She Sees the Trees, He Sees the Forest: Descriptive Gender Stereotypes of Concreteness and Abstractness. - Dodson, Samantha J.; Goodwin, Rachael D.; Wakslak, Cheryl J.; Diekmann, Kristina A.; Graham, Jesse (Journal of Personality & Social Psychology, 2025)
We utilize social role and construal-level theories to identify and explain descriptive expectations of men's and women's cognition. We find evidence of gendered construal-level stereotypes in six preregistered studies and an internal meta-analysis. First, we find that people tend to implicitly associate the names of women with low-construal terms and men with high-construal terms (Study 1; N = 229). In Studies 2 (N = 150), 3 (N = 601), and 4 (N = 333), we found that people tend to describe women as more concrete than men in general and across 48 occupations, although Study 4 (N = 333) added nuance to the story, finding that women were also described as more abstract than men. Across these studies, we also found that women were described as more concrete than abstract, whereas men would be described as more abstract than concrete. These stereotypic associations were observable in the language used to recommend LinkedIn users from varying industries and occupations (Study 5; N = 549,059). Study 6 reveals that beliefs that women are more concrete than men affect their assignments to desirable and undesirable detailed tasks (N = 841), a mechanism that could perpetuate gender roles and organizational inequity. The Supplemental Materials include three additional studies that help validate the present results. Finally, we conducted an internal meta-analysis (including supplemental and file drawer studies) to summarize the main effects. We discuss the theoretical implications of this research and provide recommendations for future research. Statement of Limitations: The research presented herein is limited in scope, specifically in its inability to capture longitudinal causal effects, generalize the effects to different historical periods, nationalities, and cultures beyond present-day U.S. populations, and provide convergent and divergent validity tests for some dependent measures. We also acknowledge the possibility of confounds that could not be accounted for in the archival data, the likelihood of imperfect predictions generated from machine learning algorithms, the potential for bias in researcher degrees of freedom (e.g., instrument choice) prior to preregistration, and some degree of artificiality in the experiments.