Researchers frequently collect position and orientation data from tracking hardware during virtual reality research studies. These data capture important information about participants' movements during experiments, but often result in large and complex datasets that can be challenging to analyze and interpret. We explore the potential of standard statistical measures that could be used to interpret position and orientation tracking data, and discuss advantages and uses of each measure. We further present three new measurement techniques - 95% range, time spent at mode, and coefficient of determination. We then evaluate the effectiveness of each statistical measure to detect differences in position and orientation on two existing data sets. We find that all of the tested measures are effective in discerning known main effects in position and orientation with varying degrees of sensitivity. This work is intended to guide researchers on future analysis and interpretation of motion tracking data sets.

A user's personal experiences and characteristics may impact the strength of an embodiment illusion and affect resulting behavioral changes in unknown ways. This paper presents a novel re-analysis of two fully-immersive embodiment user-studies (n = 189 and n = 99) using structural equation modeling, to test the effects of personal characteristics on subjective embodiment. Results demonstrate that individual characteristics (gender, participation in science, technology, engineering or math - Experiment 1, age, video gaming experience - Experiment 2) predicted differing self-reported experiences of embodiment Results also indicate that increased self-reported embodiment predicts environmental response, in this case faster and more accurate responses within the virtual environment. Importantly, head-tracking data is shown to be an effective objective measure for predicting embodiment, without requiring researchers to utilize additional equipment.

Current optical see-through displays in the field of augmented reality are limited in their ability to display colors with low lightness in the hue, saturation, lightness (HSL) color space, causing such colors to appear transparent. This hardware limitation may add unintended bias into scenarios with virtual humans. Humans have varying skin tones including HSL colors with low lightness. When virtual humans are displayed with optical see-through devices, people with low lightness skin tones may be displayed semi-transparently while those with high lightness skin tones will be displayed more opaquely. For example, a Black avatar may appear semi-transparent in the same scene as a White avatar who will appear more opaque. We present an exploratory user study ( N=160) investigating whether differing opacity levels result in dehumanizing avatar and human faces. Results support that dehumanization occurs as opacity decreases. This suggests that in similar lighting, low lightness skin tones (e.g., Black faces) will be viewed as less human than high lightness skin tones (e.g., White faces). Additionally, the perceived emotionality of virtual human faces also predicts perceived humanness. Angry faces were seen overall as less human, and at lower opacity levels happy faces were seen as more human. Our results suggest that additional research is needed to understand the effects and interactions of emotionality and opacity on dehumanization. Further, we provide evidence that unintentional racial bias may be added when developing for optical see-through devices using virtual humans. We highlight the potential bias and discuss implications and directions for future research.

Shooter bias is the tendency to more quickly shoot at unarmed Black suspects compared to unarmed White suspects. The primary goal of this research was to investigate the efficacy of shooter bias simulation studies in a more realistic immersive virtual scenario instead of the traditional methodologies using desktop computers. In this paper we present results from a user study (N=99) investigating shooter and racial bias in an immersive virtual environment. Our results highlight how racial bias was observed differently in an immersive virtual environment compared to previous desktop-based simulation studies. Latency to shoot, the standard shooter bias measure, was not found to be significantly different between race or socioeconomic status in our more realistic scenarios where participants chose to raise a weapon and pull a trigger. However, more nuanced head and hand motion analysis was able to predict participants' racial shooting accuracy and implicit racism scores. Discussion of how these nuanced measures can be used for detecting behavior changes for body-swap illusions, and implications of this work related to racial justice and police brutality are discussed.

A psychological phenomenon termed "stereotype threat" has been shown to contribute to women's underperformance and underrepresentation in math and science fields. Within the virtual reality literature, a recent study utilized gendered body-swap illusions (i.e., women in male virtual bodies) to mitigate the effects of stereotype threat among a sample of female participants. The present research provides a much needed replication of this intervention, as well as a critical extension of virtual reality research on the Proteus Effect to test whether stereotype threat can be induced among male participants immersed in a female virtual body. Results supported both the replication and extension hypotheses; female participants embodied in male avatars were buffered from stereotype threat whereas male participants embodied in female avatars suffered from stereotype threat. Avatar gender also influenced participants' math confidence and awareness of the negative societal stereotype regarding women's math ability.

This poster details an ongoing experiment aiming to test the prevalence of shooter bias in virtual reality. Further, we examine the interaction between shooter bias and perceived socioeconomic status. Even though shooter bias is a well-documented topic in psychology research, little experimentation has used virtual reality, instead opting for unrealistic two-dimensional simulations. This study will yield new insight into shooter bias, especially concerning virtual reality as a tool for use in future work, and will provide new understanding about the relationship between socioeconomic status and shooter bias.

The underrepresentation of women in technical and STEM fields is a well-known problem, and stereotype threatening situations have been linked to the inability to recruit and retain women into these fields. Virtual reality enables the unique ability to perform body-swap illusions, and research has shown that these illusions can change participant behavior. Characteristically people take on the traits of the avatar they are embodying. We hypothesized that female participants embodying male avatars when a stereotype threat was made salient would demonstrate stereotype lift. We tested our hypothesis through a between-participants user study in an immersive virtual environment by measuring working memory. Our results support that stereotype threat can be induced in an immersive virtual environment, and that stereotype lift is possible with fully-immersive body-swap illusions. Additionally, our results suggest that participants in a gender-swapped avatar without an induced stereotype threat have significantly impaired working memory; however, this impairment is lifted when a threat is made salient. We discuss possible theories as to why a body-swap illusion from a female participant into a male avatar would only increase working memory impairment when not under threat, as well as applications and future research directions. Our results offer additional insight into understanding the cognitive effects of body-swap illusions, and provide evidence that virtual reality may be an applicable tool for decreasing the gender gap in technology.