Cited by 1,829Visual perception and processing mechanismsFace Recognition and PerceptionNeural dynamics and brain function

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Decoding the visual and subjective contents of the human brain

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Kamitani, Yukiyasu, Tong, Frank·2005·Nature Neuroscience

DOI 10.1038/nn1444PMC PMC1808230

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Abstract

The potential for human neuroimaging to read out the detailed contents of a person's mental state has yet to be fully explored. We investigated whether the perception of edge orientation, a fundamental visual feature, can be decoded from human brain activity measured with functional magnetic resonance imaging (fMRI). Using statistical algorithms to classify brain states, we found that ensemble fMRI signals in early visual areas could reliably predict on individual trials which of eight stimulus orientations the subject was seeing. Moreover, when subjects had to attend to one of two overlapping orthogonal gratings, feature-based attention strongly biased ensemble activity toward the attended orientation. These results demonstrate that fMRI activity patterns in early visual areas, including primary visual cortex (V1), contain detailed orientation information that can reliably predict subjective perception. Our approach provides a framework for the readout of fine-tuned representations in the human brain and their subjective contents.

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Minsky, M., & Papert, S. A. (1969) · Cited by 3,273
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Sereno, M. I., Dale, A. M., Reppas, J. B., Kwong, K. K., Belliveau, J. W., Brady, T. J., Rosen, B. R., & Tootell, R. B. H. (1995) · Science · doi:10.1126/science.7754376 · Cited by 2,663
A direct demonstration of functional specialization in human visual cortex
Zeki, S., Watson, J. D. G., Lueck, C. J., Friston, K. J., Christopher Kennard, C., & Frackowiak, R. S. J. (1991) · Journal of Neuroscience · doi:10.1523/jneurosci.11-03-00641.1991 · Cited by 1,717
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Authors: Kamitani, Y., & Tong, F.
Affiliations: Advanced Telecommunications Research Institute International (JP)
Vanderbilt University (US)
Corresponding author: Kamitani, Yukiyasu
ORCID: Kamitani, Yukiyasu
Tong, Frank
Venue: Nature Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2005-04-24
Volume: 8
Issue: 5
Pages: 679-685
ISSN: 1097-6256 (ISSN-L)
1546-1726
DOI: 10.1038/nn1444
PMC: PMC1808230
OpenAlex: W2036084760
Cited by: 1,829
References: 42
Citations by year: 2026: 46 · 2025: 76 · 2024: 64 · 2023: 63 · 2022: 65 · 2021: 86 · 2020: 65 · 2019: 91 · 2018: 83 · 2017: 113 · 2016: 100 · 2015: 99 · 2014: 111 · 2013: 119 · 2012: 107
FWCI: 30.12
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Neuroscience (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health
Japan Society for the Promotion of Science
National Institute of Information and Communications Technology
National Institute of Mental Health (P50 MH62196, P50MH062196)
National Eye Institute (R01-EY14202, R01EY014202)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords: fMRI decoding, primary visual cortex, early visual areas, feature-based attention, orientation discrimination, visual perception
MeSH terms: Adult, Algorithms, Attention, Brain Mapping, Cognition, Evoked Potentials, Visual, Humans, Magnetic Resonance Imaging, Models, Neurological, Orientation, Pattern Recognition, Visual, Photic Stimulation, Visual Cortex, Visual Pathways, Visual Perception

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Decoding the visual and subjective contents of the human brain

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