Visible to the public Measuring Human Trust in a Virtual Assistant using Physiological Sensing in Virtual Reality

TitleMeasuring Human Trust in a Virtual Assistant using Physiological Sensing in Virtual Reality
Publication TypeConference Paper
Year of Publication2020
AuthorsGupta, K., Hajika, R., Pai, Y. S., Duenser, A., Lochner, M., Billinghurst, M.
Conference Name2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
Keywordsand virtual realities, artificial intelligence technology, auditory assistance, augmented, Cognition, cognitive load, custom VR environment, eeg, electroencephalography, galvanic skin response, Games, H.1.2 [User/Machine Systems]: Human factors, H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems— Artificial, H.5.2 [User Interfaces]: Evaluation/methodology, heart rate variability, heart-rate variability, Human Behavior, human computer interaction, human factors, human trust, human trust formation, interactive systems, medical signal processing, physiological sensing, physiological sensor data, physiology, psychology, pubcrawl, Shape, Skin, smart devices, subjective mental effort questionnaire, Task Analysis, user interfaces, virtual agents, Virtual Assistant, virtual reality, virtual reality based search task
AbstractWith 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 user's 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 questionnaire 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.
Citation Keygupta_measuring_2020