JMIR Publications

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 although often with an emphasis on interventions being tailored. Autistic children, for instance, appear to prefer avatars over real images when assessing facial expressions.

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 completed a survey on the internet 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:

Only few faces showed factor loadings around 0.7, which means 49% shared variance with the latent factor the face is linked with. The standardized thresholds are mostly around average, and the highest correlation are between faces showing happiness and anger.

Conclusions:

Adjustments are needed to increase faces’ discrimination (e.g., increasing reliabilities). The faces showed average levels of difficulty, meaning they are neither very difficult nor very easy to perceive, which fits a general population. Adjustments could be made for specific populations.