Color constancy supports cross-illumination color selection.

We rely on color to select objects as the targets of our actions (e.g., the freshest fish, the ripest fruit). To be useful for selection, color must provide accurate guidance about object identity across changes in illumination. Although the visual system partially stabilizes object color appearance across illumination changes, how such color constancy supports object selection is not understood. To study how constancy operates in real-life tasks, we developed a novel paradigm in which subjects selected which of two test objects presented under a test illumination appeared closer in color to a target object presented under a standard illumination. From subjects' choices, we inferred a selection-based match for the target via a variant of maximum likelihood difference scaling, and used it to quantify constancy. Selection-based constancy was good when measured using naturalistic stimuli, but was dramatically reduced when the stimuli were simplified, indicating that a naturalistic stimulus context is critical for good constancy. Overall, our results suggest that color supports accurate object selection across illumination changes when both stimuli and task match how color is used in real life. We compared our selection-based constancy results with data obtained using a classic asymmetric matching task and found that the adjustment-based matches predicted selection well for our stimuli and instructions, indicating that the appearance literature provides useful guidance for the emerging study of constancy in natural tasks.

[1]  Page Widick,et al.  The Assessment of Art Attributes , 2010 .

[2]  L. Maloney,et al.  Perceived surface color in binocularly viewed scenes with two light sources differing in chromaticity. , 2004, Journal of vision.

[3]  D. Pelli,et al.  Display Characterization , 1998 .

[4]  C. D. Weert,et al.  Naming versus matching in color constancy , 1991, Perception & psychophysics.

[5]  L. Arend,et al.  Simultaneous color constancy: paper with diverse Munsell values. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[6]  David H Brainard,et al.  The effect of photometric and geometric context on photometric and geometric lightness effects. , 2014, Journal of vision.

[7]  Steven K Shevell,et al.  Stereo disparity improves color constancy , 2002, Vision Research.

[8]  Karl R Gegenfurtner,et al.  Effects of memory colour on colour constancy for unknown coloured objects , 2012, i-Perception.

[9]  Kenneth Knoblauch,et al.  Modeling Psychophysical Data in R , 2012 .

[10]  Jingge Wu A Color-Rendition Chart , 2017 .

[11]  Laurence T Maloney,et al.  Maximum likelihood difference scaling. , 2003, Journal of vision.

[12]  Hiroyuki Shinoda,et al.  Color Constancy in a Photograph Perceived as a Three-Dimensional Space , 2004 .

[13]  Q. Zaidi,et al.  Limits of lightness identification for real objects under natural viewing conditions. , 2004, Journal of vision.

[14]  Phillip J. Moore,et al.  Verbal and visual learning styles , 1988 .

[15]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[16]  D. Foster Color constancy , 2011, Vision Research.

[17]  Qasim Zaidi,et al.  Lightness identification of patterned three-dimensional, real objects. , 2006, Journal of vision.

[18]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[19]  Sérgio M C Nascimento,et al.  Effect of Scene Dimensionality on Colour Constancy with Real Three-Dimensional Scenes and Objects , 2010, Perception.

[20]  D. Purves,et al.  The effects of color on brightness , 1999, Nature Neuroscience.

[21]  Ana Radonjić,et al.  Depth effect on lightness revisited: The role of articulation, proximity and fields of illumination , 2013, i-Perception.

[22]  A. Stockman,et al.  The spectral sensitivities of the middle- and long-wavelength-sensitive cones derived from measurements in observers of known genotype , 2000, Vision Research.

[23]  D. F. Marks,et al.  Visual imagery differences in the recall of pictures. , 1973, British journal of psychology.

[24]  David H Brainard,et al.  Surface-Illuminant Ambiguity and Color Constancy: Effects of Scene Complexity and Depth Cues , 2002, Perception.

[25]  Alexa I Ruppertsberg,et al.  Color constancy improves for real 3D objects. , 2009, Journal of vision.

[26]  David H Brainard,et al.  Color constancy in a naturalistic, goal-directed task. , 2015, Journal of vision.

[27]  Matjaž Jogan,et al.  A new two-alternative forced choice method for the unbiased characterization of perceptual bias and discriminability. , 2014, Journal of vision.

[28]  Lewis D. Griffin Partitive mixing of images: a tool for investigating pictorial perception , 1999 .

[29]  D H Brainard,et al.  Color constancy in the nearly natural image. I. Asymmetric matches. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[30]  A C Hurlbert,et al.  Measurements of Colour Constancy by Using a Forced-Choice Matching Technique , 1996, Perception.

[31]  Marc Ebner,et al.  Color Constancy , 2007, Computer Vision, A Reference Guide.

[32]  D. Brainard,et al.  Surface gloss and color perception of 3D objects , 2008, Visual Neuroscience.

[33]  L. Arend,et al.  Simultaneous color constancy. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[34]  Qasim Zaidi,et al.  Color strategies for object identification , 2008, Vision Research.

[35]  David H Brainard,et al.  RenderToolbox3: MATLAB tools that facilitate physically based stimulus rendering for vision research. , 2014, Journal of vision.

[36]  S. Ishihara TESTS FOR COLOUR BLINDNESS , 1952 .