Visualizing Gendered Representations of Male and Female Teachers Using a Reverse Correlation Paradigm

Abstract

Abstract. Stereotypically, men are expected to outperform women in science, technology, engineering, and mathematics (STEM) domains, and women to outperform men in language. We conceptually replicated this association using reverse correlation tasks. Without available gender information, participants generated male images of physics teachers and female images of language teachers (Studies 1 and 3). Personal endorsement of respective ability stereotypes inconsistently predicted these effects (Studies 1 and 3). With unambiguous gender information (Study 2), participants generated feminized images of female language teachers and masculinized images of female physics teachers, whereas images of male teachers were unaffected by academic domain. Stereotype endorsement affected perceptions of female but not male teachers, suggesting that appearing feminine in STEM domains still signals professional mismatch.