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Several findings are consistent with norm-based coding of face identity.
First, people can abstract averages or prototypes from sets of seen faces, a process that operates from early infancy (Bruce, Doyle, Dench, & Burton, 1991; Cabeza, Bruce, Kato, & Oda, 1999; Cabeza & Kato, 2000; De Haan, Johnson, Maurer, & Perrett, 2001; Haberman & Whitney, 2007; Inn, Walden, & Solso, 1993; Mac Lin & Webster, 2001; Reed, 1972; Rhodes, Jeffery, Watson, Clifford, & Nakayama, 2003; Rhodes, Jeffery, Watson, Jaquet, Winkler, & Clifford, 2004; Solso & Mc Carthy, 1981a, 1981b; Strauss, 1979; Walton & Bower, 1993; Webster & Mac Lin, 1999).
Second, distinctive faces (far from average) are recognized better than typical ones (close to average; Valentine, 1991, 2001).
These results indicate that, despite the common structure shared by all faces, identity is coded using sex-specific norms.
We suggest that the use of category-specific norms may increase coding efficiency and help us discriminate thousands of faces despite their similarity as patterns.
Identity aftereffects are generally larger for adapt–test pairs that lie opposite an average face, which functions as a norm for coding identity, than those that do not.
Many theorists propose that faces are coded relative to a perceptual norm that represents the central tendency (average) of our perceptual diet of faces (Diamond & Carey, 1986; Goldstein & Chance, 1980; Hebb, 1949; Hochberg, 1978; Leopold, O'Toole, Vetter, & Blanz, 2001; Rhodes, 1996; Rhodes, Brennan, & Carey, 1987; Rhodes & Leopold, in press; Valentine, 1991).
Norm-based coding makes explicit what is distinctive about each face, allowing us to readily discriminate and recognize thousands of faces despite their similarity as visual patterns.