{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T03:38:55Z","timestamp":1794368335486,"version":"4.9.0"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Commun"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are enabling models of the brain with performances beyond what was possible just a decade ago. However, because the stimuli of these datasets typically live within a common naturalistic visual distribution, they make it challenging to implement out-of-distribution (OOD) generalization tests crucial for the development of robust brain models. Here, we address this by releasing NSD-synthetic, a dataset of 7T fMRI responses from the same eight NSD participants for 284 synthetic images. We show that NSD-synthetic\u2019s fMRI responses reliably encode stimulus-related information and are OOD with respect to NSD; that OOD generalization tests on NSD-synthetic reveal differences between brain models that are not detected in-distribution; and that the degree of OOD (quantified as the test data distance from the training data) is predictive of the magnitude of model failures. Together, NSD-synthetic enables OOD generalization tests that facilitate the development of more robust models of visual processing.<\/jats:p>","DOI":"10.1038\/s41467-026-69345-9","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T09:34:18Z","timestamp":1770629658000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision"],"prefix":"10.1038","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8923-9477","authenticated-orcid":false,"given":"Alessandro T.","family":"Gifford","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4190-6071","authenticated-orcid":false,"given":"Radoslaw M.","family":"Cichy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Naselaris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6604-9155","authenticated-orcid":false,"given":"Kendrick","family":"Kay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"69345_CR1","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1038\/s41593-021-00962-x","volume":"25","author":"EJ Allen","year":"2022","unstructured":"Allen, E. J. et al. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat. Neurosci. 25, 116\u2013126 (2022).","journal-title":"Nat. Neurosci."},{"key":"69345_CR2","doi-asserted-by":"publisher","first-page":"119754","DOI":"10.1016\/j.neuroimage.2022.119754","volume":"264","author":"AT Gifford","year":"2022","unstructured":"Gifford, A. T., Dwivedi, K., Roig, G. & Cichy, R. M. A large and rich EEG dataset for modeling human visual object recognition. NeuroImage 264, 119754 (2022).","journal-title":"NeuroImage"},{"key":"69345_CR3","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.82580","volume":"12","author":"MN Hebart","year":"2023","unstructured":"Hebart, M. N. et al. THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior. eLife 12, e82580 (2023).","journal-title":"eLife"},{"key":"69345_CR4","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.tins.2024.09.011","volume":"47","author":"ER Kupers","year":"2024","unstructured":"Kupers, E. R., Knapen, T., Merriam, E. P. & Kay, K. N. Principles of intensive human neuroimaging. Trends Neurosci. 47, 856\u2013864 (2024).","journal-title":"Trends Neurosci."},{"key":"69345_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-50310-3","volume":"15","author":"B Lahner","year":"2024","unstructured":"Lahner, B. et al. Modeling short visual events through the BOLD moments video fMRI dataset and metadata. Nat. Commun. 15, 6241 (2024).","journal-title":"Nat. Commun."},{"key":"69345_CR6","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cobeha.2020.12.008","volume":"40","author":"T Naselaris","year":"2021","unstructured":"Naselaris, T., Allen, E. & Kay, K. Extensive sampling for complete models of individual brains. Curr. Opin. Behav. Sci. 40, 45\u201351 (2021).","journal-title":"Curr. Opin. Behav. Sci."},{"key":"69345_CR7","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.neuron.2024.12.003","volume":"113","author":"P Papale","year":"2025","unstructured":"Papale, P. et al. An extensive dataset of spiking activity to reveal the syntax of the ventral stream. Neuron 113, 539\u2013553 (2025).","journal-title":"Neuron"},{"key":"69345_CR8","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.tics.2019.01.009","volume":"23","author":"RM Cichy","year":"2019","unstructured":"Cichy, R. M. & Kaiser, D. Deep neural networks as scientific models. Trends Cogn. Sci. 23, 305\u2013317 (2019).","journal-title":"Trends Cogn. Sci."},{"key":"69345_CR9","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1038\/s41583-023-00705-w","volume":"24","author":"A Doerig","year":"2023","unstructured":"Doerig, A. et al. The neuroconnectionist research programme. Nat. Rev. Neurosci. 24, 431\u2013450 (2023).","journal-title":"Nat. Rev. Neurosci."},{"key":"69345_CR10","doi-asserted-by":"crossref","unstructured":"Kietzmann, T. C., McClure, P. & Kriegeskorte, N. Deep neural networks in computational neuroscience. Oxford Res. Encyclopedia Neurosci. (2019).","DOI":"10.1093\/acrefore\/9780190264086.013.46"},{"key":"69345_CR11","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1038\/s41593-019-0520-2","volume":"22","author":"BA Richards","year":"2019","unstructured":"Richards, B. A. et al. A deep learning framework for neuroscience. Nat. Neurosci. 22, 1761\u20131770 (2019).","journal-title":"Nat. Neurosci."},{"key":"69345_CR12","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/s41583-020-00395-8","volume":"22","author":"A Saxe","year":"2021","unstructured":"Saxe, A., Nelli, S. & Summerfield, C. If deep learning is the answer, what is the question? Nat. Rev. Neurosci. 22, 55\u201367 (2021).","journal-title":"Nat. Rev. Neurosci."},{"key":"69345_CR13","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1038\/nn.4244","volume":"19","author":"DLK Yamins","year":"2016","unstructured":"Yamins, D. L. K. & DiCarlo, J. J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356\u2013365 (2016).","journal-title":"Nat. Neurosci."},{"key":"69345_CR14","doi-asserted-by":"publisher","unstructured":"Gifford, A. T. et al. The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenes. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2301.03198 (2023).","DOI":"10.48550\/ARXIV.2301.03198"},{"key":"69345_CR15","doi-asserted-by":"publisher","unstructured":"Schrimpf, M. et al. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? Preprint at https:\/\/doi.org\/10.1101\/407007 (2018).","DOI":"10.1101\/407007"},{"key":"69345_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-53147-y","volume":"15","author":"C Conwell","year":"2024","unstructured":"Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A. & Konkle, T. A large-scale examination of inductive biases shaping high-level visual representation in brains and machines. Nat. Commun. 15, 9383 (2024).","journal-title":"Nat. Commun."},{"key":"69345_CR17","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.1038\/s42256-025-01072-0","volume":"7","author":"A Doerig","year":"2025","unstructured":"Doerig, A. et al. High-level visual representations in the human brain are aligned with large language models. Nat. Mach. Intell. 7, 1220\u20131234 (2025).","journal-title":"Nat. Mach. Intell."},{"key":"69345_CR18","doi-asserted-by":"publisher","first-page":"4159","DOI":"10.1016\/j.cub.2022.08.009","volume":"32","author":"M Khosla","year":"2022","unstructured":"Khosla, M., Ratan Murty, N. A. & Kanwisher, N. A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition. Curr. Biol. 32, 4159\u20134171.e9 (2022).","journal-title":"Curr. Biol."},{"key":"69345_CR19","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1038\/s42003-023-04546-2","volume":"6","author":"N Jain","year":"2023","unstructured":"Jain, N. et al. Selectivity for food in human ventral visual cortex. Commun. Biol. 6, 175 (2023).","journal-title":"Commun. Biol."},{"key":"69345_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-38674-4","volume":"14","author":"G St-Yves","year":"2023","unstructured":"St-Yves, G., Allen, E. J., Wu, Y., Kay, K. & Naselaris, T. Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations. Nat. Commun. 14, 3329 (2023).","journal-title":"Nat. Commun."},{"key":"69345_CR21","doi-asserted-by":"publisher","first-page":"eadl1776","DOI":"10.1126\/sciadv.adl1776","volume":"10","author":"JS Prince","year":"2024","unstructured":"Prince, J. S., Alvarez, G. A. & Konkle, T. Contrastive learning explains the emergence and function of visual category-selective regions. Sci. Adv. 10, eadl1776 (2024).","journal-title":"Sci. Adv."},{"key":"69345_CR22","doi-asserted-by":"publisher","unstructured":"Liu, J. et al. Towards out-of-distribution generalization: a survey. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2108.13624 (2021).","DOI":"10.48550\/ARXIV.2108.13624"},{"key":"69345_CR23","doi-asserted-by":"publisher","first-page":"107515","DOI":"10.1016\/j.neunet.2025.107515","volume":"190","author":"K Shirakawa","year":"2025","unstructured":"Shirakawa, K. et al. Spurious reconstruction from brain activity. Neural Netw. 190, 107515 (2025).","journal-title":"Neural Netw."},{"key":"69345_CR24","doi-asserted-by":"crossref","unstructured":"Madan, S. et al. Benchmarking out-of-distribution generalization capabilities of DNN-based encoding models for the ventral visual cortex. in Advances in neural information processing systems (eds Globerson, A. et al.) vol. 37, 89249\u201389277 (Curran Associates, Inc., 2024).","DOI":"10.52202\/079017-2832"},{"key":"69345_CR25","doi-asserted-by":"publisher","first-page":"29330","DOI":"10.1073\/pnas.1912334117","volume":"117","author":"T Golan","year":"2020","unstructured":"Golan, T., Raju, P. C. & Kriegeskorte, N. Controversial stimuli: Pitting neural networks against each other as models of human cognition. Proc. Natl. Acad. Sci. USA 117, 29330\u201329337 (2020).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"69345_CR26","doi-asserted-by":"publisher","first-page":"e1011145","DOI":"10.1371\/journal.pcbi.1011145","volume":"20","author":"Y Ren","year":"2024","unstructured":"Ren, Y. & Bashivan, P. How well do models of visual cortex generalize to out of distribution samples? PLoS Comput. Biol. 20, e1011145 (2024).","journal-title":"PLoS Comput. Biol."},{"key":"69345_CR27","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.neuron.2012.01.010","volume":"73","author":"JJ DiCarlo","year":"2012","unstructured":"DiCarlo, J. J., Zoccolan, D. & Rust, N. C. How does the brain solve visual object recognition? Neuron 73, 415\u2013434 (2012).","journal-title":"Neuron"},{"key":"69345_CR28","doi-asserted-by":"publisher","first-page":"2470","DOI":"10.1126\/science.1063414","volume":"293","author":"PE Downing","year":"2001","unstructured":"Downing, P. E., Jiang, Y., Shuman, M. & Kanwisher, N. A cortical area selective for visual processing of the human body. Science 293, 2470\u20132473 (2001).","journal-title":"Science"},{"key":"69345_CR29","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1038\/33402","volume":"392","author":"R Epstein","year":"1998","unstructured":"Epstein, R. & Kanwisher, N. A cortical representation of the local visual environment. Nature 392, 598\u2013601 (1998).","journal-title":"Nature"},{"key":"69345_CR30","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1016\/S0042-6989(01)00073-6","volume":"41","author":"K Grill-Spector","year":"2001","unstructured":"Grill-Spector, K., Kourtzi, Z. & Kanwisher, N. The lateral occipital complex and its role in object recognition. Vis. Res. 41, 1409\u20131422 (2001).","journal-title":"Vis. Res."},{"key":"69345_CR31","doi-asserted-by":"publisher","first-page":"11163","DOI":"10.1073\/pnas.1005062107","volume":"107","author":"N Kanwisher","year":"2010","unstructured":"Kanwisher, N. Functional specificity in the human brain: a window into the functional architecture of the mind. Proc. Natl. Acad. Sci. USA 107, 11163\u201311170 (2010).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"69345_CR32","doi-asserted-by":"publisher","first-page":"4302","DOI":"10.1523\/JNEUROSCI.17-11-04302.1997","volume":"17","author":"N Kanwisher","year":"1997","unstructured":"Kanwisher, N., McDermott, J. & Chun, M. M. The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception. J. Neurosci. 17, 4302\u20134311 (1997).","journal-title":"J. Neurosci."},{"key":"69345_CR33","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1113\/jphysiol.1985.sp015776","volume":"365","author":"KH Foster","year":"1985","unstructured":"Foster, K. H., Gaska, J. P., Nagler, M. & Pollen, D. A. Spatial and temporal frequency selectivity of neurones in visual cortical areas V1 and V2 of the macaque monkey. J. Physiol. 365, 331\u2013363 (1985).","journal-title":"J. Physiol."},{"key":"69345_CR34","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1113\/jphysiol.1978.sp012490","volume":"283","author":"JA Movshon","year":"1978","unstructured":"Movshon, J. A., Thompson, I. D. & Tolhurst, D. J. Spatial and temporal contrast sensitivity of neurones in areas 17 and 18 of the cat\u2019s visual cortex. J. Physiol. 283, 101\u2013120 (1978).","journal-title":"J. Physiol."},{"key":"69345_CR35","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1113\/jphysiol.1968.sp008455","volume":"195","author":"DH Hubel","year":"1968","unstructured":"Hubel, D. H. & Wiesel, T. N. Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195, 215\u2013243 (1968).","journal-title":"J. Physiol."},{"key":"69345_CR36","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/40398","volume":"388","author":"S Engel","year":"1997","unstructured":"Engel, S., Zhang, X. & Wandell, B. Colour tuning in human visual cortex measured with functional magnetic resonance imaging. Nature 388, 68\u201371 (1997).","journal-title":"Nature"},{"key":"69345_CR37","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/S1364-6613(03)00134-7","volume":"7","author":"BD McCandliss","year":"2003","unstructured":"McCandliss, B. D., Cohen, L. & Dehaene, S. The visual word form area: expertise for reading in the fusiform gyrus. Trends Cogn. Sci. 7, 293\u2013299 (2003).","journal-title":"Trends Cogn. Sci."},{"key":"69345_CR38","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/S0042-6989(98)00113-8","volume":"39","author":"GM Boynton","year":"1999","unstructured":"Boynton, G. M., Demb, J. B., Glover, G. H. & Heeger, D. J. Neuronal basis of contrast discrimination. Vis. Res. 39, 257\u2013269 (1999).","journal-title":"Vis. Res."},{"key":"69345_CR39","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1016\/0042-6989(82)90113-4","volume":"22","author":"RL De Valois","year":"1982","unstructured":"De Valois, R. L., Albrecht, D. G. & Thorell, L. G. Spatial frequency selectivity of cells in macaque visual cortex. Vis. Res 22, 545\u2013559 (1982).","journal-title":"Vis. Res"},{"key":"69345_CR40","doi-asserted-by":"publisher","unstructured":"Ha, J., Broderick, W. F., Kay, K. & Winawer, J. Spatial Frequency Maps in Human Visual Cortex: A Replication and Extension. Preprint at https:\/\/doi.org\/10.1101\/2025.01.21.634150 (2025).","DOI":"10.1101\/2025.01.21.634150"},{"key":"69345_CR41","doi-asserted-by":"crossref","unstructured":"Kriegeskorte, N., Mur, M. & Bandettini, P. A. Representational similarity analysis\u2014connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, (2008).","DOI":"10.3389\/neuro.06.004.2008"},{"key":"69345_CR42","first-page":"352","volume":"323","author":"ML Davison","year":"2000","unstructured":"Davison, M. L. & Sireci, S. G. Multidimensional Scaling. Handb. Appl. Multivar. Stat. Math. Modeling 323, 352 (2000).","journal-title":"Handb. Appl. Multivar. Stat. Math. Modeling"},{"key":"69345_CR43","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1002\/wcs.1203","volume":"4","author":"MC Hout","year":"2013","unstructured":"Hout, M. C., Papesh, M. H. & Goldinger, S. D. Multidimensional scaling. WIREs Cogn. Sci. 4, 93\u2013103 (2013).","journal-title":"WIREs Cogn. Sci."},{"key":"69345_CR44","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1038\/nature06713","volume":"452","author":"KN Kay","year":"2008","unstructured":"Kay, K. N., Naselaris, T., Prenger, R. J. & Gallant, J. L. Identifying natural images from human brain activity. Nature 452, 352\u2013355 (2008).","journal-title":"Nature"},{"key":"69345_CR45","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.conb.2019.04.002","volume":"55","author":"N Kriegeskorte","year":"2019","unstructured":"Kriegeskorte, N. & Douglas, P. K. Interpreting encoding and decoding models. Curr. Opin. Neurobiol. 55, 167\u2013179 (2019).","journal-title":"Curr. Opin. Neurobiol."},{"key":"69345_CR46","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.neuroimage.2010.07.073","volume":"56","author":"T Naselaris","year":"2011","unstructured":"Naselaris, T., Kay, K. N., Nishimoto, S. & Gallant, J. L. Encoding and decoding in fMRI. NeuroImage 56, 400\u2013410 (2011).","journal-title":"NeuroImage"},{"key":"69345_CR47","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1146\/annurev.neuro.29.051605.113024","volume":"29","author":"MC-K Wu","year":"2006","unstructured":"Wu, M. C.-K., David, S. V. & Gallant, J. L. Complete functional characterization of sensory neurons by system identification. Annu. Rev. Neurosci. 29, 477\u2013505 (2006).","journal-title":"Annu. Rev. Neurosci."},{"key":"69345_CR48","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.1038\/nn.3776","volume":"17","author":"JP Cunningham","year":"2014","unstructured":"Cunningham, J. P. & Yu, B. M. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17, 1500\u20131509 (2014).","journal-title":"Nat. Neurosci."},{"key":"69345_CR49","doi-asserted-by":"publisher","unstructured":"Krizhevsky, A. One weird trick for parallelizing convolutional neural networks. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.1404.5997 (2014).","DOI":"10.48550\/ARXIV.1404.5997"},{"key":"69345_CR50","doi-asserted-by":"publisher","unstructured":"Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2010.11929 (2020).","DOI":"10.48550\/ARXIV.2010.11929"},{"key":"69345_CR51","first-page":"778","volume":"770","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. Proc. IEEE Conf. Computer Vis. Pattern Recognit. 770, 778 (2016).","journal-title":"Proc. IEEE Conf. Computer Vis. Pattern Recognit."},{"key":"69345_CR52","doi-asserted-by":"crossref","unstructured":"He, K. et al. Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 9729\u20139738 (2020).","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"69345_CR53","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y. & Deny, S. Barlow twins: Self-supervised learning via redundancy reduction. in Proceedings of the 38th international conference on machine learning (eds Meila, M. & Zhang, T.) vol. 139, 12310\u201312320 (PMLR, 2021)."},{"key":"69345_CR54","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.neuroimage.2017.08.016","volume":"180","author":"KN Kay","year":"2018","unstructured":"Kay, K. N. Principles for models of neural information processing. NeuroImage 180, 101\u2013109 (2018).","journal-title":"NeuroImage"},{"key":"69345_CR55","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms15037","volume":"8","author":"T Horikawa","year":"2017","unstructured":"Horikawa, T. & Kamitani, Y. Generic decoding of seen and imagined objects using hierarchical visual features. Nat. Commun. 8, 15037 (2017).","journal-title":"Nat. Commun."},{"key":"69345_CR56","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.neuroimage.2017.07.018","volume":"180","author":"K Seeliger","year":"2018","unstructured":"Seeliger, K. et al. Convolutional neural network-based encoding and decoding of visual object recognition in space and time. NeuroImage 180, 253\u2013266 (2018).","journal-title":"NeuroImage"},{"key":"69345_CR57","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-28091-4","volume":"13","author":"T Konkle","year":"2022","unstructured":"Konkle, T. & Alvarez, G. A. A self-supervised domain-general learning framework for human ventral stream representation. Nat. Commun. 13, 491 (2022).","journal-title":"Nat. Commun."},{"key":"69345_CR58","doi-asserted-by":"publisher","first-page":"e1005508","DOI":"10.1371\/journal.pcbi.1005508","volume":"13","author":"J Diedrichsen","year":"2017","unstructured":"Diedrichsen, J. & Kriegeskorte, N. Representational models: a common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLoS Comput. Biol. 13, e1005508 (2017).","journal-title":"PLoS Comput. Biol."},{"key":"69345_CR59","unstructured":"Schaeffer, R. et al. Does maximizing neural regression scores teach us about the brain? in UniReps: 2nd edition of the workshop on unifying representations in neural models (2024)."},{"key":"69345_CR60","doi-asserted-by":"publisher","unstructured":"Finzi, D., Margalit, E., Kay, K., Yamins, D. L. K. & Grill-Spector, K. A single computational objective can produce specialization of streams in visual cortex. Preprint at https:\/\/doi.org\/10.1101\/2023.12.19.572460 (2023).","DOI":"10.1101\/2023.12.19.572460"},{"key":"69345_CR61","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1016\/j.neuron.2023.03.022","volume":"111","author":"K Kay","year":"2023","unstructured":"Kay, K., Bonnen, K., Denison, R. N., Arcaro, M. J. & Barack, D. L. Tasks and their role in visual neuroscience. Neuron 111, 1697\u20131713 (2023).","journal-title":"Neuron"},{"key":"69345_CR62","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1523\/JNEUROSCI.1724-17.2017","volume":"38","author":"J Zhou","year":"2018","unstructured":"Zhou, J., Benson, N. C., Kay, K. N. & Winawer, J. Compressive temporal summation in human visual cortex. J. Neurosci. 38, 691\u2013709 (2018).","journal-title":"J. Neurosci."},{"key":"69345_CR63","doi-asserted-by":"publisher","unstructured":"Gifford, A. T. et al. The Algonauts Project 2025 challenge: how the human brain makes sense of multimodal movies. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2501.00504 (2025).","DOI":"10.48550\/ARXIV.2501.00504"},{"key":"69345_CR64","doi-asserted-by":"crossref","unstructured":"Boyle, J. et al. The Courtois NeuroMod project: quality assessment of the initial data release (2020). Conference on Cognitive Computational Neuroscience (2023).","DOI":"10.32470\/CCN.2023.1602-0"},{"key":"69345_CR65","doi-asserted-by":"publisher","first-page":"e1006633","DOI":"10.1371\/journal.pcbi.1006633","volume":"15","author":"G Shen","year":"2019","unstructured":"Shen, G., Horikawa, T., Majima, K. & Kamitani, Y. Deep image reconstruction from human brain activity. PLOS Comput. Biol. 15, e1006633 (2019).","journal-title":"PLOS Comput. Biol."},{"key":"69345_CR66","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adj3906","volume":"9","author":"FL Cheng","year":"2023","unstructured":"Cheng, F. L. et al. Reconstructing visual illusory experiences from human brain activity. Sci. Adv. 9, eadj3906 (2023).","journal-title":"Sci. Adv."},{"key":"69345_CR67","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1167\/3.6.5","volume":"3","author":"GA Rousselet","year":"2003","unstructured":"Rousselet, G. A., Mac\u00e9, M. J.-M. & Fabre-Thorpe, M. Is it an animal? Is it a human face? Fast processing in upright and inverted natural scenes. J. Vis. 3, 5 (2003).","journal-title":"J. Vis."},{"key":"69345_CR68","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1037\/h0083717","volume":"11","author":"CM Mooney","year":"1957","unstructured":"Mooney, C. M. Age in the development of closure ability in children. Can. J. Psychol. Rev. Can. Psychol. 11, 219\u2013226 (1957).","journal-title":"Can. J. Psychol. Rev. Can. Psychol."},{"key":"69345_CR69","doi-asserted-by":"publisher","first-page":"9661","DOI":"10.1073\/pnas.1015666108","volume":"108","author":"DB Walther","year":"2011","unstructured":"Walther, D. B., Chai, B., Caddigan, E., Beck, D. M. & Fei-Fei, L. Simple line drawings suffice for functional MRI decoding of natural scene categories. Proc. Natl. Acad. Sci. USA 108, 9661\u20139666 (2011).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"69345_CR70","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1523\/JNEUROSCI.1546-22.2022","volume":"43","author":"JJD Singer","year":"2023","unstructured":"Singer, J. J. D., Cichy, R. M. & Hebart, M. N. The spatiotemporal neural dynamics of object recognition for natural images and line drawings. J. Neurosci. 43, 484\u2013500 (2023).","journal-title":"J. Neurosci."},{"key":"69345_CR71","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1167\/jov.22.4.3","volume":"22","author":"WF Broderick","year":"2022","unstructured":"Broderick, W. F., Simoncelli, E. P. & Winawer, J. Mapping spatial frequency preferences across human primary visual cortex. J. Vis. 22, 3 (2022).","journal-title":"J. Vis."},{"key":"69345_CR72","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1364\/JOSA.69.001183","volume":"69","author":"DIA MacLeod","year":"1979","unstructured":"MacLeod, D. I. A. & Boynton, R. M. Chromaticity diagram showing cone excitation by stimuli of equal luminance. J. Opt. Soc. Am. 69, 1183 (1979).","journal-title":"J. Opt. Soc. Am."},{"key":"69345_CR73","doi-asserted-by":"publisher","first-page":"2491","DOI":"10.1364\/JOSAA.10.002491","volume":"10","author":"A Stockman","year":"1993","unstructured":"Stockman, A., MacLeod, D. I. A. & Johnson, N. E. Spectral sensitivities of the human cones. J. Opt. Soc. Am. A 10, 2491 (1993).","journal-title":"J. Opt. Soc. Am. A"},{"key":"69345_CR74","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.22341","volume":"6","author":"KN Kay","year":"2017","unstructured":"Kay, K. N. & Yeatman, J. D. Bottom-up and top-down computations in word- and face-selective cortex. eLife 6, e22341 (2017).","journal-title":"eLife"},{"key":"69345_CR75","doi-asserted-by":"crossref","unstructured":"Kay, K. N., Rokem, A., Winawer, J., Dougherty, R. F. & Wandell, B. A. GLMdenoise: a fast, automated technique for denoising task-based fMRI data. Front. Neurosci. 7, (2013).","DOI":"10.3389\/fnins.2013.00247"},{"key":"69345_CR76","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.neuroimage.2018.08.064","volume":"183","author":"I Charest","year":"2018","unstructured":"Charest, I., Kriegeskorte, N. & Kay, K. N. GLMdenoise improves multivariate pattern analysis of fMRI data. NeuroImage 183, 606\u2013616 (2018).","journal-title":"NeuroImage"},{"key":"69345_CR77","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giaa133","volume":"9","author":"A Rokem","year":"2020","unstructured":"Rokem, A. & Kay, K. Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. GigaScience 9, giaa133 (2020).","journal-title":"GigaScience"},{"key":"69345_CR78","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.77599","volume":"11","author":"JS Prince","year":"2022","unstructured":"Prince, J. S. et al. Improving the accuracy of single-trial fMRI response estimates using GLMsingle. eLife 11, e77599 (2022).","journal-title":"eLife"},{"key":"69345_CR79","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O. et al. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211\u2013252 (2015).","journal-title":"Int. J. Comput. Vis."}],"container-title":["Nature Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-69345-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-69345-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-69345-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T14:03:33Z","timestamp":1770905013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-69345-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":79,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["69345"],"URL":"https:\/\/doi.org\/10.1038\/s41467-026-69345-9","relation":{},"ISSN":["2041-1723"],"issn-type":[{"value":"2041-1723","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]},"assertion":[{"value":"23 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1589"}}