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Zero-shot visual reasoning through probabilistic analogical mapping

Author

Listed:
  • Taylor Webb

    (University of California)

  • Shuhao Fu

    (University of California)

  • Trevor Bihl

    (Sensors Directorate, Air Force Research Laboratory)

  • Keith J. Holyoak

    (University of California)

  • Hongjing Lu

    (University of California
    University of California)

Abstract

Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive direct training on visual reasoning tasks, and yield limited generalization to problems with novel content. In contrast, a long tradition of research in cognitive science has focused on elucidating the computational principles underlying human analogical reasoning; however, this work has generally relied on manually constructed representations. Here we present visiPAM (visual Probabilistic Analogical Mapping), a model of visual reasoning that synthesizes these two approaches. VisiPAM employs learned representations derived directly from naturalistic visual inputs, coupled with a similarity-based mapping operation derived from cognitive theories of human reasoning. We show that without any direct training, visiPAM outperforms a state-of-the-art deep learning model on an analogical mapping task. In addition, visiPAM closely matches the pattern of human performance on a novel task involving mapping of 3D objects across disparate categories.

Suggested Citation

  • Taylor Webb & Shuhao Fu & Trevor Bihl & Keith J. Holyoak & Hongjing Lu, 2023. "Zero-shot visual reasoning through probabilistic analogical mapping," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40804-x
    DOI: 10.1038/s41467-023-40804-x
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    References listed on IDEAS

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    1. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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    Cited by:

    1. Hongrui Zhang & Yanjin Chen & Zhuo Wang & Tie Jun Cui & Philipp Hougne & Lianlin Li, 2024. "Semantic regularization of electromagnetic inverse problems," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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