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Facial first impressions are known to influence how we behave towards others. As a result of the COVID-19 pandemic, we often view incomplete faces due to the commonplace wearing of face masks. Previous research has shown that perceptions of attractiveness are often increased due to these coverings, with initial evidence suggesting that this may be caused by viewers using a mental representation of the average face to complete any missing information. Here, we directly address this hypothesis by presenting participants with incomplete faces (either the lower or upper half removed) and asking them to decide how they thought the actual, full face looked. Participants were able to manipulate the missing half of the face onscreen by increasing or decreasing the averageness of its shape. Our results demonstrated that participants did not select the original versions of the faces but instead chose more average versions when manipulating both the lower and upper face. Further, the typicality of the original image influenced responses, with less typical faces (in comparison with more typical ones) being completed using an even more average version of the missing half of the faces. Taken together, these findings provide the first direct evidence that people utilise an average/typical internal representation when inferring information about incomplete faces. This result has theoretical importance in terms of visual perception, as well as real-world relevance in a time where face masks are commonplace due to the COVID-19 pandemic.

In the HTML version of the article originally published, the author group 'The Schizophrenia Working Group of the Psychiatric Genomics Consortium' was displayed incorrectly. The error has been corrected in the HTML version of the article.

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Deciding whether two different face photographs or voice samples are from the same person represent fundamental challenges within applied settings. To date, most research has focussed on average performance in these tests, failing to consider individual differences and within-person consistency in responses. Here, participants completed the same face (Experiment 1) or voice matching test (Experiment 2) on two separate occasions, allowing comparison of overall accuracy across the two timepoints as well as consistency in trial-level responses. In both experiments, participants were highly consistent in their performances. In addition, we demonstrated a large association between consistency and accuracy, with the most accurate participants also tending to be the most consistent. This is an important result for applied settings in which organisational groups of super-matchers are deployed in real-world contexts. Being able to reliably identify these high performers based upon only a single test informs regarding recruitment for law enforcement agencies worldwide.

First impressions based on facial appearance affect our behaviour towards others. Since the same face will appear different across images, over time, and so on, our impressions may not be equally weighted across exposures but are instead disproportionately influenced by earlier or later instances. Here, we followed up on previous work which identified an anchoring effect, whereby higher attractiveness ratings were given to a person after viewing naturally varying images of their face presented in descending (high-to-low), rather than ascending (low-to-high), order of attractiveness of these images. In Experiment 1 (n = 301), we compared these 'descending' and 'ascending' conditions for unfamiliar identities by presenting six-image sequences. Although we found higher attractiveness ratings for the 'descending' condition, this small effect equated to only 0.22 points on a 1–7 response scale. In Experiment 2 (n = 307), we presented these six-image sequences in a random order and found no difference in attractiveness ratings given to these randomly ordered sequences when compared with those resulting from both our 'descending' and 'ascending' conditions. Further, we failed to detect an influence of the earlier images in these random sequences on attractiveness ratings. Taken together, we found no compelling evidence that anchoring could have an effect on real-world impression formation. [ABSTRACT FROM AUTHOR]

Within the domain of face processing, researchers have been interested in quantifying the relationship between objective (i.e., performance on laboratory tests of recognition and matching) and subjective measures of ability (typically, self-report questionnaires). Put simply, do people show high levels of metacognitive insight into their own abilities with faces? Although several studies have suggested that the association between these two types of measures may only be moderate, there remain several important issues that require consideration before this question can be sensibly investigated. First, specificity is needed regarding both objective and subjective measurements because both tend to span a wide range of potentially separable abilities. Second, experimental tasks appear to focus on different contexts to those tapped in self-report questionnaire items. Third, recent issues with statistical approaches and visualisation can result in numerical artefacts and misinterpretations. Finally, the sizes of population-level insights suggested by recent work provide only limited information regarding individuals within these populations, and so researchers aiming to identify people at the extremes of ability must be careful when drawing conclusions. Taken together, we argue that more attention to these issues is needed when attempting to investigate metacognitive insight within this domain. [ABSTRACT FROM AUTHOR]

Facial first impressions influence jurors in both laboratory experiments and real courtrooms. Often, more attractive defendants are perceived as less guilty and receive more lenient sentences. However, the type of crime under consideration, as well as the ecological validity of the stimuli presented, may determine the nature of this bias. Here, extending previous work, we considered three crime types (robbery, sexual assault, and murder) and utilised short video clips of male defendants, accompanied by real-world crime descriptions. Crucially, we varied attractiveness by presenting a large set of identities, in comparison with the typical use of one "high" and one "low" attractive face. Using null hypothesis significance testing, we found no evidence that either attractiveness or crime type influenced guilt perceptions. Taking a Bayesian perspective, our results provided some evidence that more attractive defendants were rated as less guilty of murder but more guilty of sexual assault, with no bias observed for robbery. Importantly, however, none of these effects had high certainty and all were small in size. By comparing the extremes of attractiveness, we again found inconclusive evidence of any attractiveness effects, with small differences in all cases. The implications for this departure from previous findings are discussed in terms of ecological validity and the need to consider attractiveness as a continuous rather than binary measure. [ABSTRACT FROM AUTHOR]

Background/Objectives: It is vital to identify children whose weight status means that they may benefit from medical or behavioural support, but adult visual judgements of child weight status are inaccurate, and children are seldom routinely weighed and measured. Consequently, there is a need for validated visual tools for use in training, communication, and interventions relating to child weight. Methods: This paper presents validation data for a set of innovative photo-realistic colour body size scales depicting boys and girls aged 4–5 and 10–11. Each age- and gender-specific scale consists of 7 figures based on three-dimensional (3D) scans of 388 children to accurately represent the change in body size caused by changing adiposity. To assess scale validity, 238 adult participants (105 men, 132 women, 1 non-binary individual) undertook two tasks: rating figure adiposity using a visual analogue scale and ranking figures in ascending order of adiposity (OSF Reference: gdp9j). Results: Participants accurately estimated the relative adiposity of each figure, i.e., they were able to tell the difference between figures and correctly rank them by size. This demonstrates scale validity for use in body size tasks. One hundred and fifty-one participants also provided 3-day test–retest data, which demonstrates excellent short-term reliability. Conclusions: Overall, the MapMe child body size scales provide an anthropometrically accurate, valid, reliable, and usable tool for size-related tasks and communication with adults regarding child weight. [ABSTRACT FROM AUTHOR]

It is unclear whether witness mental illness and special measures used with witnesses in court impacts juror decision-making. Participants (N = 204) from the general public and student population completed a measure assessing attitudes towards mental illness before reading a mock trial vignette where witness mental illness (depression, schizophrenia, no mental illness) and the special measure used in court (screen, intermediary, no special measure) were manipulated. Participants were then instructed to formulate judgements about the witness testimony provided (reliability, competency, credibility) and their likelihood of finding the defendant guilty. The findings showed that witnesses with depression were perceived as significantly more competent than witnesses with schizophrenia, or with no mental illness. Witnesses with depression were also perceived as significantly more competent than witnesses with schizophrenia when a screen was used in court. There was however no difference in competency ratings for witnesses with depression versus those with schizophrenia when no special measure was used, or when an intermediary was used, although witnesses with depression were still viewed more favourably overall. These findings suggest that some awareness of these biases is needed in court. Improving clarity about why special measures are used in court might also go some way towards addressing this issue. [ABSTRACT FROM AUTHOR]

The cheerleader effect describes how faces are perceived as more attractive when presented in a group rather than alone. Given differences in how familiar versus unfamiliar faces are perceived and represented, we hypothesized that the cheerleader effect may be diminished/absent for familiar faces. In Experiment 1, targets were rated for attractiveness when presented alone and in groups, with these selected to span the continuum of prior familiarity. Our results identified the cheerleader effect, alongside an increase in attractiveness with increasing familiarity, but no interaction between these two effects. In Experiment 2, we instructed participants to rate the target “person” rather than “face” to increase the salience of any pre-existing impressions. Again, the results showed no influence of familiarity on the size of the cheerleader effect. Taken together, the cheerleader effect was robust with respect to face familiarity, perhaps suggesting underlying mechanisms that are more general rather than face specific. [ABSTRACT FROM AUTHOR]

Artificial intelligence can now synthesise face images which people cannot distinguish from real faces. Here, we investigated the wisdom of the (outer) crowd (averaging individuals' responses to the same trial) and inner crowd (averaging the same individual's responses to the same trial after completing the test twice) as routes to increased performance. In Experiment 1, participants viewed synthetic and real faces, and rated whether they thought each face was synthetic or real using a 1–7 scale. Each participant completed the task twice. Inner crowds showed little benefit over individual responses, and we found no associations between performance and personality factors. However, we found increases in performance with increasing sizes of outer crowd. In Experiment 2, participants judged each face only once, providing a binary 'synthetic/real' response, along with a confidence rating and an estimate of the percentage of other participants that they thought agreed with their answer. We compared three methods of aggregation for outer crowd decisions, finding that the majority vote provided the best performance for small crowds. However, the 'surprisingly popular' solution outperformed the majority vote and the confidence‐weighted approach for larger crowds. Taken together, we demonstrate the use of outer crowds as a robust method of improvement during synthetic face detection, comparable with previous approaches based on training interventions. [ABSTRACT FROM AUTHOR]

Perceiving facial attractiveness is an important behaviour across psychological science due to these judgments having real-world consequences. However, there is little consensus on the measurement of this behaviour, and practices differ widely. Research typically asks participants to provide ratings of attractiveness across a multitude of different response scales, with little consideration of the psychometric properties of these scales. Here, we make psychometric comparisons across nine different response scales. Specifically, we analysed the psychometric properties of a binary response, a 0–100 scale, a visual analogue scale, and a set of Likert scales (1–3, 1–5, 1–7, 1–8, 1–9, 1–10) as tools to measure attractiveness, calculating a range of commonly used statistics for each. While certain properties suggested researchers might choose to favour the 1–5, 1–7 and 1–8 scales, we generally found little evidence of an advantage for one scale over any other. Taken together, our investigation provides consideration of currently used techniques for measuring facial attractiveness and makes recommendations for researchers in this field. [ABSTRACT FROM AUTHOR]

Investigations of face identification have typically focussed on matching faces to photographic IDs. Few researchers have considered the task of searching for a face in a crowd. In Experiment 1, we created the Chokepoint Search Test to simulate real-time search for a target. Performance on this test was poor (39% accuracy) and showed moderate associations with tests of face matching and memory. In addition, trial-level confidence predicted accuracy, and for those participants who were previously familiar with one or more targets, higher familiarity was associated with increased accuracy. In Experiment 2, we found improvements in performance on the test when three recent images of the target, but not three social media images, were displayed during searches. Taken together, our results highlight the difficulties inherent in real-time searching for faces, with important implications for those security personnel who carry out this task on a daily basis.

Eyewitness descriptions provide critical information for the police and other agencies to use during investigations. While researchers have typically considered the impact of memory, little consideration has been given to the utility of facial descriptions themselves, without the additional memory demands. In Experiment 1, participants described face images to their partners, who were then required to select these faces from photographic lineups. Performance was error-prone when the same image appeared in the lineup (73% correct), and decreased further when a different image of the same face was presented (22% correct). We found some evidence to suggest this was due, in part, to difficulties with recognizing that two different images depicted the same person. In Experiment 2, we demonstrated that descriptions of the same face given by different people showed only moderate agreement. Taken together, these results highlight the problematic nature of facial descriptions, even without memory, and their limited utility.

This letter to the editor discusses two problematic analysis strategies used in recent studies on the metacognition of face perception. The first strategy involves dividing a sample into quartiles based on one measure and studying the relationship between paired measures. This can lead to numerical artifacts that may be mistaken for meaningful patterns of behavior. The second strategy involves studying the correlation between self-estimates of ability and actual performance within quartiles, which can be influenced by range restriction. The authors recommend alternative methods for assessing metacognitive insight in face perception research. [Extracted from the article]

Low-quality images are problematic for face identification, for example, when the police identify faces from CCTV images. Here, we test whether face averages, comprising multiple poor-quality images, can improve both human and computer recognition. We created averages from multiple pixelated or nonpixelated images and compared accuracy using these images and exemplars. To provide a broad assessment of the potential benefits of this method, we tested human observers (n = 88; Experiment 1), and also computer recognition, using a smartphone application (Experiment 2) and a commercial one-to-many face recognition system used in forensic settings (Experiment 3). The third experiment used large image databases of 900 ambient images and 7,980 passport images. In all three experiments, we found a substantial increase in performance by averaging multiple pixelated images of a person's face. These results have implications for forensic settings in which faces are identified from poor-quality images, such as CCTV.

Cryptophyte and chlorarachniophyte algae are transitional forms in the widespread secondary endosymbiotic acquisition of photosynthesis by engulfment of eukaryotic algae. Unlike most secondary plastid-bearing algae, miniaturized versions of the endosymbiont nuclei (nucleomorphs) persist in cryptophytes and chlorarachniophytes. To determine why, and to address other fundamental questions about eukaryote eukaryote endosymbiosis, we sequenced the nuclear genomes of the cryptophyte Guillardia theta and the chlorarachniophyte Bigelowiella natans. Both genomes have 21,000 protein genes and are intron rich, and B. natans exhibits unprecedented alternative splicing for a single-celled organism. Phylogenomic analyses and subcellular targeting predictions reveal extensive genetic and biochemical mosaicism, with both host- and endosymbiont-derived genes servicing the mitochondrion, the host cell cytosol, the plastid and the remnant endosymbiont cytosol of both algae. Mitochondrion-to-nucleus gene transfer still occurs in both organisms but plastid-to-nucleus and nucleomorph-to-nucleus transfers do not, which explains why a small residue of essential genes remains locked in each nucleomorph.

Research in face recognition has tended to focus on discriminating between individuals, or "telling people apart." It has recently become clear that it is also necessary to understand how images of the same person can vary, or "telling people together." Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed across different photos of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncratic--the dimensions of variability in one face do not generalize well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework.