Past studies have already showed that even if considered automatic and quite difficult to suppress, racial bias may be successfully reduced when a novel social category is made salient in priming task involving automatic evaluation of (previously learned) faces. However the efficacy of bias reduction models proposed in literature have not yet been compared as they lack of a common frame of reference. We propose five studies based on the common paradigm of Social Categorization Task (SCT) to investigate how different racial bias reduction models interfere with the implicit social map that emerges from the SCT.
Decategorization, Recategorization, Cross classification and dual identity have been shown to be efficacious in reducing inter-group bias in several independent studies. However a common frame of reference is still missing to compare their effectiveness in terms of social categorization. The present project proposes 5 studies to investigate how and when each model interferes in spatial ordering of racial categories emerging from the social categorization process. All the five studies we propose instead are all based on the basic SCT paradigm that allows to determine whether the intergroup differentiation (i.e. the spatial ordering of social categories) has been reached or not, but also to compare the efficacy of each model in racial bias reduction. Results will be discussed considering intergroup bias theories and racial bias literature.