<h4>Background</h4>This study examined age-related differences and interrelationships among psychological symptoms, personality traits, and emotional expression styles in a community sample of 151 participants aged 10-77 years, spanning four age groups: adolescents, young adults, middle-aged adults, and older adults.<h4>Methods</h4>Psychological symptoms were assessed using the SCL-90, personality traits using the Big Five Inventory-2 (BFI-2), and emotional expression patterns were derived from facial expression recognition via a convolutional neural network (CNN) model. Kruskal-Wallis H tests were used to examine age-related differences. K-means cluster analysis was applied to identify emotional expression patterns, and logistic regression was used to construct a mental health risk screening model.<h4>Results</h4>The young adult group (19-35 years) achieved the highest scores on the depression (M = 1.73) and anxiety (M = 1.61) dimensions, indicating a higher level of psychological distress during this life stage. Personality traits showed a significant developmental trajectory: neuroticism decreased with age (H(3) = 17.09, <i>p</i> < 0.001, η² = 0.11), declining from 2.69 in the young adult group to 2.17 in the older adult group; conscientiousness increased with age (H(3) = 37.39, <i>p</i> < 0.001, η² = 0.24), representing the most substantial age-related effect. K-means clustering identified three distinct emotional expression patterns: Cluster 1 was characterised by happiness, Cluster 2 by anger, disgust, and fear, and Cluster 3 by neutrality, sadness, and surprise. Cluster 2 exhibited the highest scores on neuroticism, anxiety, depression, and mood swings, and scored significantly higher than the other two clusters on interpersonal sensitivity, depression, anxiety, and hostility (<i>p</i> < 0.05). Mental health risk screening indicated that 26.5% of participants were classified as high-risk. Logistic regression analysis (AUC = 0.742) showed that neuroticism was the strongest predictor of elevated mental health risk (OR = 4.58), while extraversion (OR = 0.41) and conscientiousness (OR = 0.57) were significant protective factors.<h4>Conclusions</h4>These findings provide exploratory evidence regarding age-related patterns of psychological symptoms and personality traits in a convenience sample and offer preliminary support for personality-based mental health risk screening. Notably, the SCL-90 was employed as a screening tool rather than for clinical diagnosis. Given the unequal age group sizes, particularly the small young adult subgroup, generalisability across the lifespan should not be assumed.

Significance Current theory of face-based trait impressions focuses on their foundation in facial morphology, from which emerges a correlation structure of face impressions due to shared feature dependence, “face trait space.” Here, we proposed that perceivers’ lay conceptual beliefs about how personality traits correlate structure their face impressions. We demonstrate that “conceptual trait space” explains a substantial portion of variance in face trait space. Further, we find that perceivers who believe any set of personality traits (e.g., trustworthiness, intelligence) is more correlated in others use more similar facial features when making impressions of those traits. These findings suggest lay conceptual beliefs about personality play a crucial role in face-based trait impressions and may underlie both their similarities and differences across perceivers.

The main theme of the present dissertation was the measurement of personality traits through someone’s verbal and non-verbal behavior. In most studies, personality traits were measured using the HEXACO model of personality, a theoretical framework – based on cross-cultural lexical research – that organizes personality using six factors: Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience. In one of the studies the Big Five Model was used, which contains similar factors except for Honesty-Humility. Behavior was measured through three modalities: (a) audio, including voice characteristics, such as voice intensity or pitch, (b) visual, including facial expressions and head movements, and (c) verbal, including written or spoken text. All three modalities were automatically extracted using software developed to measure the three types of features at a granular level. Below are presented the main findings across the four empirical chapters of the present dissertation. The findings of the four empirical chapters have significant implications for practitioners and personality psychologists, alike. Regarding practitioners (e.g., AVI vendors), the results suggest that the content of job interview questions should be carefully designed to activate the traits someone is interested in measuring. The more the content of the interview questions aligns with the constructs to-be-measured (e.g., personality traits), the more the behaviors

Appearance is known to influence social interactions, which in turn could potentially influence personality development. In this study we focus on discovering the relationship between self-reported personality traits, first impressions and facial characteristics. The results reveal that several personality traits can be read above chance from a face, and that facial features influence first impressions. Despite the former, our prediction model fails to reliably infer personality traits from either facial features or first impressions. First impressions, however, could be inferred more reliably

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,

Whether facial information constitutes a valid cue for inferring personality remains a matter of academic debate. Contradictory findings suggest that the debate on the predictive validity of faces should shift from dichotomous conceptions to more detailed approaches, focusing on the specific conditions under which facial cues reveal personality traits. In this critical review, we examine the most striking findings on facial perception of narcissism, with the aim of analyzing the extent to which facial features can be considered valid cues for inferring narcissism. Our analysis suggests that, c

We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions. For fusing cues, we explore decision and feature-level fusion, and an additive attention-based fusion strategy which quantifies the relat

Mental health disorders, such as depression, anxiety, and borderline personality disorder (BPD), are common, often begin early, and can cause profound impairment. Traditional assessments rely heavily on subjective reports and clinical observation, which can be inconsistent and biased. Recent advances in AI offer a promising complement by analyzing objective, observable cues from speech, language, facial expressions, physiological signals, and digital behavior. Explainable AI ensures these patterns remain interpretable and clinically meaningful. A synthesis of 24 recent systematic and scoping reviews shows that depression is linked to self-focused negative language, slowed and monotonous speech, reduced facial expressivity, disrupted sleep and activity, and altered phone or online behavior. Anxiety disorders present with negative language bias, monotone speech with pauses, physiological hyperarousal, and avoidance-related behaviors. BPD exhibits more complex patterns, including impersonal or externally focused language, speech dysregulation, paradoxical facial expressions, autonomic dysregulation, and socially ambivalent behaviors. Some cues, like reduced heart rate variability and flattened speech, appear across conditions, suggesting shared transdiagnostic mechanisms, while BPD's interpersonal and emotional ambivalence stands out. These findings highlight the potential of observable, digitally measurable cues to complement traditional assessments, enabling earlier detection, ongoing monitoring, and more personalized interventions in psychiatry.

reports of college students regarding those traits argued that static traits, such as beauty or ugliness of features, hold a position subordinate to groups Physical attractiveness is the extent to which a person's physical features are considered aesthetically pleasing or beautiful. The term often implies sexual attractiveness or desirability but can also be distinct from them. Many factors influence one person's attraction to another, with physical aspects being one of them. Physical attraction includes universal perceptions common across human cul The degree of differences between male and female anatomical traits is called sexual dimorphism. Female respondents in the follicular phase of their menstrual cycle were significantly more likely to choose a masculine face than those in menses and luteal phases, (or in those taking hormonal contraception). This distinction supports the sexy son hypothesis, which posits that it is evolutionarily advantageous for women to select potential fathers who are more genetically attractive, rather than the best caregivers. However, women's likeliness to exert effort to view male faces does not seem to depend on their masculinity, but to a general increase with women's testosterone levels. It is suggested that the masculinity of facial features is a reliable indication of good health, or, alternatively, that masculine-looking males are more likely to achieve high status. However, the correlation between attractive facial features and health has been questioned. Sociocultural factors, such as self-perceived attractiveness, status in a relationship and degree of gender-conformity, have been reported to play a role in female preferences for male faces. Studies have found that women who perceive themselves as physically attractive are more likely to choose men with masculine facial dimorphism, than are women who perceive themselves as physically unattractive. In men, facial masculinity significantly correlates with facial symmetry – it has been suggested that both are signals of developmental stability and genetic health. One study called into question the importance of facial masculinity in physical attractiveness in men, arguing that when perceived health, which is factored into facial masculinity, is discounted it makes little difference in physical attractiveness. In a cross-country study involving 4,794 women in their early twenties, a difference was found in women's average "masculinity preference" between countries. A study found that the same genetic factors cause facial masculinity in both males and females such that a male with a more masculine face would likely have a sister with a more masculine face due to the siblings having shared genes. The study also found…