From traits to trades: Empathizing and systemizing map onto data-driven occupation dimensions.

Empathizing-systemizing theory suggests people differ in their drives to understand others' emotions (empathizing) and rule-based systems (systemizing). Prior studies report that individuals with higher systemizing compared to empathizing scores are more likely to work in science, technology, engineering, and mathematics (STEM), but have relied on broad occupational categories that obscure variation within and between fields. To address this limitation, we used the Visualization of Latent Components Assessed in O*NET Occupations (VOLCANO) tool to examine how empathizing and systemizing traits relate to data-driven dimensions of real-world occupations. Data were drawn from 3957 adults who completed the Empathy Quotient (EQ) and Systemizing Quotient (SQ) questionnaires and reported their occupation. SQ-EQ profiles were computed for each individual and their occupations were scored along three continuous VOLCANO dimensions: occupational complexity, STEM versus humanities, and computational versus health sciences. Regression analyses revealed that higher systemizing relative to empathizing scores significantly predicted occupations positioned toward STEM (β =1.08, p < 0.001) and computational fields (β = 1.40, p < 0.001). The association with STEM was stronger for males. These findings replicate and extend prior work, demonstrating that SQ-EQ traits map onto graded, data-driven occupational characteristics and highlighting the value of dimensional approaches for understanding links between cognitive style and career choice.