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First impressions formed after seeing someone’s face or hearing their voice can affect many social decisions, including voting in political elections. Despite the many studies investigating the independent contribution of face and voice cues to electoral success, their integration is still not well understood. Here, we examine a novel electoral context, student representative ballots, allowing us to test the generalisability of previous studies. We also examine the independent contributions of visual, auditory and audiovisual information to social judgements of the candidates, and their relationship to election outcomes. Results showed that perceived trustworthiness was the only trait significantly related to election success. These findings contrast with previous reports on the importance of perceived competence using audio or visual cues only in the context of national political elections. The present study highlights the role of real-world context and emphasises the importance of using ecologically valid stimulus presentation in understanding real-life social judgement.

A paradoxical finding from recent studies of face perception is that observers are error-prone and inconsistent when judging the identity of unfamiliar faces, but nevertheless reasonably consistent when judging traits. Our aim is to understand this difference. Using everyday ambient images of faces, we show that visual image statistics can predict observers' consensual impressions of trustworthiness, attractiveness and dominance, which represent key dimensions of evaluation in leading theoretical accounts of trait judgement. In Study 1, image statistics derived from ambient images of multiple face identities were able to account for 51% of the variance in consensual impressions of entirely novel ambient images. Shape properties were more effective predictors than surface properties, but a combination of both achieved the best results. In Study 2 and Study 3, statistics derived from multiple images of a particular face achieved the best generalisation to new images of that face, but there was nonetheless significant generalisation between images of the faces of different individuals. Hence, whereas idiosyncratic variability across different images of the same face is sufficient to cause substantial problems in judging the identities of unfamiliar faces, there are consistencies between faces which are sufficient to support (to some extent) consensual trait judgements. Furthermore, much of this consistency can be captured in simple operational models based on image statistics.

Forgetting someone’s name is a common failure of memory, and often occurs despite being able to recognise that person’s face. This gives rise to the widespread view that memory for names is generally worse than memory for faces. However, this everyday error confounds stimulus class (faces vs. names) with memory task: recognition versus recall. Here we compare memory for faces and names when both are tested in the same recognition memory framework. Contrary to the common view, we find a clear advantage for names over faces. Across three experiments, we show that recognition of previously unfamiliar names exceeds recognition of previously unfamiliar faces. This advantage persists, even when the same face pictures are repeated at learning and test—a picture-memory task known to produce high levels of performance. Differential performance between names and faces disappears in recognition memory for familiar people. The results are discussed with reference to representational complexity and everyday memory errors.

Models of social evaluation aim to capture the information people use to form first impressions of unfamiliar others. However, little is currently known about the relationship between perceived traits across gender. In Study 1, we asked viewers to provide ratings of key social dimensions (dominance, trustworthiness etc.) for multiple images of 40 unfamiliar identities. We observed clear sex differences in the perception of dominance – with negative evaluations of high dominance in unfamiliar females, but not males. In Study 2, we used the social evaluation context to investigate key predictions about the importance of pictorial information in familiar and unfamiliar face processing. We compared the consistency of ratings attributed to different images of the same identities and demonstrated that ratings of images depicting the same familiar identity are more tightly clustered than those of unfamiliar identities. Such results imply a shift from image rating to person rating with increased familiarity, a finding which generalises results previously observed in studies of identification.

Unfamiliar face matching is a surprisingly difficult task, yet we often rely on people’s matching decisions in applied settings (e.g., border control). Most attempts to improve accuracy (including training and image manipulation) have had very limited success. In a series of studies, we demonstrate that using smiling rather than neutral pairs of images brings about significant improvements in face matching accuracy. This is true for both match and mismatch trials, implying that the information provided through a smile helps us detect images of the same identity as well as distinguishing between images of different identities. Study 1 compares matching performance when images in the face pair display either an open-mouth smile or a neutral expression. In Study 2, we add an intermediate level, closed-mouth smile, to identify the effect of teeth being exposed, and Study 3 explores face matching accuracy when only information about the lower part of the face is available. Results demonstrate that an open-mouth smile changes the face in an idiosyncratic way which aids face matching decisions. Such findings have practical implications for matching in the applied context where we typically use neutral images to represent ourselves in official documents.

It has been known for many years that identifying familiar faces is much easier than identifying unfamiliar faces, and that this familiar face advantage persists across a range of tasks. However, attempts to understand face familiarity have mostly used a binary contrast between ‘familiar’ and ‘unfamiliar’ faces, with no attempt to incorporate the vast range of familiarity we all experience. From family members to casual acquaintances and from personal to media exposure, familiarity is a more complex categorisation than is usually acknowledged. Here we model levels of familiarity using a generic statistical analysis (PCA combined with LDA) computed over some four thousand naturally occurring images that include a large variation in the numbers of images for each known person. Using a strong test of performance with entirely novel, untrained everyday images, we show that such a model can simulate widely documented effects of familiarity in face recognition and face matching, and offers a natural account of the internal feature advantage for familiar faces. Furthermore, as with human viewers, the benefits of familiarity seem to accrue from being able to extract consistent information across different photos of the same face. We argue that face familiarity is best understood as reflecting increasingly robust statistical descriptions of idiosyncratic within-person variability. Understanding how faces become familiar appears to rely on both bottom-up statistical image descriptions (modelled here with PCA), and top-down processes that cohere superficially different images of the same person (modelled here with LDA).

Our social evaluation of other people is influenced by their faces and their voices. However, rather little is known about how these channels combine in forming "first impressions." Over 5 experiments, we investigate the relative contributions of facial and vocal information for social judgments: dominance and trustworthiness. The experiments manipulate each of these sources of information within-person, combining faces and voices giving rise to different social attributions. We report that vocal pitch is a reliable source of information for judgments of dominance (Study 1), but not trustworthiness (Study 4). Faces and voices make reliable, but independent, contributions to social evaluation. However, voices have the larger influence in judgments of dominance (Study 2), whereas faces have the larger influence in judgments of trustworthiness (Study 5). The independent contribution of the 2 sources appears to be mandatory, as instructions to ignore 1 channel do not eliminate its influence (Study 3). Our results show that information contained in both the face and the voice contributes to first impression formation. This combination is, to some degree, outside conscious control, and the weighting of channel contribution varies according the trait being perceived. (PsycINFO Database Record

We describe three experiments in which viewers complete face detection tasks as well as standard measures of unfamiliar face identification. In the first two studies, participants view pareidolic images of objects (Experiment 1) or cloud scenes (Experiment 2), and their propensity to see faces in these scenes is measured. In neither case is performance significantly associated with identification, as measured by the Cambridge Face Memory or Glasgow Face Matching Tests. In Experiment 3 we show participants real faces in cluttered scenes. Viewers’ ability to detect these faces is unrelated to their identification performance. We conclude that face detection dissociates from face identification.

Viewers are highly accurate at recognizing sex and race from faces-though it remains unclear how this is achieved. Recognition of familiar faces is also highly accurate across a very large range of viewing conditions, despite the difficulty of the problem. Here we show that computation of sex and race can emerge incidentally from a system designed to compute identity. We emphasize the role of multiple encounters with a small number of people, which we take to underlie human face learning. We use highly variable everyday 'ambient' images of a few people to train a Linear Discriminant Analysis (LDA) model on identity. The resulting model has human-like properties, including a facility to cohere previously unseen ambient images of familiar (trained) people-an ability which breaks down for the faces of unknown (untrained) people. The first dimension created by the identity-trained LDA classifies both familiar and unfamiliar faces by sex, and the second dimension classifies faces by race- even though neither of these categories was explicitly coded at learning. By varying the numbers and types of face identities on which a further series of LDA models were trained, we show that this incidental learning of sex and race reflects covariation between these social categories and face identity, and that a remarkably small number of identities need be learnt before such incidental dimensions emerge. The task of learning to recognize familiar faces is sufficient to create certain salient social categories.

A full understanding of face recognition will involve identifying the visual information that is used to discriminate different identities and how this is represented in the brain. The aim of this study was to explore the importance of shape and surface properties in the recognition and neural representation of familiar faces. We used image morphing techniques to generate hybrid faces that mixed shape properties (more specifically, second order spatial configural information as defined by feature positions in the 2D-image) from one identity and surface properties from a different identity. Behavioural responses showed that recognition and matching of these hybrid faces was primarily based on their surface properties. These behavioural findings contrasted with neural responses recorded using a block design fMRI adaptation paradigm to test the sensitivity of Haxby et al.'s (2000) core face-selective regions in the human brain to the shape or surface properties of the face. The fusiform face area (FFA) and occipital face area (OFA) showed a lower response (adaptation) to repeated images of the same face (same shape, same surface) compared to different faces (different shapes, different surfaces). From the behavioural data indicating the critical contribution of surface properties to the recognition of identity, we predicted that brain regions responsible for familiar face recognition should continue to adapt to faces that vary in shape but not surface properties, but show a release from adaptation to faces that vary in surface properties but not shape. However, we found that the FFA and OFA showed an equivalent release from adaptation to changes in both shape and surface properties. The dissociation between the neural and perceptual responses suggests that, although they may play a role in the process, these core face regions are not solely responsible for the recognition of facial identity.

A person’s ability to recognize familiar faces across a wide range of viewing conditions is one of the most impressive facets of human cognition. As shown in Figure 1, it is easy to conclude, for a known individual, that each image in the set shows the same person (British Prime Minister David Cameron), despite a wide range of variation in viewing angle, physical appearance, camera and lighting. In fact, familiar face recognition performance is often at or near ceiling level, even when the images are of poor quality [1] or artificially distorted. [2] At first glance, the aptitude for familiar face recognition may suggest a similar level of expertise for the recognition of unfamiliar faces, thus the reliance on face-to-photo ID for identity verification. [3] This is not the case, as recent research shows people are surprisingly poor at recognizing new instances of an unfamiliar person. The poor recognition of unfamiliar faces is a concern for the United States. Many preliminary screenings involve facial recognition by security agents. In order for this method to be effective, more robust training for security agents needs to be established. The Department of Defense utilizes facial and iris recognition technologies in order to eliminate human error in identifying persons of interest during surveillance operations. [4] DoD guidelines should be implemented by security agent guidance programs to ensure best practices in identification of potential threats.

Objective People with diabetes frequently develop vascular disease. We investigated the relationship between blood 25-hydroxy vitamin D (25OH-D) concentration and vascular disease risk in type 2 diabetes. Research design and methods The relationships between blood 25OH-D concentration at baseline and the incidence of macrovascular (including myocardial infarction, stroke) and microvascular (retinopathy, nephropathy, neuropathy, and amputation) disease were analysed with Cox proportional-hazards models and logistic regression in an observational study of patients in the 5-year Fenofibrate Intervention and Event Lowering in Diabetes trial. Results 50% of the patients had low vitamin D concentrations, as indicated by median blood 25OH-D concentration of 49nmol/L. These patients with a blood 25OH-D concentration < 50nmol/L had a higher cumulative incidence of macrovascular and microvascular events than those with levels ≥ 50nmol/L. Multivariate analysis, stratified by treatment and adjusted for relevant confounders, identified blood 25OH-D concentration as an independent predictor of macrovascular events. A 50nmol/L difference in blood 25OH-D concentration was associated with a 23% (P=0.007) change in risk of macrovascular complications during the study and further adjustments for seasonality, hs-CRP and physical activity level had little impact. The unadjusted risk of microvascular complications was 18% (P=0.006) higher during the study, though the excess risk declined to 11-14% and lost significance with adjustment for HbA1C, seasonality or physical activity. Conclusions Low blood 25OH-D concentrations are associated with an increased risk of macrovascular and microvascular disease events in type 2 diabetes. However, a causal link remains to be demonstrated.