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

TL;DR

This paper investigates the minimal computational requirements for symbolic thought, proposing that hierarchical integration of role-bindings and structure mappings, alongside dynamic binding, are essential for symbolic reasoning in biological brains.

Contribution

It introduces a systematic simulation-based analysis demonstrating that multi-place predicates and structure mapping are necessary for symbolic thought beyond dynamic binding.

Findings

Multi-place predicates and structure mapping are essential for symbolic reasoning.

Simulations show these capabilities are minimal requirements for symbolic thought.

Results differentiate biological intelligence from machine learning approaches.

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.