No 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.

Social stereotypes are prevalent and consequential, yet sometimes inaccurate. How do people learn these inaccurate beliefs in the first place and why do these beliefs persist in the face of counter evidence? Building on past research on cognitive limitations and environmental sample biases, we propose an integrative perspective: Insufficient statistical learning (Insta-learn). Instalearn posits that humans are active learners of the environment. Starting from a small sample, people are able to extract statistical patterns within the sample accurately and quickly. However, people do not continue sampling sufficiently. If they decide not to collect more samples once they are (prematurely) satisfied, inaccurate stereotypes can emerge even when more data would show otherwise. We investigated this hypothesis across six online experiments (N = 1565), using novel pairs of computer-generated faces and social behaviors. Fixing the population level statistics of face-behavior associations to zero and varying the initial sample statistics, we found that participants quickly learned the initial sample statistics (from as few as three examples) and persisted in using such spurious associations in their final decisions. Granting the sampling power to participants — samples were endogenously generated by participants and not defined by the experimenters — we found insufficient sampling caused spurious associations to persist. Insta-learn provides a domain-general framework for a mechanistic explanation of the emergence and persistence of social stereotypes.

Social stereotypes are prevalent and consequential, yet sometimes inaccurate. How do people learn these inaccurate beliefs in the first place and why do these beliefs persist in the face of counter evidence? Building on past research on cognitive limitations and environmental sample biases, we propose an integrative perspective: Insufficient statistical learning (Insta-learn). Instalearn posits that humans are active learners of the environment. Starting from a small sample, people are able to extract statistical patterns within the sample accurately and quickly. However, people do not continue sampling sufficiently. If they decide not to collect more samples once they are (prematurely) satisfied, inaccurate stereotypes can emerge even when more data would show otherwise. We investigated this hypothesis across six online experiments (N = 1565), using novel pairs of computer-generated faces and social behaviors. Fixing the population level statistics of face-behavior associations to zero and varying the initial sample statistics, we found that participants quickly learned the initial sample statistics (from as few as three examples) and persisted in using such spurious associations in their final decisions. Granting the sampling power to participants — samples were endogenously generated by participants and not defined by the experimenters — we found insufficient sampling caused spurious associations to persist. Insta-learn provides a domain-general framework for a mechanistic explanation of the emergence and persistence of social stereotypes.

Trustworthy-looking faces are also perceived as more attractive, but are there other meaningful cues that contribute to trustworthiness judgments? 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 eliminate the attractiveness confounds, 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. 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 key cues used for trustworthiness judgments.