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

• URL: http://arxiv.org/abs/2508.15082v1
• Date: Wed, 20 Aug 2025 21:38:06 GMT
• Title: From Basic Affordances to Symbolic Thought: A Computational Phylogenesis of Biological Intelligence
• Authors: John E. Hummel, Rachel F. Heaton,
• Abstract summary: We propose that two kinds of hierarchical integration are minimal requirements, on top of basic dynamic binding, to realize symbolic thought.<n>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.<n>The results inform our understanding of how human brains give rise to symbolic thought and speak to the differences between biological intelligence and modern approaches to machine learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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