How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence

2026-03-18

AI Summary

This work addresses fundamental limitations in contemporary artificial intelligence stemming from structural deficiencies inherent in its psychological foundations—namely behaviorism, cognitivism, and constructivism—which impede the realization of truly adaptive and systemic behavior. To overcome these constraints, the paper introduces the ReSynth framework, which decouples reasoning (Intellect), goals (Identity), and knowledge (Memory), and incorporates an Eastern philosophical perspective that positions memory as a precursor to understanding through a multi-stage structural view. By synthesizing a genealogical analysis of psychological learning theories, systematic representational design, cross-cultural comparisons of learning paradigms, and Aizawa’s critique of classicism versus connectionism, the study exposes core bottlenecks in current AI systems regarding knowledge updating, interpretability, and compositional generalization, thereby offering a novel theoretical pathway toward artificial general intelligence whose intrinsic attribute is systemic behavior.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Philosophy and Ethics of AI: Philosophical Foundations of AI
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

The dominant paradigms of artificial intelligence were shaped by learning theories from psychology: behaviorism inspired reinforcement learning, cognitivism gave rise to deep learning and memory-augmented architectures, and constructivism influenced curriculum learning and compositional approaches. This paper argues that each AI paradigm inherited not only the strengths but the structural limitations of the psychological theory that inspired it. Reinforcement learning cannot account for the internal structure of knowledge, deep learning compresses representations into opaque parameter spaces resistant to principled update, and current integrative approaches lack a formal account of how new understanding is constructed from existing components. The paper further examines a cross-cultural divergence in the interpretation of rote learning, arguing that the Eastern conception of memorization as a structured, multi-phase precursor to understanding offers an underexploited bridge between psychological theory and AI methodology. Drawing on the systematicity debate and critique of Aizawa of both classicism and connectionism, this paper introduces ReSynth, a trimodular framework that separates reasoning (Intellect), purpose (Identity), and knowledge (Memory) as architecturally independent components. The paper traces the genealogy from psychological paradigm to AI method, diagnoses the inherited limitations at each stage, and argues that adaptability, the central challenge of artificial general intelligence requires a representational architecture in which systematic behavior is a necessary consequence rather than an accidental property.

Problem

• artificial intelligence
• psychological learning paradigms
• structural limitations
• adaptability
• artificial general intelligence

Innovation

• ReSynth
• trimodular architecture
• systematicity
• psychological paradigms
• representational architecture