Abstract

Existing models of facial first impressions indicate between two and four factors that underpin all social trait judgements. Here, we submitted several large databases of these first impression ratings to unsupervised learning algorithms with the aim of clustering together faces, rather than traits, to examine the ways in which impressions may be grouped together. Experiment 1 revealed two clusters of faces that exist in both a full-dimensional, and two- or three-factor representations, of social impressions, while Experiment 2 indicated that these clusters also emerged in additional datasets. In Experiment 3, using Bayesian modelling approaches, we extracted the impression profile of each cluster and also derived a vector that maximally separated the clusters. The resulting vector related strongly to the valence and approachability components in existing models. In a further test of our model, we showed in Experiment 4 that mere facial appearance, rather than perceptions, is sufficient to separate these clusters, demonstrating probabilistically that facial cues like smiling may drive the perceptual profile that gives rise to the perceptual clusters. Finally, Experiment 5 showed that observer responses to faces in these two clusters mapped closely on to approach-avoidance behaviour, with observers responding rapidly and without instruction to approach faces from one cluster over the other. Taken together, our findings provide compelling evidence, drawing upon both computational and behavioural approaches, that existing models of social impressions are realised practically in terms of basic approach-avoidance mechanisms.