Evaluating User Experience Factors Using Experiments: Expressive Artificial Faces Embedded in Contexts

There is an ongoing debate on what kind of factors contribute to the general positive user experience while playing a game. The following chapter introduces an experimental setting to measure user experience aroused by facial expression of embodied conversational agents (ECAs). The experimental setup enables to measure the implications of ECAs in three contextual settings called still, animated, and interaction. Within the experiment, artificially generated facial expressions are combined with emotion-eliciting situations and are presented via different presentation platforms. Stimuli (facial expressions/emotion-eliciting situations) are assembled in either consonant (for example, facial expression: joy, emotion-eliciting situation: joy ) or dissonant (for example, facial expression: joy, emotion-eliciting situation: anger ) constellations. The contextual setting called interaction is derived from the video games domain, granting an interactive experience of a given emotional situation. The aim of the study is to establish a comparative experimental framework to analyze subjects’ user experience on emotional stimuli in different context dimensions. This comparative experimental framework utilizes theoretical models of emotion theory along with approaches from human–computer interaction to close a gap in the intersection of affective computing and research on facial expressions. Results showed that the interaction situation is rated as providing a better user experience, independent of showing consonant or dissonant contextual descriptions. The still setting is given a higher user experience rating than the animated setting.