A long-standing scientific interest in the crowding phenomenon (Bouma,
1970; Korte,
1923; Stuart & Burian,
1962) has produced a wealth of experimental data and theories (Levi,
2008). A prevalent theory argues that crowding is a result of integrating features over inappropriately large areas (Greenwood, Bex, & Dakin,
2010; Levi & Carney,
2009; Pelli et al.,
2004; Pelli & Tillman,
2008; van den Berg, Roerdink, & Cornelissen,
2010), but it has also been suggested that crowding is primarily a result of substituting object positions due to spatial uncertainty (Chung & Legge,
2009; Greenwood, Bex, & Dakin,
2009; Huckauf & Heller,
2002; Nandy & Tjan,
2007; Strasburger,
2005) or of attentional limitations (Chakravarthi & Cavanagh,
2007; He, Cavanagh, & Intriligator,
1996; Petrov & Popple,
2007). Despite the abundance of data and theories, quantitative models for crowding are sparse (Whitney & Levi,
2011) and have been put forward only recently (Dakin, Cass, Greenwood, & Bex,
2010; Greenwood et al.,
2009; Nandy & Tjan,
2007; Solomon & Dayan,
2011; van den Berg et al.,
2010). Whereas several researchers have proposed that crowding may be explained using ideal-observer theory, as yet it is unclear to what extent the behavioral hallmark of crowding, namely critical spacing that scales with target eccentricity, would be compatible with an ideal-observer account of crowding.