Trustworthiness judgments without the halo effect: A data-driven computational modeling approach.

Trustworthy-looking faces are also perceived as more attractive, but are there other meaningful cues that contribute to perceived trustworthiness? Using data-driven models, we identify these cues after removing attractiveness cues. In Experiment 1, we show that both judgments of trustworthiness and attractiveness of faces manipulated by a model of perceived trustworthiness change in the same direction. To control for the effect of attractiveness, we build two new models of perceived trustworthiness: a subtraction model, which forces the perceived attractiveness and trustworthiness to be negatively correlated (Experiment 2), and an orthogonal model, which reduces their correlation (Experiment 3).

Learning the affective value of people: More than affect-based mechanisms.

People's ability to learn about the affective value of others is impressive. However, it is unclear whether this learning solely reflects general affect-based processes or a mixture of affect-based and person-attribution processes. Consistent with the former possibility, people's ability to learn the affective value of people and places have been shown to be comparable (Falvello, Vinson, Ferrari, & Todorov, 2015).