Abstract
Social norms shape a vast range of human behaviors, from everyday interactions to major life choices. Yet, existing theories of norm emergence typically focus either on why certain norms arise (substantive properties) or on how they spread and persist (dynamical properties), often making conflicting assumptions. Here, we propose a unified account in which norms prescribing how one ought to act emerge naturally from the fundamental algorithms that guide learning-whether in social or nonsocial settings. Our account builds on recent advances in decision making and emotion research that have highlighted "actor-critic" models as a core mechanism of learning from feedback. We extend this mechanism to social settings by assuming that it is not only we who critique our actions; others critique our actions as well. By simulating this interindividual form of learning, we show that it uniquely produces group behavior that exhibits both substantive and dynamical properties of real-world social norms, including prosociality, ingroup bias, stickiness, S-shaped curves, and local conformity/global diversity. Our framework thus offers a uniquely parsimonious way to bridge the gap between individual learning and group behavior. (PsycInfo Database Record (c) 2026 APA, all rights reserved).