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

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Decoding the visual and subjective contents of the human brain

Open access

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

DOI 10.1038/nn1444PMC PMC1808230

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1038/nn1444

Cites

42 of 42 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Statistical Learning Theory
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Receptive fields and functional architecture of monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,685
Distributed and overlapping representations of faces and objects in ventral temporal cortex
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001) · Science · doi:10.1126/science.1063736 · Cited by 3,940
Perceptrons: An Introduction to Computational Geometry
Minsky, M., & Papert, S. A. (1969) · Cited by 3,273
Borders of Multiple Visual Areas in Humans Revealed by Functional Magnetic Resonance Imaging
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
Automated Image Registration: I. General Methods and Intrasubject, Intramodality Validation
Woods, R. P., Grafton, S. T., Holmes, C. J., Cherry, S. R., & Mazziotta, J. C. (1998) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199801000-00027 · Cited by 1,707
Retinotopic organization in human visual cortex and the spatial precision of functional MRI
Engel, S. A. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.2.181 · Cited by 1,606
Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans
Wolpaw, J. R., & McFarland, D. J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403504101 · Cited by 1,512

Show all 42 references

Sorted by citations

Items 501–600 of 1,000

Modeling and analysis of mechanisms underlying fMRI-based decoding of information conveyed in cortical columns
Chaimow, D., Yacoub, E. S., Uǧurbil, K., & Shmuel, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.037 · Cited by 55
Patterns of resting state connectivity in human primary visual cortical areas: A 7T fMRI study
Raemaekers, M. A. H., Schellekens, W., Wezel, R. J. A. V., Petridou, N., Kristo, G., & Ramsey, N. F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.060 · Cited by 55
The Neural Code for Face Orientation in the Human Fusiform Face Area
Ramírez, F., Cichy, R. M., Allefeld, C., & Haynes, J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3156-13.2014 · Cited by 54
Local pattern classification differentiates processes of economic valuation
Clithero, J. A., Carter, R., & Huettel, S. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.074 · Cited by 55
The importance of actions and the worth of an object: dissociable neural systems representing core value and economic value
Brosch, T., Coppin, G., Schwartz, S., & Sander, D. (2011) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsr036 · Cited by 56
Decoding Reveals Plasticity in V3A as a Result of Motion Perceptual Learning
Shibata, K., Chang, L.-H., Kim, D., Náñez, J. E., Kamitani, Y., Watanabe, T., & Sasaki, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044003 · Cited by 53
Self-citation
The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs
Coutanche, M. N., & Thompson‐Schill, S. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.076 · Cited by 53
Distributed Representations of Rule Identity and Rule Order in Human Frontal Cortex and Striatum
Reverberi, C., Görgen, K., & Haynes, J. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2344-12.2012 · Cited by 54
Repetition suppression for performed hand gestures revealed by fMRI
Hamilton, A. F. D. C., & Grafton, S. T. (2008) · Human Brain Mapping · doi:10.1002/hbm.20717 · Cited by 53
Neural Decoding and “Inner” Psychophysics: A Distance-to-Bound Approach for Linking Mind, Brain, and Behavior
Ritchie, J. B., & Carlson, T. A. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00190 · Cited by 59
Neural representations of the multidimensional self in the cortical midline structures
Feng, C., Yan, X., Huang, W., Han, S., & Ma, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.018 · Cited by 53
Correlates of perceptual awareness in human primary auditory cortex revealed by an informational masking experiment
Wiegand, K., & Gutschalk, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.067 · Cited by 53
The neural correlates of visual working memory encoding: A time-resolved fMRI study
Todd, J. J., Han, S. W., Harrison, S. A., & Marois, R. (2011) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2011.01.040 · Cited by 60
Decoding episodic memory in ageing: A Bayesian analysis of activity patterns predicting memory
Morcom, A. M., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.071 · Cited by 54
Nonlinear estimation of the BOLD signal
Johnston, L. A., Duff, E. P., Mareels, I., & Egan, G. F. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.024 · Cited by 52
Decoding the Formation of Reward Predictions across Learning
Kahnt, T., Heinzle, J., Park, S. Q., & Haynes, J. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.3412-11.2011 · Cited by 54
Distributed task coding throughout the multiple demand network of the human frontal–insular cortex
Stiers, P. L. J., Mennes, M., & Sunaert, S. G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.078 · Cited by 54
Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment
Oblak, E. F., Lewis‐Peacock, J. A., & Sulzer, J. S. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005681 · Cited by 52
What the success of brain imaging implies about the neural code
Guest, O., & Love, B. C. (2017) · eLife · doi:10.7554/elife.21397 · Cited by 53
Processing local signals into global patterns
Sasaki, Y. (2007) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2007.03.003 · Cited by 53
Imaging neural signatures of consciousness: 'what', 'when', 'where' and 'how' does it work?
Sergent, C., & Naccache, L. (2012) · PubMed · doi:10.4449/aib.v150i2.1270 · Cited by 54
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal NeuronsPreprint
Parthasarathy, N., Batty, E., Falcon, W., Rutten, T., Rajpal, M., Chichilnisky, E. J., & Paninski, L. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/153759 · Cited by 52
The role of motion in the neural representation of social interactions in the posterior temporal cortex
Landsiedel, J., Daughters, K., Downing, P. E., & Koldewyn, K. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119533 · Cited by 51
Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks
Jang, H., & Tong, F. (2024) · Nature Communications · doi:10.1038/s41467-024-45679-0 · Cited by 57
Self-citation
Crack segmentation on steel structures using boundary guidance model
He, Z., Chen, W., Zhang, J., & Wang, Y.-H. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105354 · Cited by 51
Multisensory functional magnetic resonance imaging: a future perspective
Goebel, R., & van Atteveldt, N. (2009) · Experimental Brain Research · doi:10.1007/s00221-009-1881-7 · Cited by 53
Similar coding of freely chosen and externally cued intentions in a fronto-parietal network
Wisniewski, D., Goschke, T., & Haynes, J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.044 · Cited by 55
Inferring brain-computational mechanisms with models of activity measurements
Kriegeskorte, N., & Diedrichsen, J. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0278 · Cited by 53
An Information-Driven 2-Pathway Characterization of Occipitotemporal and Posterior Parietal Visual Object Representations
Vaziri-Pashkam, M., & Xu, Y. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy080 · Cited by 52
Radial bias is not necessary for orientation decoding
Pratte, M. S., Sy, J. L., Swisher, J. D., & Tong, F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.066 · Cited by 50
Self-citation
Can expectation suppression be explained by reduced attention to predictable stimuli?
Alink, A., & Blank, H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117824 · Cited by 50
Toward a Brain-Computer Interface for Alzheimer's Disease Patients by Combining Classical Conditioning and Brain State Classification
Liberati, G., da Rocha, J. L. D., van der Heiden, L., Raffone, A., Birbaumer, N., Belardinelli, M. O., & Sitaram, R. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-112129 · Cited by 50
Neural decoding of goal locations in spatial navigation in humans with fMRI
Rodriguez, P. F. (2009) · Human Brain Mapping · doi:10.1002/hbm.20873 · Cited by 50
Learning Alters the Tuning of Functional Magnetic Resonance Imaging Patterns for Visual Forms
Zhang, J., Meeson, A., Welchman, A. E., & Kourtzi, Z. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2204-10.2010 · Cited by 50
Loss of reliable temporal structure in event-related averaging of naturalistic stimuli
Ben-Yakov, A., Honey, C. J., Lerner, Y., & Hasson, U. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.008 · Cited by 50
Amodal Processing in Human Prefrontal Cortex
Tamber-Rosenau, B. J., Dux, P. E., Tombu, M. N., Asplund, C. L., & Marois, R. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4601-12.2013 · Cited by 53
Parcellation of Human and Monkey Core Auditory Cortex with fMRI Pattern Classification and Objective Detection of Tonotopic Gradient Reversals
Schönwiesner, M., Dechent, P., Voit, D., Petkov, C. I., & Krumbholz, K. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu124 · Cited by 51
The Statistical Analysis of Multi-Voxel Patterns in Functional Imaging
Schreiber, K. M., & Krekelberg, B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069328 · Cited by 49
Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images
Güçlü, U., & van Gerven, M. A. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003724 · Cited by 49
History Modulates Early Sensory Processing of Salient Distractors
Adam, K. C. S., & Serences, J. T. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.3099-20.2021 · Cited by 49
Brain decoding: Reading minds
Smith, K. (2013) · Nature · doi:10.1038/502428a · Cited by 49
Visual Representations: Insights from Neural Decoding
Robinson, A. K., Quek, G. L., & Carlson, T. A. (2023) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100120-025301 · Cited by 48
Basing Perceptual Decisions on the Most Informative Sensory Neurons
Scolari, M., & Serences, J. T. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00273.2010 · Cited by 52
Neural representation of scene boundaries
Ferrara, K., & Park, S. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.05.012 · Cited by 48
Inverted Encoding Models of Human Population Response Conflate Noise and Neural Tuning Width
Liu, T., Cable, D. M., & Gardner, J. L. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2453-17.2017 · Cited by 51
Information Theory and Cognition: A Review
Sayood, K. (2018) · Entropy · doi:10.3390/e20090706 · Cited by 48
Conscious perception as integrated information patterns in human electrocorticography
Haun, A. M., Oizumi, M., Kovach, C. K., Kawasaki, H., Oya, H., Howard, M. A., Adolphs, R., & Tsuchiya, N. (2017) · eneuro · doi:10.1523/eneuro.0085-17.2017 · Cited by 47
DreaMR: Diffusion-Driven Counterfactual Explanation for Functional MRI
Bedel, H. A., & Çukur, T. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3507008 · Cited by 53
Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective
Chen, M., Han, J., Hu, X., Jiang, X., Guo, L., & Liu, T. (2013) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9238-z · Cited by 48
Advancing Emotion Theory with Multivariate Pattern Classification
Kragel, P. A., & LaBar, K. S. (2014) · Emotion Review · doi:10.1177/1754073913512519 · Cited by 54
Rivalry-Like Neural Activity in Primary Visual Cortex in Anesthetized Monkeys
Xu, H., Han, C., Chen, M., Li, P., Zhu, S., Fang, Y., Hu, J., Ma, H., & Lu, H. D. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3660-15.2016 · Cited by 47
Ghosts in machine learning for cognitive neuroscience: Moving from data to theory
Carlson, T. A., Goddard, E., Kaplan, D. M., Klein, C., & Ritchie, J. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.019 · Cited by 51
Action and object words are differentially anchored in the sensory motor system - A perspective on cognitive embodiment
Horoufchin, H., Bzdok, D., Buccino, G., Borghi, A. M., & Binkofski, F. (2018) · Scientific Reports · doi:10.1038/s41598-018-24475-z · Cited by 47
Understanding Others
Singer, T., & Tusche, A. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-416008-8.00027-9 · Cited by 51
Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images
Jang, H., McCormack, D. R., & Tong, F. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001418 · Cited by 53
Self-citation
A single-trial analytic framework for EEG analysis and its application to target detection and classification
Poolman, P., Frank, R. M., Luu, P., Pederson, S. M., & Tucker, D. M. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.031 · Cited by 47
Natural Image Reconstruction From fMRI Using Deep Learning: A Survey
Rakhimberdina, Z., Jodelet, Q., Liu, X., & Murata, T. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.795488 · Cited by 49
Targeting the functional properties of cortical neurons using fMR-adaptation
Malach, R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.002 · Cited by 50
Recent developments in multivariate pattern analysis for functional MRI
Yang, Z., Fang, F., & Weng, X. (2012) · Neuroscience Bulletin · doi:10.1007/s12264-012-1253-3 · Cited by 49
Cortical Correlates of Human Motion Perception Biases
Vintch, B., & Gardner, J. L. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2809-13.2014 · Cited by 47
Visual attention as an important visual function: an outline of manifestations, diagnosis and management of impaired visual attention
Das, M., Bennett, D. M., & Dutton, G. N. (2007) · British Journal of Ophthalmology · doi:10.1136/bjo.2006.104844 · Cited by 46
Integration of texture and disparity cues to surface slant in dorsal visual cortex
Murphy, A. P., Ban, H., & Welchman, A. E. (2013) · Journal of Neurophysiology · doi:10.1152/jn.01055.2012 · Cited by 46
A Fully Implantable Wireless ECoG 128-Channel Recording Device for Human Brain–Machine Interfaces: W-HERBS
Matsushita, K., Hirata, M., Suzuki, T., Ando, H., Yoshida, T., Ota, Y., Satō, F., Morris, S., Sugata, H., Goto, T., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00511 · Cited by 48
Subjective vividness of motor imagery has a neural signature in human premotor and parietal cortex
Zabicki, A., de Haas, B., Zentgraf, K., Stark, R. M., Munzert, J., & Krüger, B. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.073 · Cited by 50
False-positive neuroimaging: Undisclosed flexibility in testing spatial hypotheses allows presenting anything as a replicated finding
Hong, Y.-W., Yoo, Y., Han, J.-H., Wager, T. D., & Woo, C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.070 · Cited by 49
Probing principles of large‐scale object representation: Category preference and location encoding
Cichy, R. M., Sterzer, P., Heinzle, J., Elliott, L. T., Ramírez, F., & Haynes, J. (2012) · Human Brain Mapping · doi:10.1002/hbm.22020 · Cited by 46
Combined orientation and colour information in human V1 for both L–M and S-cone chromatic axes
Sumner, P., Anderson, E. J., Sylvester, R. J., Haynes, J., & Rees, G. E. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.013 · Cited by 46
Distributed representation of single touches in somatosensory and visual cortex
Beauchamp, M. S., LaConte, S. M., & Yasar, N. E. (2009) · Human Brain Mapping · doi:10.1002/hbm.20735 · Cited by 46
Working memory needs pointers
Awh, E., & Vogel, E. K. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.12.006 · Cited by 45
High-Field fMRI for Human Applications: An Overview of Spatial Resolution and Signal Specificity
Olman, C. A., & Yacoub, E. S. (2011) · The Open Neuroimaging Journal · doi:10.2174/1874440001105010074 · Cited by 49
How attention extracts objects from noise
Pratte, M. S., Ling, S., Swisher, J. D., & Tong, F. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00127.2013 · Cited by 45
Self-citation
Transcranial magnetic stimulation of early visual cortex interferes with subjective visual awareness and objective forced-choice performance
Koivisto, M., Railo, H., & Salminen‐Vaparanta, N. (2010) · Consciousness and Cognition · doi:10.1016/j.concog.2010.09.001 · Cited by 45
Remembered or Forgotten?—An EEG-Based Computational Prediction Approach
Sun, X., Qian, C., Chen, Z., Wu, Z., Luo, B., & Pan, G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0167497 · Cited by 45
Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants
Cortese, A., Amano, K., Koizumi, A., Lau, H. C., & Kawato, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.069 · Cited by 45
Accurate Reconstruction of Image Stimuli From Human Functional Magnetic Resonance Imaging Based on the Decoding Model With Capsule Network Architecture
Qiao, K., Zhang, C., Wang, L., Chen, J., Zeng, L., Tong, L., & Yan, B. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00062 · Cited by 45
Neural signatures of dynamic emotion constructs in the human brain
Grootswagers, T., Kennedy, B. L., Most, S. B., & Carlson, T. A. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.10.016 · Cited by 45
Attention driven phantom vision: measuring the sensory strength of attentional templates and their relation to visual mental imagery and aphantasia
Keogh, R., & Pearson, J. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0688 · Cited by 45
Mapping spatial frequency preferences across human primary visual cortex
Broderick, W. F., Simoncelli, E. P., & Winawer, J. A. (2022) · Journal of Vision · doi:10.1167/jov.22.4.3 · Cited by 47
Decoding the direction of imagined visual motion using 7 T ultra-high field fMRI
Emmerling, T. C., Zimmermann, J., Sorger, B., Frost, M. A., & Goebel, R. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.022 · Cited by 44
Dependencies between stimuli and spatially independent fMRI sources: Towards brain correlates of natural stimuli
Ylipaavalniemi, J., Savia, E., Malinen, S., Hari, R., Vigário, R., & Samuel Kaski, A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.056 · Cited by 44
Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis
Suk, H., Chong-yaw Wee, A., Lee, S., & Shen, D. G. (2014) · Neuroinformatics · doi:10.1007/s12021-014-9241-6 · Cited by 44
Direct evidence for encoding of motion streaks in human visual cortex
Apthorp, D., Schwarzkopf, D. S., Kaul, C., Bahrami, B., Alais, D., & Rees, G. E. (2012) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2012.2339 · Cited by 45
Tuning to Binaural Cues in Human Auditory Cortex
McLaughlin, S. A., Higgins, N. C., & Stecker, G. C. (2015) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-015-0546-4 · Cited by 44
Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion
Zafar, R., Dass, S. C., & Malik, A. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178410 · Cited by 45
The precision of attentional selection is far worse than the precision of the underlying memory representation
Kerzel, D. (2019) · Cognition · doi:10.1016/j.cognition.2019.02.001 · Cited by 46
Modeling fMRI data generated by overlapping cognitive processes with unknown onsets using Hidden Process Models
Hutchinson, R. A., Niculescu, R. S., Keller, T. A., Rustandi, I., & Mitchell, T. M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.025 · Cited by 44
Validation of brain‐derived signals in near‐infrared spectroscopy through multivoxel analysis of concurrent functional magnetic resonance imaging
Moriguchi, Y., Noda, T., Nakayashiki, K., Takata, Y., Setoyama, S., Kawasaki, S., Kunisato, Y., Mishima, K., Nakagome, K., & Hanakawa, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23734 · Cited by 43
Motion Direction Biases and Decoding in Human Visual Cortex
Wang, H. X., Merriam, E. P., Freeman, J., & Heeger, D. J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1034-14.2014 · Cited by 43
Demonstration of Tuning to Stimulus Orientation in the Human Visual Cortex: A High-Resolution fMRI Study with a Novel Continuous and Periodic Stimulation Paradigm
Sun, P., Gardner, J. L., Costagli, M., Ueno, K., Waggoner, R. A., Tanaka, K., & Cheng, K. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs149 · Cited by 43
Decoding sequential finger movements from preparatory activity in higher‐order motor regions: a functional magnetic resonance imaging multi‐voxel pattern analysis
Nambu, I., Hagura, N., Hirose, S., Wada, Y., Kawato, M., & Naito, E. (2015) · European Journal of Neuroscience · doi:10.1111/ejn.13063 · Cited by 43
Neural Systems for Visual Scene Recognition
Epstein, R. A. (2014) · The MIT Press eBooks · doi:10.7551/mitpress/9780262027854.003.0006 · Cited by 43
Task-dependent enhancement of facial expression and identity representations in human cortex
Dobs, K., Schultz, J., Bülthoff, I., & Gardner, J. L. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.013 · Cited by 43
BrainCLIP: Brain Representation via CLIP for Generic Natural Visual Stimulus Decoding
Ma, Y., Liu, Y., Chen, L., Zhu, G., Chen, B., & Zheng, N. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3537287 · Cited by 78
The effect of spatial smoothing on fMRI decoding of columnar-level organization with linear support vector machine
Misaki, M., Luh, W., & Bandettini, P. A. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.11.004 · Cited by 42
Learning-dependent plasticity with and without training in the human brain
Zhang, J., & Kourtzi, Z. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1002506107 · Cited by 42
Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function
Aquino, K. M., Robinson, P. A., Schira, M. M., & Breakspear, M. J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.001 · Cited by 42
Evidence for cue-independent spatial representation in the human auditory cortex during active listening
Higgins, N. C., McLaughlin, S. A., Rinne, T., & Stecker, G. C. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707522114 · Cited by 42
A geometric framework for modeling dynamic decisions among arbitrarily many alternatives
Kvam, P. D. (2018) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2019.03.001 · Cited by 42
Noninvasively Decoding the Contents of Visual Working Memory in the Human Prefrontal Cortex within High-gamma Oscillatory Patterns
Polanía, R., Paulus, W., & Nitsche, M. A. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00151 · Cited by 42
Multi-view manifold learning of human brain-state trajectories
Busch, E. L., Huang, J., Benz, A., Wallenstein, T., Lajoie, G., Wolf, G., Krishnaswamy, S., & Turk‐Browne, N. B. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00419-0 · Cited by 41

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Decoding the visual and subjective contents of the human brain

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks