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Artistic representations: Clues to efficient coding in human vision

Published online by Cambridge University Press:  16 May 2011

DANIEL J. GRAHAM  and
MING MENG
DANIEL J. GRAHAM*
Affiliation:
Department of Psychological Basic Research, University of Vienna, Vienna, Austria Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire
MING MENG
Affiliation:
Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire
*
*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
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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.

Keywords

Coding efficiencyArt perceptionFace detectionNatural scene statisticsCortex
Type
Evolution and eye design
Information
Visual Neuroscience , Volume 28 , Issue 4 , July 2011 , pp. 371 - 379
Copyright
Copyright © Cambridge University Press 2011

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