Measuring Human Trust in a Virtual Assistant using Physiological Sensing in VR
Abstract

With the advancement of Artificial Intelligence technology to make smart devices, understanding how humans develop trust in virtual agents is emerging as a critical research field. Through our research, we report on a novel methodology to investigate users’ trust in auditory assistance in a Virtual Reality (VR) based search task, under both high and low cognitive load and under varying levels of agent accuracy. We collected physiological sensor data such as electroencephalography (EEG), galvanic skin response (GSR), and heart-rate variability (HRV), subjective data through questionnaires such as System Trust Scale (STS), Subjective Mental Effort Questionnaire (SMEQ), and NASA-TLX. We also collected a behavioral measure of trust (congruency of users’ head motion in response to valid/ invalid verbal advice from the agent). Our results indicate that our custom VR environment enables researchers to measure and understand human trust in virtual agents using the matrices, and both cognitive load and agent accuracy play an important role in trust formation. We discuss the implications of the research and directions for future work.

Gupta, Kunal, Ryo Hajika, Yun Suen Pai, Andreas Duenser, Martin Lochner, and Mark Billinghurst. "Measuring human trust in a virtual 
assistant using physiological sensing in virtual reality." InĀ 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR),
pp. 756-765. IEEE, 2020.
IEEE VR 2020, Atlanta