Physiological sensing often complements studies of human behavior in virtual reality (VR) to detect users’ affective and cognitive states. Some psychological states, such as fear and frustration, can be particularly hard to differentiate from a physiological perspective as they are close in the arousal and valence emotional space. Moreover, it is largely unclear how users’ physiological reactions are expressed in response to transient psychological states such as fear, frustration, and insight—especially since these are rich indicators for characterizing users’ responses to dynamic systems but are hard to capture in highly interactive settings. We conducted a study (N=24) to analyze participants’ pulmonary, electrodermal, cardiac, and pupillary responses to moments of fear, frustration, and insight in immersive settings. Participants interacted in five VR environments, throughout which we measured their physiological reactions and analyzed the patterns. We also measured subjective fear and frustration using questionnaires.

Cybersickness has been one of the main impediments to the widespread adoption of Virtual Reality for decades. It has been argued that several factors can influence the occurrence of cybersickness, such as technical factors, interaction design, but also users’ demographics and their perceived presence. Yet, previous studies had comparably small sample sizes and demographically homogeneous samples; comparisons across studies (e.g., regarding demographic factors) are challenging due to the large variation in the studied virtual environments. In this paper, we address these limitations and report the results of a lab-in-the-field experiment on cybersickness with a large and heterogeneous sample of N=837 participants who navigated and interacted inside a virtual environment (ages 18–80, M=29.34, SD=9.50, 431 males, 400 females, 6 non-binaries).

In this paper, we introduce the term “Affective and Cognitive VR” and its main research lines. This survey also clarifies the different models of Affective and Cognitive States (ACS), presents the methods for measuring them with their respective advantages and drawbacks in VR, and showcases Affective and Cognitive VR studies done in an Immersive Virtual Environment (IVE) in a non-clinical context. We provide a comprehensive list of references with the analysis of 63 research articles and summarize future works directions.

This paper describes an ``all-in-one’’ solution for the real-time recognition of users’ mental workloads in VR through the customization of a commercial HMD with physiological sensors. We conducted an extensive user study (N = 75) to collect mental workload data. The classification accuracy of 4 levels of mental workload using sensors embedded in the HMD is compared to the classification performance using commercial grade systems. In addition, we discuss the role of the signal normalization procedures and the contribution of the different physiological signals on the recognition accuracy.