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Identifying natural images from human brain activity

Author

Listed:
  • Kendrick N. Kay

    (University of California, Berkeley, California 94720, USA)

  • Thomas Naselaris

    (Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720, USA)

  • Ryan J. Prenger

    (University of California, Berkeley, California 94720, USA)

  • Jack L. Gallant

    (University of California, Berkeley, California 94720, USA
    Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720, USA)

Abstract

Reading the mind Recent functional magnetic resonance imaging (fMRI) studies have shown that, based on patterns of activity evoked by different categories of visual images, it is possible to deduce simple features in the visual scene, or to which category it belongs. Kay et al. take this approach a tantalizing step further. Their newly developed decoding method, based on quantitative receptive field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas, can identify with high accuracy which specific natural image an observer saw, even for an image chosen at random from 1,000 distinct images. This prompts the thought that it may soon be possible to decode subjective perceptual experiences such as visual imagery and dreams, an idea previously restricted to the realm of science fiction.

Suggested Citation

  • Kendrick N. Kay & Thomas Naselaris & Ryan J. Prenger & Jack L. Gallant, 2008. "Identifying natural images from human brain activity," Nature, Nature, vol. 452(7185), pages 352-355, March.
    • Handle: RePEc:nat:nature:v:452:y:2008:i:7185:d:10.1038_nature06713
    DOI: 10.1038/nature06713
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