Second, PE and PL differ in terms of the degree of specificity in learning. By definition, the excellent recognition skills in PE generalize to new but similar exemplars of the trained category. We can easily think of real-world examples, for instance, a music-reading expert can read newly composed music pieces efficiently, and bird experts can discriminate between new birds without trouble (see also Gauthier & Tarr,
1997; Gauthier et al.,
1998; Wong, Palmeri, & Gauthier,
2009). Laboratory training studies using birds or novel objects also support such generalization in learning (Gauthier et al.,
1998; Tanaka et al.,
2005; Wong, Palmeri, & Gauthier,
2009). In contrast, PL is marked by the high specificity of learning effects, for instance, to the trained orientation and spatial frequency (Fiorentini & Berardi,
1980), eye (Karni & Sagi,
1991), visual field (Fahle, Edelman, & Poggio,
1995), motion direction (Ball & Sekuler,
1987), shape (Sigman & Gilbert,
2000), and task (Fahle,
1997). For example, changing the spatial frequency or rotating training shapes by 90° often lead to a large decrease in performance, down to the pretraining level (Fiorentini & Berardi,
1980). Although recent evidence suggests that the degree of generalization in PL can be modulated by various factors including task precision (Jeter, Dosher, Petrov, & Lu,
2009), task difficulty (Ahissar & Hochstein,
1997), and preexposure in spatial locations (Xiao et al.,
2008), the specificity of PL has been extensively replicated and remains a defining characteristic of this type of learning.