"The Correctness Aversion Effect": Why do people dislike correct information and others that point out their errors? A comprehensive review of the theory.

In everyday conversations, classrooms, workplaces, and online platforms, people often respond to accurate corrections with interpersonal coldness, distrust, or even punishment of the corrector. This review synthesizes dispersed evidence and theory to propose the Correctness Aversion Effect (CAE): a person-directed backlash that emerges when (a) a corrective message is relatively correct and hard to dismiss (high epistemic constraint), (b) that correctness is appraised as threatening to self, face, status, autonomy, or identity, and (c) negative responses are directed at the corrector rather than merely the message content. Moving beyond the correction literature’s dominant focus on belief updating and misinformation reduction, CAE highlights a layered outcome signature in which person evaluation and sanctioning can deteriorate even when epistemic accuracy improves or remains unchanged. We clarify CAE’s construct boundaries relative to shooting-the-messenger effects, backfire/correction resistance, psychological reactance, face-threatening acts, and the intergroup sensitivity effect, and we integrate these lines into a convergent process model: correction episodes (e.g., public exposure, status asymmetry, blunt tone, identity-laden domains) evoke multidimensional threat appraisals that recruit coordinated affective pathways (shame/embarrassment and envy escalating into defensive anger and resentment) and cognitive coping (motive derogation, moralization, source delegitimization, and “pulling-down” reframes), producing interpersonal avoidance, exclusion, and punitive intentions. Social comparison is specified as a key moderator (e.g., comparison orientation, rank concern) that amplifies rank-relevant encoding of being corrected. Finally, we provide an operational blueprint for future research—manipulations, mediator batteries, and layered dependent measures—designed to render CAE falsifiable and to support cumulative multi-study programs and intervention testing.