Human trust is a psycho-physiological state that is difficult to measure, yet is becoming increasingly important for the design of human-computer interactions. This paper explores if human trust can be measured using physiological measures when interacting with a computer interface, and how it correlates with cognitive load. In this work, we present a pilot study in Virtual Reality (VR) that uses a multi-sensory approach of Electroencephalography (EEG), galvanic skin response (GSR), and Heart Rate Variability (HRV) to measure trust with a virtual agent and explore the correlation between trust and cognitive load. The goal of this study is twofold; 1) to determine the relationship between biosignals, or physiological signals with trust and cognitive load, and 2) to introduce a pilot study in VR based on cognitive load level to evaluate trust. Even though we could not report any significant main effect or interaction of cognitive load and trust from the physiological signal, we found that in low cognitive load tasks, EEG alpha band power reflects trustworthiness on the agent. Moreover, cognitive load of the user decreases when the agent is accurate regardless of task’s cognitive load. This could be possible because of small sample size, tasks not stressful enough to induce high cognitive load due to lab study and comfortable environment or timestamp synchronisation error due to fusing data from various physiological sensors with different sample rate.
Gupta, Kunal, Ryo Hajika, Yun Suen Pai, Andreas Duenser, Martin Lochner, and Mark Billinghurst. "In ai we trust: Investigating the
relationship between biosignals, trust and cognitive load in vr." In 25th ACM Symposium on Virtual Reality Software and Technology,
pp. 1-10. 2019.