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.

翻译：人工智能的主流范式深受心理学学习理论的塑造：行为主义启发了强化学习，认知主义催生了深度学习与记忆增强架构，而建构主义则影响了课程学习与组合方法。本文论证，每种AI范式不仅继承了其灵感来源的心理理论的优势，也继承了其结构性局限。强化学习无法解释知识的内部结构，深度学习将表征压缩至抗拒原则性更新的不透明参数空间，而当前的整合方法缺乏对如何从现有组件构建新理解的正式说明。本文进一步探讨了机械学习解释中的跨文化差异，提出东方将记忆视为结构化、多阶段理解前驱的认知，为心理学理论与AI方法论之间提供了未被充分开发的桥梁。基于系统性的辩论以及对肯尼斯·艾扎瓦对古典主义与连接主义的批判，本文引入ReSynth——一个将推理（智力）、目的（身份）与知识（记忆）分离为架构独立组件的三模块框架。本文追溯从心理范式到AI方法的谱系，诊断各阶段继承的局限，并论证适应性——通用人工智能的核心挑战——要求一种表征架构，其中系统性行为是必然结果而非偶然属性。