The sorting paired features (SPF) task measures four associations in a single response block. Using four response options (e.g., good-Republicans, bad-Republicans, good-Democrats, and bad-Democrats), each trial requires participants to categorize two stimuli at once to a category pair (e.g., wonderful-Clinton to good-Democrats). Unlike other association measures, the SPF requires simultaneous categorization of both components of the association in the same trial. Providing measurement flexibility, it is sensitive to both focal, attended concepts and nonfocal, unattended stimulus features (e.g., gender of individuals in a politics SPF). Three studies measure race, gender, and political evaluations, differentiate automatic evaluations between known groups, provide evidence of convergent and discriminant validity with other attitude measures, and illustrate the SPF’s unique measurement qualities.
Deep learning for quantile regression under right censoring: DeepQuantreg
