Facial first impressions form two clusters representing approach-avoidance

Decoding the language of first impressions: Comparing models of first impressions of faces derived from free-text descriptions and trait ratings

First impressions formed from facial appearance predict important social outcomes. Existing models of these impressions indicate they are underpinned by dimensions of Valence and Dominance, and are typically derived by applying data reduction methods to explicit ratings of faces for a range of traits. However, this approach is potentially problematic because the trait ratings may not fully capture the dimensions on which people spontaneously assess faces. Here, we used natural language processing to extract 'topics' directly from participants' free-text descriptions (i.e., their first impressions) of 2222 face images. Two topics emerged, reflecting first impressions related to positive emotional valence and warmth (Topic 1) and negative emotional valence and potential threat (Topic 2). Next, we investigated how these topics were related to Valence and Dominance components derived from explicit trait ratings. Collectively, these components explained only ~44% of the variance in the topics extracted from free-text descriptions and suggested that first impressions are underpinned by correlated valence dimensions that subsume the content of existing trait-rating-based models. Natural language offers a promising new avenue for understanding social cognition, and future work can examine the predictive utility of natural language and traditional data-driven models for impressions in varying social contexts.

Data-driven studies in face identity processing rely on the quality of the tests and data sets

There is growing interest in how data-driven approaches can help understand individual differences in face identity processing (FIP). However, researchers employ various FIP tests interchangeably, and it is unclear whether these tests 1) measure the same underlying ability/ies and processes (e.g., confirmation of identity match or elimination of identity match) 2) are reliable, 3) provide consistent performance for individuals across tests online and in laboratory. Together these factors would influence the outcomes of data-driven analyses. Here, we asked 211 participants to perform eight tests frequently reported in the literature. We used Principal Component Analysis and Agglomerative Clustering to determine factors underpinning performance. Importantly, we examined the reliability of these tests, relationships between them, and quantified participant consistency across tests. Our findings show that participants' performance can be split into two factors (called here confirmation and elimination of an identity match) and that participants cluster according to whether they are strong on one of the factors or equally on both. We found that the reliability of these tests is at best moderate, the correlations between them are weak, and that the consistency in participant performance across tests and is low. Developing reliable and valid measures of FIP and consistently scrutinising existing ones will be key for drawing meaningful conclusions from data-driven studies.

Trustworthiness judgments without the halo effect: A data-driven computational modeling approach

Trustworthy-looking faces are also perceived as more attractive, but are there other meaningful cues that contribute to perceived trustworthiness? Using data-driven models, we identify these cues after removing attractiveness cues. In Experiment 1, we show that both judgments of trustworthiness and attractiveness of faces manipulated by a model of perceived trustworthiness change in the same direction. To control for the effect of attractiveness, we build two new models of perceived trustworthiness: a subtraction model, which forces the perceived attractiveness and trustworthiness to be negatively correlated (Experiment 2), and an orthogonal model, which reduces their correlation (Experiment 3). In both experiments, faces manipulated to appear more trustworthy were indeed perceived to be more trustworthy, but not more attractive. Importantly, in both experiments, these faces were also perceived as more approachable and with more positive expressions, as indicated by both judgments and machine learning algorithms. The current studies show that the visual cues used for trustworthiness and attractiveness judgments can be separated, and that apparent approachability and facial emotion are driving trustworthiness judgments and possibly general valence evaluation.

Not Just About Faces in Context: Face-Context Relation Moderates the Impact of Contextual Threat on Facial Trustworthiness

Recent work showed that the attribution of facial trustworthiness can be influenced by the surrounding context in which a face is embedded: contexts that convey threat make faces less trustworthy. In four studies (N = 388, three preregistered) we tested whether face-context integration is influenced by how faces and contexts are encoded relationally. In Experiments 1a to 1c, face-context integration was stronger when threatening stimuli were attributable to the human action. Faces were judged less trustworthy when shown in threatening contexts that were ascribable (vs. non-ascribable) to the human action. In Experiment 2, we manipulated face-context relations using instructions. When instructions presented facial stimuli as belonging to the "perpetrators" of the threatening contexts, no difference with the control (no-instructions) condition was found in face-context integration. Instead, the effect was reduced when faces were presented as "victims." We discussed the importance of considering relational reasoning when studying face-context integration.

Analysis of personality traits' correlation to facial width-to-height ratio (fWHR) and mandibular line angle based on 16 personality factor in Chinese college students

Facial appearance reveals clues about personality. Studies have found that facial width-to-height ratio (fWHR) correlates with some personality traits, and mandibular morphology as a potential facial feature that might have correlation with personality traits. Therefore, a face recognition study was carried out to explore the personality traits' correlation to both fWHR and bilateral mandibular line angles. Specifically, face images of 904 college students in China were collected and measured, with the personality traits evaluated using the 16 Personality Factor Questionnaire. Analyses revealed that the average bilateral mandibular line angle of the male were significantly more extensive than that of the female, while the fWHR of the female was significantly more extensive than that of the male. We found facial features (fWHR and average bilateral mandibular line angle) were correlated with 16PF in the canonical correlation analysis and the loadings of bilateral mandibular line angles were greater than that of fWHR. The fWHR was significantly negatively correlated with the scores of sensitivity and self-reliance in male but none of the factors related to fWHR in female. The bilateral mandibular line angles were significantly negatively correlated with the scores of social boldness in male, and were significantly negatively correlated with the scores of vigilance and apprehension in female. Over all, the correlations between fWHR, average bilateral mandibular line angle and certain 16PF factors in male and female tend to be different, suggesting that such correlations might vary with gender. In the future, mandibular morphology could be selected as a potential indicator in facial perception. The limitations of this study were the participants were limited to 18-30 years of age and the mandibular morphology was not measured with anthropometry, which could be further improved in future studies.

Mapping physical characteristics in face images to social judgements

Sutherland and Young (Br. J. Psychol., 113, 2022, 1056) provide a comprehensive and timely overview of recent developments in research on social judgements of faces, emphasizing the utility of data-driven approaches. Here, we expand on this theme, focusing on how data-driven approaches can provide new insights into the physical characteristics in face images that best predict social judgements.

A Cautionary Note on Predicting Social Judgments from Faces with Deep Neural Networks

People spontaneously infer other people’s psychology from faces, encompassing inferences of their affective states, cognitive states, and stable traits such as personality. These judgments are known to be often invalid, but nonetheless bias many social decisions. Their importance and ubiquity have made them popular targets for automated prediction using deep convolutional neural networks (DCNNs). Here, we investigated the applicability of this approach: how well does it generalize, and what biases does it introduce? We compared three distinct sets of features (from a face identification DCNN, an object recognition DCNN, and using facial geometry), and tested their prediction across multiple out-of-sample datasets. Across judgments and datasets, features from both pre-trained DCNNs provided better predictions than did facial geometry. However, predictions using object recognition DCNN features were not robust to superficial cues (e.g., color and hair style). Importantly, predictions using face identification DCNN features were not specific: models trained to predict one social judgment (e.g., trustworthiness) also significantly predicted other social judgments (e.g., femininity and criminal), and at an even higher accuracy in some cases than predicting the judgment of interest (e.g., trustworthiness). Models trained to predict affective states (e.g., happy) also significantly predicted judgments of stable traits (e.g., sociable), and vice versa. Our analysis pipeline not only provides a flexible and efficient framework for predicting affective and social judgments from faces but also highlights the dangers of such automated predictions: correlated but unintended judgments can drive the predictions of the intended judgments.