People rapidly form impressions from facial appearance, and these impressions affect social decisions. We argue that data-driven, computational models are the best available tools for identifying the source of such impressions. Here we validate seven computational models of social judgments of faces: attractiveness, competence, dominance, extroversion, likability, threat, and trustworthiness. The models manipulate both face shape and reflectance (i.e., cues such as pigmentation and skin smoothness). We show that human judgments track the models' predictions (Experiment 1) and that the models differentiate between different judgments, though this differentiation is constrained by the similarity of the models (Experiment 2). We also make the validated stimuli available for academic research: seven databases containing 25 identities manipulated in the respective model to take on seven different dimension values, ranging from -3 SD to +3 SD (175 stimuli in each database). Finally, we show how the computational models can be used to control for shared variance of the models. For example, even for highly correlated dimensions (e.g., dominance and threat), we can identify cues specific to each dimension and, consequently, generate faces that vary only on these cues.

Studies on first impressions from facial appearance have rapidly proliferated in the past decade. Almost all of these studies have relied on a single face image per target individual, and differences in impressions have been interpreted as originating in stable physiognomic differences between individuals. Here we show that images of the same individual can lead to different impressions, with within-individual image variance comparable to or exceeding between-individuals variance for a variety of social judgments (Experiment 1). We further show that preferences for images shift as a function of the context (e.g., selecting an image for online dating vs. a political campaign; Experiment 2), that preferences are predictably biased by the selection of the images (e.g., an image fitting a political campaign vs. a randomly selected image; Experiment 3), and that these biases are evident after extremely brief (40-ms) presentation of the images (Experiment 4). We discuss the implications of these findings for studies on the accuracy of first impressions.

Trustworthiness and dominance impressions summarize trait judgments from faces. Judgments on these key traits are negatively correlated to each other in impressions of female faces, implying less differentiated impressions of female faces. Here we test whether this is true across many trait judgments and whether less differentiated impressions of female faces originate in different facial information used for male and female impressions or different evaluation of the same information. Using multidimensional rating datasets and data-driven modeling, we show that (1) impressions of women are less differentiated and more valence-laden than impressions of men, and find that (2) these impressions are based on similar visual information across face genders. Female face impressions were more highly intercorrelated and were better explained by valence (Study 1). These intercorrelations were higher when raters more strongly endorsed gender stereotypes. Despite the gender difference, male and female impression models-derived from separate trustworthiness and dominance ratings of male and female faces-were similar to each other (Study 2). Further, both male and female models could manipulate impressions of faces of both genders (Study 3). The results highlight the high-level, evaluative effect of face gender in impression formation-women are judged negatively to the extent their looks do not conform to expectations, not because people use different facial information across genders, but because people evaluate the information differently across genders.

People often make approachability decisions based on perceived facial trustworthiness. However, it remains unclear how people learn trustworthiness from a population of faces and whether this learning influences their approachability decisions. Here we investigated the neural underpinning of approach behavior and tested two important hypotheses: whether the amygdala adapts to different trustworthiness ranges and whether the amygdala is modulated by task instructions and evaluative goals. We showed that participants adapted to the stimulus range of perceived trustworthiness when making approach decisions and that these decisions were further modulated by the social context. The right amygdala showed both linear response and quadratic response to trustworthiness level, as observed in prior studies. Notably, the amygdala's response to trustworthiness was not modulated by stimulus range or social context, a possible neural dynamic adaptation. Together, our data have revealed a robust behavioral adaptation to different trustworthiness ranges as well as a neural substrate underlying approach behavior based on perceived facial trustworthiness.

Trustworthiness and dominance impressions summarize trait judgments from faces.Judgments on these key traits are negatively correlated to each other in impressions of female faces, implying less differentiated impressions of female faces. Here we test whether this is true across many trait judgments and whether less differentiated impressions of female faces originate in different facial information used for male and female impressions or different evaluation of the same information. Using multidimensional rating datasets and data-driven modeling, we show that (1) impressions of women are less differentiated and more valence-laden than impressions of men, and find that (2) these impressions are based on similar visual information across face genders. Female face impressions were more highly intercorrelated and were better explained by valence (Study 1). These intercorrelations were higher when raters more strongly endorsed gender stereotypes.Despite the gender difference, male and female impression models -derived from separate trustworthiness and dominance ratings of male and female faces -were similar to each other (Study 2). Further, both male and female models could manipulate impressions of faces of both genders (Study 3). The results highlight the high-level, evaluative effect of face gender in impression formation -women are judged negatively to the extent their looks do not conform to expectations, not because people use different facial information across genders, but because people evaluate the information differently across genders.