The measurement and impact of implicit or automatic biases has been a central focus of stereotyping and prejudice research over the past decade. The often pernicious effects of implicit biases have been recognized well beyond the laboratory. Indeed, implicit biases are identified as obstacles to equality in many domains. For example, implicit gender bias has been implicated as one of eight core social and environmental factors contributing to women's chronic underrepresentation in the science, technology, mathematics, and engineering professions (Hill, Corbett, & St. Rose, 2010). Similarly, research concerning racial health disparities indicates that physicians' implicit race bias has a profound impact on the diagnosis and treatment of African Americans (Green et al., 2007). Clearly there is a pressing need for strategies that can be applied to the reduction of these biases. Social psychological research and theory have advanced our knowledge of the existence and