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Abstract. In an evaluative context, does the impression we think we convey to others matter, such that the more positive we think the impression conveyed is, the better we perform? Does this belief need to be accurate to perform better? We investigate the role of meta-perception and meta-accuracy in a public speaking context by asking participants to deliver a speech in front of an audience in virtual reality. Main results showed that participants’ meta-perception (i.e., how positive they think the audience perceives them) was positively associated to their performance above and beyond other-perception (i.e., how the audience actually perceives them). Results also revealed that performance increased as scores of meta-perception and other-perception increased together (i.e., meta-accuracy), up to a certain threshold.

The use of corpora represents a widespread methodology in interpersonal perception and impression formation studies. Nonetheless, the development of a corpus using the traditional approach involves a procedure that is both time- and cost-intensive and might lead to methodological flaws (e.g., high invasiveness). This might in turn lower the internal and external validities of the studies. Drawing on the technological advances in artificial intelligence and machine learning, we propose an innovative approach based on deepfake technology to develop corpora while tackling the challenges of the traditional approach. This technology makes it possible to generate synthetic videos showing individuals doing things that they have never done. Through an automatized process, this approach allows to create a large scale corpus at a lesser cost and in a short time frame. This method is characterized by a low degree of invasiveness given that it requires minimal input from participants (i.e., a single image or a short video) to generate a synthetic video of a person. Furthermore, this method allows a high degree of control over the content of the videos. As a first step, a referent video is created in which an actor performs the desired behavior. Then, based on this referent video and participant input, the videos that will compose the corpus are generated by a specific class of machine learning algorithms such that either the facial features or the behavior exhibited in the referent video are transposed to the face or the body of another person. In the present paper, we apply deepfake technology to the field of social skills and more specifically to interpersonal perception and impression formation studies and provide technical information to researchers who are interested in developing a corpus using this innovative technology.

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