Self-utility distance as a computational approach to understanding self-concept clarity.

Abstract

Self-concept stability and cohesion are crucial for psychological functioning and well-being, yet the mechanisms that underpin this fundamental aspect of human cognition remain underexplored. Integrating insights from cognitive and personality psychology with reinforcement learning, we introduce Self-Utility Distance (SUD)-a metric quantifying the dissimilarities between individuals' self-concept attributes and their expected utility value. In Study 1 (n = 155), participants provided self- and expected utility ratings using a set of predefined adjectives. SUD showed a significant negative relationship with Self-Concept Clarity that persisted after accounting for individuals' Self-Esteem. In Study 2 (n = 323), we found that SUD provides incremental predictive accuracy over Ideal-Self and Ought-Self discrepancies in the prediction of Self-Concept Clarity. In Study 3 (n = 85), we investigated the mechanistic principles underlying Self-Utility Distance. Participants conducted a social learning task where they learned about trait utilities from a reference group. We formalized different computational models to investigate the strategies individuals use to adjust trait utility estimates in response to environmental feedback. Through Hierarchical Bayesian Inference, we found evidence that participants utilized their self-concept to modulate trait utility learning, effectively avoiding the maximization of Self-Utility Distance. Our findings provide insights into self-concept dynamics that might help understand the maintenance of adaptive and maladaptive traits.