Virtual reality interfaces and population-specific models to mitigate public speaking anxiety

Abstract

Public speaking is key to effectively exchanging ideas, persuading others, and making a tangible impact. Yet, public speaking anxiety (PSA) ranks as a top social phobia among many people. This paper leverages bio-behavioural indices captured from wearable devices and virtual reality (VR) interfaces to quantify PSA. The significance of individual-specific factors, such as general trait anxiety and personality, as well as contextual factors, such as age, gender, highest education, and native language, in moderating the association between bio-behavioral indices and PSA is further examined through group-based machine learning models. Results highlight the importance of including such factors for detecting PSA with the proposed group-based PSA models yielding Spearman's correlation of 0.55(p < 0.05) between the actual and predicted state-based anxiety scores. This work further analyzes whether systematic exposure to public speaking tasks in the VR environment can help alleviate PSA. Results indicate that systematic exposure to public speaking in VR can alleviate PSA in terms of both self-reported (p < 0.05) and physiological (p < 0.05) indices. Findings of this study will enable researchers to better understand antedecedents and causes of PSA contributing to behavioral interventions using VR.