AI的成功揭示了人类学习中的关联主义机制。
The New Associationism: Lessons from Deep Learning
- 用评价反馈驱动的监督学习是现代AI的核心机制
- 从大模型到游戏智能体，皆依赖反馈信号进行学习
- 适合对认知科学与机器学习交叉感兴趣的读者
现代人工智能的成功能为我们提供关于人类学习的什么启示？本文认为，将人工智能视为人类学习的模型，支持一种温和但真实的关联主义。核心发现是，监督学习——由评价性反馈驱动的学习——贯穿于当代各类AI系统，包括大语言模型和博弈智能体，其差异主要体现在生成反馈信号所需的工作量。这证实了关联主义所倡导的跨领域统一、渐进且以错误为驱动的学习机制，并化解了过去认为关联主义无法解释人类认知能力的质疑。然而，深度学习的成功依赖于远超经典关联主义设想的计算架构，监督学习只是其中一环，并非学习的完整解释。
原文摘要 · Abstract (English)
What can the success of modern AI tell us about how humans learn? This paper argues that taking AI seriously as a model of human learning supports a modest but genuine associationism. The central finding is that supervised learning -- learning driven by evaluative feedback -- underlies a surprisingly wide range of contemporary AI systems, from large language models to game-playing agents, differing primarily in how much work is required to generate the relevant feedback signal. This vindicates associationist ideals of a uniform, gradual, error-driven learning mechanism operating across domains, and defuses the once-influential argument that associationist mechanisms are too limited to account for human cognitive capacities. At the same time, the successes of deep learning depend on computational architectures that go well beyond anything classical associationists envisaged, and supervised learning operates within these as one component rather than a complete account of learning.
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