How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence

• URL: http://arxiv.org/abs/2603.18203v2
• Date: Sun, 22 Mar 2026 22:23:15 GMT
• Title: How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence
• Abstract summary: The dominant paradigms of artificial intelligence were shaped by learning theories from psychology.<n>This paper argues that each AI paradigm inherited not only the strengths but the structural limitations of the psychological theory that inspired it.
• Score: 5.576793481209459
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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