First, the qualitative observations and descriptions generated by early work are now replaced by rigorous computational theories. This advance can be traced through a number of developments. Hochberg's and Attneave's commitment to quantitative characterization of perceptual phenomena (Attneave,
1954,
1959; Hochberg & McAlister,
1953; Hochberg & Silverstein,
1956) have inspired a generation of more rigorous psychophysical approaches to spatial (e.g., Feldman
1997; Field, Hayes, & Hess,
1993; Kubovy, Holcombe, & Wagemans,
1998; Oyama,
1961; Pizlo, Salach-Golyska, & Rosenfeld,
1997) and spatiotemporal (e.g., Adelson & Movshon,
1982; Burt & Sperling,
1981; Gepshtein & Kubovy,
2000,
2007; Ullman,
1979) perceptual organization. Brunswik's early and, at the time, radical ideas about the “ecological validity” of perceptual organization (Brunswik & Kamiya,
1953) led to contemporary Bayesian models of perceptual organization grounded in the statistics of our visual environment (e.g., Elder & Goldberg,
2002; Geisler, Perry, Super, & Gallogly,
2001; Martin, Fowlkes, Tal, & Malik,
2001). Theory and technique from the computer vision community have provided candidate algorithms and computational frameworks that are being tested as models of perception (e.g., Lee & Mumford,
2003; Lowe,
1985; Roberts,
1965; Tu & Zhu,
2002; Witkin & Tenenbaum,
1983; Zucker, Hummel, & Rosenfeld,
1977). Researchers have sought organizational principles that could explain perceived surface lightness and color (Adelson,
1993,
2000; Gilchrist,
2006; Gilchrist et al.,
1999) and developed models based on the physics of light-surface interaction (Boyaci, Doerschner, Snyder, & Maloney,
2006) or the statistical structure of natural scenes (Brainard et al.,
2006).