There are a number of approaches that may be used to develop predictive models for appearance data. One approach is
mechanistic and seeks to formulate models that incorporate known features of the neural processing chain. Examples of models in this category are those that invoke ideas such as gain control (e.g., Burnham, Evans, & Newhall,
1957; Stiles,
1967; von Kries,
1902/1970), contrast coding (e.g., Shevell,
1978; Walraven,
1976), and color opponency (e.g., Hurvich & Jameson,
1957; Webster & Mollon,
1995) to explain various color appearance phenomena. A second approach abstracts further and considers the problem as one of characterizing the properties of a black-box system. Here, the effort is on testing principles of system performance (such as linearity) that allow prediction of color appearance in many contexts based on measurements from just a few (Brainard & Wandell,
1992; Krantz,
1968). A third approach is
computational in the sense defined by Marr (
1982), with the idea being to develop models by thinking about what useful function color context effects serve and how a system designed to accomplish this function would perform. In the case of color, one asks how could a visual system process image data to recover descriptions of object surface reflectance that are stable across changes in the scene. We have taken this approach in much of our prior work on color and lightness constancy (Brainard, Brunt, & Speigle,
1997; Brainard, Kraft, & Longère,
2003; Brainard, Wandell, & Chichilnisky,
1993; Bloj et al.,
2004; Speigle & Brainard,
1996; see also Adelson & Pentland,
1996; Bloj, Kersten, & Hurlbert,
1999; Boyaci, Doerschner, & Maloney,
2004; Boyaci, Maloney, & Hersh,
2003; Doerschner, Boyaci, & Maloney,
2004; Land,
1964; Maloney & Yang,
2001; McCann, McKee, & Taylor,
1976) and develop it further here. It is useful to keep in mind that the various approaches are not mutually exclusive and that the distinctions between them are not always sharp.