Affective Socialization Theory as a Foundation for Recursive Intelligence: A Position Paper

Human intelligence is fundamentally social intelligence, emerging from recursive learning processes that occur under specific affective conditions. Affective Socialization Theory (AST) provides a formal mathematical framework for understanding how environments shape learning, agency, and behavior. This paper proposes that AGI architectures must either replicate these affective conditions or discover equivalent substrates for recursive social learning. The core insight is formalized in the Intelligence Equation, which specifies the conditions under which generalizable intelligence emerges.