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

arXiv: https://arxiv.org/abs/2501.18620 · HTML: https://mlsanity.com/paper/2501.18620/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Adversarial Examples Are a Natural Consequence of Test Error in Noise](https://mlsanity.com/paper/1901.10513/) (2019)
- [Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations](https://mlsanity.com/paper/1807.01697/) (2018)
- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [The Vector Grounding Problem](https://mlsanity.com/paper/2304.01481/) (2023)
- [Unsupervised deep learning identifies semantic disentanglement in single inferotemporal neurons](https://mlsanity.com/paper/2006.14304/) (2020)
- [A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions](https://mlsanity.com/paper/2406.05785/) (2024)
- [Emergent Graphical Conventions in a Visual Communication Game](https://mlsanity.com/paper/2111.14210/) (2021)
- [Unsupervised, Bottom-up Category Discovery for Symbol Grounding with a Curious Robot](https://mlsanity.com/paper/2404.03092/) (2024)
- [In-depth analysis of music structure as a text network](https://mlsanity.com/paper/2303.13631/) (2023)

## Abstract (arXiv)

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.