Social Attitudes and Categorizing Contemporary Information: Individual Differences in Sorting Topics into Larger Domains

Misinformation is motivating social scientists to explore factors contributing to scientific beliefs and evidence rejection. Research shows political ideology and cognitive styles can influence beliefs and the acceptance of fake news through four frameworks—knowledge deficit, motivated reasoning, identity protective cognitions, and analytic thinking. This thesis expands that line of research by exploring how category learning (Generalized Context Model and Prototype Theory), socio-political attitudes (Right-Wing Authoritarianism), and belief in science (Belief in Science Scale) relate to how people categorize information into scientific, political, religious, and neutral domains. Results showed a main effect of political orientation on domain ratings (e.g., liberal and non-partisan information was rated as more scientific and conservative information was more religious). Results also showed a main effect of RWA on domain ratings and multiple correlations similar to research on scientific beliefs (e.g., conservatives had lower scientific ratings and higher religious ratings). Implications for future research are discussed.