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    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.
    Redies, Christoph and Brachmann, Anselm 2017. Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art. Frontiers in Neuroscience, Vol. 11, Issue. ,
    Nascimento, Sérgio M.C. Linhares, João M.M. Montagner, Cristina João, Catarina A.R. Amano, Kinjiro Alfaro, Catarina and Bailão, Ana 2017. The colors of paintings and viewers’ preferences. Vision Research, Vol. 130, Issue. , p. 76.
    Essock, Edward A. and Schweinhart, April M. 2016. Structural Content in Paintings II: Artists Commissioned to Reproduce a Specific Image Over-Regularize Orientation Biases in Their Paintings. Perception, Vol. 45, Issue. 6, p. 657.
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    Montagner, Cristina Linhares, João M. M. Vilarigues, Márcia and Nascimento, Sérgio M. C. 2016. Statistics of colors in paintings and natural scenes. Journal of the Optical Society of America A, Vol. 33, Issue. 3, p. A170.
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    Schweinhart, April M and Essock, Edward A 2013. Structural Content in Paintings: Artists Overregularize Oriented Content of Paintings Relative to the Typical Natural Scene Bias. Perception, Vol. 42, Issue. 12, p. 1311.
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Artistic representations: Clues to efficient coding in human vision

  • DANIEL J. GRAHAM (a1) (a2) and MING MENG (a2)
Abstract

In what ways is mammalian vision—and in particular, human vision—efficiently adapted to its ecology? We suggest that human visual artwork, which is made for the human eye, holds clues that could help answer this question. Paintings are readily perceived as representations of natural objects and scenes, yet statistical relationships between natural images and paintings are nontrivial. Although spatial frequency content is generally similar for art and natural images, paintings cannot reproduce the dynamic range of luminance in scenes. Through a variety of image manipulations designed to alter image intensity distributions and spatial contrast, we here investigate the notion that artists’ representational strategies can efficiently capture salient features of natural images, and in particular, of faces. We report that humans perform near flawless discrimination of faces and nonfaces in both paintings and natural images, even for stimulus presentation durations of 12 ms. In addition, contrast negation and up-down inversion have minimal to no effect on performance for both image types, whereas 1/f noise addition significantly affects discrimination performance for art more than for natural images. Together, these results suggest artists create representations that are highly efficient for transmitting perceptual information to the human brain.

Copyright
COPYRIGHT: © Cambridge University Press 2011
Corresponding author
*Address correspondence and reprint requests to: Daniel J. Graham, Department of Psychological Basic Research University of Vienna, Liebiggasse 5, Vienna 1010, Austria. E-mail: artstats@gmail.com
References
Hide All
Atick, J.J. & Redlich, A.N. (1992). What does the retina know about natural scenes? Neural Computation 4, 196–210.
Bex, P.J., Mareschal, I. & Dakin, S.C. (2007). Contrast gain control in natural scenes. Journal of Vision 7, 1–12.
Biederman, I. & Kim, J.G. (2008). 17,000 years of depicting the junction of two smooth shapes. Perception 37, 161–164.
Bindemann, M. & Burton, A.M. (2009). The role of color in human face detection. Cognitive Science 33, 1144–1156.
Brainard, D.H. (1997). The psychophysics toolbox. Spatial Vision 10, 433–436.
Clarke, M. (2001). The Art of All Colours: Mediaeval Recipe Books for Painters and Illuminators. London: Archetype.
Cutting, J.E., DeLong, J.E. & Nothelfer, C.E. (2010). Attention and the evolution of Hollywood film. Psychological Science 21, 440–447.
de Waal, F. (2001). The Ape and the Sushi Master: Cultural Reflections by a Primatologist. New York: Basic Books.
Fagot, J., Martin-Malivel, J. & Depy, D. (2000). What is the evidence for an equivalence between objects and pictures in birds and non-human primates? In Picture Perception in Animals, ed. Fagot, J., Hove, UK: Psychology Press.
Felsen, G. & Dan, Y. (2005). A natural approach to studying vision. Nature Neuroscience 8, 1643–1646.
Field, D.J. (1987). Relations between the statistics of natural images and the response profiles of cortical cells. Journal of the Optical Society of America. A, Optics and Image Science 4, 2379–2394.
Field, D. (1994). What is the goal of sensory coding? Neural Computation 6, 559–601.
Field, D.J., Hayes, A. & Hess, R. (1993). Contour integration by the human visual system: Evidence for a local ‘association field’. Vision Research 33, 173–193
Geisler, W.S. (2008). Visual perception and the statistical properties of natural scenes. Annual Review of Psychology 59, 167–192.
Gilad, S., Meng, M. & Sinha, P. (2009). Role of ordinal contrast relationships in face encoding. Proceedings of the National Academy of Sciences of the United States of America 106, 5353–5358.
Graham, D.J. & Field, D.J. (2007). Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearities. Spatial Vision 21, 149–164.
Graham, D.J. & Field, D.J. (2008 a). Global nonlinear luminance compression in painted art. Proceedings of SPIE 6810, 68100K.
Graham, D.J. & Field, D.J. (2008 b). Natural images: Coding efficiency. In Encyclopedia of Neuroscience, Vol. VI, ed. Squire, L.R., pp. 19–27. Oxford: Academic Press.
Graham, D.J. & Field, D.J. (2008 c). Variations in intensity statistics for representational and abstract art, and for art from the eastern and western hemispheres. Perception 37, 1341–1352.
Graham, D.J., Friedenberg, J.D., McCandless, C.H. & Rockmore, D.N. (2010 a). Preference for artwork: Similarity, statistics, and selling price. Proceedings of SPIE 7527, 75271A.
Graham, D.J., Friedenberg, J.D. & Rockmore, D.N. (2009). Efficient visual system processing of spatial and luminance statistics in representational and non-representational art. Proceedings of SPIE 7240, 72401N.
Graham, D.J., Friedenberg, J.D., Rockmore, D.N. & Field, D.J. (2010 b). Mapping the similarity space of paintings: Image statistics and visual perception. Visual Cognition 18, 559–573.
Graham, D.J. & Meng, M. (2011). Altered spatial frequency content in paintings by artists with schizophrenia. i-Perception 2, 1–9.
Graham, D.J. & Redies, C. (2010). Statistical regularities in art: Relations with visual coding and perception. Vision Research 50, 1503–1509.
Hasson, U., Malach, R. & Heeger, D. (2009). Reliability of cortical activity during natural stimulation. Trends in Cognitive Sciences 14, 40–48.
Hasson, U., Nir, Y., Levy, I., Fuhrmann, G. & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. Science 303, 1634–1640.
Haxby, J.V., Hoffman, E.A. & Gobbini, M.I. (2000). The distributed human neural system for face perception. Trends in Cognitive Sciences 4, 223–233.
Honey, C., Kirchner, H. & VanRullen, R. (2008). Faces in the cloud: Fourier power spectrum biases ultrarapid face detection. Journal of Vision 8, 1–13.
Hughes, J.M., Graham, D.J., Jacobsen, C.R. & Rockmore, D.N. (2011). Higher-order spatial statistics and perceptual judgments in the stylometric analysis of art. In Proceedings of EUSIPCO 2011 (19th European Signal Processing Conference), Barcelona, Spain, 29 Aug 2011.
Koch, M., Denzler, J. & Redies, C. (2010). 1/f2 Characteristics and Isotropy in the Fourier Power Spectra of Visual Art, Cartoons, Comics, Mangas, and Different Categories of Photographs. PLoS ONE 5(8): e12268. DOI: 10.1371/journal.pone.0012268.
Li, F.-F., Iyer, A., Koch, C. & Perona, P. (2007). What do we perceive in a glance of a real-world scene? Journal of Vision 7, 1–29.
Liu, C.H., Collin, C.A., Burton, A.M. & Chaudhuri, A. (1999). Lighting direction affects recognition of untextured faces in photographic positive and negative. Vision Research 39, 4003–4009.
McCann, J. (2005). Rendering high-dynamic range images: Algorithms that mimic human vision. Proceedings of the AMOS Technical Conference 2005, 19–28.
Meng, M. & Potter, M.C. (2008). Detecting and remembering pictures with and without visual noise. Journal of Vision 8:7, 1–10.
Oliva, A. (2005). Gist of the scene. In Encyclopedia of Neurobiology of Attention, ed. Itti, L., Rees, G. & Tsotsos, J.K., pp. 251–256. San Diego, CA: Elsevier.
Olmos, A. & Kingdom, F.A.A. (2004). McGill Calibrated Colour Image Database. Available from http://tabby.vision.mcgill.ca.
Olshausen, B.A. & Field, D.J. (2004). Sparse coding of sensory inputs. Current Opinion in Neurobiology 14, 481–487.
Olshausen, B.A. & Field, D.J. (2005). How close are we to understanding V1? Neural Computation 17, 1665–1699.
Patel, A.D. (2008). Music, Language, and the Brain. Oxford: Oxford University Press.
Pinto, N., Cox, D.D. & DiCarlo, J.J. (2008). Why is real-world visual object recognition hard? PLoS Computational Biology 41, e27.
Pinto, P.D., Linhares, J.M.M., Carvalhal, J.A. & Nascimento, S.M.C. (2006). Psychophysical estimation of the best illumination for appreciation of Renaissance paintings. Visual Neuroscience 23, 669–674.
Potter, M.C. & Levy, E.I. (1969). Recognition memory for a rapid sequence of pictures. Journal of Experimental Psychology 81, 10–15.
Redies, C., Hänisch, J., Blickhan, M. & Denzler, J. (2007 a). Artists portray faces with the Fourier statistics of complex natural scenes. Network: Computation in Neural Systems 18, 235–248.
Redies, C., Hasenstein, J. & Denzler, J. (2007 b). Fractal-like image statistics in visual art: Similarity to natural scenes. Spatial Vision 21, 137–148.
Rust, N.C. & Movshon, J.A. (2005). In praise of artifice. Nature Neuroscience 8, 1647–1650.
Thorpe, S.J., Fize, D. & Marlot, C. (1996). Speed of processing in the human visual system. Nature 381, 520–522.
Torralba, A. & Oliva, A. (2003). Statistics of natural image categories. Network: Computation in Neural Systems 14, 391–412.
VanRullen, R. & Thorpe, S.J. (2001). Is it a bird? Is it a plane? Ultra-rapid visual categorisation of natural and artifactual objects. Perception 30, 655–668.
Velisavljevic, L. & Elder, J.H. (2009). Cue dynamics underlying rapid detection of animals in natural scenes, Journal of Vision 9, 1–20.
Wallraven, C., Kaulard, K., Kürner, C., Pepperell, R. & Bülthoff, H.H. (2007). Psychophysics for perception of indeterminate art. In Proceedings of the 4th Symposium on Applied Perception in Graphics and Visualization APGV‘07, 115-122. New York: ACM Press.
Winkielman, P., Schwarz, N., Reber, R. & Fandeiro, T.A. (2003). Cognitive and affective consequences of visual fluency: When seeing is easy on the mind. In Persuasive Imagery: A Consumer Response Perspective, ed. Scott, L.M. & Batra, R., p. 152. Mahwah, NJ: Lawrence Erlbaum Associates.
Yamane, Y., Carlson, E.T., Bowman, K.C., Wang, Z. & Connor, C.E. (2008). A neural code for three-dimensional object shape in macaque inferotemporal cortex. Nature Neuroscience 11, 1352–1360.
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Visual Neuroscience
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