How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence
arXiv:2603.18203v1 Announce Type: new 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 understa
arXiv:2603.18203v1 Announce Type: new 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.
Executive Summary
This article critiques the dominant artificial intelligence (AI) paradigms, arguing that they were shaped by learning theories from psychology and inherited their structural limitations. The authors examine how behaviorism, cognitivism, and constructivism influenced AI methods such as reinforcement learning, deep learning, and compositional approaches. They propose a trimodular framework, ReSynth, to address the challenge of artificial general intelligence, which requires adaptability. The authors also highlight the importance of systematic behavior in AI and suggest a cross-cultural divergence in the interpretation of rote learning as a potential bridge between psychological theory and AI methodology.
Key Points
- ▸ AI paradigms were shaped by learning theories from psychology.
- ▸ Each AI paradigm inherited the structural limitations of the psychological theory that inspired it.
- ▸ The authors propose a trimodular framework, ReSynth, to address the challenge of artificial general intelligence.
Merits
Strength in theoretical foundations
The article provides a comprehensive analysis of the relationship between psychological learning paradigms and AI methods, demonstrating a deep understanding of the underlying theories.
Demerits
Limitation in practical applications
The article primarily focuses on theoretical foundations and may not provide sufficient guidance for practical implementation of the proposed framework, ReSynth.
Expert Commentary
This article provides a thought-provoking analysis of the relationship between psychological learning paradigms and AI methods. The authors' critique of the dominant AI paradigms and their proposal of a trimodular framework, ReSynth, offer a fresh perspective on the challenge of achieving artificial general intelligence. However, the article's focus on theoretical foundations may limit its practical applicability. To further develop the proposed framework, the authors may need to provide more concrete guidance on its implementation and evaluation. Additionally, the article's implications for AI policies and regulations are not fully explored, and further research is needed to fully understand the potential consequences of these findings.
Recommendations
- ✓ Future research should focus on developing more concrete, practical applications of the proposed framework, ReSynth.
- ✓ The authors should explore the potential implications of their findings for AI policies and regulations in more detail.
