Human behavior can often be understood as optimal inference about the state of the world given noisy data. Tasks as varied as motor control (Faisal & Wolpert,
2009; Körding & Wolpert,
2004), animal learning (Courville, Daw, & Touretzky,
2006; Daw, Courville, & Dayan,
2008), semantic memory (Shiffrin & Steyvers,
1997; Steyvers, Griffiths, & Dennis,
2006), and categorization (Anderson,
1991; Heller, Sanborn, & Chater,
2009; Kemp, Perfors, & Tenenbaum,
2007) have all been successfully modeled in these terms. Vision has been a particular poster child for the approach, with a venerable and influential history of ideal observer (de Vries,
1943; Green & Swets,
1966; Peterson, Birdsall, & Fox,
1954; Rose,
1942) and sequential ideal observer (Geisler,
1989) analyses. As with other applications of Bayesian methods, optimal inference lays assumptions bare and, in its failures, suggests approximations. However, optimal inference has been used only somewhat sparingly in contrast discrimination (Chirimuuta & Tolhurst,
2005); this is our focus.