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

Abstract

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). In both experiments, faces manipulated to appear more trustworthy were indeed perceived to be more trustworthy, but not more attractive. Importantly, in both experiments, these faces were also perceived as more approachable and with more positive expressions, as indicated by both judgments and machine learning algorithms. The current studies show that the visual cues used for trustworthiness and attractiveness judgments can be separated, and that apparent approachability and facial emotion are driving trustworthiness judgments and possibly general valence evaluation.

What is Beautiful is Still Good: The Attractiveness Halo Effect in the era of Beauty Filters

“…Hence, our findings might not generalise to real-world scenarios where attractiveness perceptions interact with other factors, such as situational dynamics, personality and social context. Nonetheless, most of the previous work that has studied this cognitive bias has adopted a similar methodology to ours [1,2,8,24,44,[80][81][82][83][84] and faces play a significant role in our judgements of the attributes studied in this work [1,2,6,8,10,11,24,[43][44][45][46].…”

“…Each question was presented to participants as "How [trait term] is this person? ", following the same approach as previous studies in the literature [1,8,44,[80][81][82][83]. The responses were entered on a slider initially placed at the mid-point, and where both the mid and end points were labelled.…”

“…For the remaining questions, participants were asked to provide their answers on a 7-point Likert scale presented as a slider (indicated with a ➞ symbol below) where the end and middle points are labelled, as per previous research [1,8,44,[80][81][82][83]…”

Listen to this: Why consumer behavior researchers should care about listening

“…Not surprisingly, those who smile are perceived to be kinder, but they are also perceived to be more attractive (Otta et al, 1996). Nevertheless, controlling for attractiveness ratings, those who smile are also viewed as more trustworthy (Oh et al, 2023) and more sincere, sociable, and competent (Belkin & Rothman, 2017;Reis et al, 1990). Smiling also has positive downstream consequences.…”

Exploring Predictors of Facial Impressions: An Integrated Approach Utilizing VAE, Grad-CAM, and GPT-4V

“…A subsequent study showed that the eye region is critical for attractiveness (Sano & Kawabata, 2023). These DL methods, as well as computational methods such as morphometrics (Windhager et al, 2018;; image statistical analysis (Arce-Lopera et al, 2013;Fink et al, 2001); and 3D modeling (Nakamura & Watanabe, 2019;Oh et al, 2023;Said & Todorov, 2011) can comprehensively explore critical facial features.…”