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 strong evidence for the universal law in a naturalistic high-dimensional regime by analyzing an existing data set of 214,200 human similarity judgments and a newly collected data set of 390,819 human generalization judgments (N = 2,406 U.S. participants) across three sets of natural images. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Chaos
January 2025
Department of Applied Mathematics, University of Colorado, Boulder, Colorado 80309-0526, USA.
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Faculty of Architecture, and Urban Systems Institute, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR.
Surfacic networks are structures built upon a 2D manifold. Many systems, including transportation networks and various urban networks, fall into this category. The fluctuations of node elevations imply significant deviations from typical plane networks and require specific tools to understand their impact.
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Department of Hepatology and Gastroenterology, Aarhus University Hospital, Aarhus N, Denmark.
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Health Systems Program, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
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