Trustworthiness perception is disrupted in artificial faces

Racial Bias in Pediatric Pain Perception

Racial disparities in pediatric pain care are prevalent across a variety of health care settings, and likely contribute to broader disparities in health, morbidity, and mortality. The present research expands on prior work demonstrating potential perceptual contributions to pain care disparities in adults and tests whether racial bias in pain perception extends to child targets. We examined the perception and hypothetical treatment of pain in Black and White boys (experiment 1), Black and White boys and girls (experiment 2), Black and White boys and adult men (experiment 3), and Black, White, Asian, and Latinx boys (experiment 4). Across this work, pain was less readily perceived on Black (vs White) boys' faces-though this bias was not observed within girls. Moreover, this perceptual bias was comparable in magnitude to the same bias measured with adult targets and consistently predicted bias in hypothetical treatment. Notably, bias was not limited to Black targets-pain on Hispanic/Latinx boys' faces was also relatively underperceived. Taken together, these results offer strong evidence for racial bias in pediatric pain perception. PERSPECTIVE: This article demonstrates perceptual contributions to racial bias in pediatric pain recognition. Participants consistently saw pain less readily on Black boys' faces, compared with White boys, and this perceptual bias consistently predicted race-based gaps in treatment. This work reveals a novel factor that may support pediatric pain care disparities.

Real is the new sexy: the influence of perceived realness on self-reported arousal to sexual visual stimuli

As state-of-art technology can create artificial images that are indistinguishable from real ones, it is urgent to understand whether believing that a picture is real or not has some import over affective phenomena such as sexual arousal. Thus, in two pre-registered online studies, we tested whether 60 images depicting models in underwear elicited higher self-reported sexual arousal when believed to be (N = 57) or presented as (N = 108) real photographs as opposed to artificially generated. In both cases, Realness correlated with significantly higher scores on self-reported sexual arousal. Consistently with the literature on downregulation of emotional response to fictional works, our result indicates that sexual images that are perceived to be fake are less arousing than those believed to portray real people.

The effects of social presence on cooperative trust with algorithms

Algorithms support many processes in modern society. Research using trust games frequently reports that people are less inclined to cooperate when believed to play against an algorithm. Trust is, however, malleable by contextual factors and social presence can increase the willingness to collaborate. We investigated whether situating cooperation with an algorithm in the presence of another person increases cooperative trust. Three groups of participants played a trust game against a pre-programmed algorithm in an online webhosted experiment. The first group was told they played against another person who was present online. The second group was told they played against an algorithm. The third group was told they played against an algorithm while another person was present online. More cooperative responses were observed in the first group compared to the second group. A difference in cooperation that replicates previous findings. In addition, cooperative trust dropped more over the course of the trust game when participants interacted with an algorithm in the absence another person compared to the other two groups. This latter finding suggests that social presence can mitigate distrust in interacting with an algorithm. We discuss the cognitive mechanisms that can mediate this effect.

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.

Should I trust you? Investigating trustworthiness judgements of painful facial expressions

Past research indicates that patients' reports of pain are often met with skepticism and that observers tend to underestimate patients' pain. The mechanisms behind these biases are not yet fully understood. One relevant domain of inquiry is the interaction between the emotional valence of a stranger's expression and the onlooker's trustworthiness judgment. The emotion overgeneralization hypothesis posits that when facial cues of valence are clear, individuals displaying negative expressions (e.g., disgust) are perceived as less trustworthy than those showing positive facial expressions (e.g., happiness). Accordingly, we hypothesized that facial expressions of pain (like disgust) would be judged more untrustworthy than facial expressions of happiness. In two separate studies, we measured trustworthiness judgments of four different facial expressions (i.e., neutral, happiness, pain, and disgust), displayed by both computer-generated and real faces, via both explicit self-reported ratings (Study 1) and implicit motor trajectories in a trustworthiness categorization task (Study 2). Ratings and categorization findings partly support our hypotheses. Our results reveal for the first time that when judging strangers' facial expressions, both negative expressions were perceived as more untrustworthy than happy expressions. They also indicate that facial expressions of pain are perceived as untrustworthy as disgust expressions, at least for computer-generated faces. These findings are relevant to the clinical setting because they highlight how overgeneralization of emotional facial expressions may subtend an early perceptual bias exerted by the patient's emotional facial cues onto the clinician's cognitive appraisal process.

The role of valence, dominance, and pitch in perceptions of artificial intelligence (AI) conversational agents' voices

There is growing concern that artificial intelligence conversational agents (e.g., Siri, Alexa) reinforce voice-based social stereotypes. Because little is known about social perceptions of conversational agents' voices, we investigated (1) the dimensions that underpin perceptions of these synthetic voices and (2) the role that acoustic parameters play in these perceptions. Study 1 (N = 504) found that perceptions of synthetic voices are underpinned by Valence and Dominance components similar to those previously reported for natural human stimuli and that the Dominance component was strongly and negatively related to voice pitch. Study 2 (N = 160) found that experimentally manipulating pitch in synthetic voices directly influenced dominance-related, but not valence-related, perceptions. Collectively, these results suggest that greater consideration of the role that voice pitch plays in dominance-related perceptions when designing conversational agents may be an effective method for controlling stereotypic perceptions of their voices and the downstream consequences of those perceptions.

On the realness of people who do not exist: The social processing of artificial faces

Today more than ever, we are asked to evaluate the realness, truthfulness and trustworthiness of our social world. Here, we focus on how people evaluate realistic-looking faces of non-existing people generated by generative adversarial networks (GANs). GANs are increasingly used in marketing, journalism, social media, and political propaganda. In three studies, we investigated if and how participants can distinguish between GAN and REAL faces and the social consequences of their exposure to artificial faces. GAN faces were more likely to be perceived as real than REAL faces, a pattern partly explained by intrinsic stimulus characteristics. Moreover, participants' realness judgments influenced their behavior because they displayed increased social conformity toward faces perceived as real, independently of their actual realness. Lastly, knowledge about the presence of GAN faces eroded social trust. Our findings point to potentially far-reaching consequences for the pervasive use of GAN faces in a culture powered by images at unprecedented levels.

A data-driven, hyper-realistic method for visualizing individual mental representations of faces

Research in person and face perception has broadly focused on group-level consensus that individuals hold when making judgments of others (e.g., “X type of face looks trustworthy”). However, a growing body of research demonstrates that individual variation is larger than shared, stimulus-level variation for many social trait judgments. Despite this insight, little research to date has focused on building and explaining individual models of face perception. Studies and methodologies that have examined individual models are limited in what visualizations they can reliably produce to either noisy and blurry or computer avatar representations. Methods that produce low-fidelity visual representations inhibit generalizability by being clearly computer manipulated and produced. In the present work, we introduce a novel paradigm to visualize individual models of face judgments by leveraging state-of-the-art computer vision methods. Our proposed method can produce a set of photorealistic face images that correspond to an individual's mental representation of a specific attribute across a variety of attribute intensities. We provide a proof-of-concept study which examines perceived trustworthiness/untrustworthiness and masculinity/femininity. We close with a discussion of future work to substantiate our proposed method.

Sensitivity to trustworthiness cues in own- and other-race faces: The role of spatial frequency information

Research has shown that adults are better at processing faces of the most represented ethnic group in their social environment compared to faces from other ethnicities, and that they rely more on holistic/configural information for identity discrimination in own-race than other-race faces. Here, we applied a spatial filtering approach to the investigation of trustworthiness perception to explore whether the information on which trustworthiness judgments are based differs according to face race. European participants (N = 165) performed an online-delivered pairwise preference task in which they were asked to select the face they would trust more within pairs randomly selected from validated White and Asian broad spectrum, low-pass filter and high-pass filter trustworthiness continua. Results confirmed earlier demonstrations that trustworthiness perception generalizes across face ethnicity, but discrimination of trustworthiness intensity relied more heavily on the LSF content of the images for own-race faces compared to other-race faces. Results are discussed in light of previous work on emotion discrimination and the hypothesis of overlapping perceptual mechanisms subtending social perception of faces.

What is So Special About Contemporary CG Faces? Semiotics of MetaHumans

This paper analyses the features of the 2021 software for the creation of ultrarealistic digital characters "MetaHuman Creator" and reflects on the causes of such perceived effect of realism to understand if the faces produced with such software represent an actual novelty from an academic standpoint. Such realism is first of all defined as the result of semio-cognitive processes which trigger interpretative habits specifically related to faces. These habits are then related to the main properties of any realistic face: being face-looking, face-meaning and face-acting. These properties, in turn, are put in relation with our interactions with faces in terms of face detection, face recognition, face reading and face agency. Within this theoretical framework, we relate the characteristics of these artificial faces with such interpretative habits. To do so, we first of all make an examination of the technological features behind both the software and the digital faces it produces. This analysis highlights four main points of interest: the mathematical accuracy, the scanned database, the high level of details and the transformative capacities of these artificial faces. We then relate these characteristics with the cultural and cognitive aspects involved in recognizing and granting meaning to faces. This reveals how metahuman faces differs from previous artificial faces in terms of indexicality, intersubjectivity, informativity and irreducibility. But it also reveals some limits of such effect of reality in terms of intentionality and historical context. This examination consequently brings us to conclude that metahuman faces are qualitatively different from previous artificial faces and, in the light of their potentials and limits, to highlight four main lines of future research based on our findings.

Face masks reduce interpersonal distance in virtual reality

During the COVID-19 pandemic several behavioral measures have been implemented to reduce viral transmission. While these measures reduce the risk of infections, they may also increase risk behavior. Here, we experimentally investigate the influence of face masks on physical distancing. Eighty-four participants with or without face masks passed virtual agents in a supermarket environment to reach a target while interpersonal distance was recorded. Agents differed in wearing face masks and age (young, elderly). In addition, situational constraints varied in whether keeping a distance of 1.5 m required an effortful detour or not. Wearing face masks (both self and other) reduced physical distancing. This reduction was most prominent when keeping the recommended distance was effortful, suggesting an influence of situational constraints. Similarly, increased distances to elderly were only observed when keeping a recommended distance was effortless. These findings highlight contextual constraints in compensation behavior and have important implications for safety policies.

Robot faces elicit responses intermediate to human faces and objects at face-sensitive ERP components

Face recognition is supported by selective neural mechanisms that are sensitive to various aspects of facial appearance. These include event-related potential (ERP) components like the P100 and the N170 which exhibit different patterns of selectivity for various aspects of facial appearance. Examining the boundary between faces and non-faces using these responses is one way to develop a more robust understanding of the representation of faces in extrastriate cortex and determine what critical properties an image must possess to be considered face-like. Robot faces are a particularly interesting stimulus class to examine because they can differ markedly from human faces in terms of shape, surface properties, and the configuration of facial features, but are also interpreted as social agents in a range of settings. In the current study, we thus chose to investigate how ERP responses to robot faces may differ from the response to human faces and non-face objects. In two experiments, we examined how the P100 and N170 responded to human faces, robot faces, and non-face objects (clocks). In Experiment 1, we found that robot faces elicit intermediate responses from face-sensitive components relative to non-face objects (clocks) and both real human faces and artificial human faces (computer-generated faces and dolls). These results suggest that while human-like inanimate faces (CG faces and dolls) are processed much like real faces, robot faces are dissimilar enough to human faces to be processed differently. In Experiment 2 we found that the face inversion effect was only partly evident in robot faces. We conclude that robot faces are an intermediate stimulus class that offers insight into the perceptual and cognitive factors that affect how social agents are identified and categorized.

Why is the literature on first impressions so focused on White faces?

We spontaneously attribute to strangers a wide variety of character traits based on their facial appearance. While these first impressions have little or no basis in reality, they exert a strong influence over our behaviour. Cognitive scientists have revealed a great deal about first impressions from faces including their factor structure, the cues on which they are based, the neurocognitive mechanisms responsible, and their developmental trajectory. In this field, authors frequently strive to remove as much ethnic variability from stimulus sets as possible. Typically, this convention means that participants are asked to judge the likely traits of White faces only. In the present article, we consider four possible reasons for the lack of facial diversity in this literature and find that it is unjustified. Next, we illustrate how the focus on White faces has undermined scientific efforts to understand first impressions from faces and argue that it reinforces socially regressive ideas about 'race' and status. We go on to articulate our concern that opportunities may be lost to leverage the knowledge derived from the study of first impressions against the dire consequences of prejudice and discrimination. Finally, we highlight some promising developments in the field.

Automatic gaze to the nose region cannot be inhibited during observation of facial expression in Eastern observers

Humans can extract a great deal of information about others very quickly. This is partly because the face automatically captures observers' attention. Specifically, the eyes can attract overt attention. Although it has been reported that not only the eyes but also the nose can capture initial oculomotor movement in Eastern observers, its generalizability remains unknown. In this study, we applied the "don't look" paradigm wherein participants are asked not to fixate on a specific facial region (i.e., eyes, nose, and mouth) during an emotion recognition task with upright (Experiment 1) and inverted (Experiment 2) faces. In both experiments, we found that participants were less able to inhibit the initial part of their fixations to the nose, which can be interpreted as the nose automatically capturing attention. Along with previous studies, our overt attention tends to be attracted by a part of the face, which is the nose region in Easterner observers.

Facial first impressions form two clusters representing approach-avoidance

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.

Sensitivity to face animacy and inversion in childhood: Evidence from EEG data

Adults exhibit relative behavioral difficulties in processing inanimate, artificial faces compared to real human faces, with implications for using artificial faces in research and designing artificial social agents. However, the developmental trajectory of inanimate face perception is unknown. To address this gap, we used electroencephalography to investigate inanimate faces processing in cross-sectional groups of 5-10-year-old children and adults. A face inversion manipulation was used to test whether face animacy processing relies on expert face processing strategies. Groups of 5-7-year-olds (N = 18), 8-10-year-olds (N = 18), and adults (N = 16) watched pictures of real or doll faces presented in an upright or inverted orientation. Analyses of event-related potentials revealed larger N170 amplitudes in response to doll faces, irrespective of age group or face orientation. Thus, the N170 is sensitive to face animacy by 5-7 years of age, but such sensitivity may not reflect high-level, expert face processing. Multivariate pattern analyses of the EEG signal additionally assessed whether animacy information could be reliably extracted during face processing. Face orientation, but not face animacy, could be reliably decoded from occipitotemporal channels in children and adults. Face animacy could be decoded from whole scalp channels in adults, but not children. Together, these results suggest that 5-10-year-old children exhibit some sensitivity to face animacy over occipitotemporal regions that is comparable to adults.

Implicit race attitudes modulate visual information extraction for trustworthiness judgments

Black people are still considered to be one of the most stigmatized groups and have to face multiple prejudices that undermine their well-being. Assumptions and beliefs about other racial groups are quite pervasive and have been shown to impact basic social tasks such as face processing. For example, individuals with high racial prejudice conceptualize other-race faces as less trustworthy and more criminal. However, it is unknown if implicit racial bias could modulate even low-level perceptual mechanisms such as spatial frequency (SF) extraction when judging the level of trustworthiness of other-race faces. The present study showed that although similar facial features are used to judge the trustworthiness of White and Black faces, own-race faces are processed in lower SF (i.e. coarse information such as the contour of the face and blurred shapes as opposed to high SF representing fine-grained information such as eyelashes or fine wrinkles). This pattern was modulated by implicit race biases: higher implicit biases are associated with a significantly higher reliance on low SF with White than with Black faces.

Trust in AI Agent: A Systematic Review of Facial Anthropomorphic Trustworthiness for Social Robot Design

As an emerging artificial intelligence system, social robot could socially communicate and interact with human beings. Although this area is attracting more and more attention, limited research has tried to systematically summarize potential features that could improve facial anthropomorphic trustworthiness for social robot. Based on the literature from human facial perception, product, and robot face evaluation, this paper systematically reviews, evaluates, and summarizes static facial features, dynamic features, their combinations, and related emotional expressions, shedding light on further exploration of facial anthropomorphic trustworthiness for social robot design.

Turning Heads: The Effects of Face View and Eye Gaze Direction on the Perceived Attractiveness of Expressive Faces

We investigated whether the perceived attractiveness of expressive faces was influenced by head turn and eye gaze towards or away from the observer. In all experiments, happy faces were consistently rated as more attractive than angry faces. A head turn towards the observer, whereby a full-face view was shown, was associated with relatively higher attractiveness ratings when gaze direction was aligned with face view (Experiment 1). However, preference for full-face views of happy faces was not affected by gaze shifts towards or away from the observer (Experiment 2a). In Experiment 3, the relative duration of each face view (front-facing or averted at 15°) during a head turn away or towards the observer was manipulated. There was benefit on attractiveness ratings for happy faces shown for a longer duration from the front view, regardless of the direction of head turn. Our findings support previous studies indicating a preference for positive expressions on attractiveness judgements, which is further enhanced by the front views of faces, whether presented during a head turn or shown statically. In sum, our findings imply a complex interaction between cues of social attention, indicated by the view of the face shown, and reward on attractiveness judgements of unfamiliar faces.

An Assessment of Computer-Generated Stimuli for Use in Studies of Body Size Estimation and Bias

Inaccurate body size judgments are associated with body image disturbances, a clinical feature of many eating disorders. Accordingly, body-related stimuli have become increasingly important in the study of estimation inaccuracies and body image disturbances. Technological advancements in the last decade have led to an increased use of computer-generated (CG) body stimuli in body image research. However, recent face perception research has suggested that CG face stimuli are not recognized as readily and may not fully tap facial processing mechanisms. The current study assessed the effectiveness of using CG stimuli in an established body size estimation task (the “bodyline” task). Specifically, we examined whether employing CG body stimuli alters body size judgments and associated estimation biases. One hundred and six 17- to 25-year-old females completed the CG bodyline task, which involved estimating the size of full-length CG body stimuli along a visual analogue scale. Our results show that perception of body size for CG stimuli was non-linear. Participants struggled to discriminate between extreme bodies sizes and overestimated the size change between near to average bodies. Furthermore, one of our measured size estimation biases was larger for CG stimuli. Our collective findings suggest using caution when employing CG stimuli in experimental research on body perception.

The Role of Low-Spatial Frequency Components in the Processing of Deceptive Faces: A Study Using Artificial Face Models

Interpreting another's true emotion is important for social communication, even in the face of deceptive facial cues. Because spatial frequency components provide important clues for recognizing facial expressions, we investigated how we use spatial frequency information from deceptive faces to interpret true emotion. We conducted two different tasks: a face-generating experiment in which participants were asked to generate deceptive and genuine faces by tuning the intensity of happy and angry expressions (Experiment 1) and a face-classification task in which participants had to classify presented faces as either deceptive or genuine (Experiment 2). Low- and high-spatial frequency (LSF and HSF) components were varied independently. The results showed that deceptive happiness (i.e., anger is the hidden expression) involved different intensities for LSF and HSF. These results suggest that we can identify hidden anger by perceiving unbalanced intensities of emotional expression between LSF and HSF information contained in deceptive faces.

Face perception: A brief journey through recent discoveries and current directions

Faces are a rich source of information about the people around us. Identity, state of mind, emotions, intentions, age, gender, ethnic background, attractiveness and a host of other attributes about an individual can be gleaned from a face. When face perception fails, dramatic psycho-social consequences can follow at the individual level, as in the case of prosopagnosic parents who are unable to recognize their children at school pick-up. At the species level, social interaction patterns are shaped by human face perception abilities. The computational feat of recognizing faces and facial attributes, and the challenges overcome by the human brain to achieve this feat, have fascinated generations of vision researchers. In this paper, we present a brief overview of some of the milestones of discovery as well as outline a selected set of current directions and open questions on this topic.

Perceiving Animacy in Own-and Other-Species Faces

Though artificial faces of various kinds are rapidly becoming more and more life-like due to advances in graphics technology (Suwajanakorn et al., 2015; Booth et al., 2017), observers can typically distinguish real faces from artificial faces. In general, face recognition is tuned to experience such that expert-level processing is most evident for faces that we encounter frequently in our visual world, but the extent to which face animacy perception is also tuned to in-group vs. out-group categories remains an open question. In the current study, we chose to examine how the perception of animacy in human faces and dog faces was affected by face inversion and the duration of face images presented to adult observers. We hypothesized that the impact of these manipulations may differ as a function of species category, indicating that face animacy perception is tuned for in-group faces. Briefly, we found evidence of such a differential impact, suggesting either that distinct mechanisms are used to evaluate the "life" in a face for in-group and out-group faces, or that the efficiency of a common mechanism varies substantially as a function of visual expertise.

Avatars in Pain: Visible Harm Enhances Mind Perception in Humans and Robots

Previous research has shown that when people read vignettes about the infliction of harm upon an entity appearing to have no more than a liminal mind, their attributions of mind to that entity increased. Currently, we investigated if the presence of a facial wound enhanced the perception of mental capacities (experience and agency) in response to images of robotic and human-like avatars, compared with unharmed avatars. The results revealed that harmed versions of both robotic and human-like avatars were imbued with mind to a higher degree, irrespective of the baseline level of mind attributed to their unharmed counterparts. Perceptions of capacity for pain mediated attributions of experience, while both pain and empathy mediated attributions of abilities linked to agency. The findings suggest that harm, even when it appears to have been inflicted unintentionally, may augment mind perception for robotic as well as for nearly human entities, at least as long as it is perceived to elicit pain.