LAUSR

Reverse correlation (RC) method has been recently used to visualize mental representations of self. Previous studies have mainly examined the relationship between psychological aspects measured by self-reports and classification images of self (self-CIs), which are visual proxies of self-image generated through the RC method. In Experiment 1 (N = 118), to extend the validity of self-CIs, we employed social evaluation on top of self-reports as criterion variables and examined the relationship between self-CIs and social evaluation provided by clinical psychologists. Experiment 1 revealed that the valence ratings of self-CIs evaluated by independent raters predicted social evaluation after controlling for the effects of self-reported self-esteem and extraversion. Furthermore, in Experiment 2 (N = 127), we examined whether a computational scoring method – a method to assess self-CIs without employing independent raters – could be applied to evaluate the valence of participants’ self-CIs. Experiment 2 found that the computational scores of self-CIs were comparable to independent valence ratings of self-CIs. We provide evidence that self-CIs can add independent information to self-reports in predicting social evaluation. We also suggest that the computational scoring method can complement the independent rating process of self-CIs. Overall, our findings reveal that self-CIs are a valid and useful tool to examine self-image more profoundly.