Department of Social Psychology, University of Basel.
J Pers Soc Psychol. 2019 Sep;117(3):483-499. doi: 10.1037/pspa0000157. Epub 2019 Mar 21.
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. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
自我提升是指个体倾向于对自我持有不切实际的积极看法的现象。传统的自我提升衡量方法通常涉及自我评估和参照值，例如他人的评估或对一般他人的评估。然而，将个体的自我评估与这些参照值进行比较存在风险。参照值并不一定比自我评估更准确，也无法区分自我提升者和确实优于他人的个体。在这里，我们提出了两种新的方法来衡量自我提升，通过使用参与者自己的面孔作为参照值来规避这些问题。在研究 1 中，我们系统地操纵了先前发现会影响参与者面孔吸引力、可爱度和大五人格维度感知的面部特征，并要求他们识别自己。在研究 2 中，我们使用一种新颖的方法将随机噪声模式应用于参与者的面孔，并要求他们指出自己更能识别哪个版本。聚合这些随机噪声模式以更自下而上、数据驱动的方式揭示了自我识别的方向。在这两项研究中，我们都发现了关于吸引力、可爱度和大五人格维度的自我提升的证据。（PsycINFO 数据库记录（c）2019 APA，保留所有权利）。
