation interface design but also in the proper use of automation in real-life situations.

中文翻译：

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评估人机交互中信任的有效连接性：动态因果建模 (DCM) 研究

客观的

使用动态因果建模 (DCM)，我们研究了可信度和可靠性如何影响大脑区域对彼此施加因果影响的方式——有效连接 (EC)——在自动化信任的背景下。

背景

中央执行网络（CEN）和默认模式网络（DMN）的多个大脑区域与信任判断有关。然而，就大脑区域之间的定向信息流而言，信任判断的神经相关性仍然相对未开发。

方法

16 名参与者观察了空军多属性任务组合 (AF-MATB) 的系统监控子任务的四种计算机算法的性能，这些算法在可信度和可靠性方面存在差异。使用 CEN 和 DMN 的六个大脑区域，这些区域通常被认为在人类信任中被激活，总共开发了 30 个（前向、后向和横向）连接模型。贝叶斯模型平均 (BMA) 用于量化大脑区域之间的连接强度。

结果

相对于高信任条件，低信任表现出特定连接的独特存在、来自前额叶皮层的更大连接强度以及更大的网络复杂性。高信任条件显示没有反向连接。

结论

结果表明，信任和不信任可以是人机交互中两个独特的神经过程——不信任是一个比信任更复杂的网络，这可能是由于认知负荷的增加。

应用

使用 DCM 推断的分布式大脑区域的因果架构不仅有助于设计平衡的人机界面设计，而且有助于在现实生活中正确使用自动化。