Research on Human-machine Dynamic Trust Based on Alarm Sequence

With the extensive application of automation equipment, the issue of trust in automation has become a research hotspot in the field of human-machine interaction. Too high or too low trust levels may affect the performance and security of human-machine interaction. False alarm is considered to be an important factor affecting operator's automatic trust. Many experimental results show that the false alarm rate is significantly correlated with trust level. This paper studies the relationship between the alarm sequence (composed of a series of correct alarm and false alarm events) and trust level. Based on the cockpit simulation experiment, the dynamic change of the trust level with the warning event is obtained. The experimental results show significant recency and primacy effects.

[1]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004 .

[2]  Daniel R. Ilgen,et al.  Not All Trust Is Created Equal: Dispositional and History-Based Trust in Human-Automation Interactions , 2008, Hum. Factors.

[3]  Jeffrey M. Bradshaw,et al.  The Dynamics of Trust in Cyberdomains , 2009, IEEE Intelligent Systems.

[4]  Philip Webb,et al.  The Development of a Scale to Evaluate Trust in Industrial Human-robot Collaboration , 2015, International Journal of Social Robotics.

[5]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004, Hum. Factors.

[6]  Sibel Adali,et al.  A Survey on Trust Modeling , 2015, ACM Comput. Surv..

[7]  Eric T. Chancey,et al.  Automation trust and attention allocation in multitasking workspace. , 2018, Applied ergonomics.

[8]  Christopher D. Wickens,et al.  Automation Reliability in Unmanned Aerial Vehicle Control: A Reliance-Compliance Model of Automation Dependence in High Workload , 2006, Hum. Factors.

[9]  James L. Szalma,et al.  A Meta-Analysis of Factors Influencing the Development of Trust in Automation , 2016, Hum. Factors.

[10]  Holly A. H. Handley,et al.  Trust and the Compliance–Reliance Paradigm: The Effects of Risk, Error Bias, and Reliability on Trust and Dependence , 2017, Hum. Factors.

[11]  Makoto Itoh,et al.  Driver Trust in Automated Driving Systems: The Case of Overtaking and Passing , 2018, IEEE Transactions on Human-Machine Systems.

[12]  John Richardson,et al.  Alarm timing, trust and driver expectation for forward collision warning systems. , 2006, Applied ergonomics.

[13]  Jessie Y. C. Chen,et al.  A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction , 2011, Hum. Factors.