From Basic Affordances to Symbolic Thought: A Computational Phylogenesis of Biological Intelligence

📄 arXiv: 2508.15082v1 📥 PDF

作者: John E. Hummel, Rachel F. Heaton

分类: cs.NE, cs.AI, cs.LG, q-bio.NC

发布日期: 2025-08-20

备注: 47 pages 8 figures

DOI: 10.1037/rev0000592

💡 一句话要点

提出动态绑定与层次整合以实现符号思维

🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics)

关键词: 符号思维 动态绑定 层次整合 认知架构 生物启发人工智能

📋 核心要点

  1. 核心问题：现有研究未能充分解释人类大脑如何实现符号思维，特别是动态绑定的局限性。
  2. 方法要点：提出多角色绑定和多对应关系的层次整合作为实现符号思维的必要条件，构建相应的认知架构进行验证。
  3. 实验或效果：通过17个模拟实验，结果表明多角色谓词和结构映射是符号思维的最低要求，验证了理论假设。

📝 摘要（中文）

人类大脑为何能够进行符号推理，而大多数动物却无法做到？研究表明，动态绑定是符号思维的必要条件，但并不足够。本文提出，除了基本的动态绑定外，层次整合的两种形式（多角色绑定整合和多对应关系整合）是实现符号思维的最低要求。通过17个系统模拟实验，验证了具备与不具备多角色谓词和结构映射能力的认知架构在执行任务时的表现，结果支持了这一假设。这些发现有助于理解人类大脑如何产生符号思维，并揭示生物智能与现代机器学习方法之间的差异。

🔬 方法详解

问题定义：本文旨在解决人类大脑如何实现符号思维的问题，现有方法主要集中于动态绑定，但未能解释其不足之处。

核心思路：提出在动态绑定的基础上，层次整合的多角色绑定和多对应关系是实现符号思维的必要条件，强调了认知架构的设计。

技术框架：研究设计了17个模拟实验，分别测试具备和不具备多角色谓词及结构映射能力的认知架构，任务设置尽量通用，避免依赖特定特征。

关键创新：最重要的技术创新在于提出了层次整合的概念，强调多角色绑定和结构映射在符号思维中的核心作用，这与现有方法的单一动态绑定思路本质不同。

关键设计：实验中设置了多种任务，确保任务无法通过特征诊断完成，依赖于认知架构的多角色谓词和结构映射能力，确保了实验的严谨性。

📊 实验亮点

实验结果表明，具备多角色谓词和结构映射能力的认知架构在执行任务时表现优于不具备这些能力的架构，验证了理论假设。这一发现为理解符号思维的形成提供了重要证据。

🎯 应用场景

该研究的潜在应用领域包括生物启发的人工智能、认知机器人以及人机交互等。通过理解人类符号思维的机制，可以为开发更智能的机器学习系统提供理论基础，推动人工智能的进步与应用。

📄 摘要（原文）

What is it about human brains that allows us to reason symbolically whereas most other animals cannot? There is evidence that dynamic binding, the ability to combine neurons into groups on the fly, is necessary for symbolic thought, but there is also evidence that it is not sufficient. We propose that two kinds of hierarchical integration (integration of multiple role-bindings into multiplace predicates, and integration of multiple correspondences into structure mappings) are minimal requirements, on top of basic dynamic binding, to realize symbolic thought. We tested this hypothesis in a systematic collection of 17 simulations that explored the ability of cognitive architectures with and without the capacity for multi-place predicates and structure mapping to perform various kinds of tasks. The simulations were as generic as possible, in that no task could be performed based on any diagnostic features, depending instead on the capacity for multi-place predicates and structure mapping. The results are consistent with the hypothesis that, along with dynamic binding, multi-place predicates and structure mapping are minimal requirements for basic symbolic thought. These results inform our understanding of how human brains give rise to symbolic thought and speak to the differences between biological intelligence, which tends to generalize broadly from very few training examples, and modern approaches to machine learning, which typically require millions or billions of training examples. The results we report also have important implications for bio-inspired artificial intelligence.