Abstract
Complex evaluative judgments from facial appearance are made efficiently and are consequential. We review some of the most important findings and methods over the last two decades of research on face evaluation. Such evaluative judgments emerge early in development and show a surprising consistency over time and across cultures. Judgments of trustworthiness, in particular, are closely associated with general valence evaluation of faces and are grounded in resemblance to emotional expressions, signaling approach versus avoidance behaviors. Data-driven computational models have been critical for the discovery of the configurations of features, including resemblance to emotional expressions, driving specific judgments. However, almost all models are based on judgments aggregated across individuals, essentially masking idiosyncratic differences in judgments. Yet, recent research shows that most of the meaningful variance of complex judgments such as trustworthiness is idiosyncratic: explained not by stimulus features, but by participants and participants by stimuli interactions. Hence, to understand complex judgments, we need to develop methods for building models of judgments of individual participants. We describe one such method, combining the strengths of well-established methods with recent developments in machine learning.