Spontaneous emergence of linguistic statistical laws in images via artificial neural networks

TL;DR

This paper demonstrates that neural networks processing images spontaneously develop statistical regularities similar to linguistic laws, revealing that structured, symbolic-like units can emerge naturally from perceptual processing.

Contribution

It shows that visual representations derived from neural networks follow linguistic statistical laws without explicit symbolic training, offering a new perspective on emergent structured representations.

Findings

Image-derived units follow Zipf's, Heaps', and Benford's laws

Statistical regularities emerge spontaneously without explicit symbols

Neural networks can develop quasi-symbolic structures from perception

Abstract

As a core element of culture, images transform perception into structured representations and undergo evolution similar to natural languages. Given that visual input accounts for 60% of human sensory experience, it is natural to ask whether images follow statistical regularities similar to those in linguistic systems. Guided by symbol-grounding theory, which posits that meaningful symbols originate from perception, we treat images as vision-centric artifacts and employ pre-trained neural networks to model visual processing. By detecting kernel activations and extracting pixels, we obtain text-like units, which reveal that these image-derived representations adhere to statistical laws such as Zipf's, Heaps', and Benford's laws, analogous to linguistic data. Notably, these statistical regularities emerge spontaneously, without the need for explicit symbols or hybrid architectures. Our results indicate that connectionist networks can automatically develop structured, quasi-symbolic units through perceptual processing alone, suggesting that text- and symbol-like properties can naturally emerge from neural networks and providing a novel perspective for interpretation.