Metamers of the ventral stream
Open accessAbstract
The human capacity to recognize complex visual patterns emerges in a sequence of brain areas known as the ventral stream, beginning with primary visual cortex (V1). We developed a population model for mid-ventral processing, in which nonlinear combinations of V1 responses are averaged in receptive fields that grow with eccentricity. To test the model, we generated novel forms of visual metamers, stimuli that differ physically but look the same. We developed a behavioral protocol that uses metameric stimuli to estimate the receptive field sizes in which the model features are represented. Because receptive field sizes change along the ventral stream, our behavioral results can identify the visual area corresponding to the representation. Measurements in human observers implicate visual area V2, providing a new functional account of neurons in this area. The model also explains deficits of peripheral vision known as crowding, and provides a quantitative framework for assessing the capabilities and limitations of everyday vision.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Receptive fields vary linearly with eccentricity.
“Because receptive fields vary linearly with eccentricity67 while cortical magnification varies logarithmically64,82, the optimal configuration packs electrodes less tightly in regions of the cortex representing the fovea than regions representing the periphery (see “STAR methods”).”
From A virtual patient simulation modeling the neural and perceptual effects of human visual cortical stimulation, from pulse trains to percepts (2024) · cites this paper as [67]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759 · see citing works
(1989) · neural information processing systems · Cited by 3,736 · see citing works
Spatiotemporal energy models for the perception of motion
Adelson, E. H., & Bergen, J. R. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000284 · Cited by 3,442 · see citing works
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309 · see citing works
‘What’ and ‘where’ in the human brain
Ungerleider, L. G., & Haxby, J. V. (1994) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(94)90066-3 · Cited by 1,913 · cited 2× · see citing works
“In the V1 model (see below), we only included the central sample (that is, k = l = 0) for which equations (1) and (2) reduce to weighted variances.”
BackgroundMethodA Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806 · see citing works
Self-citationRobust Object Recognition with Cortex-Like Mechanisms
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., & Poggio, T. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.56 · Cited by 1,667 · see citing works
Inferotemporal cortex and object vision
Tanaka, K. (1996) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.19.030196.000545 · Cited by 1,402 · see citing works
Interaction Effects in Parafoveal Letter Recognition
Bouma, H. (1970) · Nature · doi:10.1038/226177a0 · Cited by 1,325 · see citing works
Population receptive field estimates in human visual cortex
Dumoulin, S. O., & Wandell, B. A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.034 · Cited by 1,181 · see citing works
A change of the leading player in flow Visualization technique
Mizuno, A., & Ohta, Y. (2006) · Journal of Visualization · doi:10.1007/bf03181556 · Cited by 1,042 · see citing works
Crowding—An essential bottleneck for object recognition: A mini-review
Levi, D. M. (2008) · Vision Research · doi:10.1016/j.visres.2007.12.009 · Cited by 976 · see citing works
Graham, N. (1989) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195051544.001.0001 · Cited by 959 · see citing works
A model of neuronal responses in visual area MT
Simoncelli, E. P., & Heeger, D. J. (1998) · Vision Research · doi:10.1016/s0042-6989(97)00183-1 · Cited by 941 · see citing works
Self-citationCompulsory averaging of crowded orientation signals in human vision
Parkes, L., Lund, J. S., Angelucci, A., Solomon, J., & Morgan, M. (2001) · Nature Neuroscience · doi:10.1038/89532 · Cited by 937 · see citing works
Julesz, B. (1962) · IEEE Transactions on Information Theory · doi:10.1109/tit.1962.1057698 · Cited by 900 · see citing works
Crowding is unlike ordinary masking: Distinguishing feature integration from detection
Pelli, D. G., Palomares, M. C., & Majaj, N. J. (2004) · Journal of Vision · doi:10.1167/4.12.12 · Cited by 832 · see citing works
Visuotopic organization and extent of V3 and V4 of the macaque
Gattass, R., Sousa, A., & Gross, C. (1988) · The Journal of Neuroscience · doi:10.1523/jneurosci.08-06-01831.1988 · Cited by 676 · see citing works
The uncrowded window of object recognition
Pelli, D. G., & Tillman, K. A. (2008) · Nature Neuroscience · doi:10.1038/nn.2187 · Cited by 659 · see citing works
Do We Know What the Early Visual System Does?
Carandini, M., Demb, J. B., Mante, V., Tolhurst, D. J., Dan, Y., Olshausen, B. A., Gallant, J. L., & Rust, N. C. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.3726-05.2005 · Cited by 586 · see citing works
Visual topography of V2 in the macaque
Gattass, R., Gross, C. G., & Sandell, J. H. (1981) · The Journal of Comparative Neurology · doi:10.1002/cne.902010405 · Cited by 488 · see citing works
A summary statistic representation in peripheral vision explains visual search
Rosenholtz, R. E., Huang, J., Raj, A., Balas, B., & Ilie, L. (2012) · Journal of Vision · doi:10.1167/12.4.14 · Cited by 389 · see citing works
Phase congruency: A low-level image invariant
Kovesi, P. D. (2000) · Psychological Research · doi:10.1007/s004260000024 · Cited by 389 · see citing works
Crowding and eccentricity determine reading rate
Pelli, D. G., Tillman, K. A., Freeman, J., Su, M., Berger, T. D., & Majaj, N. J. (2007) · Journal of Vision · doi:10.1167/7.2.20 · Cited by 380 · see citing works
Self-citationSelectivity for Complex Shapes in Primate Visual Area V2
Hegdé, J., & Essen, D. C. V. (2000) · Journal of Neuroscience · doi:10.1523/jneurosci.20-05-j0001.2000 · Cited by 325 · see citing works
Exploration of the primary visual cortex, 1955–78
Hubel, D. H. (1982) · Nature · doi:10.1038/299515a0 · Cited by 306 · see citing works
In search of a general picture processing operator
Granlund, G. H. (1978) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(78)90047-3 · Cited by 299 · see citing works
A summary-statistic representation in peripheral vision explains visual crowding
Balas, B., Nakano, L., & Rosenholtz, R. (2009) · Journal of Vision · doi:10.1167/9.12.13 · Cited by 298 · see citing works
Representation of Angles Embedded within Contour Stimuli in Area V2 of Macaque Monkeys
Ito, M., & Komatsu, H. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.4364-03.2004 · Cited by 273 · see citing works
Neurons in monkey visual area V2 encode combinations of orientations
Anzai, A., Peng, X., & Van Essen, D. C. (2007) · Nature Neuroscience · doi:10.1038/nn1975 · Cited by 244 · see citing works
Crowding, reading, and developmental dyslexia
Martelli, M., Filippo, G. D., Spinelli, D., et al. (2009) · Journal of Vision · doi:10.1167/9.4.14 · Cited by 242 · see citing works
Rust, N. C., & Movshon, J. A. (2005) · Nature Neuroscience · doi:10.1038/nn1606 · Cited by 240 · see citing works
Precueing attention to the target location diminishes crowding and reduces the critical distance
Yeshurun, Y., & Rashal, E. (2010) · Journal of Vision · doi:10.1167/10.10.16 · Cited by 224 · see citing works
The psychophysics of categorical perception.
Macmillan, N. A., Kaplan, H. L., & Creelman, C. D. (1977) · Psychological Review · doi:10.1037/0033-295x.84.5.452 · Cited by 205 · see citing works
Positional averaging explains crowding with letter-like stimuli
Greenwood, J. A., Bex, P. J., & Dakin, S. C. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901352106 · Cited by 193 · see citing works
Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex
Zoccolan, D., Kouh, M., Poggio, T., & DiCarlo, J. J. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1897-07.2007 · Cited by 169 · see citing works
Lateral masking for letters with unlimited viewing time
Townsend, J. T., Taylor, S. G., & Brown, D. R. (1971) · Perception & Psychophysics · doi:10.3758/bf03207464 · Cited by 167 · see citing works
Comparison of Spatial Summation Properties of Neurons in Macaque V1 and V2
Shushruth, S., Ichida, J. M., Levitt, J. B., & Angelucci, A. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00512.2009 · Cited by 134 · see citing works
Spectral Receptive Field Properties Explain Shape Selectivity in Area V4
David, S. V., Hayden, B. Y., & Gallant, J. L. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00575.2006 · Cited by 133 · see citing works
Scolari, M., Kohnen, A., Barton, B., et al. (2007) · Journal of Vision · doi:10.1167/7.2.7 · Cited by 123 · see citing works
Learning to identify crowded letters: Does it improve reading speed?
Chung, S. T. L. (2007) · Vision Research · doi:10.1016/j.visres.2007.08.017 · Cited by 111 · see citing works
Neural Representation of Natural Images in Visual Area V2
Willmore, B. D. B., Prenger, R., & Gallant, J. L. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.4099-09.2010 · Cited by 108 · see citing works
Retinal ganglion cell dendritic fields in old-world monkeys are oriented radially
Schall, J. D., Perry, V. H., & Leventhal, A. G. (1986) · Brain Research · doi:10.1016/0006-8993(86)91037-1 · Cited by 93 · see citing works
Lettvin, J. Y. (1976) · The Sciences · doi:10.1002/j.2326-1951.1976.tb01231.x · Cited by 90 · see citing works
Excitatory and suppressive receptive field subunits in awake monkey primary visual cortex (V1)
Chen, X., Han, F., Poo, M., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706938104 · Cited by 79 · see citing works
Vortex flow patterns of a heaving foil
Yang, C.-J., & Lee, Y. (2006) · Journal of Visualization · doi:10.1007/bf03181564 · Cited by 51 · see citing works
Task-determined strategies of visual process
Geiger, G., Lettvin, J. Y., & Zegarra‐Moran, O. (1992) · Cognitive Brain Research · doi:10.1016/0926-6410(92)90004-b · Cited by 50 · see citing works
Davoudeaden, F., & Liu, N. (2004) · Journal of Visualization · doi:10.1007/bf03181478 · Cited by 32 · see citing works
Attentive texture similarity as a categorization task: Comparing texture synthesis models
Balas, B. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.08.007 · Cited by 24 · see citing works
Schmid, A. (2009) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.015.2009 · Cited by 22 · see citing works
Rodionova, E. I., Revishchin, A. V., & Пигарев, И. Н. (2004) · Experimental Brain Research · doi:10.1007/s00221-004-1967-1 · Cited by 18 · see citing works
The Neurophysiology and Computational Mechanisms of Object Representation
Rolls, E. T. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511635465.015 · Cited by 12 · see citing works
Local image operators and iconic structure
Koenderink, J. J., & van Doorn, A. J. (1997) · Lecture notes in computer science · doi:10.1007/bfb0017861 · Cited by 11 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [33]Kovesi P Image features from phase congruency Videre: Journal of Computer Vision Research 1999
Cited by
Sorted by citations
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924 · see citing works
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621 · see citing works
Seth, A. K. (2014) · Cognitive Neuroscience · doi:10.1080/17588928.2013.877880 · Cited by 346 · see citing works
Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion
Dunn, T. W., Mu, Y., Narayan, S., Randlett, O., Naumann, E. A., Yang, C.-T., Schier, A. F., Freeman, J., Engert, F., & Ahrens, M. B. (2016) · eLife · doi:10.7554/elife.12741 · Cited by 312 · see citing works
Self-citationA Completely Blind Video Integrity Oracle
Mittal, A., Saad, M. A., & Bovik, A. C. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2502725 · Cited by 357 · see citing works
Consciousness in humans and non-human animals: recent advances and future directions
Boly, M., Seth, A. K., Wilke, M., Ingmundson, P., Baars, B., Laureys, S., Edelman, D. B., & Tsuchiya, N. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00625 · Cited by 291 · see citing works
Deep neural networks in computational neuroscience
Kietzmann, T. C., McClure, P., & Kriegeskorte, N. (2019) · Oxford Research Encyclopedia of Neuroscience · doi:10.1093/acrefore/9780190264086.013.46 · Cited by 186 · see citing works
Spatial attention alters visual appearance
Carrasco, M., & Barbot, A. (2019) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2018.10.010 · Cited by 161 · see citing works
Melloni, L., Mudrik, L., Pitts, M., Bendtz, K., Ferrante, O., Gorska, U., Hirschhorn, R., Khalaf, A., Kozma, C., Lepauvre, A., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0268577 · Cited by 113 · see citing works
“Large (2.3˚), high contrast, faces and objects will be presented for 250 ms at one of four locations (two in the left, two in the right visual field, 6.4˚ eccentricity), while scrambled texture patterns [56] made from superimposing faces and objects will be presented at the other three locations.”
InfluentialConnecting neural codes with behavior in the auditory system of Drosophila
Clemens, J., Girardin, C. C., Coen, P., Guan, X.-J., Dickson, B. J., & Murthy, M. (2015) · Neuron · doi:10.1016/j.neuron.2015.08.014 · Cited by 69 · see citing works
Model metamers reveal divergent invariances between biological and artificial neural networks
Feather, J., Leclerc, G., Mądry, A., & McDermott, J. H. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01442-0 · Cited by 59 · see citing works
Fine, I., & Boynton, G. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-65337-1 · Cited by 19 · see citing works
Axonal stimulation affects the linear summation of single-point perception in three Argus II users
Hou, Y., Nanduri, D., Granley, J., Weiland, J. D., & Beyeler, M. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad31c4 · Cited by 14 · see citing works
- Authors
- Freeman, J., & Simoncelli, E. P.
- Affiliations
- New York University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Freeman, Jeremy
- Venue
- Nature Neuroscience
- Published
- 2011-08-14
- Volume
- 14
- Issue
- 9
- Pages
- 1195-1201
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC3164938
- OpenAlex
- W2034676897
- Cited by
- 667
- References
- 53
- Citations by year
- 2026: 50 · 2025: 44 · 2024: 59 · 2023: 53 · 2022: 41 · 2021: 46 · 2020: 46 · 2019: 38 · 2018: 42 · 2017: 47 · 2016: 55 · 2015: 53 · 2014: 38 · 2013: 31 · 2012: 21
- FWCI
- 14.95
- 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
- Howard Hughes Medical Institute
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- ventral stream, metamers, receptive field size, eccentricity, visual area V2, visual crowding, peripheral vision, population coding, primary visual cortex, visual pattern recognition, perceptual equivalence
- MeSH terms
- Adult, Brain Mapping, Color Perception, Eye Movements, Female, Humans, Male, Models, Biological, Pattern Recognition, Visual, Photic Stimulation, Psychometrics, Psychophysics, Visual Cortex, Visual Fields, Visual Pathways, Young Adult
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
