Relational Constraints On Neural Networks Reproduce Human Biases towards Abstract Geometric Regularity

TL;DR

This paper demonstrates that neural networks can exhibit human-like biases towards geometric regularity without symbolic processing by using architectural constraints and curriculum training, revealing insights into geometric reasoning.

Contribution

It introduces a novel architectural constraint enabling neural networks to discover and manipulate relational structure, reproducing human biases in geometric reasoning tasks.

Findings

Neural networks with the proposed constraint show human-like symmetry biases.

Training with curriculum enhances the model's ability to generalize geometric regularity.

The approach offers an alternative to symbolic models for geometric reasoning.

Abstract

Uniquely among primates, humans possess a remarkable capacity to recognize and manipulate abstract structure in the service of task goals across a broad range of behaviors. One illustration of this is in the visual perception of geometric forms. Studies have shown a uniquely human bias toward geometric regularity, with task performance enhanced for more regular and symmetric forms compared to their geometrically irregular counterparts. Such studies conclude that this behavior implies the existence of discrete symbolic structure in human mental representations, and that replicating such behavior in neural network architectures will require mechanisms for symbolic processing. In this study, we argue that human biases towards geometric regularity can be reproduced in neural networks, without explicitly providing them with symbolic machinery, by augmenting them with an architectural constraint that enables the system to discover and manipulate relational structure. When trained with the appropriate curriculum, this model exhibits human-like biases towards symmetry and regularity in two distinct tasks involving abstract geometric reasoning. Our findings indicate that neural networks, when equipped with the necessary training objectives and architectural elements, can exhibit human-like regularity biases and generalization. This approach provides insights into the neural mechanisms underlying geometric reasoning and offers an alternative to prevailing symbolic "Language of Thought" models in this domain.