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Abstract People readily (but often inaccurately) attribute traits to others based on faces. While the details of attributions depend on the language available to describe social traits, psychological theories argue that two or three dimensions (such as valence and dominance) summarize social trait attributions from faces. However, prior work has used only a small number of trait words (12 to 18), limiting conclusions to date. In two large-scale, preregistered studies we ask participants to rate 100 faces (obtained from existing face stimuli sets), using a list of 100 English trait words that we derived using deep neural network analysis of words that have been used by other participants in prior studies to describe faces. In study 1 we find that these attributions are best described by four psychological dimensions, which we interpret as “warmth”, “competence”, “femininity”, and “youth”. In study 2 we partially reproduce these four dimensions using the same stimuli among additional participant raters from multiple regions around the world, in both aggregated and individual-level data. These results provide a comprehensive characterization of trait attributions from faces, although we note our conclusions are limited by the scope of our study (in particular we note only white faces and English trait words were included). 

Abstract 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. 

Decision-making in complex environments relies on flexibly using prior experience. This process depends on the medial frontal cortex (MFC) and the medial temporal lobe, but it remains unknown how these structures implement selective memory retrieval. We recorded single neurons in the MFC, amygdala, and hippocampus while human subjects switched between making recognition memory–based and categorization-based decisions. The MFC rapidly implemented changing task demands by using different subspaces of neural activity and by representing the currently relevant task goal. Choices requiring memory retrieval selectively engaged phase-locking of MFC neurons to amygdala and hippocampus field potentials, thereby enabling the routing of memories. These findings reveal a mechanism for flexibly and selectively engaging memory retrieval and show that memory-based choices are preferentially represented in the frontal cortex when required. 

The role of the pre-supplementary motor area (pre-SMA) in linking goals to actions remains unclear. Using single-neuron recordings in human pre-SMA, Wang et al. identify cells that signal when a fixation lands on a target across several different visual search tasks. One function of pre-SMA may thus be goal detection.