Abstract
BACKGROUND: The availability and potential of virtual reality (VR) has led to an increase of its application. VR is suggested to be helpful in training elements of social competence but with an emphasis on interventions being tailored. Recognizing facial expressions is an important social skill and thus a target for training. Using VR in training these skills could have advantages over desktop alternatives. Children with autism, for instance, appear to prefer avatars over real images when assessing facial expressions. Available software provides the opportunity to transform profile pictures into avatars, thereby giving the possibility of tailoring according to an individual's own environment. However, the emotions provided by such software should be validated before application. OBJECTIVE: Our aim was to investigate whether available software is a quick, easy, and viable way of providing emotion expressions in avatars transformed from real images. METHODS: A total of 401 participants from a general population completed a survey on the web containing 27 different images of avatars transformed, using a software, from real images. We calculated the reliability of each image and their level of difficulty using a structural equation modeling approach. We used Bayesian confirmatory factor analysis testing under a multidimensional first-order correlated factor structure where faces showing the same emotions represented a latent variable. RESULTS: Few emotions were correctly perceived and rated as higher than other emotions. The factor loadings indicating the discrimination of the image were around 0.7, which means 49% shared variance with the latent factor that the face is linked with. The standardized thresholds indicating the difficulty level of the images are mostly around average, and the highest correlation is between faces showing happiness and anger. CONCLUSIONS: Only using a software to transform profile pictures to avatars is not sufficient to provide valid emotion expressions. Adjustments are needed to increase faces' discrimination (eg, increasing reliabilities). The faces showed average levels of difficulty, meaning that they are neither very difficult nor very easy to perceive, which fits a general population. Adjustments should be made for specific populations and when applying this technology in clinical practice.