Marco Bertamini
  1. Bertamini, M. Oletto, C.M., & Contemori, G. (2024). The role of uniform textures in making texture elements visible in the visual periphery. Open Mind: Discoveries in Cognitive Science, 8, 462-482.
    doi: 10.1162/opmi_a_00136
  2.  Contemori, G., Oletto, C.M., Battaglini, L., R.P. & Bertamini, M. (2024). On the Relationship Between Foveal Mask Interference and Mental Imagery in Peripheral Object Recognition. Proceedings of the Royal Society, B. 291.
    doi: 10.1098/rspb.2023.2867
  3. Bertamini, M., Cretenoud, A.F., & Herzog, M.H. (2019). Exploring the extent in the visual field of the Honeycomb and Extinction illusions. i-Perception,10, 4.
    doi:10.1177/2041669519854784
  4. Bertamini, M. Herzog, M.H., & Bruno, N. (2016). The Honeycomb illusion: Uniform textures not perceived as such. i-Perception, 7, 4.
    doi: 10.1177/2041669516660727
  5.  Wright, D. Makin, A.D.J., & Bertamini, M. (2017). Electrophysiological responses to symmetry presented in the left or in the right visual hemifield. Cortex, 86, 93-108.
    doi: 10.1016/j.cortex.2016.11.001
David Brainard
  1. Wandell, B. A., Brainard, D. H. (2023). Principles and consequences of the initial visual encoding. In The New Handbook of Mathematical Psychology, Vol. 3 (Ashby, F. G., Colonius, H., Dzhafarov, E. N., eds.).  Cambridge University Press.
    https://color2.psych.upenn.edu/brainard/papers/Principles_and_Consequences_Color.pdf
  2. Brainard, D. H., Cottaris, N. P., Radonjić, A. (2018). The perception of color and material in natural tasks. Royal Society Interface Focus, 8(4),
    doi: 10.1098/rsfs.2018.0012.
  3. Brainard, D. H. (2015). Color and the cone mosaic. Annual Review of Vision Science, 1:519–546, doi: 10.1146/annurev-vision-082114-035341.
    https://color2.psych.upenn.edu/brainard/papers/Brainard_2015_ColorAndMosaic.pdf
  4. Brainard, D. H. & Maloney, L. T. (2011). Surface color perception and equivalent illumination models. Journal of Vision, 11(5:1),
    http://www.journalofvision.org/content/11/5/1, doi: 10.1167/11.5.1.
Peter Dayan
  1. Peter Dayan (1998). A Hierarchical Model of Binocular Rivalry. Neural computation, 10(5), 1119-1135.
    https://doi.org/10.1162/089976698300017377
  2. Shervin Safavi, Peter Dayan (2022). Multistability, perceptual value, and internal foraging. Neuron, 110(19), 3076-3090.
    https://doi.org/10.1016/j.neuron.2022.07.024

  3. Shervin Safavi, Peter Dayan (2025). A decision-theoretic model of multistability: perceptual switches as internal actions. Preprint.
    https://doi.org/10.1101/2024.12.06.627286

  4. Peter Dayan, Joshua A. Solomon (2010). Selective Bayes: Attentional load and crowding. Vision Research, 50(22), 2248-2260
    https://doi.org/10.1016/j.visres.2010.04.014

  5. Peter Dayan (2023). Metacognitive Information Theory. Open mind: discoveries in cognitive science, 7, 392-411
    https://doi.org/10.1162/opmi_a_00091
Roland Fleming
  1. Schmidt F*, Hebart MN*, Schmid & RW Fleming (2025). Core dimensions of human material perception.  Proceedings of the National Academy of Sciences, 122(10): e2417202122.
    https://doi.org/10.1073/pnas.2417202122
  2. Storrs KR, Anderson BL & RW Fleming (2021).  Unsupervised learning predicts human perception and misperception of gloss.  Nature Human Behavior 5, 1402–1417.
    https://doi.org/10.1038/s41562-021-01097-6
  3. Phillips F* & RW Fleming* (2020). The Veiled Virgin illustrates visual segmentation of shape by cause. Proceedings of the National Academy of Sciences, 201917565.
    https://doi.org/10.1073/pnas.1917565117
  4. RW Fleming (2014). Visual Perception of Materials and their Properties. Vision Research, 94: 62-75.
    https://doi.org/10.1016/j.visres.2013.11.004
  5. Fleming RW, Holtmann-Rice D & HH Bülthoff (2011).  Estimation of 3D shape from image orientations.  Proceedings of the National Academy of Sciences, 108(51) 20438–20443.
    https://doi.org/10.1073/pnas.1114619109

[* equal authorship]
Pascal Fries
  1. Rhythms for Cognition: Communication through Coherence. Fries P. Neuron. 2015 Oct 7;88(1):220-35.
    doi: 10.1016/j.neuron.2015.09.034.
  2. Rhythmic attentional scanning. Fries P. Neuron. 2023 Apr 5;111(7):954-970.
    doi: 10.1016/j.neuron.2023.02.015.
  3. Attentional stimulus selection through selective synchronization between monkey visual areas. Bosman CA, Schoffelen JM, Brunet N, Oostenveld R, Bastos AM, Womelsdorf T, Rubehn B, Stieglitz T, De Weerd P, Fries P. Neuron. 2012 Sep 6;75(5):875-88.
    doi: 10.1016/j.neuron.2012.06.037.
  4. Visual areas exert feedforward and feedback influences through distinct frequency channels. Bastos AM, Vezoli J, Bosman CA, Schoffelen JM, Oostenveld R, Dowdall JR, De Weerd P, Kennedy H, Fries P. Neuron. 2015 Jan 21;85(2):390-401.
    doi: 10.1016/j.neuron.2014.12.018.
  5. Stimulus-specific plasticity of macaque V1 spike rates and gamma. Peter A, Stauch BJ, Shapcott K, Kouroupaki K, Schmiedt JT, Klein L, Klon-Lipok J, Dowdall JR, Schölvinck ML, Vinck M, Schmid MC, Fries P. Cell Rep. 2021 Dec 7;37(10):110086.
    doi: 10.1016/j.celrep.2021.110086.
Wilson Geisler
  1. Geisler, W.S. & Diehl, R.L. (2003) A Bayesian approach to the evolution of perceptual and cognitive systems.  Cognitive Science, 27, 379-402.
    https://doi.org/10.1207/s15516709cog2703_3
  2. Geisler, W.S. (2008) Visual perception and the statistical properties of natural scenes. Annual Review of Psychology, 59, 167-192.
    https://doi.org/10.1146/annurev.psych.58.110405.085632
  3. Geisler WS (2011) Contributions of ideal observer theory to vision research. Vision Research, 51, 771-781.
    https://doi.org/10.1016/j.visres.2010.09.027
  4. Sebastian S., Abrams J. & Geisler W.S. (2017) Constrained-sampling experiments reveal principles of detection in natural scenes. Proceedings of the National Academy of Sciences, 14:28, E5731–E5740.
    https://doi.org/10.1073/pnas.1619487114
  5. Zhang, A., & Geisler, W. S. (2025). Optimal visual search with highly heuristic decision rules. Journal of Vision, 25(4):5, 1–21.
    https://doi.org/10.1167/jov.25.4.5
Tadashi Isa
  1. Kato R, Zeghbib A, Redgrave P. Isa T (2021) Visual instrumental learning in blindsight monkeys. Scientific Reports, 11, 14819.
    https://doi.org/10.1038/s41598-021-94192-7
  2. Isa T, Yoshida M (2021) Neural mechanism of blindsight in a macaque model. Neuroscience, (Forefront review), 469: 138-161.
    https://doi.org/10.1016/j.neuroscience.2021.06.022
  3. Kinoshita M, Kato R, Isa K, Kobayashi K, Kobayashi K, Onoe H, Isa T (2019) Dissecting the circuit for blindsight to reveal the critical role of the pulvinar and superior colliculus. Nature Communications, 10(1):135.
  4. https://doi.org/10.1038/s41467-018-08058-0
  5. Yoshida M, Itti L, Berg D, Ikeda T, Kato R, Takaura K, White B, Munoz D, Isa T (2012) Saliency detection during free-viewing in monkeys with blindsight. Current Biology, 22:1429-1434.
    https://doi.org/10.1016/j.cub.2012.05.046
  6. Yoshida M, Takaura K, Kato R, Ikeda T, Isa T (2008) Striate cortical lesions affect deliberate decision and control of saccade: implication for blindsight. Journal of Neuroscience, 28: 10517-10530.
    https://doi.org/10.1523/JNEUROSCI.1973-08.2008
Tomas Knapen
  1. Hedger, N., Naselaris, T., Kay, K. & Knapen, T. Vicarious Somatotopic Maps Tile Visual Cortex. bioRxiv2024.10.21.619382 (2024).
    https://doi.org/10.1101/2024.10.21.619382
  2. Kupers, E. R., Knapen, T., Merriam, E. P. & Kay, K. N. Principles of intensive human neuroimaging. Trends Neurosci.(2024).
    https://doi.org/10.1016/j.tins.2024.09.011
  3. Groen, I. I. A., Dekker, T. M., Knapen, T. & Silson, E. H. Visuospatial coding as ubiquitous scaffolding for human cognition. Trends Cogn Sci (2021).
    https://doi.org/10.1016/j.tics.2021.10.011
  4. Aqil, M., Knapen, T. & Dumoulin, S. O. Divisive normalization unifies disparate response signatures throughout the human visual hierarchy. Proc National Acad Sci 118, e2108713118 (2021).
    https://doi.org/10.1073/pnas.2108713118
  5. Knapen, T. Topographic connectivity reveals task-dependent retinotopic processing throughout the human brain. Proc National Acad Sci 118, e2017032118 (2021).
    https://doi.org/10.1073/pnas.2017032118
Jan Koenderink & Andrea Van Doorn
  1. Jan Koenderink, Andrea van Doorn, Karl Gegenfurtner (2021). RGB Colors and Ecological Optics. Frontiers in Computer Science, Sec. Computer Vision, Volume 3 – 2021
    https://doi.org/10.3389/fcomp.2021.630370
  2. Jan Koenderink (2018). Colour in the wild. De Clootcrans Press
    https://gestaltrevision.be/wp-content/uploads/clootcrans_press/2018_ColourInTheWild.pdf
  3. Jan Koenderink, Andrea van Door (2018). Local Image Structure and Procrustes Metrics. SIAM Journal on Imaging Sciences,Vol. 11, No. 1, pp. 293–324
    https://doi.org/10.1137/17M113607
  4. Jan Koenderink (2020). Curvature Against the Grain. Growth and Form, Volume 1, Issue 1, 2020, Pages 9 – 19
    https://doi.org/10.2991/gaf.k.200124.004 
  5. Jan Koenderink; Matteo Valsecchi; Andrea van Doorn; Johan Wagemans; Karl Gegenfurtner (2017). Eidolons: Novel stimuli for vision research. Journal of Vision, 17 (2), Article 7.
    https://doi.org/10.1167/17.2.7
Keith May
  1. May, K., & Zhaoping, L. (2022). Li and Atick’s theory of efficient binocular coding: A tutorial and mini-review. Vision Research, 201, 107950.
    https://doi.org/10.1016/j.visres.2021.08.005
  2. May, K. A., & Zhaoping, L. (2019). Face perception inherits low-level binocular adaptation. Journal of Vision, 19(7), Article 7.
    https://doi.org/10.1167/19.7.7
  3. May, K. A., & Zhaoping, L. (2016). Efficient coding theory predicts a tilt aftereffect from viewing untilted patterns. Current Biology, 26, 1571–1576.
    https://doi.org/10.1016/j.cub.2016.04.037
  4. May, K. A., Zhaoping, L., & Hibbard, P. B. (2012). Perceived direction of motion determined by adaptation to static binocular images. Current Biology, 22, 28–32.
    https://doi.org/10.1016/j.cub.2011.11.025
Marcello Rosa
  1. Piotr Majka, Shi Bai, Sophia Bakola, Sylwia Bednarek, Jonathan M. Chan, Natalia Jermakow, Lauretta Passarelli, David H. Reser, Panagiota Theodoni, Katrina H. Worthy, Xiao Jing Wang, Daniel K. Wójcik, Partha P. Mitra, Marcello G.P. Rosa (2020). Open access resource for cellular-resolution analyses of corticocortical connectivity in the marmoset monkey. Nature Communications volume 11, Article number: 1133.
    https://doi.org/10.1038/s41467-020-14858-0
  2. Yu, H.-H., Rowley, D. P., Price, N. S. C., Rosa, M. G. P. & Zavitz, E. (2020) A twisted visual field map in the primate dorsomedial cortex predicted by topographic continuity. Science Advances. 6, 44, 10 p., eaaz8673.
    https://doi.org/10.1126/sciadv.aaz8673
  3. Atapour, N., Worthy, K. H. & Rosa, M. G. P. (2022). Remodeling of lateral geniculate nucleus projections to extrastriate area MT following long-term lesions of striate cortex. Proceedings of the National Academy of Sciences of the United States of America. 119, 4, 10 p., e2117137119.
    https://www.pnas.org/doi/full/10.1073/pnas.2117137119
  4. Atapour, N., Rosa, M. G. P., Bai, S., Bednarek, S., Kulesza, A., Saworska, G., Teymornejad, S., Worthy, K. H. & Majka, P. (2024). Distribution of calbindin-positive neurons across areas and layers of the marmoset cerebral cortex. PLoS Computational Biology. 20, 9, 28 p., e1012428.
    https://doi.org/10.1371/journal.pcbi.1012428
  5. Daisuke Shimaoka, Yan Tat Wong, Marcello G.P. Rosa, Nicholas Seow Chiang Price (2024). Naturalistic movies and encoding analysis define areal borders in marmoset third-tier visual cortex. Progress in Neurobiology Volume 240, 102657
    https://doi.org/10.1016/j.pneurobio.2024.102657
Jonathan Victor
  1. Victor, J.D., Conte, M.M., and Chubb, C. F. (2017) Textures as probes of visual processing.  Annual Reviews of Vision Science 3, 275-296.
    https://www.annualreviews.org/doi/10.1146/annurev-vision-102016-061316
  2. Victor, J.D., Thengone, D.J., Rizvi, S.M., and Conte, M.M. (2015) A perceptual space of local image statistics.  Vision Research 117, 117-135.
    http://www.sciencedirect.com/science/article/pii/S0042698915002242
  3. Hermundstad, A.H., Briguglio, J.J., Conte, M.M, Victor, J.D., Balasubramanian, V., and Tkačik, G., (2014) Variance predicts salience in central sensory processing.  eLife 2014;10.7554/eLife.03722.
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  4. Yu, Y., Schmid, A.M., and Victor, J.D. (2015) Visual processing of informative multipoint correlations arises primarily in V2.  Elife. 2015;10.7554/eLife.06604.
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Li Zhaoping
  1. Zhaoping, L. (2025) Testing the top-down feedback in the central visual field using the reversed depth illusion iScience, Volume 28, Issue 4, 112223
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  2. Zhaoping, L. (2019) A new framework for understanding vision from the perspective of the primary visual cortex Current Opinion in Neurobiology, volume 58, page 1-10.
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    https://doi.org/10.1073/pnas.180385411
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