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Vision Transformers Learn Gestalt-Like Figure-Ground Cues from Natural Images

Summary

A new study shows that Vision Transformers (ViTs) can learn Gestalt-like figure-ground cues such as surroundedness, convexity, and symmetry from natural images. Testing 25 ViT models, the researchers found robust encoding of surroundedness and convexity, while symmetry cues only worked for uniformly colored regions. The work demonstrates that Gestalt cues can be learned from natural scene statistics and positions ViTs as a model system for studying perceptual organization.

SourcearXiv Computer VisionAuthor: Matthias Tangemann, Benjamin Lo, Zygmunt Pizlo, Kaleem Siddiqi, Dirk B. Walther, Sven Dickinson
Vision Transformers Learn Gestalt-Like Figure-Ground Cues from Natural Images
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[Submitted on 9 Jul 2026]

Title:Vision Transformers Learn Gestalt-Like Figure-Ground Cues from Natural Images

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Abstract:Figure-ground organization in the human visual system relies on several shape-based cues, including surroundedness, convexity, and symmetry. While these cues have been extensively studied using abstract stimuli, little is known about how they operate under natural conditions or how they arise from the statistics of natural scenes. Deep neural networks offer a promising path forward: a model that relies on the same figure-ground cues as humans would provide tractable experimental access to the underlying mechanisms. In this study, we evaluate shape-based figure-ground organization in Vision Transformers (ViTs), for which prior work has demonstrated the emergence of object-based grouping. We test 25 ViTs spanning supervised and self-supervised training objectives, by fitting linear probes to predict figure-ground assignment from intermediate patch representations using both natural images and controlled artificial stimuli that isolate individual cues. Our results show that ViTs robustly encode surroundedness and convexity, and that probes trained on natural images generalize zero-shot to artificial stimuli across several models. For symmetry we observe mixed results: the cue is encoded for uniformly colored but not for textured regions. Taken together, our findings demonstrate that Gestalt-like figure-ground cues can be learned from natural scene statistics and position ViTs as a compelling model system for studying the computational mechanisms of perceptual organization.

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Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.08932 [cs.CV]

(or arXiv:2607.08932v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.08932

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Matthias Tangemann [view email] [v1] Thu, 9 Jul 2026 21:00:46 UTC (5,079 KB)

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Key points and analysis

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Key points

  • ViTs robustly encode surroundedness and convexity figure-ground cues.
  • Symmetry cues are encoded only in uniformly colored regions, not textured ones.
  • The study shows Gestalt-like cues can be learned from natural scene statistics.
  • ViTs offer a compelling model system for studying computational mechanisms of perceptual organization.

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