Machine learning unveils latent architecture of superiority illusion that predicts visual illusion perception and metacognitive performance

Abstract

Humans are typically inept at evaluating their abilities and predispositions, while often disregarding such lack of metacognitive insight into their capacities and even augmenting (albeit illusorily) self-evaluation such that they should have more desirable traits than an average peer. This superiority illusion helps maintain a healthy mental state. However, the scope and range of its influence on broader human behavior, especially perceptual tasks, remain elusive. As belief shapes the way people perceive and recognize, the illusory self-superiority belief potentially regulates our perceptual and metacognitive performance. In this study, we used hierarchical Bayesian estimation and machine learning of signal detection theoretic measures to understand how superiority illusion influences visual perception and metacognition for Ponzo illusion. Our results demonstrated that superiority illusion correlated with visual illusion magnitude and metacognitive performance. Next, we used machine learning with a relaxed elastic net and unveiled the latent architecture that underlies the correlations. We revealed that the "extraversion" superiority dimension tapped into visual illusion magnitude and metacognitive ability. In contrast, the "honesty-humility" and "neuroticism" dimensions were only predictive of visual illusion magnitude and metacognitive ability, respectively. These results suggest common and distinct influences of superiority features on perceptual sensitivity and metacognition. Our findings contribute to the accumulating body of evidence indicating that the superiority illusion leverage is far-reaching, even to visual perception.