Abstract
When we look at someone’s face, we rapidly and automatically form robust impressions of how trustworthy they appear. Such impressions are vitally important, as our everyday decisions of whom to trust can have profound impacts on collective societal outcomes. Yet while people’s impressions of trustworthiness show a high degree of reliability and agreement with one another, evidence for the accuracy of these impressions is extremely weak.
How do such appearance-based biases survive in the face of weak evidence? We explored this question using an iterated learning paradigm, in which memories relating facial and behavioral trustworthiness were passed through many generations of participants. Stimuli consisted of pairs of computer-generated people’s faces and exact dollar amounts that those fictional people shared with partners in a trust game. Importantly, the faces were designed to vary considerably along a dimension of facial trustworthiness. Each participant learned (and then reproduced from memory) some mapping between the faces and the dollar amounts shared (i.e., between facial and behavioral trustworthiness). Much like in the game of “telephone”, their reproductions then became the training stimuli initially presented to the next participant, and so on for each transmission chain. Critically, the first participant in each chain was taught a completely random mapping between facial and behavioral trustworthiness.
Nevertheless, within only a few generations of participants, stereotyped patterns of transmission behavior spontaneously emerged from the initially noisy relationship. The most common of these patterns was a positive linear relationship between facial and behavioral trustworthiness, consistent with the commonly held stereotype. However, some chains yielded other simple patterns, such as negative linear relationships, or simple clusters of low/high facial/behavioral trustworthiness. These results demonstrate the power of facial stereotypes, and the ease with which they can be propagated to others, even in the absence of any reliable signal from the environment.