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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Universal dimensions of visual representation.

Zirui Chen1, Michael F Bonner1

  • 1Department of Cognitive Science, Johns Hopkins University, Baltimore 21218, USA.

Science Advances
|July 2, 2025
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Summary
This summary is machine-generated.

Diverse visual neural networks learn universal brain-aligned representations, suggesting core similarities in how artificial and biological vision process natural images. These universal features are key to brain alignment, independent of network specifics.

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Visual neural networks (VNNs) and biological vision share architectural constraints and task objectives.
  • The role of universal features in natural image processing for VNNs remains unclear.

Purpose of the Study:

  • To investigate whether VNNs align with brain representations due to shared constraints/objectives or universal image processing features.
  • To characterize the universality of representational dimensions across diverse VNNs.

Main Methods:

  • Analyzed hundreds of thousands of representational dimensions from VNNs with varying architectures, tasks, and training data.
  • Compared VNN representations with human brain representations using functional magnetic resonance imaging (fMRI).

Main Results:

  • Diverse VNNs independently learn a shared set of latent dimensions for representing natural images.
  • The most brain-aligned VNN representations are universal and independent of specific network characteristics.
  • Reducing networks to fewer than 10 universal dimensions minimally impacts representational similarity to the brain.

Conclusions:

  • Underlying similarities between artificial and biological vision stem from a core set of universal representations.
  • These universal representations are convergently learned by diverse artificial and biological systems.
  • Focusing on universal dimensions may be key to understanding and improving brain-aligned AI.