Beyond attractiveness: A multi-method approach to study enhancement in self-recognition on the Big Two personality dimensions

Self-enhancement refers to the phenomenon that individuals tend to have unrealistically positive self-views. Traditional measures of self-enhancement typically imply self-evaluations and reference values, such as evaluations by others or evaluations of the average other. Comparing individuals' self-evaluations with such reference values, however, bears risks. It is not evident that the reference values are more accurate than the self-evaluations and it is not possible to distinguish self-enhancers from individuals who are indeed superior to others. Here, we present two novel methods to measure self-enhancement that circumvent these problems by using participants' own faces as reference values. In Study 1 we systematically manipulate facial characteristics that have previously been found to impact perceptions of attractiveness, likability, and the Big Two personality dimensions in participants' faces and ask them to recognize themselves. In Study 2 we use a novel approach to apply random noise patterns to participants' faces and ask them to indicate in which version they recognize themselves more. Aggregating these random noise patterns reveals the direction of self-recognition in a more bottom-up, data-driven way. Across both studies we find evidence for self-enhancement regarding attractiveness, likability, and the Big Two personality dimensions.