Identifying natural images from human brain activity

Kendrick N. Kay, Thomas Naselaris, Ryan J. Prenger, Jack L. Gallant

Research output: Contribution to journalArticlepeer-review

869 Scopus citations


A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.

Original languageEnglish (US)
Pages (from-to)352-355
Number of pages4
Issue number7185
StatePublished - Mar 20 2008
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgements This work was supported by a National Defense Science and Engineering Graduate fellowship (K.N.K.), the National Institutes of Health, and University of California, Berkeley intramural funds. We thank B. Inglis for assistance with MRI, K. Hansen for assistance with retinotopic mapping, D. Woods and X. Kang for acquisition of whole-brain anatomical data, and A. Rokem for assistance with scanner operation. We also thank C. Baker, M. D’Esposito, R. Ivry, A. Landau, M. Merolle and F. Theunissen for comments on the manuscript. Finally, we thank S. Nishimoto, R. Redfern, K. Schreiber, B. Willmore and B. Yu for their help in various aspects of this research.


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