Surveys that rely on self-reporting are often prone to error. Therefore, measures to identify meaningless, careless, or fraudulent responses in surveys can improve data quality. In the past, various indicators have been proposed to identify data with implausible responses in questionnaires, e.g. by including attention check questions or by applying statistical techniques. Immersive virtual reality (VR) environments are equipped with several biosignal sensors. We propose and investigate whether sensory data already provided by the headset or additional biosignal sensors can be useful for automatically detecting unreliable data in in-VR surveys. Our results show that biosensor signals, such as eye tracking and electrocardiography, can provide useful cues, and that this information can be combined with statistical techniques (namely the validity score) to further improve classification accuracy.

Virtual reality becomes a medium to be explored for itself, to study human factors and human behavior within these worlds, and to infer possible behavior in the real world. Among many advantages, building test routines in virtual environments remains a challenge due to the lack of established procedures and toolkits. To encourage research in this direction and lower the barrier to entry, it is necessary to simplify the process of setting up a research environment in virtual reality by providing appropriate toolkits. This paper discusses what challenges need to be overcome, what features might be relevant, and compares available toolkits.