Hyper-realistic reverse correlation reveals a novel gender bias in representations of leadership across political orientation

Appearance influences election outcomes via leadership stereotypes -- past work has shown that adults and even children can predict real-world elections solely on the basis of perceived competence judgments via photographs with relatively high accuracy. What are our visual stereotypes of leadership? And how do they differ according to political orientation? Here we explored this question using a novel reverse correlation technique powered by hyper-realistic generative face models (Albohn et al., 2022). Participants (N=300) viewed generated faces one at a time and judged whether they looked like a “good leader”, a “bad leader”, or “not sure”. Applying a simple algorithm to the aggregated choices yielded visually compelling and interpretable mental representations at both individual and group levels. While political group-averaged representations were similar along many subjective attributes (e.g., perceived "trustworthiness”, “attractiveness”; Peterson et al., 2022), they revealed a novel gender bias: right-leaning participants’ "good leaders" were more masculine than those of left-leaning participants. We directly replicated this result using richer latent face representations (N=300). We then validated individual participant models on new observers (N=150), probing their willingness to vote for different faces generated by past participants in an imaginary election. As predicted, participants were not only more willing to vote for "good" leader faces, but were most willing for faces generated by participants sharing their political orientation. Taken together, our results demonstrate how political orientation is linked to a novel gender bias in leadership representations, showcasing the utility of our reverse correlation technique.