# The Universal Law of Generalization Holds for Naturalistic Stimuli

arXiv: https://arxiv.org/abs/2306.08564 · HTML: https://mlsanity.com/paper/2306.08564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Gibbs Sampling with People](https://mlsanity.com/paper/2008.02595/) (2020)
- [Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings](https://mlsanity.com/paper/2011.11015/) (2020)
- [Words are all you need? Language as an approximation for human similarity judgments](https://mlsanity.com/paper/2206.04105/) (2022)
- [Extracting low-dimensional psychological representations from convolutional neural networks](https://mlsanity.com/paper/2005.14363/) (2020)
- [Evidence for the size principle in semantic and perceptual domains](https://mlsanity.com/paper/1705.03260/) (2017)

## Built on by (most cited first, 4 of 4)

- [What is a Number, That a Large Language Model May Know It?](https://mlsanity.com/paper/2502.01540/) (2025)
- [Levels of Analysis for Large Language Models](https://mlsanity.com/paper/2503.13401/) (2025)
- [Shifting Attention to You: Personalized Brain-Inspired AI Models](https://mlsanity.com/paper/2502.04658/) (2025)
- [Ad hoc conventions generalize to new referents](https://mlsanity.com/paper/2509.05566/) (2025)

## Abstract (arXiv)

Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity. In its broadest form, the universal law states that the level of perceived similarity between a pair of stimuli should decay as a concave function of their distance when embedded in an appropriate psychological space. While extensively studied, evidence in support of the universal law has relied on low-dimensional stimuli and small stimulus sets that are very different from their real-world counterparts. This is largely because pairwise comparisons -- as required for similarity judgments -- scale quadratically in the number of stimuli. We provide direct evidence for the universal law in a naturalistic high-dimensional regime by analyzing an existing dataset of 214,200 human similarity judgments and a newly collected dataset of 390,819 human generalization judgments (N=2406 US participants) across three sets of natural images.