The impact of eye gaze on social interactions of females in virtual reality: The mediating role of the uncanniness of avatars and the moderating role of task type.

The introduction of the Metaverse and the rise of social virtual reality platforms such as VRChat has led to increased communication and collaboration in virtual reality. As interactions in social virtual reality take place through avatars, the behaviour, and in particular the eye gaze of an avatar can have an impact on the user experience. However, it is still unclear which features of the user experience are most influenced. Therefore, this study used data from 44 female participants to investigate how avatar gaze behaviour (static eyes vs. real-time tracked eyes) affects their perceived quality of communication and the amount of one-sided eye contact participants make during a communicative or collaborative task in virtual reality. In addition, the study investigated the mediating role of perceived avatar "uncanniness" (i.e., the finding that humanoid objects that imperfectly resemble real people evoke feelings of discomfort) in this relationship and the moderation effect of two multi-user scenarios (collaboration vs. communication). The results showed that uncanniness directly affected the perceived quality of communication. However, it did not significantly mediate the relationship between avatar eye gaze behaviour and the quality of communication or the amount of one-sided eye contact. Finally, there were no significant differences in user experience between the two scenarios. From this, we can conclude that the uncanniness of an avatar being interacted with in VR is not enough to hinder communication and collaboration in an immersive medium. And even if an avatar is perceived as uncanny, normative communication cues such as eye contact are still present. Notably, due to sample availability, the results of this study are based on a female-only sample. Thus, future research can benefit from exploring the outlined effects in a more gender-balanced sample.

Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context.

Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence. As a consequence, human assembly workers will need to adapt quickly to new and more complex assembly procedures, which are most likely to increase cognitive workload, or potentially induce overload. Measurement and optimization protocols need to be developed in order to be able to monitor workers' cognitive load. Previous studies have used electroencephalographic (EEG, measuring brain activity) and electrooculographic (EOG, measuring eye movements) signals, using basic computer-based static tasks and without creating an experience of overload. In this study, EEG and EOG data was collected of 46 participants performing an ecologically valid assembly task while inducing three levels of cognitive load (low, high and overload). The lower individual alpha frequency (IAF) was identified as a promising marker for discriminating between different levels of cognitive load and overload.