First impressions of child faces: Facial trustworthiness influences adults' interpretations of children's behavior in ambiguous situations

Facial impressions of niceness influence children's interpretations of peers' ambiguous behavior

Children infer personality traits from faces when they are asked explicitly which face appears nice or mean. Less is known about how children use face-trait information implicitly to make behavioral evaluations. We used the Ambiguous Situations Protocol to explore how children use face-trait information to form interpretations of ambiguous situations when the behavior or intention of the target child was unclear. On each trial, children (N = 144, age range = 4-11.95 years; 74 girls, 67 boys, 3 gender not specified; 70% White, 10% other or mixed race, 5% Asian, 4% Black, 1% Indigenous, 9% not specified) viewed a child's face (previously rated high or low in niceness) before seeing the child's face embedded within an ambiguous scene (Scene Task) or hearing a vignette about a misbehavior done by that child (Misbehavior Task). Children described what was happening in each scene and indicated whether each misbehavior was done on purpose or by accident. Children also rated the behavior of each child and indicated whether the child would be a good friend. Facial niceness influenced children's interpretations of ambiguous behavior (Scene Task) by 4 years of age, and ambiguous intentions (Misbehavior Task) by 6 years. Our results suggest that the use of face-trait cues to form interpretations of ambiguous behavior emerges early in childhood, a bias that may lead to differential treatment for peers perceived with a high-nice face versus a low-nice face.

Iterated learning reveals stereotypes of facial trustworthiness that propagate in the absence of evidence

When we look at someone's face, we rapidly and automatically form robust impressions of how trustworthy they appear. Yet while people's impressions of trustworthiness show a high degree of reliability and agreement with one another, evidence for the accuracy of these impressions is weak. How do such appearance-based biases survive in the face of weak evidence? We explored this question using an iterated learning paradigm, in which memories relating (perceived) facial and behavioral trustworthiness were passed through many generations of participants. Stimuli consisted of pairs of computer-generated people's faces and exact dollar amounts that those fictional people shared with partners in a trust game. Importantly, the faces were designed to vary considerably along a dimension of perceived facial trustworthiness. Each participant learned (and then reproduced from memory) some mapping between the faces and the dollar amounts shared (i.e., between perceived facial and behavioral trustworthiness). Much like in the game of 'telephone', their reproductions then became the training stimuli initially presented to the next participant, and so on for each transmission chain. Critically, the first participant in each chain observed some mapping between perceived facial and behavioral trustworthiness, including positive linear, negative linear, nonlinear, and completely random relationships. Strikingly, participants' reproductions of these relationships showed a pattern of convergence in which more trustworthy looks were associated with more trustworthy behavior - even when there was no relationship between looks and behavior at the start of the chain. These results demonstrate the power of facial stereotypes, and the ease with which they can be propagated to others, even in the absence of any reliable origin of these stereotypes.

Contextual modulation of appearance-trait learning

When we encounter a stranger for the first time, we spontaneously attribute to them a wide variety of character traits based on their facial appearance. There is increasing consensus that learning plays a key role in these first impressions. According to the Trait Inference Mapping (TIM) model, first impressions are the products of mappings between 'face space' and 'trait space' acquired through domain-general associative processes. Drawing on the associative learning literature, TIM predicts that first-learned associations between facial appearance and character will be particularly influential: they will be difficult to unlearn and will be more likely to generalise to novel contexts than appearance-trait associations acquired subsequently. The study of face-trait learning de novo is complicated by the fact that participants, even young children, already have extensive experience with faces before they enter the lab. This renders the study of first-learned associations from faces intractable. Here, we overcome this problem by using Greebles - a class of novel synthetic objects about which participants had no previous knowledge or preconceptions - as a proxy for faces. In four experiments (total N = 640) with adult participants we adapt classic AB-A and AB-C renewal paradigms to study appearance-trait learning. Our results indicate that appearance-trait associations are subject to contextual control, and are resistant to counter-stereotypical experience.

The influence of fake news on face-trait learning

Humans spontaneously attribute a wide range of traits to conspecifics based on their facial appearance. Unsurprisingly, previous findings indicate that this 'person evaluation' is affected by information provided about the target's past actions and behaviours. Strikingly, many news items shared on social media sites (e.g., Twitter) describe the actions of individuals who are often shown in accompanying images. This kind of material closely resembles that encountered by participants in previous studies of face-trait learning. We therefore sought to determine whether Twitter posts that pair facial images with favourable and unfavourable biographical information also modulate subsequent trait evaluation of the people depicted. We also assessed whether the effects of this information-valence manipulation were attenuated by the presence of the "disputed tag", introduced by Twitter as a means to combat the influence of fake-news. Across two preregistered experiments, we found that fictional tweets that paired facial images with details of the person's positive or negative actions affected the extent to which readers subsequently judged the faces depicted to be trustworthy. When the rating phase followed immediately after the study phase, the presence of the disputed tag attenuated the effect of the behavioural information (Experiment 1: N = 128; Mage = 34.06; 89 female, 36 male, 3 non-binary; 116 White British). However, when the rating phase was conducted after a 10-minute delay, the presence of the disputed tag had no significant effect (Experiment 2: N = 128; Mage = 29.12; 78 female, 44 male, 4 non-binary, 2 prefer not to say; 110 White British). Our findings suggest that disputed tags may have relatively little impact on the long-term face-trait learning that occurs via social media. As such, fake news stories may have considerable potential to shape users' person evaluation.

Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review

Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.

The cultural learning account of first impressions

Humans spontaneously attribute character traits to strangers based on their facial appearance. Although these 'first impressions' typically have no basis in reality, some authors have assumed that they have an innate origin. By contrast, the Trait Inference Mapping (TIM) account proposes that first impressions are products of culturally acquired associative mappings that allow activation to spread from representations of facial appearance to representations of trait profiles. According to TIM, cultural instruments, including propaganda, illustrated storybooks, art and iconography, ritual, film, and TV, expose many individuals within a community to common sources of correlated face-trait experience, yielding first impressions that are shared by many, but typically inaccurate. Here, we review emerging empirical findings, many of which accord with TIM, and argue that future work must distinguish first impressions based on invariant facial features (e.g., shape) from those based on facial behaviours (e.g., expressions).

Face masks have a limited influence on first impressions: evidence from three experiments

Considering the widespread use of face masks during the COVID-19 pandemic, the goal of the current study was to examine how occlusion of the lower half of the face may impact first impression formation. We conducted three experiments, each building on previous research, investigating the effect of face masks on first impressions of faces across the lifespan (children, young and older adults). Experiment 1 examined whether the mandatory influence of happy facial expressions on perceived trustworthiness in young adult faces is influenced by face masks. Experiment 2 examined behavioural consequences of adults' first impressions of child faces to determine whether masks reduce the effect of facial niceness on interpretations of ambiguous behaviour. Experiment 3 investigated consensus for first impressions of trustworthiness and competence in older adult faces with and without masks, as well as consensus on underlying facial cues. The results of all three experiments present converging evidence that masks do not have a significant impact on first impressions and their behavioural consequences.