How do people attribute traits to other people based on faces?
How do people form impressions of others based on faces? Existing psychological theories argue that people attribute traits to others from faces along two or three dimensions. While these theories have now been incorporated into numerous empirical and theoretical studies, they were derived from a small set of trait attributions, which limits their generalizability and leaves the true nature of the psychological dimensions unclear. The present study applied deep neural networks to representatively sample an inclusive list of traits and faces, generating a comprehensive set of 100 traits and 100 faces that we administered in two large-scale preregistered studies. These comprehensive trait attributions (Study 1, 750,000 ratings) revealed a novel four-dimensional space: warmth, competence, female-stereotype, and youth-stereotype, challenging existing theories. Study 2 collecting dense individual-level data in seven different countries (2,100,000 trials) reproduced this four-dimensional space across cultures and in individual participants. These findings, together with test-retest reliability of all trait attributions and direct comparisons with existing theories, provide a new, most comprehensive characterization of trait attributions from faces.
Open Science Pre-registration: 1, 2, 3, & 4
Open Science Data & Codes: 1 & 2
with Umit Keles and Ralph Adolphs
Can people infer corruptible politicians based on their faces?
While inferences of traits from unfamiliar faces prominently reveal stereotypes, some facial inferences also correlate with real-world outcomes. We investigated whether facial inferences are associated with an important real-world outcome closely linked to the face bearer’s behavior: political corruption. In four preregistered studies (N = 325), participants made trait judgments of unfamiliar government officials on the basis of their photos. Relative to peers with clean records, federal and state officials convicted of political corruption (Study 1) and local officials who violated campaign finance laws (Study 2) were perceived as more corruptible, dishonest, selfish, and aggressive but similarly competent, ambitious, and masculine (Study 3). Mediation analyses and experiments in which the photos were digitally manipulated showed that participants’ judgments of how corruptible an official looked were causally influenced by the face width of the stimuli (Study 4). The findings shed new light on the complex causal mechanisms linking facial appearances with social behavior.
Open Science Pre-registration: 1, 2, & 3
Open Science Data & Codes: Link
with Ralph Adolphs and R. Michael Alvarez
Is social network index associated with gray matter volume? A data-driven investigation
Recent studies in adult humans have reported correlations between individual differences in Social Network Index (SNI) and gray matter volume (GMV) across multiple regions of the brain. However, the cortical and subcortical loci identified are inconsistent across studies. These discrepancies might arise because different regions of interest were hypothesized and tested in different studies without controlling for multiple comparisons, and/or from insufficiently large sample sizes to fully protect against statistically unreliable findings. Here we took a data-driven approach in a pre-registered study to comprehensively investigate the relationship between SNI and GMV in every cortical and subcortical region, using three predictive modeling frameworks. We also included psychological predictors such as cognitive and emotional intelligence, personality, and mood. In a sample of 92 healthy adults, neither multivariate frameworks (e.g., ridge regression with cross-validation) nor univariate frameworks (e.g., univariate linear regression with cross-validation) showed a significant association between SNI and any GMV or psychological feature after multiple comparison corrections (all R-squared values ≤ 0.1). These results emphasize the importance of large sample sizes and hypothesis-driven studies to derive statistically reliable conclusions, and suggest that future meta-analyses will be needed to more accurately estimate the true effect sizes in this field.
Open Science Pre-registration: Link
Open Science Data & Codes: Link
with Umit Keles, J. Michael Tyszka, Marcos Gallo, Lynn Paul, and Ralph Adolphs
For a complete list of publications, please refer to my CV.