Abstract Previous studies demonstrated the positive effects of smiling on interpersonal outcomes. The present research examined if enhancing one’s smile in a virtual environment could lead to a more positive communication experience. In the current study, participants’ facial expressions were tracked and mapped on a digital avatar during a real-time dyadic conversation. The avatar’s smile was rendered such that it was either a slightly enhanced version or a veridical version of the participant’s actual smile. Linguistic analyses using the Linguistic Inquiry Word Count (LIWC) revealed that participants who communicated with each other via avatars that exhibited enhanced smiles used more positive words to describe their interaction experience compared to those who communicated via avatars that displayed smiling behavior reflecting the participants’ actual smiles. In addition, self-report measures showed that participants in the ‘enhanced smile’ condition felt more positive affect after the conversation and experienced stronger social presence compared to the ‘normal smile’ condition.

These results are particularly striking when considering the fact that most participants (>90%) were unable to detect the smiling manipulation. This is the first study to demonstrate the positive effects of transforming unacquainted individuals’ actual smiling behavior during a real-time avatar-networked conversation. Apparatus We used a markerless motion capture software with a frame rate of 30 frames per second. The software used was Faceshift, which uses a Facial Action Coding System solver to categorize facial motions into 51 unique expressions (e.g., open mouth, smile, etc.) and tracks the extent to which each expression is activated []. A depth camera that uses infrared sensors and adaptive depth detection ( Asus Xtion PRO Live) was used to track facial expressions in addition to the translation (x, y, z) and orientation (pitch, yaw, roll) of the participant’s physical head and eyes.

This tracking data was then used to update the avatar’s head, eyes, and facial expressions. The Unity game engine was used to generate and program the avatar interaction platform. Study Design The present study adopted a dyadic design with three conditions: Normal Smile, Enhanced Smile, and Mouth Open-Close only. Both participants in the dyad were assigned an avatar with identical smiling conditions (e.g., participants in the enhanced smile condition both had avatars that enhanced their actual smiles), although they were only able to see their partners’ avatars. In order to determine the extent to which the smile should be enhanced in the Enhanced Smile condition, we ran a pilot study with 23 participants who were not a part of this study. The coefficient values of the smile tracking data were initially multiplied by 1.2 when updating the avatar’s smile in the enhanced smile condition. However, participants provided feedback that the smiling behaviors of the enhanced and normal smile conditions were similar.

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Thus, we increased the enhancement of the smiling coefficients to 1.5, which did lead to perceptions that the avatars in the enhanced smile condition smiled more than the normal smile condition. More importantly, post-pilot interviews with participants showed that they had not been aware of the smiling manipulation and thought that the conversational flow was natural. Based on these results, we chose to multiply the smiling data by 1.5; manipulation checks that were conducted after the actual study also confirmed the validity of this choice. In contrast, only the coefficient value that represented the extent to which a mouth was open was used to update the avatar in the mouth open-close condition. To allow for a fair comparison across the three avatars, the avatar in the mouth open-close only condition was designed to exhibit a slight smile. The different avatar representations of these conditions is available in. Materials All participants were represented by avatars that matched their (biological) genders.