Overview
Zones, agency expectancy types, and recursive learning in one readable map.
Affective Socialization Theory (AST)
AST explains how intelligence and identity emerge from repeated emotional-social feedback. Instead of treating cognition as isolated problem-solving, AST models learning as a recursive loop between felt states, social response, and behavior adaptation over time.
Core insight: stable intelligence is not just computation; it is socialized adaptation under affective pressure. Agents become more coherent when emotional meaning and social structure are jointly learned.
Explore the AST Modules
Core Concepts
Definitions for MAT, MSI, SED', HMC, CCC, and HV.
Key Equations
Formal expressions and plain-language interpretation of each term.
AGI Connection
How AST maps onto artificial general intelligence design and alignment.
Falsifiability
Testability standards, threshold predictions, and simulation pathways.
AST Tracker
How the AST Tracker app turns core variables into daily and weekly measurements.