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Generic decoding of seen and imagined objects using hierarchical visual features

Author

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  • Tomoyasu Horikawa

    (ATR Computational Neuroscience Laboratories)

  • Yukiyasu Kamitani

    (ATR Computational Neuroscience Laboratories
    Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan)

Abstract

Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, decoding of imagined objects reveals progressive recruitment of higher-to-lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.

Suggested Citation

  • Tomoyasu Horikawa & Yukiyasu Kamitani, 2017. "Generic decoding of seen and imagined objects using hierarchical visual features," Nature Communications, Nature, vol. 8(1), pages 1-15, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15037
    DOI: 10.1038/ncomms15037
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    Citations

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    Cited by:

    1. Guohua Shen & Tomoyasu Horikawa & Kei Majima & Yukiyasu Kamitani, 2019. "Deep image reconstruction from human brain activity," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-23, January.
    2. Serra E. Favila & Brice A. Kuhl & Jonathan Winawer, 2022. "Perception and memory have distinct spatial tuning properties in human visual cortex," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    3. Hojin Jang & Frank Tong, 2024. "Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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