The Universal Law of Generalization Holds for Naturalistic Stimuli

TL;DR

This study provides strong evidence that Shepard's universal law of generalization applies to high-dimensional, naturalistic stimuli by analyzing large datasets of human similarity and generalization judgments.

Contribution

The paper demonstrates the universal law's validity in complex, real-world stimuli using extensive datasets, extending prior low-dimensional findings.

Findings

Universal law holds for naturalistic stimuli.

Large-scale datasets confirm the law's applicability.

High-dimensional stimuli follow the predicted decay of similarity.

Abstract

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.