MAPPING VARIED MENTAL REPRESENTATIONS: THE CASE OF REPRESENTING ILLEGALIZED IMMIGRANTS

Average images are often estimated within a sample or theory-derived variables (e. g., conservatives vs. liberals) to understand how social categories are mentally represented. However, average representations can mask large internal heterogeneity, thereby missing unexpected or complex representational clustering. We propose an inverted data-driven approach that first clusters representations by similarity, then identifies variables that differentiate clusters. We apply this approach to characterize mental representations of illegalized immigrants. Representations were collected in Texas and California (N = 1002) using face-based reverse correlation along with variables theorized to influence perceptions of immigrants: attitudes, demographics, ideologies, geography, and a label manipulation (i.e., "undocumented" vs. "illegal" immigrant). Sample- and variableaggregated images hid representational clusters that differed on visualized facial phenotype and affective expressions. Clustered representations ranged from highly shared to smaller clusters differentiated by demography and social geography: age and local population size perceptions. Data-driven approaches can help reveal meaningful variation in visual representations.